FIRST PROJECT_MACHINE LEARNING_AWESOME BANK.ipynb

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   "source": [
    "# Final Project - The Awesome Bank Marketing Campaign\n",
    "\n",
    "\n",
    "After your success in the first project at The Awesome Bank, an even more exciting new project appears!\n",
    "\n",
    "The marketing team wants to launch a new campaign aimed at convincing customers to open term deposits.\n",
    "\n",
    "Until now, the strategy was to call as many people as possible, indiscriminately, and try to sell them the product. However, this approach, in addition to spending more resources because it involves having several people calling all customers, is also uncomfortable for some customers who do not like to be disturbed by this type of call. After the calculations, it was concluded that:\n",
    "\n",
    "    - For each customer identified as a good candidate, and is the target of the campaign but does not adhere to the term deposit, the bank has a cost of 500 euros.\n",
    "      - For each customer who is identified as a candidate, and as such is not the target of the campaign but was actually a good candidate and would join, the bank has a cost of 2000 euros.\n",
    "    \n",
    "Based on this information, can you help the marketing team by creating a model that selects the best candidates to be targeted by the campaign, in order to reduce costs?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "**The dataset contains information about all customers who were targeted by the campaign:**\n",
    "\n",
    "    1 - age \n",
    "    2 - job : type of job \n",
    "    3 - marital : marital status\n",
    "    4 - education\n",
    "    5 - default: has credit in default? \n",
    "    6 - housing: has housing loan? \n",
    "    7 - loan: has personal loan? \n",
    "    8 - pdays: number of days that passed by after the client was last contacted from a previous campaign \n",
    "    9 - previous: number of contacts performed before this campaign and for this client \n",
    "    10 - poutcome: outcome of the previous marketing campaign \n",
    "    11 - emp.var.rate: employment variation rate - quarterly indicator \n",
    "    12 - cons.price.idx: consumer price index - monthly indicator\n",
    "    13 - cons.conf.idx: consumer confidence index - monthly indicator \n",
    "    14 - euribor3m: euribor 3 month rate - daily indicator \n",
    "    15 - nr.employed: number of employees - quarterly indicator \n",
    "\n",
    "**Output:**\n",
    "    \n",
    "    y: has the client subscribed a term deposit? \n",
    "    \n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "#### Questions:\n",
    "\n",
    "     1. How many features are available? How many customers?\n",
    "     2. How many customers do you have in the dataset who actually subscribed to the term deposit? And how many didn't?\n",
    "     3. What are the most relevant features to decide whether a customer is more likely to adhere to term deposit?\n",
    "     4. Which algorithm led to the best results?\n",
    "     5. Which evaluation metrics did you use to compare the performance of the various models? Why?\n",
    "     6. What is the cost that the bank has without any model?\n",
    "     7. What cost does the bank have with your model?\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jNLkrQajeZS6"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import ast\n",
    "\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import pearsonr, spearmanr\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import KBinsDiscretizer\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import  precision_recall_curve, roc_auc_score, confusion_matrix, accuracy_score, recall_score, precision_score, f1_score,auc, roc_curve\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn import tree\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "import category_encoders\n",
    "from category_encoders import OneHotEncoder as OHE\n",
    "from category_encoders import BinaryEncoder\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "\n",
    "pd.options.mode.chained_assignment = None\n",
    "\n",
    "seed = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "n8lubR7ReZS-"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/bank_marketing_campaign.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "<p>41188 rows × 16 columns</p>\n",
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      ],
      "text/plain": [
       "       age          job  marital            education  default housing loan  \\\n",
       "0       56    housemaid  married             basic.4y       no      no   no   \n",
       "1       57     services  married          high.school  unknown      no   no   \n",
       "2       37     services  married          high.school       no     yes   no   \n",
       "3       40       admin.  married             basic.6y       no      no   no   \n",
       "4       56     services  married          high.school       no      no  yes   \n",
       "...    ...          ...      ...                  ...      ...     ...  ...   \n",
       "41183   73      retired  married  professional.course       no     yes   no   \n",
       "41184   46  blue-collar  married  professional.course       no      no   no   \n",
       "41185   56      retired  married    university.degree       no     yes   no   \n",
       "41186   44   technician  married  professional.course       no      no   no   \n",
       "41187   74      retired  married  professional.course       no     yes   no   \n",
       "\n",
       "       pdays  previous     poutcome  emp.var.rate  cons.price.idx  \\\n",
       "0        999         0  nonexistent           1.1          93.994   \n",
       "1        999         0  nonexistent           1.1          93.994   \n",
       "2        999         0  nonexistent           1.1          93.994   \n",
       "3        999         0  nonexistent           1.1          93.994   \n",
       "4        999         0  nonexistent           1.1          93.994   \n",
       "...      ...       ...          ...           ...             ...   \n",
       "41183    999         0  nonexistent          -1.1          94.767   \n",
       "41184    999         0  nonexistent          -1.1          94.767   \n",
       "41185    999         0  nonexistent          -1.1          94.767   \n",
       "41186    999         0  nonexistent          -1.1          94.767   \n",
       "41187    999         1      failure          -1.1          94.767   \n",
       "\n",
       "       cons.conf.idx  euribor3m  nr.employed    y  \n",
       "0              -36.4      4.857       5191.0   no  \n",
       "1              -36.4      4.857       5191.0   no  \n",
       "2              -36.4      4.857       5191.0   no  \n",
       "3              -36.4      4.857       5191.0   no  \n",
       "4              -36.4      4.857       5191.0   no  \n",
       "...              ...        ...          ...  ...  \n",
       "41183          -50.8      1.028       4963.6  yes  \n",
       "41184          -50.8      1.028       4963.6   no  \n",
       "41185          -50.8      1.028       4963.6   no  \n",
       "41186          -50.8      1.028       4963.6  yes  \n",
       "41187          -50.8      1.028       4963.6   no  \n",
       "\n",
       "[41188 rows x 16 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 1. How many features are available? How many customers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This dataset has 16 features available and our dataset contains 41188 customers.\n"
     ]
    }
   ],
   "source": [
    "observations = df.shape[0]\n",
    "features = df.shape[1]\n",
    "\n",
    "print(f'This dataset has {features} features available and our dataset contains {observations} customers.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 2. How many customers do you have in the dataset who actually signed up for the term deposit? And how many didn't?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               0\n",
       "job               0\n",
       "marital           0\n",
       "education         0\n",
       "default           0\n",
       "housing           0\n",
       "loan              0\n",
       "pdays             0\n",
       "previous          0\n",
       "poutcome          0\n",
       "emp.var.rate      0\n",
       "cons.price.idx    0\n",
       "cons.conf.idx     0\n",
       "euribor3m         0\n",
       "nr.employed       0\n",
       "y                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Just to check if there were missing values in the y variable, and thus take the opportunity to check for missing values in the entire dataset.\n",
    "# As we can see, there are no missing values in this dataset\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Our dataset has no missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     36548\n",
       "yes     4640\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['y'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In this dataset, there are 4640 customers who effectively adhered to the term deposit, and 36548 customers who effectively did not adhere to the term deposit.\n"
     ]
    }
   ],
   "source": [
    "no = (df['y']=='no').sum()\n",
    "yes = (df['y']=='yes').sum()\n",
    "\n",
    "print(f'In this dataset, there are {yes} customers who effectively adhered to the term deposit, and {no} customers who effectively did not adhere to the term deposit.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 3. What are the most relevant features to decide whether a customer is more likely to adhere to the term deposit?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we analyze the correlation between the variables, let's deal with the categorical variables that we want to add to our model. Just like the target variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 3.1. Quick observation of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
       "       'pdays', 'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',\n",
       "       'cons.conf.idx', 'euribor3m', 'nr.employed', 'y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking into account our target variable, and the objective of this project, which is to select the best candidates to receive the marketing campaign, in order to reduce costs for the Awesome Bank.\n",
    "\n",
    "Taking into account the knowledge we have about the context and the variables present, they all seem to me to be quite interesting for solving our problem. I will continue with all of them instead of excluding those that seem less important at this point, and see how they behave."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41188 entries, 0 to 41187\n",
      "Data columns (total 16 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   age             41188 non-null  int64  \n",
      " 1   job             41188 non-null  object \n",
      " 2   marital         41188 non-null  object \n",
      " 3   education       41188 non-null  object \n",
      " 4   default         41188 non-null  object \n",
      " 5   housing         41188 non-null  object \n",
      " 6   loan            41188 non-null  object \n",
      " 7   pdays           41188 non-null  int64  \n",
      " 8   previous        41188 non-null  int64  \n",
      " 9   poutcome        41188 non-null  object \n",
      " 10  emp.var.rate    41188 non-null  float64\n",
      " 11  cons.price.idx  41188 non-null  float64\n",
      " 12  cons.conf.idx   41188 non-null  float64\n",
      " 13  euribor3m       41188 non-null  float64\n",
      " 14  nr.employed     41188 non-null  float64\n",
      " 15  y               41188 non-null  object \n",
      "dtypes: float64(5), int64(3), object(8)\n",
      "memory usage: 5.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This dataset has 8 categorical variables and 8 numeric variables (including the target variable).\n"
     ]
    }
   ],
   "source": [
    "var_cat = len(df.select_dtypes(include=['object']).columns)\n",
    "\n",
    "var_num = len(df.select_dtypes(include=['int', 'float']).columns)\n",
    "\n",
    "\n",
    "print(f'This dataset has {var_cat} categorical variables and {var_num} numeric variables (including the target variable).')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We can see above that we have numerical and categorical variables. Let's look at them one by one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 3.1.1. Encoding da variável target: `y`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Como eu quero que a minha variável target `y` apareça no meu heatmap e no meu boxplot**  vou fazer agora o encoding da minha variável target. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     36548\n",
       "yes     4640\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Observar os valores da nossa target \n",
    "\n",
    "df.y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    36548\n",
       "1.0     4640\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encoding da variável target\n",
    "# Ordinal Encoding\n",
    "\n",
    "\n",
    "ord_encoder_y = OrdinalEncoder(categories=[['no', 'yes']])\n",
    "\n",
    "df['y'] = ord_encoder_y.fit_transform(df[['y']])\n",
    "\n",
    "df['y'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    36548\n",
       "1     4640\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Porque eu quero que seja número inteiro e não float\n",
    "\n",
    "df['y'] = df['y'].astype(int)\n",
    "\n",
    "df['y'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['no', 'yes'], dtype=object)]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ord_encoder_y.categories_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2. Variance Threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_num = df.select_dtypes(include=['int', 'float'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                 108.602451\n",
       "pdays             34935.687284\n",
       "previous              0.244927\n",
       "emp.var.rate          2.467915\n",
       "cons.price.idx        0.335056\n",
       "cons.conf.idx        21.420215\n",
       "euribor3m             3.008308\n",
       "nr.employed        5220.283250\n",
       "y                     0.099966\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the variance of each variable\n",
    "\n",
    "df_num.std()**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.6000e+01, 9.9900e+02, 0.0000e+00, ..., 4.8570e+00, 5.1910e+03,\n",
       "        0.0000e+00],\n",
       "       [5.7000e+01, 9.9900e+02, 0.0000e+00, ..., 4.8570e+00, 5.1910e+03,\n",
       "        0.0000e+00],\n",
       "       [3.7000e+01, 9.9900e+02, 0.0000e+00, ..., 4.8570e+00, 5.1910e+03,\n",
       "        0.0000e+00],\n",
       "       ...,\n",
       "       [5.6000e+01, 9.9900e+02, 0.0000e+00, ..., 1.0280e+00, 4.9636e+03,\n",
       "        0.0000e+00],\n",
       "       [4.4000e+01, 9.9900e+02, 0.0000e+00, ..., 1.0280e+00, 4.9636e+03,\n",
       "        1.0000e+00],\n",
       "       [7.4000e+01, 9.9900e+02, 1.0000e+00, ..., 1.0280e+00, 4.9636e+03,\n",
       "        0.0000e+00]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_th = VarianceThreshold(threshold=0.05)\n",
    "var_th.fit_transform(df_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_th.get_support()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'pdays', 'previous', 'emp.var.rate', 'cons.price.idx',\n",
       "       'cons.conf.idx', 'euribor3m', 'nr.employed', 'y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_num.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">  According to the Variance Threshold, we can keep all the variables. Which means that according to the Variance Threshold for a threshold value of 0.05, the variables in our dataset have a good enough variance, not being necessary to remove them due to a low variance. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 3.3. Correlations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check correlations between numerical variables, to see how the variables behave and whether they are correlated with each other or not. Let's check out the Pearson Correlation and the Spearman Correlation.Vamos verificar correlações entre variáveis numéricas, para ver como é que as variáveis se comportam e se estão correlacionadas entre eles ou não. Vamos verificar aao de  Correlação de Pearson e a Correlação de Spearman. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3.1. Numeric variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### 3.3.1.1. Pearson's Correlation Coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "\n",
    "corr = df.corr(method=\"pearson\")\n",
    "\n",
    "mask = np.triu(np.ones_like(corr))\n",
    "\n",
    "sns.heatmap(\n",
    "    corr, vmin=-1, vmax=1, annot=True, square=True, cmap=\"coolwarm\",\n",
    "    mask=mask\n",
    ")\n",
    "\n",
    "ax.set_title('Pearson Correlation Coefficient')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age -- > PearsonRResult(statistic=0.030398803040004113, pvalue=6.802136418820705e-10)\n",
      "pdays -- > PearsonRResult(statistic=-0.3249144776166292, pvalue=0.0)\n",
      "previous -- > PearsonRResult(statistic=0.23018100321659568, pvalue=0.0)\n",
      "emp.var.rate -- > PearsonRResult(statistic=-0.2983344261593794, pvalue=0.0)\n",
      "cons.price.idx -- > PearsonRResult(statistic=-0.13621121281925047, pvalue=9.318964779840192e-170)\n",
      "cons.conf.idx -- > PearsonRResult(statistic=0.05487794605319662, pvalue=7.536664996950438e-29)\n",
      "euribor3m -- > PearsonRResult(statistic=-0.3077714039407251, pvalue=0.0)\n",
      "nr.employed -- > PearsonRResult(statistic=-0.35467829592144284, pvalue=0.0)\n",
      "y -- > PearsonRResult(statistic=1.0, pvalue=0.0)\n"
     ]
    }
   ],
   "source": [
    "#df_num = df.select_dtypes(include=['int', 'float'])\n",
    "\n",
    "\n",
    "for col in df_num.columns:\n",
    "    \n",
    "    corr_pearson = stats.pearsonr(df_num[col], df_num['y'])\n",
    "    \n",
    "    print(f'{col} -- > {corr_pearson}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the variables with the highest correlation are `euribor3m` `emp.var.rate`, we will probably exclude one of these variables. Also `nr.employed` is strongly correlated with `euribor3m`, as well as the variables `nr.employed` and `emp.var.rate`. These variables that were mentioned have a correlation above 0.90 which is worrying. They are variables that make sense to be correlated, and we want our independent variables from our dataset that will be implemented in the model to be as independent as possible, so some of these variables will have to be eliminated.\n",
    "\n",
    "\n",
    "Regarding the correlation with the target, the variable that has the highest correlation with the target is (1) `nr.employed` with 0.35, followed by (2) `pdays` with 0.32, then (3) `euribor3m` with 0.31, followed by the variables (4) `emp.var.rate` with 0.3, (5) `previous`, (6) `cons.price.idx` with 0.055, (7) `cons.conf.idx ` and finally (8) `age` with 0.03.\n",
    "\n",
    "That said, a possible decision for the feature selection will be to exclude the variables:\n",
    "- `euribor3m`\n",
    "- `emp.var.rate`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
       "       'pdays', 'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',\n",
       "       'cons.conf.idx', 'euribor3m', 'nr.employed', 'y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### 3.3.1.2. Spearman Correlation Coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Zhyx37tzBzZs35cq2bdsGAwMDVKhQAUDm+53Cw8NhZmYmy6h9fmRldLImBZ9/2RcEAWvXrs2Vvn0LZW0AgNWrV+fqfSwtLdGnTx8cP34c/v7+SmPCw8Pxzz//AADq16+Pu3fvypZyZvH394dEIkHdunV/uC0tWrSAIAh49eqV0p9PmTJlsr32e35m35MpqV+/vmwi/Dl/f3/o6urm2tb0Pj4+EAQBffv2RWpqqsL5tLQ0HDp0CABkS1y3bNkiF3P16lWEhYWhfv36AIDq1avD2NgYd+/eVTqelSpVyjbj9L2aNm0KAFi5cuV3XdeiRQu8e/cOGRkZStvn7Oz83W35lmwjEVFBxswXEYmucePGKFasGLy8vODi4gKpVIrQ0FDMnz8f+vr6GD58uFy8tbU1WrZsiSlTpsDKygpbtmxBYGAgZs+eLXt/1IgRI7B3717UqlULI0eORNmyZSGVSvH8+XOcOHECo0ePRtWqVdGwYUNoamqic+fOGDduHJKTk7Fy5UrExsaK1v9q1arBxMQEAwYMwOTJk6GhoYGtW7cqTDBzw4IFC/D48WP06NEDx48fR5s2bWBhYYHo6GgEBgZi48aN2LFjB8qWLYuRI0fC398fzZs3x7Rp02BnZ4cjR45gxYoVGDhwIJycnH64HdWrV0e/fv3Qs2dPhISEoFatWtDT00NERATOnz+PMmXKYODAgUqv/Z6fWZkyZbBv3z6sXLkSFStWhJqammxp65cmT56Mw4cPo27dupg0aRJMTU2xdetWHDlyBHPmzIGRkdEP9/dznp6eWLlyJQYNGoSKFSti4MCBKF26NNLS0nDjxg2sWbMGbm5u8PLygrOzM/r164elS5dCTU0NTZs2xdOnTzFx4kTY2Nhg5MiRAAB9fX0sXboU3bt3R0xMDNq3b48iRYrg7du3uHnzJt6+ffvdk6Xs1KxZE97e3pg+fTrevHmDFi1aQEtLCzdu3ICuri6GDh2q9LpOnTph69ataNasGYYPH44qVapAQ0MDL1++RFBQEFq1aoU2bdp8V1vc3NwAAGvWrIGBgQG0tbXh4OCgdMkiEVGBlJ+7fRCRatq5c6fQpUsXwdHRUdDX1xc0NDQEW1tbwdvbW7h7965crJ2dndC8eXNhz549QunSpQVNTU3B3t5eWLBggUK979+/FyZMmCA4OzsLmpqagpGRkVCmTBlh5MiRcluZHzp0SChXrpygra0tFC1aVBg7dqxw7NgxhV3UateuLZQuXVrhPlm7/X2+1b0gfN+uchcvXhQ8PT0FXV1doXDhwkKfPn2E69evy7b+ztK9e3dBT09PoQ1Zuwp+i/T0dGHTpk1CvXr1BFNTU6FQoUJC4cKFhaZNmwrbtm2T27Hx2bNnQpcuXQQzMzNBQ0NDcHZ2FubOnSsXk13/P2/X51uif27Dhg1C1apVBT09PUFHR0coUaKE0K1bNyEkJESuz1/uWPitP7OYmBihffv2grGxsSCRSOTGCF/sdigIgnDr1i3By8tLMDIyEjQ1NYVy5crJjb8gZP9zzRqHL+OzExoaKnTv3l2wtbUVNDU1BT09PaF8+fLCpEmT5LZLz8jIEGbPni04OTkJGhoagrm5ufDrr78KL168UKjz7NmzQvPmzQVTU1NBQ0NDKFq0qNC8eXO5tv7sbodZbVq4cKHg5uYm+7vl6ekpHDp0SBbz5W6HgiAIaWlpwrx582Q/O319fcHFxUXo37+/8PDhQ1lc1t/zLymrc9GiRYKDg4Ogrq7+XeNPRFQQSARBEMSf8hERfRt7e3u4ubnh8OHD+d0UIiIiop/CZ76IiIiIiIhEwMkXERERERGRCLjskIiIiIiISATMfBERERERUYF27tw5eHl5wdraGhKJBAcOHPjqNWfPnkXFihWhra2N4sWLY9WqVQoxe/fuhaurK7S0tODq6or9+/fnQes/4eSLiIiIiIgKtA8fPqBcuXJYtmzZN8U/efIEzZo1Q82aNXHjxg38/vvvGDZsGPbu3SuLCQ4ORseOHeHt7Y2bN2/C29sbHTp0wOXLl/OqG1x2SERERERE/x4SiQT79+9H69ats40ZP348/vrrL4SFhcnKBgwYgJs3byI4OBgA0LFjRyQkJODYsWOymCZNmsDExATbt2/Pk7Yz80VERERERKJLSUlBQkKC3JGSkpIrdQcHB6NRo0ZyZY0bN0ZISAjS0tJyjLl48WKutEGZQnlWMxERERERFWhHNJzz7d5X/+iMqVOnypVNnjwZU6ZM+em6IyMjYWFhIVdmYWGB9PR0REdHw8rKKtuYyMjIn75/djj5IiIiIiIi0fn4+GDUqFFyZVpaWrlWv0Qikfuc9bTV5+XKYr4sy02cfBERERERqSiJRt5NNL5GS0srVydbn7O0tFTIYEVFRaFQoUIwMzPLMebLbFhu4jNfRERERET0n+Lp6YnAwEC5shMnTqBSpUrQ0NDIMaZatWp51i5mvoiIiIiIqEB7//49Hj16JPv85MkThIaGwtTUFLa2tvDx8cGrV6/g7+8PIHNnw2XLlmHUqFHo27cvgoODsX79erldDIcPH45atWph9uzZaNWqFQ4ePIiTJ0/i/PnzedYPbjVPRERERKSiAgxL5du9mySEfT3o/86cOYO6desqlHfv3h1+fn7o0aMHnj59ijNnzsjOnT17FiNHjsSdO3dgbW2N8ePHY8CAAXLX79mzBxMmTMDjx49RokQJzJgxA23btv3hPn0NJ19ERERERCrq3zL5+q/gskMiIiIiIhUl0eAWEGLiaBMREREREYmAky8iIiIiIiIRcNkhEREREZGKUiuUf+/5UkXMfBEREREREYmAmS8iIiIiIhUl0WDmS0zMfBEREREREYmAmS8iIiIiIhXFZ77ExcwXERERERGRCDj5IiIiIiIiEgGXHRIRERERqShuuCEuZr6IiIiIiIhEwMwXEREREZGK4oYb4mLmi4iIiIiISAScfBEREREREYmAyw6JiIiIiFSURJ3LDsXEzBcREREREZEImPkiIiIiIlJRasx8iYqZLyIiIiIiIhEw80VEREREpKIkasx8iYmZLyIiIiIiIhFw8kVERERERCQCLjskIiIiIlJREnXmYsTE0SYiIiIiIhIBJ1+5JCAgADVq1ICxsTHMzMzQokULhIeHy85fvHgR7u7u0NbWRqVKlXDgwAFIJBKEhobKYu7evYtmzZpBX18fFhYW8Pb2RnR0dD70hoiIiIhUgZq6JN8OVcTJVy758OEDRo0ahatXr+LUqVNQU1NDmzZtIJVKkZiYCC8vL5QpUwbXr1/Hn3/+ifHjx8tdHxERgdq1a8Pd3R0hISEICAjAmzdv0KFDh3zqERERERER5SY+85VL2rVrJ/d5/fr1KFKkCO7evYvz589DIpFg7dq10NbWhqurK169eoW+ffvK4leuXIkKFSpg5syZsrINGzbAxsYGDx48gJOTk8I9U1JSkJKSIlempaUFLS2tXO4dERERERH9LGa+ckl4eDi6dOmC4sWLw9DQEA4ODgCA58+f4/79+yhbtiy0tbVl8VWqVJG7/tq1awgKCoK+vr7scHFxkdWtjK+vL4yMjOQOX1/fPOohEREREf3XSNQk+XaoIma+comXlxdsbGywdu1aWFtbQyqVws3NDampqRAEARKJ/B8wQRDkPkulUnh5eWH27NkKdVtZWSm9p4+PD0aNGiVXxqwXEREREVHBxMlXLnj37h3CwsKwevVq1KxZEwBw/vx52XkXFxds3boVKSkpsslRSEiIXB0VKlTA3r17YW9vj0KFvu3HwiWGRERERPQzVHXji/zCZYe5wMTEBGZmZlizZg0ePXqE06dPy2WkunTpAqlUin79+iEsLAzHjx/HvHnzAECWERs8eDBiYmLQuXNnXLlyBY8fP8aJEyfQq1cvZGRk5Eu/iIiIiIgo93DylQvU1NSwY8cOXLt2DW5ubhg5ciTmzp0rO29oaIhDhw4hNDQU7u7u+OOPPzBp0iQAkD0HZm1tjQsXLiAjIwONGzeGm5sbhg8fDiMjI6ip8cdERERERLlPoi7Jt0MVSYQvHz4iUWzduhU9e/ZEfHw8dHR08rs5RERERKSCQmp75tu9K50Nzrd75xc+8yUSf39/FC9eHEWLFsXNmzcxfvx4dOjQgRMvIiIiIiIVwcmXSCIjIzFp0iRERkbCysoKv/zyC2bMmJHfzSIiIiIiFSbh4y2i4rJDIiIiIiIVda1u9Xy7d8WgC/l27/zCzBcRERERkYpS1Zcd5xfmGYmIiIiIiETAyRcREREREZEIuOyQiIiIiEhFqano+7byCzNfREREREREImDmi4iIiIhIRXHDDXEx80VERERERCQCZr6IiIiIiFQUX7IsLo42ERERERGRCDj5IiIiIiIiEgGXHRIRERERqShuuCEuZr6IiIiIiIhEwMwXEREREZGK4kuWxcXMFxERERERkQg4+SIiIiIiIhIBlx0SEREREakobrghLma+iIiIiIiIRMDMFxERERGRipKoMRcjJo42ERERERGRCJj5IiIiIiJSUXzmS1zMfBEREREREYmAky8iIiIiIiIRcNkhEREREZGK4rJDcTHzRUREREREJAJmvoiIiIiIVBQzX+Li5Os/Jvzx4/xuQoFTonjx/G4CERERERGXHRIREREREYmBmS8iIiIiIhUlUWMuRkwcbSIiIiIiIhEw80VEREREpKLU1LnhhpiY+SIiIiIiogJvxYoVcHBwgLa2NipWrIi///4729gePXpAIpEoHKVLl5bF+Pn5KY1JTk7Osz5w8kVEREREpKIkapJ8O77Hzp07MWLECPzxxx+4ceMGatasiaZNm+L58+dK4xcvXoyIiAjZ8eLFC5iamuKXX36RizM0NJSLi4iIgLa29g+P59dw8kVERERERAXaggUL0Lt3b/Tp0welSpXCokWLYGNjg5UrVyqNNzIygqWlpewICQlBbGwsevbsKRcnkUjk4iwtLfO0H5x8ERERERGR6FJSUpCQkCB3pKSkKMSlpqbi2rVraNSokVx5o0aNcPHixW+61/r169GgQQPY2dnJlb9//x52dnYoVqwYWrRogRs3bvx4h74BJ19ERERERCpKoqaWb4evry+MjIzkDl9fX4U2RkdHIyMjAxYWFnLlFhYWiIyM/GofIyIicOzYMfTp00eu3MXFBX5+fvjrr7+wfft2aGtro3r16nj48OHPDWoOuNshERERERGJzsfHB6NGjZIr09LSyjZeIpF/TkwQBIUyZfz8/GBsbIzWrVvLlXt4eMDDw0P2uXr16qhQoQKWLl2KJUuWfEMPvh8nX0REREREKup7N77ITVpaWjlOtrKYm5tDXV1dIcsVFRWlkA37kiAI2LBhA7y9vaGpqZljrJqaGipXrpynmS8uOyQiIiIiogJLU1MTFStWRGBgoFx5YGAgqlWrluO1Z8+exaNHj9C7d++v3kcQBISGhsLKyuqn2psTZr6IiIiIiKhAGzVqFLy9vVGpUiV4enpizZo1eP78OQYMGAAgcwnjq1ev4O/vL3fd+vXrUbVqVbi5uSnUOXXqVHh4eMDR0REJCQlYsmQJQkNDsXz58jzrBydfREREREQqKj+XHX6Pjh074t27d5g2bRoiIiLg5uaGo0ePynYvjIiIUHjnV3x8PPbu3YvFixcrrTMuLg79+vVDZGQkjIyMUL58eZw7dw5VqlTJs35IBEEQ8qx2El3448f53YQCp0Tx4vndBCIiIqIC6Vm/1vl2b7s1B/Lt3vmFmS8iIiIiIhUlUeMWEGLiaBMREREREYmAky8iIiIiIiIRcNkhEREREZGK+rdsuPFfwcwXERERERGRCJj5IiIiIiJSUdxwQ1wcbSIiIiIiIhEw80VEREREpKokfOZLTMx8ERERERERiYCTLyIiIiIiIhFw2SERERERkYriVvPiYuaLiIiIiIhIBJx8/QR7e3ssWrQov5tBRERERPRDJGpq+XaoIi47JAiCgK1btyLg2DG8f/8ezs7OGDR4MOzs7HK87vz589js74+IiAhYWVmhe/fuqFa9ulzM4cOHsXfPHsTExMDOzg79+veHm5ub7PyFCxdw7OhRPHr0CAkJCVi6bBlKlCghV8exo0dx5swZPHr0CB8/fsSu3buhr6+fewNARERERCQC1Zxykpw9u3dj/759GDhoEBYtXgwTExP88fvvSEpKyvaasLAwzPL1Rb369bF8xQrUq18fvr6+uHfvnizm7NmzWLN6NTp26oSly5ahdOnSmDRxIqKiomQxycnJcHV1RY+ePbO9V0pKCipWqoSOnTrlToeJiIiIiPIBJ1//V6dOHQwZMgRDhgyBsbExzMzMMGHCBAiCAACIioqCl5cXdHR04ODggK1btyrUsWDBApQpUwZ6enqwsbHBoEGD8P79ewDAhw8fYGhoiD179shdc+jQIejp6SExMRGpqakYMmQIrKysoK2tDXt7e/j6+uZpvwVBwIEDB9CpUydUr14d9vb2GD16NFJSUnDmzJlsrztw4ADKV6iAjh07wsbGBh07doS7uzsOHjggi9m/fz8aNWqEJk2awNbWFv0HDEDhwoVx5MgRWUz9+vXRpWtXlC9fPtt7tW7TBh06dICLi0tudJmIiIiI/k+iJsm3QxVx8vWZTZs2oVChQrh8+TKWLFmChQsXYt26dQCAHj164OnTpzh9+jT27NmDFStWyGVwAEBNTQ1LlizB7du3sWnTJpw+fRrjxo0DAOjp6aFTp07YuHGj3DUbN25E+/btYWBggCVLluCvv/7Crl27cP/+fWzZsgX29vZ52ufIyEjExsaiQoUKsjINTU2UKVMGYXfvZnvdvbAwuWsAoELFirgbFgYASEtLw6OHDxViyleokGO9RERERET/VXzm6zM2NjZYuHAhJBIJnJ2dcevWLSxcuBC1a9fGsWPHcOnSJVStWhUAsH79epQqVUru+hEjRsj+28HBAX/++ScGDhyIFStWAAD69OmDatWq4fXr17C2tkZ0dDQOHz6MwMBAAMDz58/h6OiIGjVqQCKRfPWZq9wQGxsLADA2MZErNzY2VphcfnmdsbGxwjWxMTEAgISEBEilUoV6TYyNZfckIiIiovylqhtf5BeO9mc8PDwgkXxKgXp6euLhw4cICwtDoUKFUKlSJdk5FxcXhclHUFAQGjZsiKJFi8LAwADdunXDu3fv8OHDBwBAlSpVULp0afj7+wMANm/eDFtbW9SqVQtAZnYtNDQUzs7OGDZsGE6cOJFje1NSUpCQkCB3pKSk5HhN0OnTaNumjezISE8HALl+A4CgpOxLCucFQaFMoV4lMUREREREqoCTr2+Qns0E5XPPnj1Ds2bN4Obmhr179+LatWtYvnw5gMwleFn69OkjW3q4ceNG9OzZU1ZvhQoV8OTJE/z555/4+PEjOnTogPbt22d7T19fXxgZGckdq1atyrEvVT08sGz5ctlhaGQEALKMVZb4uDiFyeXnTExMFDJYcfHxskyXoaEh1NTUFOqNi4/PsV4iIiIiEg+f+RIXJ1+fuXTpksJnR0dHuLm5IT09HSEhIbJz9+/fR1xcnOxzSEgI0tPTMX/+fHh4eMDJyQmvX79WuMevv/6K58+fY8mSJbhz5w66d+8ud97Q0BAdO3bE2rVrsXPnTuzduxcxX0xgsvj4+CA+Pl7uGDBgQI591NXVhbW1teywtbWFiYkJrt+4IYtJS0vDrVu3UMrVNdt6XEqVwo3r1+XKrl+/Dtf/L8XU0NBASUdH3PisXgC4cf16jvUSEREREf1X8Zmvz7x48QKjRo1C//79cf36dSxduhTz58+Hs7MzmjRpgr59+2LNmjUoVKgQRowYAR0dHdm1JUqUQHp6OpYuXQovLy9cuHBBaRbKxMQEbdu2xdixY9GoUSMUK1ZMdm7hwoWwsrKCu7s71NTUsHv3blhaWmabKdLS0oKWlpZ8WXT0d/VZIpGgdevW2LVzJ4paW8O6aFHs3LkTWlpaqFOnjixu3rx5MDMzQ8//bwnfqlUrjBs7Frt37YKHpycuBQcj9MYNzJ03T3ZNmzZtMH/ePDg6OsKlVCkEHDuGt2/folmzZrKYxMREREVFIebdOwDAy5cvZeNkamoKAIiJiUFsbKxsMvv06VPo6OigSJEiMDAw+K7+EhERERHlF06+PtOtWzd8/PgRVapUgbq6OoYOHYp+/foByFwi2KdPH9SuXRsWFhaYPn06Jk6cKLvW3d0dCxYswOzZs+Hj44NatWrB19cX3bp1U7hP7969sW3bNvTq1UuuXF9fH7Nnz8bDhw+hrq6OypUr4+jRo1DL4wch2//yC1JSU7F8+XLZS5anz5gBXV1dWczbqCiofbbs0tXVFb/99hv8/f2xefNmWFlZ4TcfH7nt4GvXro3ExERs27YNMTExsLe3x9Rp02BhYSGLuXTpEhYuWCD7PHvWLABAl65d8euvvwIAjh49im2fbe0/buxYAMDIUaPQsGHDXB4NIiIiItWhqsv/8otEyHqRlYqrU6cO3N3dsWjRojy/19atWzF8+HC8fv0ampqauVp3+OPHuVrff0GJ4sXzuwlEREREBVKUj2KiQCxFfP3z7d75hZkvESUlJeHJkyfw9fVF//79c33iRURERET0XbjVvKg42iKaM2cO3N3dYWFhAR8fn/xuDhERERERiYjLDv9juOxQEZcdEhERESkX9UePfLt3kRl++Xbv/MJlh0REREREKiqn99hS7uOyQyIiIiIiIhEw80VEREREpKIk3HBDVBxtIiIiIiIiETDzRURERESkoviSZXEx80VERERERCQCTr6IiIiIiIhEwGWHRERERESqihtuiIqjTUREREREJAJmvoiIiIiIVBQ33BAXM19EREREREQi4OSLiIiIiIhIBFx2SERERESkoiQS5mLExNEmIiIiIiISATNfRERERESqihtuiIqZLyIiIiIiIhEw80VEREREpKIkfMmyqDjaREREREREIuDki4iIiIiISARcdkhEREREpKIk3HBDVMx8ERERERERiYCZLyIiIiIiVcWXLIuKo01ERERERCQCTr6IiIiIiIhEwGWHREREREQqihtuiIuZLyIiIiIiIhEw8/UfIwj87cWX5u2T5ncTCpQxbfk7FyIiIvo/NX4vEBNHm4iIiIiISATMfBERERERqSiJhKumxMTMFxERERERkQg4+SIiIiIiIhIBlx0SEREREakqbrghKo42ERERERGRCJj5IiIiIiJSUXzJsriY+SIiIiIiIhIBJ19EREREREQi4LJDIiIiIiJVJWEuRkwcbSIiIiIiIhEw80VEREREpKq44YaomPkiIiIiIqICb8WKFXBwcIC2tjYqVqyIv//+O9vYM2fOQCKRKBz37t2Ti9u7dy9cXV2hpaUFV1dX7N+/P0/7wMkXEREREZGKkkjU8u34Hjt37sSIESPwxx9/4MaNG6hZsyaaNm2K58+f53jd/fv3ERERITscHR1l54KDg9GxY0d4e3vj5s2b8Pb2RocOHXD58uUfGstvIREEQciz2kl0j8Kf5HcTCpwDN+3yuwkFypi2/J0LERERZfqw+o98u7de/xnfHFu1alVUqFABK1eulJWVKlUKrVu3hq+vr0L8mTNnULduXcTGxsLY2FhpnR07dkRCQgKOHTsmK2vSpAlMTEywffv2b+/Id+C3MCIiIiIiEl1KSgoSEhLkjpSUFIW41NRUXLt2DY0aNZIrb9SoES5evJjjPcqXLw8rKyvUr18fQUFBcueCg4MV6mzcuPFX6/wZnHwREREREakqNUm+Hb6+vjAyMpI7lGWxoqOjkZGRAQsLC7lyCwsLREZGKu2WlZUV1qxZg71792Lfvn1wdnZG/fr1ce7cOVlMZGTkd9WZG7jbIRERERERic7HxwejRo2SK9PS0so2XiKR35lREASFsizOzs5wdnaWffb09MSLFy8wb9481KpV64fqzA2cfBERERERqSiJWv4thNPS0spxspXF3Nwc6urqChmpqKgohcxVTjw8PLBlyxbZZ0tLy5+u83tx2SERERERERVYmpqaqFixIgIDA+XKAwMDUa1atW+u58aNG7CyspJ99vT0VKjzxIkT31Xn92Lmi4iIiIiICrRRo0bB29sblSpVgqenJ9asWYPnz59jwIABADKXML569Qr+/v4AgEWLFsHe3h6lS5dGamoqtmzZgr1792Lv3r2yOocPH45atWph9uzZaNWqFQ4ePIiTJ0/i/PnzedYPTr6IiIiIiFRVHj7flJs6duyId+/eYdq0aYiIiICbmxuOHj0KO7vMVwpFRETIvfMrNTUVY8aMwatXr6Cjo4PSpUvjyJEjaNasmSymWrVq2LFjByZMmICJEyeiRIkS2LlzJ6pWrZpn/eB7vv5j+J4vRXzPlzy+54uIiIiyJG2YnG/31u01Nd/unV+Y+SIiIiIiUlX5uOGGKuJoExERERERiYCTLyIiIiIiIhFw2eEP6tGjB+Li4nDgwIH8bgoRERER0Y/5l2y48V/BydcPWrx4Mf7Le5UIgoBtW7cgIOAY3r9/D2dnZwwcNBh2dvY5Xnfh/Hls3uyPiIgIWFlZoVv37qhWrbrs/O1bt7B37x48evQQMTExmDBhEjy/eJfC1i2bce7cWbx9+xaFNDRQsmRJdOvWAy4uLnnR1R8iCAKun1qOe1d2IeVjAorYlEW1VhNhauGY7TUPru3H2T2/K5T3nBaKQhqZLxiMeHIV/5zbgOhXd5CU+BYNf10K+9IN8qwfRERERCQelVt2mJqamiv1GBkZwdjYOFfqKoj27NmN/fv3Y8DAQVi4aAlMTEwx4Y/fkZSUlO01YWF3MWvWTNSrVw/Llq9AvXr1MMt3Ju7duyeLSU5OhoODAwYMHJRtPUWLFsOAgYOwfMUqzJ07DxZFLDBxwu+Ij4/LzS7+lJvn1uHWeT9UazkBrQfvgo6BOY6t743UlA85XqehpY+uv5+TO7ImXgCQnvoRplbOqNZyQl53gYiIiAgSNbV8O1TRv77XderUwZAhQzBkyBAYGxvDzMwMEyZMkGWl7O3tMX36dPTo0QNGRkbo27cvAODixYuoVasWdHR0YGNjg2HDhuHDh8wvzj4+PvDw8FC4V9myZTF5cuZ2nD169EDr1q1l51JSUjBs2DAUKVIE2traqFGjBq5evSo77+fnpzBZO3DgACSfpXpv3ryJunXrwsDAAIaGhqhYsSJCQkJyZZy+hyAIOHhgPzp26oTq1WvA3t4eo0aPRkpKCs6eCcr2uoMHDqB8+Qro0LETbGxs0KFjJ5Rzd8fBg/tlMZUqV0a37j1QvXqNbOupU7cuypevACsrK9jZ2aNvv35ISkrCkycFYxt9QRBw+4I/3Ov2h4NbI5haOqHOL7OQnpaM8NDDOV4rkUiga1BY7vicjXMtVG40Ag5ujfKyC0RERESUD/71ky8A2LRpEwoVKoTLly9jyZIlWLhwIdatWyc7P3fuXLi5ueHatWuYOHEibt26hcaNG6Nt27b4559/sHPnTpw/fx5DhgwBAHTt2hWXL19GeHi4rI47d+7g1q1b6Nq1q9I2jBs3Dnv37sWmTZtw/fp1lCxZEo0bN0ZMTMw396Nr164oVqwYrl69imvXruG3336DhobGD47Kj4uMjERsbCwqVKggK9PQ0IRbmTIICwvL9rp798JQ/rNrAKBChYoIu5v9NV+TlpaGY8eOQU9PDw4OxX+4ntyUGPsSHxOjUczx03JK9UKasHKojDfPbuR4bVpqErbProdtvnUQ4DcA0a/v5nVziYiIiLInUcu/QwX9J575srGxwcKFCyGRSODs7Ixbt25h4cKFsixXvXr1MGbMGFl8t27d0KVLF4wYMQIA4OjoiCVLlqB27dpYuXIl3NzcULZsWWzbtg0TJ04EAGzduhWVK1eGk5OTwv0/fPiAlStXws/PD02bNgUArF27FoGBgVi/fj3Gjh37Tf14/vw5xo4dK3u2ydEx++eH8lJsbCwAwNjYRK7c2NgEb6Pe5HidyRfXmBibyOr7HlcuX8bs2b5ISUmBqakpps+YCSMjo++uJy98TIwGAOjom8uV6+ibITHudbbXGRV2QO32M2Fq6YTU5Pe4c3Ez/lrVFe2G7YeRuX1eNpmIiIiICoD/xJTTw8NDbvmep6cnHj58iIyMDABApUqV5OKvXbsGPz8/6Ovry47GjRtDKpXKlrZ17doVW7duBZC5zGz79u3ZZr3Cw8ORlpaG6tU/ZUI0NDRQpUqVHDNFXxo1ahT69OmDBg0aYNasWXKZN2VSUlKQkJAgd6SkpHzz/bIEBZ1Gu7atZUdGRjoAJZvfCMLXd8T54rQA4Yc20SlbrhyWLluBefMXoELFipjlOxNxcXHfX1EueHTjEDZOrig7pBlpABS6mtlXhdJPLGzd4Vi+JcysXGDlUAn1Oy+Ekbk97lzcmoetJyIiIqKC4j+R+foaPT09uc9SqRT9+/fHsGHDFGJtbW0BAF26dMFvv/2G69ev4+PHj3jx4gU6deqktP6s58skX8wyBEGQlampqSnsjpiWlib3ecqUKejSpQuOHDmCY8eOYfLkydixYwfatGmj9L6+vr6YOnWqXNnQocMwbPgIpfHZqVrVA87On3YSTEvL3JQkNjYWpqZmsvK4+DiFzNbnTEwUs1xxcXEKGbRvoa2tDWtra1hbW8PFpRT69umFE8cD0KGj8p9BXrJ1rYe2NmVlnzMyMscn6X00dA2LyMqT38dAR99M4frsSNTUULiYG+LfPcu9xhIRERF9DzVuNS+m/0Tm69KlSwqfHR0doa6urjS+QoUKuHPnDkqWLKlwaGpqAgCKFSuGWrVqYevWrdi6dSsaNGgACwsLpfVlXXf+/HlZWVpaGkJCQlCqVCkAQOHChZGYmCjb1AMAQkNDFepycnLCyJEjceLECbRt2xYbN27Mtt8+Pj6Ij4+XO/oPGJhtfHZ0dXVlEx1ra2vY2trBxMQEN65/en4pLS0Nt2/dkvVHGReXUgi9If/M043r11HKNftrvpUgCAqTVbFoaunByNxOdpgUKQkdA3O8enhRFpORnoqIJ1dhYVf+m+sVBAHvXt9T2HSDiIiIiP6b/hOZrxcvXmDUqFHo378/rl+/jqVLl2L+/PnZxo8fPx4eHh4YPHgw+vbtCz09PYSFhSEwMBBLly6VxXXt2hVTpkxBamoqFi5cmG19enp6GDhwIMaOHQtTU1PY2tpizpw5SEpKQu/evQEAVatWha6uLn7//XcMHToUV65cgZ+fn6yOjx8/YuzYsWjfvj0cHBzw8uVLXL16Fe3atcv2vlpaWtDS0vqi7N3XhuurJBIJWrVug127dsC6qDWsrYti184d0NLSQu06dWVx8+fNhZmZGXr07AUAaNmqNcaPG4Pdu3fBw8MTly4FIzT0BubM/fSz+PjxI16//vRcVOSbSISHh8PAwABFihRBcnIydu7YjqoeHjA1MUVCYgKOHD6M6Oho1KhZ86f7lhskEgncqndD6Jk1MDS3g5GZHULPrEEhDW2UcG8hiwvaNR56hhao0mQUAODayeUoYlsORuZ2SEt+j9sXt+BdxD1UbzVRdk1aygckvHsu+5wY+xLvXodBS9cI+sbW4nWSiIiIVIJERTe+yC//iclXt27d8PHjR1SpUgXq6uoYOnQo+vXrl2182bJlcfbsWfzxxx+oWbMmBEFAiRIl0LFjR7m4X375BUOHDoW6urrctvLKzJo1C1KpFN7e3khMTESlSpVw/PhxmJhkLrkzNTXFli1bMHbsWKxZswYNGjTAlClTZO1UV1fHu3fv0K1bN7x58wbm5uZo27atwrJCsbRv/wtSU1KwYvmy/79k2QV/Tp8JXV1dWczbt1GQfJaqdnV1xfjffLDZfxO2bPaHpZUVxv/mI/dy5IcPH8Dnt/Gyz+vWrgEA1G/QAKNGjYGamhpevHyBUzNOIj4+AYaGBnB0csKcufO++oJnMZWr1QcZaSm4cHAaUj8moLBNWTTttQ6aWp+WuH6Ii5D7By01OQHn909CUmI0NLUNYGZdCl79/FHksyWNb1/dwZG13WWfLx2ZDQBwrNAadX7xFaFnRERERJRXJMKXDyL9y9SpUwfu7u5YtGhRfjelQHgUXjDehVWQHLhpl99NKFDGtOVvuIiIiChT8vbZ+XZv7c7jvx70H/OfyHwREREREdEP4IYbouKvwImIiIiIiETwr898nTlzJr+bQERERET078QNN0TF0SYiIiIiIhLBvz7zRUREREREP0jCZ77ExMwXERERERGRCDj5IiIiIiIiEgGXHRIRERERqSo15mLExNEmIiIiIiISATNfRERERESqilvNi4qjTUREREREJAJOvoiIiIiIiETAZYdERERERKpKje/5EhMzX0RERERERCJg5ouIiIiISFVxww1RcbSJiIiIiIhEwMwXEREREZGqkvCZLzEx80VERERERCQCTr6IiIiIiIhEwGWHRERERESqSo25GDFxtImIiIiIiETAzBcRERERkarihhuiYuaLiIiIiIhIBJx8ERERERERiYDLDomIiIiIVJWEuRgxcbSJiIiIiIhEwMwXEREREZGq4lbzouJoExERERERiYCZLyIiIiIiVcWt5kXFydd/jJb0Y343ocAZYH04v5tQoKw+0TK/m1Cg9G+U3y0gIiIiVcFlh0RERERERCJg5ouIiIiISFVxq3lRcbSJiIiIiIhEwMwXEREREZGq4oYbomLmi4iIiIiISAScfBEREREREYmAyw6JiIiIiFSVGnMxYuJoExERERERiYCZLyIiIiIiFSVwww1RMfNFREREREQkAma+iIiIiIhUFV+yLCqONhERERERkQg4+SIiIiIiIhIBlx0SEREREakqLjsUFUebiIiIiIhIBMx8ERERERGpKG41Ly5mvoiIiIiIiETAyRcREREREZEIuOyQiIiIiEhVccMNUXG0iYiIiIiIRMDMFxERERGRquKGG6Ji5ouIiIiIiAq8FStWwMHBAdra2qhYsSL+/vvvbGP37duHhg0bonDhwjA0NISnpyeOHz8uF+Pn5weJRKJwJCcn51kfOPkiIiIiIlJVamr5d3yHnTt3YsSIEfjjjz9w48YN1KxZE02bNsXz58+Vxp87dw4NGzbE0aNHce3aNdStWxdeXl64ceOGXJyhoSEiIiLkDm1t7R8ezq/hskMiIiIiIirQFixYgN69e6NPnz4AgEWLFuH48eNYuXIlfH19FeIXLVok93nmzJk4ePAgDh06hPLly8vKJRIJLC0t87Ttn2Pmi4iIiIiIRJeSkoKEhAS5IyUlRSEuNTUV165dQ6NGjeTKGzVqhIsXL37TvaRSKRITE2FqaipX/v79e9jZ2aFYsWJo0aKFQmYst3HyRURERESkogSJJN8OX19fGBkZyR3KsljR0dHIyMiAhYWFXLmFhQUiIyO/qZ/z58/Hhw8f0KFDB1mZi4sL/Pz88Ndff2H79u3Q1tZG9erV8fDhw58b1Bxw2SEREREREYnOx8cHo0aNkivT0tLKNl7yxc6MgiAolCmzfft2TJkyBQcPHkSRIkVk5R4eHvDw8JB9rl69OipUqIClS5diyZIl39qN78LJFxERERGRqsrHlyxraWnlONnKYm5uDnV1dYUsV1RUlEI27Es7d+5E7969sXv3bjRo0CDHWDU1NVSuXJmZr/8aPz8/jBgxAnFxcfndFKUOHjmG3fsO4F1MLOxtbTCob2+UcXPNNv7mrdtYtW4jnj5/ATNTU3Rs1xpezZoojQ06+zdmzF2Aah5VMG2Cj6x809Yd2Lx9p1ysibExdm/ZmDudymW7Tl3E5qNnEB2fiOLWFhjTtSXKOxdXGns65Bb2nA7G/eevkZaWjuJFLdCvTSNUK+MsF7Ph0Gm8iIpGenoGbC3N8WuT2mhevaJYXfppgiAg+Ngy3LqwE8kfE2BlVw71OkyCuZXjN11/79oRHPUbhRJl6qNVvxVKY66cWI3zhxagfJ1uqNvuj9xsPhERERVQmpqaqFixIgIDA9GmTRtZeWBgIFq1apXtddu3b0evXr2wfft2NG/e/Kv3EQQBoaGhKFOmTK60WxlOvr5RamoqNDU1fzqmoAs6dx4r127AsIH9UNrVBUeOnYDPlD+xfsUSWBQprBAfEfkGf0yZjmaNG+K3MSNw5+49LFm5BkZGRqhV3VMu9k1UFFZv2IQypZVP5OxtbTBnxlTZZ7Xv3IJULCcuh2L+1r/wW7c2cHeyx96gSxg6fz12+46BlZmJQvz1+49RtbQjBrdvCgNdbfz1dwhGLtyITZOHwsWuKADAUE8XvbzqwcG6CAqpq+Pvm2GYum4XTAz15SZpBdnVk2txPWgjGnedBZMi9rh8fCX2LuuJnhMDoKmtn+O1CTGvcO7AbBQtUSnbmMhn/+CfCzthbv3vGA8iIiLKPaNGjYK3tzcqVaoET09PrFmzBs+fP8eAAQMAZC5hfPXqFfz9/QFkTry6deuGxYsXw8PDQ5Y109HRgZGREQBg6tSp8PDwgKOjIxISErBkyRKEhoZi+fLledaPXP12KwgC5syZg+LFi0NHRwflypXDnj17AABnzpyBRCLB8ePHUb58eejo6KBevXqIiorCsWPHUKpUKRgaGqJz585ISkqS1VmnTh0MGTIEQ4YMgbGxMczMzDBhwgQIgqC0Dffv34dEIsG9e/fkyhcsWAB7e3sIgoCMjAz07t0bDg4O0NHRgbOzMxYvXiwX36NHD7Ru3Rq+vr6wtraGk5OTwr2y2jZq1CiYm5ujYcOGsnuVKVMGenp6sLGxwaBBg/D+/XvZOPTs2RPx8fGyF7lNmTIFQObkbdy4cShatCj09PRQtWpVnDlz5od+Fj9q74G/0KRhfTRr3BB2NjYY1K83ipib4dDRAKXxh48dR5HC5hjUrzfsbGzQrHFDNGlQD7v3HZCLy8jIwMx5C9G9aydYWSpPD6urq8PUxER2GP//L0ZBsyXgHFrVqow2darCwdoCY7q2goWpMfacClYaP6ZrK3RvXheli9vA1rIwhvzSFLYW5jh3464splKpEqhXqQwcrC1gY2GOLo1qoqSNFUIfPBGrWz9FEATcOOOPKo0GwNG9EcytndD419lIT0vGvZDDOV4rlWbg6KYx8Gw2FEZmNkpjUlM+4OimsWjYeTq0dQvmnwsiIqJ/I0Gilm/H9+jYsSMWLVqEadOmwd3dHefOncPRo0dhZ2cHAIiIiJB759fq1auRnp6OwYMHw8rKSnYMHz5cFhMXF4d+/fqhVKlSaNSoEV69eoVz586hSpUquTO4SuRq5mvChAnYt28fVq5cCUdHR5w7dw6//vorChf+lDGZMmUKli1bBl1dXXTo0AEdOnSAlpYWtm3bhvfv36NNmzZYunQpxo8fL7tm06ZN6N27Ny5fvoyQkBD069cPdnZ26Nu3r0IbnJ2dUbFiRWzduhV//vmnrHzbtm3o0qULJBIJpFIpihUrhl27dsHc3BwXL15Ev379YGVlJbcDyqlTp2BoaIjAwMBsJ3ubNm3CwIEDceHCBVmMmpoalixZAnt7ezx58gSDBg3CuHHjsGLFClSrVg2LFi3CpEmTcP/+fQCAvn5mVqBnz554+vQpduzYAWtra+zfvx9NmjTBrVu34Oj4bUu3fkZaWhoePApHp/Zt5corlnfH3S8ms1nu3ruPiuXd5coqVSiPY4GnkJ6ejkKFMv+IbdmxC8aGRmjaqAFu3bmrpCbg1esIdOzWCxoaGnBxckKv7l1hLeJ7F75FWno67j19hR7N68qVe7g54Z9Hz76pDqlUig/JKTDS01V6XhAEXL37CM8iojCsQ7OfbrMY4t+9xIeEt7B3qSErK6ShiWIlK+P1kxsoW6NTttdeOrYcOvqmKOP5C149uqY05vSuaSheujbsXKrh8vGVud5+IiIiKvgGDRqEQYMGKT3n5+cn9/lbEhgLFy7EwoULc6Fl3y7XJl8fPnzAggULcPr0aXh6Zi43K168OM6fP4/Vq1ejX79+AIDp06ejevXqAIDevXvDx8cH4eHhKF4883mZ9u3bIygoSG7yZWNjg4ULF0IikcDZ2Rm3bt3CwoULlU6+AKBr165YtmyZbPL14MEDXLt2TZaG1NDQwNSpn5a3OTg44OLFi9i1a5fc5EtPTw/r1q3LcSlhyZIlMWfOHLmyESNGyNX9559/YuDAgVixYgU0NTVhZGSk8EK38PBwbN++HS9fvoS1tTUAYMyYMQgICMDGjRsxc+ZMhXunpKQovAshJTUVWj+49DE+IRFSqRQmJsZy5SYmxoi5Hqf0mpjYWJiYlFeIz8jIQHxCAsxMTXH7bhiOnTiF1UsWZHvvUs6OGDdqOIoVtUZsXBy27tiN4WN8sG7FYhgZGv5Qf/JCXOIHZEilMDMykCs3M9LHu/jEb6pjS8A5JKekomHVcnLliUkf0XTEdKSmp0NdTQ2/dWsDDzfFjGtBlJTwFgCga2gmV65rYI6EmNfZXvfq8TXcvrQH3uMPZBtz79oRvHlxF13H7smVthIREdFnvmG3QMo9uTb5unv3LpKTk2VL77KkpqbKvUW6bNmysv+2sLCArq6ubOKVVXblyhW5Ojw8POS2kfT09MT8+fORkZEBdXV1hbZ06tQJY8eOxaVLl+Dh4YGtW7fC3d0drq6fnjVatWoV1q1bh2fPnuHjx49ITU2Fu7u7XD1lypT56jNclSopPqMSFBSEmTNn4u7du0hISEB6ejqSk5Px4cMH6OnpKa3n+vXrEARBYXljSkoKzMzMlF7j6+srN4kEgBFDBmHUsME5tvlrvvwr+PVtPBW3/QQytwNNSvqIWfMXYdTQgTAyyn4SVaXS5xtL2MHVxRnd+gxE4KkgtG+T/YOU+eXL4RAEKA6cEgHBN7B6/wksGNEDpobyz0HpaWth+58jkZScgit3H2HB9kMoWtgMlUqVyL2G55Kwq3/h5I7Jss+tB6z+/38p+bOQzbikJr/HsU1j0bDTn9DRN1UakxgbgTN7Z6DdoA0opPH13ZCIiIiICrJcm3xJpVIAwJEjR1C0aFG5c1paWggPDweQmXXKIpFI5D5nlWXV9aOsrKxQt25dbNu2DR4eHti+fTv69+8vO79r1y6MHDkS8+fPh6enJwwMDDB37lxcvnxZrp7sJko5xTx79gzNmjXDgAED8Oeff8LU1BTnz59H7969kZaWlm09UqkU6urquHbtmsKEMmtZ4peUvRsh6sXjr7Y5O0aGBlBTU0NMbJxceVxcPEyMlT9nY2pigtjYWIV4dXV1GBoY4OnzF4h8E4UJ0z5l7rImZ41atoPf6mWwtrJSqFdHWxsO9nZ4+Trih/uTF4wN9KCupoboOPksV0zCe5gZGmRzVaYTl0MxbcNuzB7sjaqlFTNaampqsLEwBwA42xXFk9dR2Hj4dIGcfJUoUw+W9p8ydxnpqQCApIRo6Bt9en/Gx/fvoGdgrrSOuOgXSIh5hQNrBsrKBCHz7/7C4a7oOSEA0a8fICnxHbbM/bQUVpBm4GX4VYSe24rhC29BTU3xFzBEREREBVGuTb5cXV2hpaWF58+fo3bt2grnsyZfP+LSpUsKnx0dHZVmvbJ07doV48ePR+fOnREeHo5OnT49c/L333+jWrVqcmtGf6Z9nwsJCUF6ejrmz58v261v165dcjGamprIyMiQKytfvjwyMjIQFRWFmjVrftO9lL0bIf4ndlvU0NCAU8kSuBZ6EzWqfXrh3LXQm6hWVfmDh64uzgi+clWuLORGKJxKlkChQoVgW6wo1i5bJHd+45Zt+Jj0EYP69UZhc+VfzFPT0vD8xUuUKV3qh/uTFzQKFYKLfVFcvvMQ9Sp92ob08p0HqF2+dLbXBQTfwLT1uzBjYFfUdP/WPglIS0//yRbnDU1tfbkdDAVBgJ5hYTy7fwFFbDIzzBnpqXj56CpqthyjtA5Ti+Lo5nNIruzC4UVITfmAuu3+gIGJJXQNTBVijm/1galFcVRu0JcTLyIiop/0vRtf0M/JtcmXgYEBxowZg5EjR0IqlaJGjRpISEjAxYsXoa+vL9uJ5Ee8ePECo0aNQv/+/XH9+nUsXboU8+fPl53/cmtJAGjbti0GDhyIgQMHom7dunLZuJIlS8Lf3x/Hjx+Hg4MDNm/ejKtXr8LBwSHHdtSvXx9t2rTBkCFDso0pUaIE0tPTsXTpUnh5eeHChQtYtWqVXIy9vT3ev3+PU6dOoVy5ctDV1YWTkxO6du2Kbt26Yf78+Shfvjyio6Nx+vRplClTBs2aibPxQrvWLTF7wWI4lSwB11LOOBIQiKi30fBq1hgAsM5vM6LfxeC30Zk7xbRo2hgHDx/FyrUb0KxJQ9wNu4+AwFP4fWxmRk5TUxMO9vI/e/3/Zws/L1+93g8eVSqhSOHCiIuPx9Ydu5GUlIRG9eU3tigIfm1SCxNX74CrQzGULWmHfUGXEfkuDu3rZT7ruHTXUbyNjce0/p0BZE68Jq3dgTFdW6FMCVtExyUAALQ0NWCgqwMA2HDoNFwdiqFYETOkpWfgwj9hOHzhGny6tVXeiAJGIpGgfJ1uuHJiNYwL28OksB0un1iNQhracKnUQhZ3zH8c9I0tULPlaBTS0IK5tXwGUEsnc2lqVrl6IU2FGA1NXWjrGSuUExERERV0ubrb4Z9//okiRYrA19cXjx8/hrGxMSpUqIDff//9p5YSduvWDR8/fkSVKlWgrq6OoUOHyjbwABS3lgQAQ0NDeHl5Yffu3diwYYPcuQEDBiA0NBQdO3aERCJB586dMWjQIBw7dizHdoSHhyM6OjrHGHd3dyxYsACzZ8+Gj48PatWqBV9fX3Tr1k0WU61aNQwYMAAdO3bEu3fvMHnyZEyZMgUbN27E9OnTMXr0aLx69QpmZmbw9PQUbeIFAHVr1UBCYiK27NiFmJhY2NvZYuaUCbAokrmULCY2FlFv38rirSwtMGPKBKxctxF/HTkGMzNTDO7XW+EdX1/zNvodZs5dgPiERBgZGqKUixOWzp8tu29B0qiqO+LeJ2HtwZOIjktAiaKWWDKqN6zMM9/xFR2fgMiYOFn8vjOXkJEhxWz//Zjtv19W3qJGRUztm5mRTU5JxSz//YiKiYOWpgbsrYpgev/OaFTVXcyu/ZTKDfoiPS0Fp3dNRXJSPCzty6Hd4A1yGbLE2AhI+Bs2IiKigoMbbohKImS3h3oBUadOHbi7u2PRokX53ZR/hRcPlW/jrspM3j3K7yYUKFsTWuZ3EwqU/o3yuwVERET5J/Hq0Xy7t0Hlf8crdXJTrma+iIiIiIjoX4QrUkTF0SYiIiIiIhJBgc98fcvbqYmIiIiIiAq6Aj/5IiIiIiKivCFwww1RcdkhERERERGRCJj5IiIiIiJSVdxwQ1QcbSIiIiIiIhFw8kVERERERCQCLjskIiIiIlJRArjhhpiY+SIiIiIiIhIBM19ERERERCpK4IYbouJoExERERERiYCZLyIiIiIiVcXMl6g42kRERERERCLg5IuIiIiIiEgEXHZIRERERKSiBAm3mhcTM19EREREREQiYOaLiIiIiEhFcat5cXG0iYiIiIiIRMDJFxERERERkQi47JCIiIiISFVxww1RMfNFREREREQkAma+iIiIiIhUFDfcEBdHm4iIiIiISATMfBERERERqSgBfOZLTMx8ERERERERiYCTLyIiIiIiIhFw2SERERERkYrihhvi4mgTERERERGJgJkv+s+LN3XI7yYUKNYa6fndhAIlcmyP/G5CgWM5d0t+N4GIiMTClyyLipkvIiIiIiIiEXDyRUREREREJAIuOyQiIiIiUlECczGi4mgTERERERGJgJkvIiIiIiIVJXDDDVEx80VERERERCQCZr6IiIiIiFQUX7IsLo42ERERERGRCDj5IiIiIiIiEgGXHRIRERERqSgB3HBDTMx8ERERERERiYCZLyIiIiIiFcUNN8TF0SYiIiIiIhIBJ19EREREREQi4LJDIiIiIiIVJUi44YaYmPkiIiIiIiISATNfREREREQqilvNi4uZLyIiIiIiIhEw80VEREREpKK41by4ONpEREREREQi4OSLiIiIiIhIBFx2SERERESkorjhhriY+SIiIiIiIhIBM19ERERERCqKG26Ii6NNREREREQkAk6+iIiIiIiIRKCyk686depgxIgReVa/n58fjI2Nc4yZMmUK3N3d86wNREREREQ5ESDJt0MVqewzX/v27YOGhkae1d+xY0c0a9Ysz+rPSwePHMPufQfwLiYW9rY2GNS3N8q4uWYbf/PWbaxatxFPn7+AmakpOrZrDa9mTZTGBp39GzPmLkA1jyqYNsFHVr5p6w5s3r5TLtbE2Bi7t2zMnU79pINHArBz3194F5s5JoP79kDZ0jmNyR2sWL8JT5+/gLmpCTq2a4WWTRvLzgecDMKcxcsVrgvYuw2ampoAgM69B+JN1FuFmFbNGmP4wL650KvcJQgCTuxdgcundyPpQwJsS5ZF254TYFmsZLbXXDq9G9f+/guRLx4BAIo5uKJpx+GwLVlWFpORkY4Te5fj+oUjSIyLhqFxYVSq3QoNWg+AmlrB/f2RjmcD6NVpBnUDY6S/eYWEv7Yg7cn97C9QLwT9hm2gU6E61AyMkBEfgw+nDuLj1XOyEIm2LvSb/gJtt8pQ09FFRsxbJBzehtR7N0XoEREREf0slZt8paWlQUNDA6ampnl6Hx0dHejo6OTpPfJC0LnzWLl2A4YN7IfSri44cuwEfKb8ifUrlsCiSGGF+IjIN/hjynQ0a9wQv40ZgTt372HJyjUwMjJCreqecrFvoqKwesMmlMlm0mJva4M5M6bKPheUL9ZBf1/A8nV+GD6gD9xcXXAoIBC/TZmJjcsXZjsmPlNnolnjBvh99DDcvnsPi1etg7GhEWpV95DF6enqYtOqxXLXZk28AGDlglmQSqWyz0+evcDYidNQu4b8uBYUQYfW49yxTejUfwbMrexxav9qrJnZB+PmH4G2jp7Sa8LvXoV7tWawd3SHhoYWgg5vwJpZ/TB2zkEYmVrI6g0+uQudBs6EZbGSePH4NnatngAdHQPUbOotZhe/mXa5qjBs+SsS9vsh9ekD6HrUg0nvsYieNx7SuHdKrzH2Hgo1fSPE716LjOg3UNM3BNTUPwWoq8O032/IeJ+AuM2LkREXA3VjMwgpySL1ioiI/ou44Ya4vnu0pVIpZs+ejZIlS0JLSwu2traYMWMGAODWrVuoV68edHR0YGZmhn79+uH9+/eya3v06IHWrVtj3rx5sLKygpmZGQYPHoy0tDRZzIoVK+Do6AhtbW1YWFigffv22bYla2nfgQMH4OTkBG1tbTRs2BAvXryQxWQt7duwYQOKFy8OLS0tCIKgsOwwJSUF48aNg42NDbS0tODo6Ij169fLzt+9exfNmjWDvr4+LCws4O3tjejo6K+27XOzZs2ChYUFDAwM0Lt3byQnf/rSlJycjNKlS6Nfv36ysidPnsDIyAhr167N9j65be+Bv9CkYX00a9wQdjY2GNSvN4qYm+HQ0QCl8YePHUeRwuYY1K837Gxs0KxxQzRpUA+79x2Qi8vIyMDMeQvRvWsnWFlaKK1LXV0dpiYmssPYyCi3u/dDdh84hKYN66F54wawsymGIX17ooi5Gf46dkJp/KGAEyhS2BxD+vaEnU0xNG/cAE0b1MWu/X/JB0og119TExO508ZGRnLngq9eg7WVJcq5lc6rrv4wQRDwd8Bm1G/VD2WqNISVjSM6DZyJ1NRk3Lh4JNvrug6Zg+oNO6OofSkUKVocv/SdCkGQ4uHtS7KYZw9vwq1SPbiWrw3TwkVRrmpjOJWphhdP7ojRtR+iW6spPl49g49XziAj6jUS/9oCadw76HrWVxqv6VwWmsVdELt+LlIf3kFGbDTSXjxG2rOHshidyrUh0dVDnN9CpD19CGncO6Q9fYD0iOdidYuIiChfrVixAg4ODtDW1kbFihXx999/5xh/9uxZVKxYEdra2ihevDhWrVqlELN37164urpCS0sLrq6u2L9/f141H8APTL58fHwwe/ZsTJw4EXfv3sW2bdtgYWGBpKQkNGnSBCYmJrh69Sp2796NkydPYsiQIXLXBwUFITw8HEFBQdi0aRP8/Pzg5+cHAAgJCcGwYcMwbdo03L9/HwEBAahVq1aO7UlKSsKMGTOwadMmXLhwAQkJCejUqZNczKNHj7Br1y7s3bsXoaGhSuvp1q0bduzYgSVLliAsLAyrVq2Cvr4+ACAiIgK1a9eGu7s7QkJCEBAQgDdv3qBDhw7fPG67du3C5MmTMWPGDISEhMDKygorVqyQndfW1sbWrVuxadMmHDhwABkZGfD29kbdunXRt684S8zS0tLw4FE4KpV3lyuvWN4dd+/dU3rN3Xv3UfGL+EoVyuPBo3Ckp6fLyrbs2AVjQyM0bdQg2/u/eh2Bjt164dfe/TF99ny8joz84b7klswxeYxK5cvJlVcqXw53wpQvIbtz74FifAV33P9iTD5+TEanXgPQoUc//D51Jh6GP86xHSeDzqFpg7qQSAreGumYqJdIjIuGc9nqsrJCGpooUaoSnj648c31pKYkIyM9Hbr6nybeDs7l8fD2JbyNeAoAeP3sHp7cv4FS7jVzrf25Sl0dGkUdkPLgtlxxyoPb0LRzVHqJtmsFpL18Ar06LVB4whKYj5sLgxadgUIa8jHPHsGwTXcUnrQcZqN9oVevJVAA/zwQEdG/x7/lma+dO3dixIgR+OOPP3Djxg3UrFkTTZs2xfPnyn8J+eTJEzRr1gw1a9bEjRs38Pvvv2PYsGHYu3evLCY4OBgdO3aEt7c3bt68CW9vb3To0AGXL1/+qTHNyXctO0xMTMTixYuxbNkydO/eHQBQokQJ1KhRA2vXrsXHjx/h7+8PPb3MJUbLli2Dl5cXZs+eDQuLzGyHiYkJli1bBnV1dbi4uKB58+Y4deoU+vbti+fPn0NPTw8tWrSAgYEB7OzsUL58+RzblJaWhmXLlqFq1aoAgE2bNqFUqVK4cuUKqlSpAgBITU3F5s2bUbiw4hIxAHjw4AF27dqFwMBANGiQOTkoXry47PzKlStRoUIFzJw5U1a2YcMG2NjY4MGDB3Bycvrq2C1atAi9evVCnz59AADTp0/HyZMn5bJf7u7umD59Ovr27YvOnTsjPDwcBw4c+GrduSU+IRFSqRQmJsZy5SYmxoi5Hqf0mpjYWJiYlFeIz8jIQHxCAsxMTXH7bhiOnTiF1UsWZHvvUs6OGDdqOIoVtUZsXBy27tiN4WN8sG7FYhgZGv5s136YbEyM5bNwJsZGiImLU3pNbGyc0vjMMUmEmakJbIsVxfgRQ1Dc3hYfkpKw76+jGDZuAtYunY9i1lYKdV64dBXvP3xA4/p1c61vuSkxPjMLrG9kJleub2iG2OjX31zP0R0LYGRaBI5un5ZW1vXqg+Sk95gzpgUkauoQpBlo0mE4yldrnjuNz2VqegaQqKtDmhgvVy59Hw81A2Ol16ibFYGmvROEtDTEbVoEiZ4BjNr0gERHHwm7136KMTHHxxsXEbt+LgqZW8KwTXdATQ0fTh7I414RERHlrwULFqB3796y79KLFi3C8ePHsXLlSvj6+irEr1q1Cra2tli0aBEAoFSpUggJCcG8efPQrl07WR0NGzaEj0/mPgQ+Pj44e/YsFi1ahO3bt+dJP74r8xUWFoaUlBTUr6+4dCYsLAzlypWTTbwAoHr16pBKpbh//1OGoHTp0lBX//Qcg5WVFaKiogAADRs2hJ2dHYoXLw5vb29s3boVSUlJObapUKFCqFSpkuyzi4sLjI2NERYWJiuzs7PLduIFAKGhoVBXV0ft2rWVnr927RqCgoKgr68vO1xcXAAA4eHhObYvS1hYGDw95Z/V+fIzAIwePRrOzs5YunQpNm7cCHNz82zrTElJQUJCgtyRkpr6Te3JyZe/hxAE4SvZFvlzgiBklkokSEr6iFnzF2HU0IEwMsp+ElWlUkXUqu6J4vZ2qOheDjOmTAAABJ4K+pEu5Lov+y8IiuOUUzyErPLM/3V1cULDurVQwsEeZUu7YtL4UShW1Br7Dx1VWt/RwFOoUrE8zM3y9lnFb3X9/GH83rOS7MjIyMzoSRT/9Hxzpi7o0HrcuHgU3Ucuhoamlqw8NPgYrp0/jC6D52DkjN3oNGAmzh7ZiKvnDuRSb/KK8I1lkP3BiN++AmkvHiP13k0kHNoKnUo1P2W/JBJI3ycgYc96pL96iuSbl/D+9F/Q9cw+m0xERFSQKf0um5KiEJeamopr166hUaNGcuWNGjXCxYsXldYdHBysEN+4cWOEhITIHnnKLia7OnPDd02+ctpAIqcv6J+Xf7nDoEQikW0qYGBggOvXr2P79u2wsrLCpEmTUK5cOcRlk2FQVr+yss8nhMp8bWMMqVQKLy8vhIaGyh0PHz786rLI7xUVFYX79+9DXV0dDx8+zDHW19cXRkZGcsfyVT/+fJiRoQHU1NQQExsnVx4XF6+QycliamKC2NhYhXh1dXUYGhjgdWQkIt9EYcK0mWjUsh0atWyHwNNnEHz5Khq1bIfXERFK69XR1oaDvR1evlZ+XizZjkl8PEyyeZWAiYmxQnxs/KcxUUZNTQ3OjiXwSkl/I6Pe4vrNW2jeSPnzQvnBtWJdjPLdKzv0DDKfV8vKgGV5nxCjkA1T5szhjTh1cC36+ayFta2z3LnD2+ajXsveKF+tGaxsnVCxZkvUatoNpw+uy70O5SLph0QIGRkKWS41fSOFbJjsmoQ4ZMTHQkj+KCtLj3oNiZoa1I1NZTHp0ZGZM/+smDevoG5oDHz2Cy0iIqLvIUgk+XYo+y6rLIsVHR2NjIwM2Uq6LBYWFojM5jGVyMhIpfHp6emyfRuyi8muztzwXZMvR0dH6Ojo4NSpUwrnXF1dERoaig8fPsjKLly4ADU1tW9alpelUKFCaNCgAebMmYN//vkHT58+xenTp7ONT09PR0hIiOzz/fv3ERcXJ8tMfYsyZcpAKpXi7NmzSs9XqFABd+7cgb29PUqWLCl3fG1il6VUqVK4dOmSXNmXnwGgV69ecHNzg7+/P8aNG4e7d+9mW6ePjw/i4+PljsEDfvz5MA0NDTiVLIFrofLbVl8LvQnXbMbT1cVZIT7kRiicSpZAoUKFYFusKNYuW4TVSxbIDs+qleFexg2rlyxA4Wwye6lpaXj+4iXMTE2UnhdL5pgUx7Ub/8iVXwv9B6VLOSu9prSLE66FyseH3LgJ5/+PiTKCICD88VOYKulvwMnTMDYyhEflij/Yi9ynraMHc0s72WFRtAQMjM3x4Nan3xSlp6ciPCwE9k45Lx0OOrQBJ/evQt/xq2FT3E3hfFrqR0i+2IlJoqYOQZAqxBYIGRlIe/UEWo7yfdFyckPqM+W/UEl9+gDqhsaQfJbxK1TYEoJUioy4mP/HPEQhMwu5Z7wKFbZCRnwskJGRBx0hIiLKW8q+y2YtAVRGcSVSzitslMV/Wf69df6s75p8aWtrY/z48Rg3bhz8/f0RHh6OS5cuYf369ejatSu0tbXRvXt33L59G0FBQRg6dCi8vb0VZpTZOXz4MJYsWYLQ0FA8e/YM/v7+kEqlcHbO/JK7bNkyhSWPGhoaGDp0KC5fvozr16+jZ8+e8PDwkD3v9S3s7e3RvXt39OrVCwcOHMCTJ09w5swZ7Nq1CwAwePBgxMTEoHPnzrhy5QoeP36MEydOoFevXsj4/5ceZW373PDhw7FhwwZs2LABDx48wOTJk3HnjvxubcuXL0dwcDD8/f3RpUsXtG/fHl27dkVqNksJtbS0YGhoKHdofbZV+Y9o17oljp04iWMnTuLZixdYsXYDot5Gw6tZ5juq1vltxqz5n7ZHb9G0MaKi3mLl2g149uIFjp04iYDAU/ilbWsAmVunO9jbyR36enrQ0dWBg72dLBO6er0fbt66jYjINwi7/wDTZs5BUlISGhWAZ5x+ae2Fo4GncCzwFJ69eInlazfizdtoeDXNTFOv3bQVvguWyOK9mjTCm6i3WLHOD89evMSxwFM4FngaHdq0lMVs2r4LV6+H4nXkGzx6/ARzl6zAoydPZXVmkUqlCDgZhEb16sgt1y1oJBIJajbxxqmDa3Hr6klEvHiInav+gKamttyzWdtX+ODojoWyz0GH1iNg9xJ06P8nTApbIyHuLRLi3iIl+dMvcVwr1MGpg2tw98ZZxLx9hVtXT+Lc0U1wq1xwMoFfSjp3DDpV6kCnci2oF7GGgVdXqBmbISk48xdX+k07wKhTf1l88o2LkCa9h1GHflAvYg0NB2cYNO+Mj1fPAumZSyOSgk9CoqsPg5beUDe3hJaLO/TqtUTSxcB86SMREf03CIIk3w6l32W1tBTaaG5uDnV1dYWMVFRUVLbzDEtLS6XxhQoVgpmZWY4x3zp3+RHf/Z6viRMnolChQpg0aRJev34NKysrDBgwALq6ujh+/DiGDx+OypUrQ1dXF+3atcOCBdlvsvAlY2Nj7Nu3D1OmTEFycjIcHR2xfft2lC6dubV2dHS0wjNWurq6GD9+PLp06YKXL1+iRo0a2LBhw/d2CytXrsTvv/+OQYMG4d27d7C1tcXvv/8OALC2tsaFCxcwfvx4NG7cGCkpKbCzs0OTJk1k76JS1rbPdezYEeHh4Rg/fjySk5PRrl07DBw4EMePHwcA3Lt3D2PHjsX69ethY2MDIHMyVq5cOUycOBGzZ8/+7j79iLq1aiAhMRFbduxCTEws7O1sMXPKBFgUKQIgc4ONqLefXvxrZWmBGVMmYOW6jfjryDGYmZlicL/eCu/4+pq30e8wc+4CxCckwsjQEKVcnLB0/mzZffNT3ZrVkZCQCP8de2Rj4jv5d1j+/x1fMTGxiHr7abmdlaUFfCf/juXr/HDwSADMTE0xpF9PuXd8vX//AfOXrUJsbBz09HRRsrgDFs2ahlJO8rvhXQv9B1Fvo9G0YT1xOvsT6nr1RlpqCvZt/BMfPyTAtkRZ9PVZK/eOr9h3EZCoffpt0sXAHchIT4P/opFydTVsOwiN2w8GALTu/geO716CfRv/xPv4GBiZFIFH/V/QsO1AcTr2A5JvXoZE1wD6DdpAzdAY6ZEvEbt+ruwdX+qGxlA3/pT1FVJTELNmFgxbd4P58D8hTXqP5JuXkRiwWxYjjY9B7LrZMPD6FeajZiIjIRZJ54/jQ9Ah0ftHREQkJk1NTVSsWBGBgYFo06aNrDwwMBCtWrVSeo2npycOHZL//8gTJ06gUqVKsl/+e3p6IjAwECNHjpSLqVatWh70IpNEEIRsngAv+Pz8/DBixIivPhOmSl48zH6ZoqpSE7gk63PXE0vldxMKlMo7euR3Ewocy7lb8rsJREQkkkfhT/Lt3iVLOHxz7M6dO+Ht7Y1Vq1bB09MTa9aswdq1a3Hnzh3Y2dnBx8cHr169gr+/P4DMrebd3NzQv39/9O3bF8HBwRgwYAC2b98u2+3w4sWLqFWrFmbMmIFWrVrh4MGDmDBhAs6fPy/bST23fXfmi4iIiIiI/huE73/tb77o2LEj3r17h2nTpiEiIgJubm44evQo7OzsAGS+l/fzd345ODjg6NGjGDlyJJYvXw5ra2ssWbJENvECgGrVqmHHjh2YMGECJk6ciBIlSmDnzp15NvECmPn6z2HmSxEzX/KY+ZLHzJciZr6IiFTHw/Bn+XZvxxJ2+Xbv/PLvmOpmo0ePHpx4ERERERH9IAGSfDtU0b968kVERERERPRvwckXERERERGRCLjhBhERERGRilLV5X/5hZkvIiIiIiIiETDzRURERESkopj5EhczX0RERERERCJg5ouIiIiISEUx8yUuZr6IiIiIiIhEwMkXERERERGRCLjskIiIiIhIRQkClx2KiZkvIiIiIiIiETDzRURERESkorjhhriY+SIiIiIiIhIBJ19EREREREQi4LJDIiIiIiIVxWWH4mLmi4iIiIiISATMfBERERERqShmvsTFzBcREREREZEImPkiIiIiIlJRfMmyuJj5IiIiIiIiEgEnX0RERERERCLgskMiIiIiIhUl5YYbomLmi4iIiIiISATMfBERERERqShuNS8uZr6IiIiIiIhEwMkXERERERGRCLjs8D9GOzUxv5tQ4LzRssvvJhQoRQ3i87sJBYp++4753YQC58PFffndhAJFr1rb/G4CEVGe4Xu+xMXMFxERERERkQiY+SIiIiIiUlHccENczHwRERERERGJgJkvIiIiIiIVxWe+xMXMFxERERERkQg4+SIiIiIiIhIBlx0SEREREakobrghLma+iIiIiIiIRMDMFxERERGRiuKGG+Ji5ouIiIiIiEgEnHwRERERERGJgMsOiYiIiIhUlDS/G6BimPkiIiIiIiISATNfREREREQqihtuiIuZLyIiIiIiIhEw80VEREREpKL4kmVxMfNFREREREQkAk6+iIiIiIiIRMBlh0REREREKoobboiLmS8iIiIiIiIRMPNFRERERKSiuOGGuJj5IiIiIiIiEgEnX0RERERERCLgskMiIiIiIhUlFfK7BaqFmS8iIiIiIiIRMPNFRERERKSiuOGGuJj5IiIiIiIiEgEnXz9ozZo1sLGxgZqaGhYtWiR37unTp5BIJAgNDc32+jNnzkAikSAuLi5P20lERERElB1BkOTboYq47PAHJCQkYMiQIViwYAHatWsHIyMjufM2NjaIiIiAubl5PrUw9+07dhLbDx7Fu9h42NsUxfBeXVHO1VlpbHRMHJZt2ob74U/xMuIN2jdriOG9f5WLefz8Jdbv2If74U8R+TYaw3p2QQevJmJ05YcIgoBd2/wQGHAIH94nwtHZFX0GjoCtnUOO1wVfOIsdm9cjMuI1LK2s0aVbH1StVkt2fkDPjngbFalwXZPmrdF30EgAQLvmtZXW7d1rAFq36/wTvcpdgiBg7/b1OHX8L3x4n4CSTqXRc8Bo2NgVz/aaF88eY8/WdXgcfg/RUZHw7jMczVp1lIs5sNsfVy+ewetXz6GpqQknlzLo3GMQrIvZ5XWXfsqukxew+egZRMcnonhRC4zp2grlnZWPxemrt7Dn9EXcf/4aaWnpKF7UEv3aNEK1sp/+ju0LuoQjF64h/GXmn5dS9sUw+JemcCthK0p/ftau08HwP/Y3ouMSUbxoEYzp0gIVnJT//TkVcht7gi7j/vMIpKWno3jRIujfqgGqlXGSxfx1/hqmrN+jcG3wmmnQ0tDIs34QERH9DE6+fsDz58+RlpaG5s2bw8rKSuG8uro6LC0t86FleePU+UtYsnErRvftjjKlHHHweBDGTJ+HzYt9YVlYcYKZlp4GY0NDdGvXErsOByitMyUlFdYWhVG3WhUs3bA1r7vw0w7s2Y5D+3dhyEgfWBcthj07N2PahNFYunoLdHR1lV5zP+w2Fsyais7evVDFsyauBP+N+bOmYPqcZXBycQUAzF60GtKMDNk1z589wbQJo+FZo46sbN3mfXL13rh2GSsWz4FHNeWTsvxyaO8WHD2wAwNGTIBVURvs3+mHmZNGYMHK7dDR1VN6TWpKMopYWqNqjbrYvG6J0piw2zfQqHk7FHcsBak0Azv9V8N30gjMXbEN2to6edmlH3biUijmb/0Lv3VvC3dHe+wNuoSh89Zht+9YWJmbKMRfv/8YVd2cMPiXpjDQ1cFff1/FyIUbsGnyMLjYFwUAXLsXjsYe7ijnaA9NDQ34HwnC4LlrsHvmWBQxNVKosyA5fvkfzNt2BD7erVDO0Q57z1zG0AV+2DNjJKzMjBXirz94gqqlS2JIu0Yw0NXBwfPXMGKxP/wnDoKLnbUsTl9HC/t8R8tdy4kXEREVZLmy7FAqlWL27NkoWbIktLS0YGtrixkzZgAAbt26hXr16kFHRwdmZmbo168f3r9/L7u2R48eaN26NebNmwcrKyuYmZlh8ODBSEtLk8WsWLECjo6O0NbWhoWFBdq3b59jey5cuIDatWtDV1cXJiYmaNy4MWJjYwEAKSkpGDZsGIoUKQJtbW3UqFEDV69elV2btRzw1KlTqFSpEnR1dVGtWjXcv38fAODn54cyZcoAAIoXLw6JRIKnT5/K3V/ZssOjR4/CyckJOjo6qFu3rsI1vXr1QtmyZZGSkgIASEtLQ8WKFdG1a9dv+AnkrR2HAtCifm14NawD+2JFMbz3ryhiZooDx08rjbcqUhgjev+KpnVrQC+biUkpx+IY3L0zGtTwgEYB/7IkCAIOH9yNdh294VG9Fmzti2PoKB+kpKTg77Mns73u8ME9KFe+Itp2+BXFbOzQtsOvKFOuIg4f3C2LMTIyhompmey4djUYllZFUbqMuyzm8/Mmpma4cukC3MqWh6WVtZK75g9BEHDsr11o3aE7qlSrAxu7Ehg4ciJSU5Jx4WxgtteVcHJF115DUK1WQxTK5s+Bz9SFqN2gOWzsisPOwREDRvyB6Ldv8OTRvbzqzk/bEnAWrWpXQZs6VeFQ1AJjfm0FC1Nj7DkdrDR+zK+t0L15XZQubgtby8IY8ksz2Fqa41zoHVnMjIFd0aFBdTjbFYWDdRFM6P0LBKmAK3cfitWtH7b1xN9oXasS2tSujOLWRTC2ixcsTI2w5/QlpfFju3ihR7PaKF3cBraW5hjavjFsLcxwLjTsi0gJzI0M5A4iIvo+gpB/hyrKlcmXj48PZs+ejYkTJ+Lu3bvYtm0bLCwskJSUhCZNmsDExARXr17F7t27cfLkSQwZMkTu+qCgIISHhyMoKAibNm2Cn58f/Pz8AAAhISEYNmwYpk2bhvv37yMgIAC1atVS0opMoaGhqF+/PkqXLo3g4GCcP38eXl5eyPh/dmHcuHHYu3cvNm3ahOvXr6NkyZJo3LgxYmJi5Or5448/MH/+fISEhKBQoULo1asXAKBjx444eTLzC/eVK1cQEREBGxubHMfnxYsXaNu2LZo1a4bQ0FD06dMHv/32m1zMkiVL8OHDB1n5xIkTER0djRUrVnxl9PNWWlo6HoQ/ReVybnLlld3L4Pa9gv+lLze8iYxAXGwMylWoJCvT0NBEabdyuB92O9vrHty7g3LlK8uVuVeojPthd5TGp6Wl4VxQIOo1bAqJRPk66LjYGFy/Goz6jZr9QE/yTtSb14iLfYcy5avIyjQ0NFHKzR0P7t3K1XslffgAANA3MMzVenNLWno67j19BQ83J7lyjzJO+Ofh02+qQyqV4sPHFBjpKf/lBQAkp6QiPSMDhjnEFARp6ekIe/oaHqUd5co9SzviZvjzb6pDKpUiKTkFhnrymc6PKaloNmY2mozyxbBFfrj37HWutZuIiCgv/PSyw8TERCxevBjLli1D9+7dAQAlSpRAjRo1sHbtWnz8+BH+/v7Q08tcdrRs2TJ4eXlh9uzZsLCwAACYmJhg2bJlUFdXh4uLC5o3b45Tp06hb9++eP78OfT09NCiRQsYGBjAzs4O5cuXz7Y9c+bMQaVKleQmLaVLlwYAfPjwAStXroSfnx+aNm0KAFi7di0CAwOxfv16jB07VnbNjBkzULt25rKu3377Dc2bN0dycrIsgwcAhQsX/qblhStXrkTx4sWxcOFCSCQSODs749atW5g9e7YsRl9fH1u2bEHt2rVhYGCA+fPn49SpUwrPk30uJSVFlimTlaWmQktT86tt+lbxiYnIkEphaizfDlMjQ7yLi8+1+xRkcbGZE3NjY1O5ciNjE7x9+ybH64xN5JeYGZuYyOr70pVLf+PD+/eo26BptnWeORUAHR1duefGCoL4//fJSGGMTBGt5Jm2HyUIAjavXwJn13KwsSuRa/XmprjED8iQSmH2RRbGzNAA7+ITv6mOLcfOIjklFQ2rlss2ZumuoyhsYoSqX0xqCpq4xKTM8TDUlys3NdLHu9vfNh6bj5/Hx5RUNKpSVlZmb1UYU3q3h2MxS7xPTsb2wIvoNXMVdkwdBlvL/87ztkREeU3KreZF9dOZr7CwMKSkpKB+/fpKz5UrV0428QKA6tWrQyqVypbxAZmTI3V1ddlnKysrREVFAQAaNmwIOzs7FC9eHN7e3ti6dSuSkpKybU9W5kuZ8PBwpKWloXr16rIyDQ0NVKlSBWFh8stZypb99H/yWc91ZbXpe4WFhcHDw0Mum+Hp6akQ5+npiTFjxuDPP//E6NGjc8zwAYCvry+MjIzkjsVrN/1QG7/my0SMAGSbnfm3OxcUiK7tmsiOjIx0AMr6K0Dy1X+w5M8LQvbjdurEUZSvVAWmZtl/cTwVeAw16zSApqbW17qRp86fOY4ev9SXHenpysdIyOxwrt1346r5eP70EYaOnZprdeYVxT8tgmKhEgHBN7B6/wn4Dv4VpobKl9FtOhKE45duYN6w7tDSLNjLdmW+/DdEwDf8/QECLoVi9YGTmDWwC0w/m8CVLWGL5tXKw8nWChWcHDB7YGfYWphjx6mLud1yIiKiXPPTmS8dnewfeBcEIdsvmp+Xf/nMj0QigVQqBQAYGBjg+vXrOHPmDE6cOIFJkyZhypQpuHr1KoyNjb+7PV/eO7t2ft6mrHNZbfpewjcuapVKpbhw4QLU1dXx8OHXl/T5+Phg1KhRcmUJ4Td/qI3ZMTIwgLqaGt7Fyme5YuMTYGpUMJd9/azKVavD0bmU7HPW84exse9gYmomK4+Pi1PIbH3O2MRUIcsVHxcLI2PFa6KiInEr9BrG/v5ntvXdvX0Tr18+x+jxk7+5L3mlYpUaKOlUWvY5LS0VABAX+w4mpp8mjwnxsQrZsB+1cfUCXLtyHpN9V8DMvEiu1JkXjA30oK6mhugvslwxCe9hls1kKsuJS6GYtn4XZg/xRtUvli1m8T96BhsOncLKcf3haFtwnvvLjrGBbua/IfHv5cpjE97D1Eg/m6syHb/8D6Zt3IfZg7qgaumSOcaqqamhtEMxPH/z7qfbTERElFd+OvPl6OgIHR0dnDp1SuGcq6srQkND8eH/z2gAmZthqKmpwclJ+RcLZQoVKoQGDRpgzpw5+Oeff/D06VOcPq18s4eyZcsqbQsAlCxZEpqamjh//rysLC0tDSEhIShVqpTSa3KDq6srLl2Sf7D8y88AMHfuXISFheHs2bM4fvw4Nm7cmGO9WlpaMDQ0lDtyc8khAGhoFIJTCXtcvSn/bFPIzdtwcynYy51+lI6uLqysi8kOG1t7GJuY4p8bIbKYtLQ03Ll9E86l3LKtx8mlNG6GhsiV3bxxFc6lSivEBgUeg6GRMSpW8ci2vlMnjqJESWfYF8/5S6gYdHT1YGldTHYUs3WAsYkZboV+2rwmPS0NYbdD4eRS5qfuJQgCNq6aj6sXz2DCjKUoYlmwJxwahQrBxb4oLt9+IFd++fYDlHW0z/a6gOAbmLJ2B2YM7Iqa7q5KY/yPBGHdwZNYNqYvXIvn/KxpQaFRqBBK2Vvj8h35XyhduvsI5XLYJj/gUiimrN+NGf07omY5l6/eRxAE3H/+mptuEBF9J77nS1w/PfnS1tbG+PHjMW7cOPj7+yM8PByXLl3C+vXr0bVrV2hra6N79+64ffs2goKCMHToUHh7e8ue9/qaw4cPY8mSJQgNDcWzZ8/g7+8PqVQKZ+fM998sW7ZMbpmhj48Prl69ikGDBuGff/7BvXv3sHLlSkRHR0NPTw8DBw7E2LFjERAQgLt376Jv375ISkpC7969f3gMrly5AhcXF7x69Urp+QEDBiA8PByjRo3C/fv3sW3bNtmGIllCQ0MxadIkrF+/HtWrV8fixYsxfPhwPH78+IfblVs6eTXB4VNncfjUWTx9+QpLNmzFm+h3aN2oHgBg1ZZd+HPxarlrHj55hodPnuFjcjLiEhLx8MkzPHnxaXzS0tJlMWnp6XgbE4uHT57hZUT2z1DlF4lEghatfsHeXVtx+eI5PH/6GMsW+kJLSws1azeQxS2ZPwNb/NbIPjdv2R43r4dg/+5tePniGfbv3oZ/Qq+hRatf5OqXSqU4HXgMdeo3gbq68mR0UtIHBJ8/g/qNm+dNJ3+SRCJB05YdcHC3P64Gn8WLZ+FYuWg6NLW0Ub12Q1ncigXTsH3TStnn9LQ0PH38AE8fP0B6ejpi373F08cPEPn6pSxmw8p5OH/mOIaMmQodHV3Exb5DXOw7pH7xvGNB8muT2jhw9goOnr2CJ6/eYP7Wg4h8F4f29TIn10t3HcWk1dtl8QHBNzBpzXaM6OyFMiVsER2XgOi4BCQmfZTFbDoShBV7AzC5TwdYmZvIYpKSC+44ZOnaqCb2nwvBgXMhePw6CvO2H0bkuzi0q1sVALB0dwAmrt0liw+4FIpJ63ZjZKdmmeMRn4jo+EQkJiXLYlYfOImLtx7gZVQM7j9/jakb9uLBiwi0/3+dREREBVGuvOdr4sSJKFSoECZNmoTXr1/DysoKAwYMgK6uLo4fP47hw4ejcuXK0NXVRbt27bBgwYJvrtvY2Bj79u3DlClTkJycDEdHR2zfvl22iUZ0dDTCw8Nl8U5OTjhx4gR+//13VKlSBTo6OqhatSo6d858Ge2sWbMglUrh7e2NxMREVKpUCcePH4dJDsvHviYpKQn379+X2x7/c7a2tti7dy9GjhyJFStWoEqVKpg5c6ZsB8Xk5GR07doVPXr0gJeXFwCgd+/eOHLkCLy9vXHu3Dm5Z+LEVr+GB+IT38Nv10G8i42Dg20xzP1jNCyLZC4vexcbhzfR8kt9eo6eKPvv++FPEfh3MCwLm2PP6syffXRsrFzM9oPHsP3gMbiXdsGyP38XoVffp3X7zkhNTcGaFQvx4f17ODqXwqQ/58m94yv6bRQkkk+/z3BxdcOo8ZOwbfN67NiyHhaW1hg1forsHV9Z/gm9hui3b3LcwfD82VMQIKBGbeXPMxYEXu1+RWpqCjasnIcP7xNRwskVv09bKPeOr+i3b+TGKDYmGj7De8g+H96/DYf3b0Mpt/KY5LscAHDy2H4AwJ+/D5a734Dhf6B2g4I5GW3k4Y649x+w9mAgouMSUKKYJZaM7g0r88wlmNFxCYh8FyuL3xcUjIwMKWb778ds//2y8hY1KmFqv04AgN2nLiItPQPjlvrL3atf64bo37axCL36cY2rlkX8hw9Y+9cpRMcnokRRCywZ2QPW/3/nWXR8IiLfxcni9565gvQMKWZt/guzNv8lK/eqXgFT+2T+8iLxYzKmb9qPd/GJ0NfRhrOtNdb+1g9u/5KMIBFRQaGqW77nF4nwrQ8k0b/C2zuX87sJBc4bLbv8bkKBkir9l2zQIBKnWG7Q8CVJhvJfJKkqvWpt87sJRER5JvBm/q2gaFgubzYQi42NxbBhw/DXX5m/wGvZsiWWLl2qdL8IIPNxkgkTJuDo0aN4/PgxjIyM0KBBA8yaNQvW1p8ed6hTpw7Onj0rd23Hjh2xY8eOb25brrzni4iIiIiI/n0ESPLtyCtdunRBaGgoAgICEBAQgNDQUHh7e2cbn5SUhOvXr2PixIm4fv069u3bhwcPHqBly5YKsX379kVERITsWL16tZIas5cryw6JiIiIiIjyW1hYGAICAnDp0iVUrZr5HPDatWvh6emJ+/fvy/aN+JyRkRECAwPlypYuXYoqVarg+fPnsLX9tEGUrq7uN73nNzvMfBERERERkehSUlKQkJAgd6T85IZawcHBMDIykk28AMDDwwNGRka4ePHbHzWIj4+HRCJRWKq4detWmJubo3Tp0hgzZgwSExOVV5ANTr6IiIiIiFSUVMi/w9fXF0ZGRnKHr6/vT/UnMjISRYoovg+0SJEiiIyM/KY6kpOT8dtvv6FLly4wNPz0XtuuXbti+/btOHPmDCZOnIi9e/eibdvvey6Yyw6JiIiIiEh0Pj4+GDVqlFyZlpbyTTimTJmCqVOn5ljf1auZ7xuVSBSfJxMEQWn5l9LS0tCpUydIpVKsWLFC7lzfvn1l/+3m5gZHR0dUqlQJ169fR4UKFb5aN8DJFxERERGRysrPlx1raWlmO9n60pAhQ9CpU6ccY+zt7fHPP//gzRvF98a+ffv2q+8ZTktLQ4cOHfDkyROcPn1aLuulTIUKFaChoYGHDx9y8kVERERERP8N5ubmMDc3/2qcp6cn4uPjceXKFVSpUgUAcPnyZcTHx6NatWrZXpc18Xr48CGCgoJgZmb21XvduXMHaWlpsLKy+uZ+8JkvIiIiIiL6TyhVqhSaNGmCvn374tKlS7h06RL69u2LFi1ayO106OLigv379wMA0tPT0b59e4SEhGDr1q3IyMhAZGQkIiMjkZqaCgAIDw/HtGnTEBISgqdPn+Lo0aP45ZdfUL58eVSvXv2b28fMFxERERGRihKE/G5B7tu6dSuGDRuGRo0aAch8yfKyZcvkYu7fv4/4+HgAwMuXL2UvZHZ3d5eLCwoKQp06daCpqYlTp05h8eLFeP/+PWxsbNC8eXNMnjwZ6urq39w2Tr6IiIiIiOg/w9TUFFu2bMkxRvhs1mlvby/3WRkbGxucPXv2p9vGyRcRERERkYqSIv823FBFfOaLiIiIiIhIBJx8ERERERERiYDLDomIiIiIVNR/ccONgoyZLyIiIiIiIhEw80VEREREpKIEgRtuiImZLyIiIiIiIhEw80VEREREpKKkfOZLVMx8ERERERERiYCTLyIiIiIiIhFw2SERERERkYriVvPiYuaLiIiIiIhIBMx8ERERERGpKAHcal5MzHwRERERERGJgJMvIiIiIiIiEXDZIRERERGRiuJ7vsTFzBcREREREZEImPkiIiIiIlJR3GpeXMx8ERERERERiYCZr/+YFA29/G5CgVP81Zn8bkKBsjGpU343oUCJHzYhv5tQ4FT9rVV+N6FA+SDh7ym/pOfZOr+bQES5hJkvcfH/UYiIiIiIiETAyRcREREREZEIuOyQiIiIiEhFSQVJfjdBpTDzRUREREREJAJmvoiIiIiIVBQ33BAXM19EREREREQi4OSLiIiIiIhIBFx2SERERESkorjsUFzMfBEREREREYmAmS8iIiIiIhUlZeZLVMx8ERERERERiYCZLyIiIiIiFSXwJcuiYuaLiIiIiIhIBJx8ERERERERiYDLDomIiIiIVBS3mhcXM19EREREREQiYOaLiIiIiEhFcat5cTHzRUREREREJAJOvoiIiIjof+zddVhU2RsH8O/Q3R2CgAoIKIq9a3e3rt3dtbp21xprdwd2r52rYiFYICrqYoBIgzRzf3+wjo6A+ltx7qzz/TzPPOucOffOe+/ODPPOOee9RKQAnHZIRERERKSiWHBDsTjyRUREREREpAAc+SIiIiIiUlEc+VIsjnwREREREREpAEe+iIiIiIhUFEvNKxZHvoiIiIiIiBSAyRcREREREZECqEzyVb16dQwbNkx239nZGYsXLxYtHiIiIiIisQmCeDdVpDJrvvbv3w9NTU2FPNfKlSuxcuVKPH/+HABQsmRJTJo0CQ0aNFDI83+rQ8dOYPf+Q4iNj4dzEUcM6N0dPiU9C+x/594DrFy/Cc8jXsDCzBTtWjVHkwb1ZI+fOHMO8/9Ynme74/t2QktLK0/7jj37sX7LdrRs2ggDe/conIMqZLsv3MDmk1cQk5gCVztLjGrXAGWKOeXb9+ztEOy5eBNhL6KQlZ0DFztL9GtSA5VLusn1234mAHsu3kRUXCJMDPRQu4wnBresDW0FvW6/lSAIuH5iGR4E7EJ6WhJsipRC9daTYG5b7Ku2f3T7GE5sGQEXr1po3GuFrP3a8aW4cXKZXF89Qwv0mn6lUOMvbHYd2qJIr27QsrJA6uNwPJ45D4m3ggrsb9+xHew7t4eOvR0yXkfh+cq1eHPwaL59rRrVR8nFc/H29DncHzD8ex1Codod9Bibb4QhJiUNrhbGGFXTF2UcLb+4XfDLt+i18zxcLY2xq9uHz5WsHCk2XAvF0QfPEJ2cBiczQwytVgpVXGy/52EUqt1nA7Dl+EXEJCTDxd4aozo0QZkSRfPte/bWfew9H4CwiEhkZWXDxd4afZvXRmXvEvn2P3ktGONW7UR1X08sHNr1ex4GERH9H3745CsrKwuampowMzNT2HM5ODhgzpw5cHPL/XK9efNmNGvWDEFBQShZsuR3j+NbnP/rClas24gh/XrDy9MdR0+cwrgpM7Fh+WJYW+X9ohQZ9Qa/TZ2JhvVqY9zIobgf8hBLVq2FsZERqlapJOunr6eHTauWyG2bX+L18NETHDtxGi7O+ScyyuDkzfuYv+sExnVohNJuRbDv0i0MWrIN+6YMhK25SZ7+tx//jYoerhjcojYMdHVw+GoQhi7bga3jesO9SO4XxT+v38WS/WcwpWszlHJ1xN9vYjFp00EAwKh2/42kPfDsWgRd2Ig6HebA1MoZN06txMGV3dH5txPQ0jH47LZJca/w16G5sHPxy/dxM5tiaDFgo+y+RE29UGMvbFYN66HY+DF4NGUmEm8Hw659a/isW4EbDVogIzIqT3+7Dm3gMmoIwsZPQ9K9+zDy8UaJGZOQnZSM2HMX5fpq29nCdewIJNwMVNThfLOToRGYfzYY4+qUQWkHS+wLfoJBey9hX8/6sDXSL3C75IxMTPzzOso7WSM2NV3usRV/3cOxkL8xsZ4fipob4eqzKIw8eAWbOtaCu7Xp9z6kb3by+h38vuMIxnVpjlLFnLDv/HUMXrgBe2eNgK153vhvhz1FhZLFMKhVfRjq6eLQ5VsYtngztkwaCHcne7m+r2PisWjXMfgWzz+RIyL6mFQqdgSqRemmHQqCgHnz5sHFxQW6urooVaoU9u7dCwDYtGkTTExM5PofPHgQEolEdn/KlCkoXbo0NmzYABcXF2hra0MQhDzTDgEgOTkZHTp0gIGBAezs7LB06VK5xyMiItCsWTMYGBjAyMgIbdu2xZs3b774XE2aNEHDhg1RvHhxFC9eHDNnzoSBgQGuXbsm29bZ2RkzZsxAly5dYGBgACcnJxw6dAhv376VPae3tzdu3bpVSGf26+w9eAQN6tREo3q14eTogIG9e8DKwhxHjp/Mt/+RE6dgZWmBgb17wMnRAY3q1Ub92jWx+8Bh+Y4SwMzUVO72qbS0NMxasBgjBveDocHnv6yLadvpq2j+ky9a/lwWLraWGN2uAWxMjbDn4s18+49u1wDd6v+Eks72cLI2x+AWtVHEygwX74bJ+twNf4HSbo5oUMEHdhamqFTSDfXLeyHk79eKOqxvIggCgi9tQbk6/eBWqi7MbYujTse5yMpMR1hg/qM370mlOTi5dRQqNhgMY3PHfPuoqalD38hSdtMz+P4/pnwLxx6dEbn3ACL3HEBq+DM8mTkfGVFRsO/QNt/+Ns0a47X/XkT/eRLpL14h+tgJRO49gCK9u8t3VFOD54LZeP7HSqS9eKmAIykc226FoblPUbQs5QoXcyOMrlUGNoa62BMU/tntZpy8hfoeTvCxM8/z2NEHz9Gzogd+drWDg4kB2vq6oZKzDbbeDMtnT8pn+8m/0LxqObSoVh4udtYY3bEprM2MsffctXz7j+7YFN0aVkdJF0cUsbHA4Nb1UcTaHJeCQ+X65UilmLDaH/2a14GDpXK/T4iIVJHSJV8TJkzAxo0bsXLlSjx48ADDhw9Hp06dcPHixS9v/I8nT55g9+7d2LdvH4KDgwvsN3/+fPj4+OD27dsYN24chg8fjtOnTwPI/TLZvHlzxMXF4eLFizh9+jTCw8PRrl27/+u5cnJy4O/vj3fv3qFSpUpyjy1atAhVqlRBUFAQGjVqhM6dO6NLly7o1KkTbt++DTc3N3Tp0gWCgibFZmVl4dGTcPj5lpZrL+tbCg9C8/9CE/IwDGV9S8m1lStTGo+ehCM7O1vWlpaWjl969EW7br3x29RZeBz+NM++/li1DhX9yqJs6VJ5HlMWWdnZCI2IRCVP+SmDFT1dcSf8xVftQyqVIjU9E8Z6urK20m5FEPJ3JO4/y/1C/fJtHK7ce4yfvIsXXvDfUVLsS6QmvUUR959kbRoaWrB3K4fI5wVPtQOAGyeXQ9fADCUrtimwT0LM31g/6SdsmlYTxzcPR2LM151rMUg0NWBQ0gNxlwPk2uMuB8C4TP6vbTUtLUgzMuXapOkZMPLxgkTjwwQF50F9kRUXj8i9Bwo/8O8kKycHoVHxqORsI9desagN7ryKKXC7Q/ee4mVCCvpWyX+2QFaOFFoa8iOg2hrqCHr59tuD/s6ysrMR+vwVKnrJT8mt5FUcd578/VX7yP0cyYCRvp5c+5pDZ2BqqI/m1coXWrxERFR4lGra4bt377Bw4UKcO3dOlqi4uLjg8uXLWL16NerWrftV+8nMzMTWrVthafn59QRVqlTB2LFjAQDFixfHlStXsGjRItSpUwdnzpzB3bt38ezZMzg65v4av3XrVpQsWRI3b95EuXLlPvtc9+7dQ6VKlZCeng4DAwMcOHAAnp7y66YaNmyIvn37AgAmTZqElStXoly5cmjTJvdL6K+//opKlSrhzZs3sLGR/+LyPSQmJUMqlcLUxFiu3dTEBHEJCfluExefANNPRiNNTYyRk5ODxKRkmJuZooiDA8YMGwQXZye8S03F/sPHMHTMeKxZugAOdnYAgHOXLuNJ+FOsWDj3exxaoYlPSUWOVAqzT6ZKmRsZIDYp5av2sfX0VaRlZqKu34cvlfXLeyM+5R26z9sACAKypVK0qVYOPRr8XKjxfy+pyblfePUM5Uco9AwtkBxX8Ojd66eBeHBtLzqMPlhgHxsnH9TtOBcmls5ITY7FzVMrseeP9ug49ih09ZVvepmmqSnUNDSQGRMr154VEwstC4t8t4n76yps27TA29PnkPIgFIZenrBt3RxqWprQNDVB5tsYGJcpDds2LXCraf6jZ8oqPjUTOYIAM30duXZzPR3EvkvPd5u/45Kx5OJdbOhQCxpq+f9GWKmoDbbdDEMZB0s4mhrgxt9vcPHJK+T8B1ZwJyTnfo6YG8mP8JsZGSA2Mfmr9rH1xF9Iy8hC3fI+srbgx89x6NJN7Jw2rDDDJaIf3H/gY/OHolTJV0hICNLT01GnTh259szMTPj6+n71fpycnL6YeAHIMxJVqVIlWQXE0NBQODo6yhIvAPD09ISJiQlCQ0NlyVdBz1WiRAkEBwcjISEB+/btQ9euXXHx4kW5BMzH58MfTWtrawCAt7d3nrbo6Oh8k6+MjAxkZGTIt2VmQjuftVT/l4+mcQIABAGS/Hv+013+0fdv4vfNnu7F4en+YQTHy8Md/YaNxsEjxzGob09Ev43B8rUbMG/apHzXgSkjCT49ZiFPW36O37iHVUcuYNGAX2D20RevW2HPsP7PvzCuQyN4F3XAi7dxmO9/HGuML6BP4+qFHP23e3jrMM7vniy736TPagB5zwsEAQWdlsz0FJzaNhq12k2H7memETp7VpO7b+tcGptn1EHojYMoU6N7AVspgU//mkkkBY5iP1++BlqWFii7ZysgkSArJg6R+w/DqU93CDlSqOvrweP3WQgbPxVZ8QnfP/bv4NOXgYC8HzVA7rS5344GoF8VLziZGRa4v9G1fDH9xC20XH8cEgAOJgZo6l0Uh+89K8ywv698Pju/5nPkxLVgrD54GouGdpV9jrxLy8CE1f6Y2L0VTA0LXkdHRETiUqrkS/rPir9jx47B3l5+AbG2tjbOnz+f58tLVlZWnv3o6//7PzzvEwlBEPIkFfm1F/RcWlpasoIbfn5+uHnzJv744w+sXr1a1ufj6ovv95lfm7SAlZCzZ8/G1KlT5dqGD+qPEYMHFHyAn2FsZAg1NTXEf/LlLj4xMc/o1ntmpiaIi4+Xa0tITIS6ujqMDPP/4qSmpoYSxdzw8nUkAODRk3AkJCSi37DRsj5SqRR3H4Tg4NHjOLHfH+rqylFgwdRAD+pqanlGueKS3+UZDfvUyZv3MW3zIczr2xYVPV3lHltx6BwaVfRBy5/LAgCKOVgjLSMTM7YeQa+GVaFWwK//YnHxqgkbpw9T6HKyc6fMvUuOgb6xlaw9NSUWeob5j/YkxrxAUtwrHFnXX9YmCLmv9aUjPNH5txMwsSiSZztNbT2Y2xZH4tvnhXEohS4rPh7S7GxoWcoft6a5GbJiY/PdRpqRgYfjJiNs4nRoWZghIzoGdu1bITslBVnx8TBwLw5dR3t4r/5QtEbyz2uiWmggrtdrhvQI5VwDZqqnBXWJJM8oV1xqOsz0dPL0T83MRkhUPMLe3MbcM7cBAFJBgADAb/5urGhbDeWdrGGmp4NFLX9CRnYOEtMyYGmgiyUX78LOWPkTDxPDfz5HPhnlik9OgZnx59e7nrx+B9M27MXcAR1RoeSHaYsvo2PxOiYewxZvlrVJ//l7Wa7HOOyfMwqOVnnXzhERceRLsZQq+fL09IS2tjYiIiJQrVq1PI9bWloiOTkZ7969kyU9n1vT9SUfF8B4f9/d3V0WS0REBF68eCEb/QoJCUFiYiI8PDz+7+cSBCHPKNW3GjduHEaMGCHX9jbiyb/en6amJoq7uSIw6A5+qlRB1h4YfBdVKpTLdxtP9xIIuCFfFORWUDCKu7lCQyP/l5cgCAh/+gxF/6loWKaUD9YtWyTXZ/7iZXB0sEf71i2UJvECAE0NDXgUscW1kHDU9P3wOrgW+hTVS+Vf8hnIHfGauvkgZvdqjZ998q7jSs/Mgtonyb6amhoE5H7pVDZaOgZyFQwFQYCekSVehF2BlUPu6G5OdiZePbmJKk1G5bsPU2sXdPz1iFxbwLHFyMx4h2otx8PQJP+pttnZmYh7Ew47l7KFdDSFS8jKRsqDUJhVqYiY0+dk7WZVKiLmzIXPb5udjYyoaACAdaP6iD1/CRAEpIY/w42GreT6Fh0+EBr6+ng8Y16+FRSVhaa6OjxsTHHteRRqFneQtV97/gbV3ezz9NfX1sSe7vXk2nYHPcHNiGjMb1YZ9p8kJ9oa6rAy1ENWjhRnH71EHff8i7YoE00NDXg42+P6g8eoWdZL1n7twWNU9y34sh4nrgVj6vo9mNWvA34uLf93yNnWErtnyF92YMW+k3iXnoHRHZvCxkx+OjkREYlDqZIvQ0NDjBo1CsOHD4dUKsVPP/2EpKQkXL16FQYGBmjSpAn09PTw22+/YfDgwbhx4wY2bdr0r5/vypUrmDdvHpo3b47Tp09jz549OHbsGACgdu3a8PHxQceOHbF48WJkZ2djwIABqFatGvz88i+H/d5vv/2GBg0awNHREcnJyfD398eFCxdw4sSJfx1rfrS1taGtrS3XlvSN0/ZaN2+COQuXoHgxV3i6l8CxE6cR/TYGTRrkrrdbt3kbYmLjMHbEEABAk/p1cejocaxYtxGN6tVByMMwHD99DuNHDZPtc8vO3fAoUQz2drZITU3DgSN/4smz5xjSvzcAQE9PF0Wd5Ec4dHR0YGRkmKddGXSqUxkTNuyHp5MdfFwdsf/SLUTFJaJ1tdwEdcn+04hOSMaMHi0B5CZekzbsx+j2DeDt4oCYf37t1tbUhOE/v/xX9SmBbWcCUMLRFt4uDngRHYeVh86hWqkSUFeyUa/8SCQSlK7aBTdPr4aJpTNMLJ1w8/RqaGrpoETZxrJ+p7aNgb6xNao0GQkNTW2Y28onotq6RgAg1/7XobkoWrIGDE1tkZYchxunVyIzPQUe5Vso5uD+hRcbtsJj/kwk3Q9BUtAd2LVrBW1bW7zauQcA4DJyCLStrRA6ZgIAQNfZCUY+Xki6cw8axkZw7N4Z+sXcEDpmIgBAmpmJd4/lf1jJTs59HX3arow6+ZXAhGPX4WljBh97C+wPDkdUUipal84dAV5y8S6iU1Ixo1FFqEkkcLM0kdveTE8HWhrqcu33XsciOiUNJaxMEJ2chtVX7kMqCOhW3l2BR/bvdaz3Myau2QUPZwf4uBXB/gs3EBWbgFY1KgIAlu45juj4JEzvk1vk6cS1YExauwujOjSFt2sRxCT88zmipQFDPV1oa2nCzUH+BwvDf4r6fNpORPQxqTL+yvsDU6rkCwCmT58OKysrzJ49G0+fPoWJiQnKlCmD3377DWZmZti2bRtGjx6NNWvWoHbt2pgyZQr69Onzr55r5MiRCAwMxNSpU2FoaIgFCxagXr3cX1wlEgkOHjyIwYMHo2rV3Glf9evXz1OOPj9v3rxB586dERkZCWNjY/j4+ODEiRN51rIpoxo/V0FSUjK2+u9BXFw8nJ2KYPbk32BtlTuVLDYuHtFvP1Qos7WxxqzJ47Fi3UYcPnYC5mZmGNSnh9w1vlJS3mHhslWIj0+Avr4e3FyKYtGc6XAv/nUX31U29cp5IfFdKtYcu4iYxGS42Vlh6eCOsPvnGl8xiSmIikuU9d936RaypVLM3nEMs3cck7U3qVQa07rnJhC9GlWFRJI7/TA6IQmmBvqoWqo4BjWvpdBj+xZla/VGdlYGzu+diozURFg7lULz/hvkRsiS4yMhkfx/yWRKQhRObhmBtHcJ0DUwhY1TabQdvhtGZnlHTZRF9J8noWFiDOeBfaBtZYl3j57gbu+ByPhnqq2WlQW07T58IZaoq8GxZxfoFXWCkJ2N+Gs3EdiuC9Jf/TcuNfAl9TyKIDE9A2uuPkDMu3S4WRhjaeufZVMEY96lISop9f/aZ0Z2Dpb/dQ+vElKgp6WBKi62mN6oIgx1/hvrRutVKIXElFSsPXQWMYlJcLW3wZIR3WFnkVtEJiYhGVGxCbL++85fR3aOFHO2HsScrQdl7U2qlMXU3v+tIixERKpMIiiqjjkpxMtH98UOQemYRfKcfGxjanuxQ1AqnkOU99IGYqkwtpnYISgVwbOM2CEoHf1KzcUOgYgKyfLj4j33wAbiPbdYlG7ki4iIiIiIFEPccZgvV3j90Sj/YhIiIiIiIqIfAEe+iIiIiIhUFBcgKRZHvoiIiIiIiBSAyRcREREREZECcNohEREREZGKkkrFjkC1cOSLiIiIiIhIATjyRURERESkolhwQ7E48kVERERERD+M+Ph4dO7cGcbGxjA2Nkbnzp2RkJDw2W26desGiUQid6tYsaJcn4yMDAwePBgWFhbQ19dH06ZN8fLly/8rNiZfREREREQqSiqId/teOnTogODgYJw4cQInTpxAcHAwOnfu/MXt6tevj8jISNntzz//lHt82LBhOHDgAPz9/XH58mWkpKSgcePGyMnJ+erYOO2QiIiIiIh+CKGhoThx4gSuXbuGChUqAADWrl2LSpUqISwsDCVKlChwW21tbdjY2OT7WGJiItavX4+tW7eidu3aAIBt27bB0dERZ86cQb169b4qPo58ERERERGRwmVkZCApKUnulpGR8U37DAgIgLGxsSzxAoCKFSvC2NgYV69e/ey2Fy5cgJWVFYoXL47evXsjOjpa9lhgYCCysrJQt25dWZudnR28vLy+uN+PMfkiIiIiIlJRgiDebfbs2bJ1We9vs2fP/qbjiYqKgpWVVZ52KysrREVFFbhdgwYNsH37dpw7dw4LFizAzZs3UbNmTVkyGBUVBS0tLZiamsptZ21t/dn9forTDomIiIiISOHGjRuHESNGyLVpa2vn23fKlCmYOnXqZ/d38+ZNAIBEIsnzmCAI+ba/165dO9m/vby84OfnBycnJxw7dgwtW7YscLsv7fdTTL6IiIiIiFSU8D0rX3yBtrZ2gcnWpwYNGoT27dt/to+zszPu3r2LN2/e5Hns7du3sLa2/urYbG1t4eTkhMePHwMAbGxskJmZifj4eLnRr+joaFSuXPmr98vki4iIiIiIlJqFhQUsLCy+2K9SpUpITEzEjRs3UL58eQDA9evXkZiY+H8lSbGxsXjx4gVsbW0BAGXLloWmpiZOnz6Ntm3bAgAiIyNx//59zJs376v3yzVfRERERET0Q/Dw8ED9+vXRu3dvXLt2DdeuXUPv3r3RuHFjuUqH7u7uOHDgAAAgJSUFo0aNQkBAAJ4/f44LFy6gSZMmsLCwQIsWLQAAxsbG6NmzJ0aOHImzZ88iKCgInTp1gre3t6z64dfgyBcRERERkYoScdbhd7N9+3YMGTJEVpmwadOmWLZsmVyfsLAwJCYmAgDU1dVx7949bNmyBQkJCbC1tUWNGjWwa9cuGBoayrZZtGgRNDQ00LZtW6SlpaFWrVrYtGkT1NXVvzo2Jl9ERERERPTDMDMzw7Zt2z7bRxA+ZJ26uro4efLkF/ero6ODpUuXYunSpf86NiZfREREREQqSvgBR76UGdd8ERERERERKQCTLyIiIiIiIgXgtEMiIiIiIhUl/RErbigxjnwREREREREpAEe+iIiIiIhUFAtuKBZHvoiIiIiIiBSAI19ERERERCqKI1+KxZEvIiIiIiIiBWDyRUREREREpACcdviDESTMpz8lkeaIHYJSUedLhL5Eg38aPpZuaCV2CEon0MtX7BCUStX7QWKHQPSvSTnvUKH4NYyIiIiIiEgB+PMmEREREZGKEqRiR6BaOPJFRERERESkAEy+iIiIiIiIFIDTDomIiIiIVJTAghsKxZEvIiIiIiIiBeDIFxERERGRipKy4IZCceSLiIiIiIhIATjyRURERESkorjmS7E48kVERERERKQATL6IiIiIiIgUgNMOiYiIiIhUlJSzDhWKI19EREREREQKwJEvIiIiIiIVJXDoS6E48kVERERERKQATL6IiIiIiIgUgNMOiYiIiIhUFC/zpVgc+SIiIiIiIlIAjnwREREREakoKQtuKBRHvoiIiIiIiBSAI19ERERERCpK4KIvheLIFxERERERkQIw+SIiIiIiIlIATjskIiIiIlJRglTsCFQLR76IiIiIiIgUgCNfREREREQqSsqCGwrFkS8iIiIiIiIFYPL1iQsXLkAikSAhIUHUOJ4/fw6JRILg4GBR4yAiIiIiosLBaYeUx6Fjx7Fn/0HExsXDuYgjBvTuCW8vzwL737l3H6vWbcTziBcwNzNDu1bN0aRh/Xz7nr/4F2bOX4jKFctj2oRxsvbN2/2xdecuub6mJibYs21j4RxUIdt14SY2nw5ATGIyXO2sMLpNXZQp5pRv37NBodh98RYevXyDzOxsuNpaol/jaqhc0k3WJysnBxtOXMaRgLuITkiCs7UFhrashSof9VF2giDg2vFluHd1F9LTkmDrVAo12kyChW2xr9o+LPAY/tw8Aq7etdC094p8+9w4tRpXji6Eb7UuqN5qfGGGX+jsOrRFkV7doGVlgdTH4Xg8cx4SbwUV2N++YzvYd24PHXs7ZLyOwvOVa/Hm4NF8+1o1qo+Si+fi7elzuD9g+Pc6hEK1O/ARNl8LQUxKGlwtTTCqdlmUKWL1xe2CX0Sj17YzcLU0wa5eDeUeS07PxLILwTgX9gJJ6ZmwNzHA8Fpl8LOb/fc6jEK178Q57Dh0HLHxCSjqaI+h3TugtGfxfPvGxCdg6SZ/hD39Gy8i36BNw9oY1qODXJ8L125hy/5jeBn5Btk5OXC0tUb7JvXRoHplRRzON7Nt1waO3btCy9IC756EI3zu70i6XfB7xrZ9W9h3aAdtOztkREYhYu16RB/+8J4xr10TRXr3hK6jIyQaGkiLiMDLzVsRfeSYIg6H6D+D1/lSLKVIvrKysqCpqSl2GATg/KXLWLl2A4b074OSnu44dvwUxk2ZjvUrlsDayjJP/8ioNxg/ZQYa1quDsaOG4UHIQyxZuQbGxsaoWqWSXN830dFYvWEzvEvmn8g5F3HEvJlTZffV1JRzYPbkrQeYv+ckfvulIUq7OmLvX7cxcNkO7J88ALZmxnn6Bz6OQEUPFwxuXhOGujo4FBCMISv8se3XnnAvYgsAWH7oPI5dv4dJnRqjqI0FroaEY8Sq3dg8urusj7K7dWYtbp/fiLqd5sDU0hnXT63E/uXd0W3CCWjpGHx226S4V7h0cC7sXf0K7BP1913cu7oLFnYlCjv0QmfVsB6KjR+DR1NmIvF2MOzat4bPuhW40aAFMiKj8vS369AGLqOGIGz8NCTduw8jH2+UmDEJ2UnJiD13Ua6vtp0tXMeOQMLNQEUdzjc7GfIc808HYlz9cijtYIl9QY8xaNd57OvTGLbG+gVul5yeiYlHAlDe2Qax79LlHsvKyUG/nWdhpqeD+S1/hpWRHt4kpUJP67/xt+TMlev4Y+MOjOrdGT7uxXDw1AWMnLkQ2xfPhI2leZ7+WVnZMDEyRNdWjeF/9FS++zQyMEDXVo3hZG8LDQ0NXLkVjFnL18PU2BAVfb2/9yF9E8v6deE6djSezJiNxKBg2LZpBe9Vy3CraStkROV9z9i2a4Oiwwbj8ZTpSL7/AIbeXig2ZSKyE5MQd/ESACA7MRERa9Yh9dlzCFlZMKv2M0pMn4Ks2DjEXw1Q9CESEQH4TtMOq1evjiFDhmDMmDEwMzODjY0NpkyZIntcIpFg1apVaNasGfT19TFjxow8+3j16hXatWsHU1NTmJubo1mzZnj+/Lns8W7duqF58+aYNWsWrK2tYWJigqlTpyI7OxujR4+GmZkZHBwcsGHDBtk276fy+fv7o3LlytDR0UHJkiVx4cKFzx7Pvn37ULJkSWhra8PZ2RkLFiyQPTZt2jR4e+f9o1a2bFlMmjRJdn/jxo3w8PCAjo4O3N3dsWKF/C/7N27cgK+vL3R0dODn54egoIJ/7fue9h08jPp1aqFhvTpwcnTEgD49YWVhjiN/nsi3/9HjJ2FlaYEBfXrCydERDevVQf3aNbFn/0G5fjk5OZj1+yJ07dgetjbW+e5LXV0dZqamspuJcd5ERhlsPROAFlV80fKnMnCxtcSYtvVgY2qMPRdv5dt/TNt66F6vCryc7eFkbY4hzWuhiJU5Lt57JOtz7Ppd9GzwE372LgYHS1O0reaHSp6u2HLmmqIO65sIgoDbF7egfN1+KFaqLizsiqNex7nIzkrHw8D8R2/ek0pzcHzLKFRqOBjG5o759snMeIfjW0aj9i8zoKOnnK+Ljzn26IzIvQcQuecAUsOf4cnM+ciIioJ9h7b59rdp1hiv/fci+s+TSH/xCtHHTiBy7wEU6d1dvqOaGjwXzMbzP1Yi7cVLBRxJ4dh24yGal3JFy9JucLEwxug6frAx0sOe248+u92M4zdQv6QzfOwt8jx28E44ktIysbB1NZR2tIKdsQF8Ha1Qwtr0ex1GofI/cgpNalZF09rV4Oxgh2E9OsDK3AwHTp7Lt7+tlQWG9+yIBtWrwEBPN98+ZbzcUa1CWTg72MHBxgrtGteFq5MD7j58/D0PpVDYd+mEqP0HEbXvANKePsPTub8jIyoKtu3b5NvfqkkjRO7Zh7cnTiH95Su8PX4SUfsPwrFnN1mfxJuBiD17HmlPnyH9xUu83rYTKY8ew6iMr4KOiui/QSoVRLupou82tLB582bo6+vj+vXrmDdvHqZNm4bTp0/LHp88eTKaNWuGe/fuoUePHnLbpqamokaNGjAwMMClS5dw+fJlGBgYoH79+sjMzJT1O3fuHF6/fo1Lly5h4cKFmDJlCho3bgxTU1Ncv34d/fr1Q79+/fDixQu5/Y8ePRojR45EUFAQKleujKZNmyI2Njbf4wgMDETbtm3Rvn173Lt3D1OmTMHEiROxadMmAECPHj0QEhKCmzdvyra5e/cugoKC0K1bNwDA2rVrMX78eMycOROhoaGYNWsWJk6ciM2bNwMA3r17h8aNG6NEiRIIDAzElClTMGrUqH997v+trKwsPHoSDj/f0nLtZX1LI+Thw3y3CXkYhrKf9Pcr44tHT8KRnZ0ta9vmvxsmRsZoULd2gc//6nUk2nXpgU49+2LG3AV4nc+vnWLLys5BaEQkKnm4yrVX9HDBnacvCthKnlQqIDU9A8YffYHKzM6Btqb8QLSOpgaCnkR8e9AKkBj7EqlJb+Hk/pOsTUNTC/au5fD62ed/SLh2Yjl0DczgVSn/L1kAcG7PNBQtWQ1OJZR/+pREUwMGJT0Qd1n+l/W4ywEwLlMq323UtLQgzciUa5OmZ8DIxwsSjQ+vC+dBfZEVF4/IvQcKP/DvJCsnB6GRcajkIj+CW7GoLe68jClwu0N3wvEyIRl9f85/xObi41fwsbfAnJM3UWvxPrRecxTrr9xHjlT5L1iTlZWNsPDnKF+6pFx7+VIlcS8svFCeQxAE3LobgojXUSjtqdyjxRINDRh6euQZjYq/eg1GpQp4z2hq5n3PZGTA0Fv+PfMxkwrloefsjMTA/86oMRH9eL7btEMfHx9MnjwZAFCsWDEsW7YMZ8+eRZ06dQAAHTp0yJN0vefv7w81NTWsW7cOEokEQO7IkYmJCS5cuIC6desCAMzMzLBkyRKoqamhRIkSmDdvHlJTU/Hbb78BAMaNG4c5c+bgypUraN++vWz/gwYNQqtWrQAAK1euxIkTJ7B+/XqMGTMmTywLFy5ErVq1MHHiRABA8eLFERISgvnz56Nbt25wcHBAvXr1sHHjRpQrV04Wa7Vq1eDi4gIAmD59OhYsWICWLVsCAIoWLYqQkBCsXr0aXbt2xfbt25GTk4MNGzZAT08PJUuWxMuXL9G/f//PnuOMjAxkZGTIt2VmQltL67PbFSQxKRlSqRSmpiZy7aamJoi7nZDvNnHx8TA19c3TPycnB4lJSTA3M8P9kFAcP3UWq5csLPC5PUoUw5gRQ+Fgb4f4hARs99+DoaPGYd2KP2BsZPSvjud7iE9JRY5UgJmR/FQpcyN9xCS9+6p9bDkTgLTMLNQt++GLVyVPV2w9cw1l3IrA0dIM1x8+xYU7Ycj5j8zDTk16CwDQM5KfLqVnZIHkuNcFbvfqaSAeBOxFp18PFtgnLPAYol+EoMOovYUS6/emaWoKNQ0NZMbI/6CTFRMLLYu8IzgAEPfXVdi2aYG3p88h5UEoDL08Ydu6OdS0NKFpaoLMtzEwLlMatm1a4FbT/EfPlFV8agZyBAFm+jpy7eb6Ooh9l5bvNn/HJWHJ+WBs6FwHGgVMP34Vn4KbiSlo4FUUS9tVR0RcMuacuolsqVBgwqYsEpKTkSOVwsxY/rPNzMQYcQn3v2nfKe9S0azPCGRmZUNdTYJRvTujfKmSX95QRJqmppBoaCArNk6uPTM2FqYWeadgAkD81QDYtGqO2HPnkRISCoOSnrBp0QxqmprQNDFBZkxuYq9uYICK505CoqkJSKV4PGM2EgKuf/djIvov+Y981fhhfNfk62O2traIjo6W3ffzK3htR2BgIJ48eQJDQ0O59vT0dISHf/hVsGTJknLrgqytreHl5SW7r66uDnNzc7nnBYBKlT6sRdLQ0ICfnx9CQ0PzjSU0NBTNmjWTa6tSpQoWL16MnJwcqKuro3fv3ujRowcWLlwIdXV1bN++XTY18e3bt3jx4gV69uyJ3r17y/aRnZ0N43+m1YWGhqJUqVLQ09PLN8aCzJ49G1OnTpVrGzZoAEYMGfjFbT9H8sl9QRBkSfDXbPF+4aZEIkFqahrmLFiMEYP7w9i44CSqvF/Zj+45wdO9BLr06o/TZ8+jdYtmBW4nlk9PhyDkPW/5OX7zPlYdvYjF/dvJJXBj2tbDtG1H0WLKCkgkgIOlGZpWLo3DV4MLNe7CEnrzMM7umiy737zv6n/+lc+JKUBmegpObBmN2r9Mh66BWb59kuMjcWH/TLQcsAEamtrfGrZifXrsEkmBi5qfL18DLUsLlN2zFZBIkBUTh8j9h+HUpzuEHCnU9fXg8fsshI2fiqz4hO8f+3eQ53MFgCSfd02OVIrfDl1Bv6recDIv+DNDityEbmKD8lBXU4OnrTnepqRhy7UQpU++ZCT5f3Z+Cz1dHWz+fSpS0zNw614Ilmzyh521Fcp4uX/zvr+3PMcvkRT4GRKxai20LMxRevtmSCQSZMbG4c3Bw3Ds2R2CNEfWL+fdOwS2ag91PV2YVKwA19Ejkf7yJRL/Q2smiejH8t2Sr08LaEgkEkg/mg6ir1/wImupVIqyZcti+/bteR6ztPxQ9CG/5/jS8xakoOQiv8Tj0z8QTZo0gba2Ng4cOABtbW1kZGTIRtbeP/fatWtRoUIFue3U1dXz3d/XGjduHEaMGCHXFv3i6b/aFwAYGxlCTU0NcZ98uUtISISpSf7rbMxMTREfH5+nv7q6OowMDfE84gWi3kRjwrRZssffH2/dpq2wafUy2NnmLSihq6ODos5OePk68l8fz/dgaqAHdTUJYhPlR7nikt/B3Kjg1zSQW6hj6pbDmNenNSp6uMg9Zmaoj8X92yEjKxsJKamwMjHEHwfOws5COdevuHrXhK3zh+lA2dm5039Sk2JgYPyhgl1qciz0jPIf7UmIeYGkuFc4tObDCK8g5L5fFg/zRLfxJxAT+QipybHYPr/lhz7SHLwMv4ngv7ZjyMJ7UFNTL9Rj+1ZZ8fGQZmdDy1L+uDXNzZBVwPRmaUYGHo6bjLCJ06FlYYaM6BjYtW+F7JQUZMXHw8C9OHQd7eG9eolsG8k/PzxVCw3E9XrNkB6hnGvATPW0oS6R5CmYEfcuPc9oGACkZmYjJDIOYVHxmHsydx2lVBAgAPCbvQMrfqmJ8s42sNDXhYa6GtQ/+gGuqIURYt6lIysnB5rqyvW6+JiJoSHU1dQQl5Ao1x6fmASzAj5rv5aamhocbHPX1RYvWgR/v3yNLfuPKnXylRUfDyE7G1qfjHJpmZkh85PRsPekGRl4NHEqHk+dCU1zM2S+jYFtm/fvmYQPHQUB6f8sPXgX9gh6LkXh2KsHky8iEo1SVDv8VJkyZbBr1y5YWVnB6DtMObt27RqqVq0KIHcEKjAwEIMGDcq3r6enJy5fvizXdvXqVRQvXlyWPGloaKBr167YuHEjtLW10b59e9kolrW1Nezt7fH06VN07NixwOfYunUr0tLSoKurK4vxS7S1taGtLT8akPgvpxwCuclscTdXBAbfwU+VK8raA4PvoHKF8vnH7l4CATduyrXdCgpGcTdXaGhooIiDPdYuWyz3+MZtO5CWmoYBfXrCsoBpWJlZWYh48RLeJT3+9fF8D5oa6vAoYouA0Keo6fvhy8z10KeoXqrgdRXHb97HlC2HMbtnS1T1zr+UNABoa2rA2tQIWTk5OBsUijplCy7xLyYtHQO5CoaCIEDPyBJ/h12BlWNuzDnZmXgVfhM/Nc1//aKZtQs6jz0i13b12GJkZrxD9ZbjYWhqAz1Dszx9Tu0YB1MrF5Sr3VvpEi8AELKykfIgFGZVKiLm9IfiCWZVKiLmzIXPb5udjYyo3JF660b1EXv+EiAISA1/hhsNW8n1LTp8IDT09fF4xrx8KygqC011dXjYmuHas0jULPGhoMq1Z5GoXtwhT399bU3s6dVIrm337Ue4+fwN5rf8GfYmua+70o6WOP7gOaSCALV/fiCLiE2GhYGuUideAKCpqYESrs64cecBqlX4MOp/824Ifi5XulCfSxCArI/W3yojITsbySGhMK1UEbFnz8vaTSpVROz5C1/cNvNN7nvGsn49xF3867Mj7hKJBGrf8HeS6EckqGjhC7EoRfJ148YNdOnSBWfPnoW9vT06duyI+fPno1mzZpg2bRocHBwQERGB/fv3Y/To0XBwyPsH+/+xfPlyFCtWDB4eHli0aBHi4+MLXH82cuRIlCtXDtOnT0e7du0QEBCAZcuW5alW2KtXL3h45CYKV65ckXtsypQpGDJkCIyMjNCgQQNkZGTg1q1biI+Px4gRI9ChQweMHz8ePXv2xIQJE/D8+XP8/vvv33SM/1ar5k0xd+EfKO7mCk+PEjh24jSi38agScN6AIB1m7YiJjYOY0cOBQA0blAPh47+iZVrN6Bh/ToICQ3DidNn8dvo3BE5LS0tFHWWv/6VwT+jnh+3r16/CRXL+8HK0hIJiYnY7r8HqampqFurhiIO+//SuXYljN94ACWdbOHj4oB9f91GZHwiWlfN/RK15MBZRCckY0b35gByE6+JGw9idNt68CnqgJjEFACAtpYGDHVzf/m/9+wlohOSUcLBBtEJSVh19CKkgoBudauIcoz/L4lEgjLVuuDm6dUwtXSGiaUTbpxeDQ1NHbiXbSzrd2LrGBgYW+OnpiOhoakNCzv5RFRbN/fHlvft6hpaefpoaulBV98kT7syebFhKzzmz0TS/RAkBd2BXbtW0La1xaudewAALiOHQNvaCqFjJgAAdJ2dYOTjhaQ796BhbATH7p2hX8wNoWNy15pKMzPx7vETuefITk4GgDztyqhTeXdMOBwAT1tz+NhbYH/QE0QlpaJ1mdxrwC05H4To5DTMaFoZahIJ3KxM5LY309OBloa6XHubMsXgfysM807dwi9+JRARn4z1Vx/gl3LKXVzivfZN6mLakrXwcHWGVwk3HDp9EW9iYtG8bu5n3spte/A2LgGThnyYrv7oWW4BnrT0DCQkJePRswhoaqijqGPudc227D8Kd9eisLe2RFZ2DgJu38Xxi1cxuk9nxR/g/+nVlm0oMXsGkh+EIOnOXdi2bgkdWxtE7spd6+k8bDC0rawQ9lvue0LXqQgMvb2QdPc+NI0MYd+1M/SLuSJs/ETZPh179UDygwdIf/ESEk1NmP38E6yaNMKTGbNFOUYiIkBJkq/U1FSEhYUhKysLAKCnp4dLly7h119/RcuWLZGcnAx7e3vUqlWrUEbC5syZg7lz5yIoKAiurq44dOgQLAoYgSlTpgx2796NSZMmYfr06bC1tcW0adNklQzfK1asGCpXrozY2Ng80wt79eoFPT09zJ8/H2PGjIG+vj68vb0xbNgwAICBgQGOHDmCfv36wdfXF56enpg7d65s6qIi1aj6E5KSk7HNfzfi4uLh7FQEs6ZMgLVV7lSyuPh4RL99K+tva2ONmVMmYOW6jTh87DjMzc0wsE/PPNf4+pK3MbGYNX8hEpOSYWxkBA/34li6YK7seZVJPb+SSEhJxepjlxCTlAI3OyssG9QBduYmAIC3iSmIjPswnWjvpUBkS6WY7X8cs/2Py9qbVCyF6d1y17NlZGVj+aHzeBkTDz1tLfzkVQwzureAkV7eaVnKyq92b2RnZeDsnqnISE2EjVMptBywQW6ELDk+EhKJcl6/rTBF/3kSGibGcB7YB9pWlnj36Anu9h6IjH+m0WpZWUDbzkbWX6KuBseeXaBX1AlCdjbir91EYLsuSH9VcLGS/5J6ns5ITMvEmsv3EJOSBjdLEyxtVx12xrmvjZiUdER9ZcGa92yM9LGifU0sOBOItuuOwcpQDx3KlUC3Sso5Wvyp2lUqIDH5HTbsOYzY+ES4FLHH778Nh61V7t+i2PhEvPmkaEu3UR/WWT4Mf45Tf12DjaU59q/K/bEuLT0Dv6/Zgui4eGhracHJ3gaTh/ZG7Sryf5OU0dsTp6BhbAynfn1yL7L8+Anu9x+MjMh/3jMWFtC2/fCegbo6HLp2hq5z7nsm4cYtBHfqJnuPAYC6rg6KTfgNWtZWkGZkIPXZc4SNm4C3J/K/ThqRqpKy4oZCSQQVuqz18+fPUbRoUQQFBaF06dKFum9BEODu7o6+ffvmWYelSC8eh4j23MrK4qU410xTVpsz85/+qqpKDMq/lLUqqzBB8T/8KLO0sgVfIkNVPWj/bYWdfjRV7/PvDP13DV6cJNpzLx2mPBWtFUUpRr7+66Kjo7F161a8evUK3bt3//IGRERERESkcph8FQJra2tYWFhgzZo1MDVVzup0RERERESfYsENxVKp5MvZ2blQrqPyKRWauUlERERERP+SSiVfRERERET0AUe+FOvHLztGRERERESkBDjyRURERESkojjwpVgc+SIiIiIiIlIAJl9EREREREQKwGmHREREREQqigU3FIsjX0RERERERArAkS8iIiIiIhXF69UqFke+iIiIiIiIFIDJFxERERERkQJw2iERERERkYqSsuCGQnHki4iIiIiISAE48kVEREREpKJYcEOxOPJFREREREQ/jPj4eHTu3BnGxsYwNjZG586dkZCQ8NltJBJJvrf58+fL+lSvXj3P4+3bt/+/YuPIFxERERGRivoRL7LcoUMHvHz5EidOnAAA9OnTB507d8aRI0cK3CYyMlLu/vHjx9GzZ0+0atVKrr13796YNm2a7L6uru7/FRuTLyIiIiIi+iGEhobixIkTuHbtGipUqAAAWLt2LSpVqoSwsDCUKFEi3+1sbGzk7h86dAg1atSAi4uLXLuenl6evv8PTjskIiIiIiKFy8jIQFJSktwtIyPjm/YZEBAAY2NjWeIFABUrVoSxsTGuXr36Vft48+YNjh07hp49e+Z5bPv27bCwsEDJkiUxatQoJCcn/1/xMfkiIiIiIlJRglQQ7TZ79mzZuqz3t9mzZ3/T8URFRcHKyipPu5WVFaKior5qH5s3b4ahoSFatmwp196xY0fs3LkTFy5cwMSJE7Fv3748fb6E0w6JiIiIiEjhxo0bhxEjRsi1aWtr59t3ypQpmDp16mf3d/PmTQC5xTM+JQhCvu352bBhAzp27AgdHR259t69e8v+7eXlhWLFisHPzw+3b99GmTJlvmrfTL6IiIiIiFSUVMRS89ra2gUmW58aNGjQFysLOjs74+7du3jz5k2ex96+fQtra+svPs9ff/2FsLAw7Nq164t9y5QpA01NTTx+/JjJFxERERER/RgsLCxgYWHxxX6VKlVCYmIibty4gfLlywMArl+/jsTERFSuXPmL269fvx5ly5ZFqVKlvtj3wYMHyMrKgq2t7ZcP4B9c80VERERERD8EDw8P1K9fH71798a1a9dw7do19O7dG40bN5ardOju7o4DBw7IbZuUlIQ9e/agV69eefYbHh6OadOm4datW3j+/Dn+/PNPtGnTBr6+vqhSpcpXx8eRLyIiIiIiFfUjXudr+/btGDJkCOrWrQsAaNq0KZYtWybXJywsDImJiXJt/v7+EAQBv/zyS559amlp4ezZs/jjjz+QkpICR0dHNGrUCJMnT4a6uvpXx8bki4iIiIiIfhhmZmbYtm3bZ/sI+ax169OnD/r06ZNvf0dHR1y8ePGbY2PyRURERESkovJLQuj7YfL1g8lQ0xU7BKVzTC/v0LEq62h4QuwQlErA3ptih6B0QnVTxA5BqVhJvu66MKokZcsNsUNQKkcCs8UOQek0KcuvmET5YcENIiIiIiIiBeDPEkREREREKkr6AxbcUGYc+SIiIiIiIlIAjnwREREREamoH7HUvDLjyBcREREREZECcOSLiIiIiEhFsdS8YnHki4iIiIiISAGYfBERERERESkApx0SEREREakoQSoVOwSVwpEvIiIiIiIiBeDIFxERERGRiuJFlhWLI19EREREREQKwOSLiIiIiIhIATjtkIiIiIhIRfE6X4rFkS8iIiIiIiIF4MgXEREREZGKElhwQ6E48kVERERERKQAHPkiIiIiIlJRHPlSLI58ERERERERKQCTLyIiIiIiIgXgtEMiIiIiIhUlFaRih6BSOPJFRERERESkABz5IiIiIiJSUSy4oVgc+SIiIiIiIlIAJl9EREREREQKwGmHREREREQqitMOFYsjX0RERERERArAkS8iIiIiIhUlCBz5UiQmX5QvQRCwY/s2nDhxHCkpKShRogT6DxgIJyfnz2535fJlbN26BZGRkbC1tUWXrl1RuXIV2eP3793Dvn178eTJY8TFxWHChEmoVLmy7PHs7Gxs2bIZt27eRFRUJPT19VG6tC+6de8Bc3Pz73W4/zdBEHDuwHLcvLAbae+S4OjqgyZdJsLaoViB2zy4eQoXjqxBXHQEcrKzYW7jhJ8adINvlWayPhePrMGDW6fxNvIpNDV1UKSYL+q1GwlL26KKOKxCtef0X9h67BxiEpLgYm+DkZ1bwtfdNd++527ewd4zl/Ho71fIysqGi4Mt+rSqj0o+HgqOuvAIgoDje1biytm9SEtJglMxb7TtOR62jm4FbnPlzF7cuHQEkS8eAwAcXTzR5JehcHbzlvX5c/cKHN+7Um47Q2NzzFp74bscR2ESBAH7dq7HuZOH8C4lCW7FS6J7v1FwcHIpcJuXfz/Fnu1r8Sz8IWKio9C511A0aNZers+hPZtx8+pFvH71N7S0tFHM3Ru/dBsAOwen731IX+3QsePYs/8gYuPi4VzEEQN694S3l2eB/e/cu49V6zbiecQLmJuZoV2r5mjSsH6+fc9f/Asz5y9E5YrlMW3COFn73fsPsHvfQTwOD0dsXDymjh+LKpUqFPqxFSZBEHBy3woEnN2LtHdJKOLmjVbdJ3z2fRNwdi9u/nUYUS+fAAAcinqiUbuhcProfTNtcF3Ex7zOs22VOu3RuseEwj+QQiIIAk7tW4Hr5/Yg9V0Sirj5oGX3CbBxKPh8XDu3B4F/HUbUiw/no0G7oSji5iPrk5OTjVP7luP2lWNIToiBkYkl/Ko1Q+3m/aCmxklRRN8T32GUr7179+DAgQPo138AFi1eAlNTM0wY/xtSU1ML3CY0NARz5sxCzZo1sWz5CtSsWRNzZs/Cw4cPZX3S09NRtGhR9Os/IN99ZGRkIPzJE/zySwcsWboM4ydMxKtXrzBt6pTCPsRv8texdbhyYhOadJ6AAVN3w8DYAhvn9URG2rsCt9E1MEH1pn3Rd+JODJ55EGV/boH9a8fj8d3Lsj7PHt5Exdod0G+SP7r/uh7SnGxsmtcTmRkFn3dldCrgNhZsPYAezepi+8zR8HV3xZB5qxAVE5dv/6CH4ajg5Y4/RvfF1pmj4OfphuG/r8XD5y8VHHnhOXNoA84f24I2PX7DqNk7YWRigWUz+iD9M6+RJyE3UbZKAwyZvAEjZmyDmbktVszoi4S4N3L9bB3dMHPNedlt3IL93/twCsWRfdtw/OBOdOs7EjMWboCxqTlmTRqKtNSCz0lGRjqsbOzQvusAmJjm/wNM6P0g1GnUCtPmr8W46X9AmpONOZOGIT097Xsdyv/l/KXLWLl2Azq0bY1VSxbAu6Qnxk2ZjjfRb/PtHxn1BuOnzIB3SU+sWrIAHdq2wvI163HpSkCevm+io7F6w2Z4l8ybyKWnp8PFxRmD+vUu9GP6Xs4d2YALf25Bq+6/YfhMfxiZWGDVrN6ff9+E3kSZyg0xcMIGDJ26DabmNlg1u4/c+2bETH9MXXlBduv321oAQOmKdb/7MX2L80fW49LxzWjRbTyGztgFI2MLrJnV67PnIzzkJkpXboh+EzZg8NTtMLGwxZo5fZD40fk4f2Q9As7sRotu4zHm9yNo1GEELh7diCsntyvisEjJSKVS0W6qiMmXEtmyZQvMzc2RkZEh196qVSt06dJFYXEIgoBDBw+gXfv2qFLlJzg7O2PEyJHIyMjAxQvnC9zu0MGD8PUtg7bt2sPR0RFt27VHqdKlcejQAVkfv3Ll0KVrN1Sp8lO++9DX18fMWbPxc9WqcHBwhLu7B/r1748nTx4jOjq60I/13xAEAVdObkH1pn1RslxdWDsUR+s+c5CVmY47AUcL3M7FozxK+tWBlb0rzK2LoHK9LrB2LI7njwJlfbqNXosyP7eAtUMx2BZxR6ves5AQG4lXzx4o4tAKzfbjF9CsekU0r1EJRf8Z9bI2N8XeM1fy7T+yc0t0bVILJV2dUMTGCgPbNUERG0v8dfu+giMvHIIg4MKf21C3RW+UrlAbdkWKodPAmcjKSMety8cK3K7rkLmoWq89HJzdYWPvgl/6TYEgSBF277pcPzU1dRiZWMhuhkZm3/uQvpkgCDhxeBeate2G8pWrw9HJFf2HT0RmRjquXjxV4HauxT3RscdgVK5aBxqamvn2GTt1MarVbgQHJxc4FS2GvsMmIOZtFJ49eZhvf0Xbd/Aw6tephYb16sDJ0RED+vSElYU5jvx5It/+R4+fhJWlBQb06QknR0c0rFcH9WvXxJ79B+X65eTkYNbvi9C1Y3vY2ljn2U95v7Lo0bkjfq5c6XscVqETBAEXj29FneZ94FO+Dmwdi6FD/1nIzEzH7SsFv286D5qLn+q2h72zO6ztXdCuz1QIghSP71+T9TEwMpN7z4TcvggLa0e4epRTxKH9K4Ig4K8TW1GrWR94/3M+2v9zPoKuFnw+Og6ahyp1foG9swes7F3Qpnfe8/H34zvw8qsJT99qMLO0R6kK9VDcuzJe/Mf+1hD9FzH5UiJt2rRBTk4ODh8+LGuLiYnB0aNH0b17d4XFERUVhfj4eJQpU0bWpqmpBS9vb4SGhha43cOHofD9aBsAKFOmLEJDCt7ma7x79w4SiQQGBvrftJ/CEv/2JVISY+Dm9WE6pYamFpxLlEPE46Cv2ocgCAh/EICYyOco6u5XYL/0tGQAgJ6B8bcFrUBZ2dl4+OwFKnqXkGuv6F0Cdx8/+6p9SKVSvEtPh5GB3vcI8buLjX6JpIQYuJf6MKVWU1MLbp5l8SzszlfvJzMjHTnZ2dD/5P//26gIjO9bE5MH1sfGxaMR8+ZFocX+vUS/eY2E+Fj4+JaXtWlqasHDyxePHt4r1OdKfZcCADAwNCrU/f4bWVlZePQkHH6+peXay/qWRsjD/JPDkIdhKPtJf78yvnj0JBzZ2dmytm3+u2FiZIwGdWsXdtiiiI1+ieSEGJTw/vC+0dDUgpuHH549Cv7q/WRmpEOanV3g52Z2dhYCLx9F+eotIJFIvjXs7ybu/fnwkf9b4+rhh+ePvu5vDfDhc+Tj81G0hC8e37+Gt5HPAQCv/36IZ2FB8Cj9c6HFT0T545ovJaKrq4sOHTpg48aNaNOmDQBg+/btcHBwQPXq1RUWR3x8PADAxMRUrt3ExBRvo9/kt4lsO9NPtjE1MZXt79/IzMzEpo0bUa16dejpKUfylZwYAwAwMLaQazcwNkdCPmsKPpaemoy5Q6sjOzsTampqaNJlklwS9zFBEPDnjrlwKl4W1g7FCyd4BUhIfoccqRRmxvJffM2MDRGTmPxV+9j253mkZ2SiTgXf7xHid5eUEAsAMDKWnyZnaGyOuJjIr97P4e2LYGxmhRLeFWVtTsW80XngTFjZOSEpIRYn96/BwgmdMX7hQegbmhRK/N9DYnzuOTE2kR+lMzIxQ0x0VKE9jyAI2LZ+CUp4loKjU/5rDBUpMSkZUqkUpqYmcu2mpiaIu52Q7zZx8fEwNfXN0z8nJweJSUkwNzPD/ZBQHD91FquXLPxOkSve+89Ww0/eNwbG5vmu1yrI0Z2575viXvmP+N27eRZpqckoX7X5v45VET78rfnkfBj9f+fjT/+FMDazQrGPzkeNJr2QnpqCeaMaQ6KmDkGag/pth8K3cqPCCZ7+U1hqXrGYfCmZ3r17o1y5cnj16hXs7e2xceNGdOvWLd9f5zIyMvJMUczIyIC2tvb/9Zznz5/DsqVLZPenTJ0GAMjzlIKQT+MnPnlYgPDFTQqSnZ2NuXNmQxCkGDhw0L/bSSEIvnoEhzZOkd3vMvKfYgf5nJ8v/YqqpaOPQTP2IyM9FU9DruH4zrkws3KEi0f5PH2PbJmOqBdh6DPhvzkH/9NTIQh5T1l+TlwNxJr9J7BgRC+YGRt+l9gK282/jsJ/zTTZ/X7jluf+45OTkHsOvu4NcebQBgReOY4hUzZAU+vDe7qk74dfpu2KAEWLl8LUwQ1x/eIh1Gzc9RuOonBdvnAS65fPld0fM+n33H/k88IozMGHTat+R8TzJ5g8d3Xh7bQQ5P24+NLnxaevndwvRxKJBKmpaZizYDFGDO4PY2PxR/f+rcDLR7F73VTZ/d5jVuT+I7/XyFe+b84e3oCgq39i4MSNcu+bj12/sB/upX+CsZnVv4r7e7l9+Sj2rp8iu99zTO7fmrzH/uW/Ne+dP7IeQVf/RP+Jm+TOR3DAcQRePooOA+fBxsENr/9+iENb58DI1BLllDwpJfqvY/KlZHx9fVGqVCls2bIF9erVw71793DkyJF8+86ePRtTp06Vaxs8eAiGDB32fz1nhQoVUaKEu+x+VlYmgNyRLDOzD7+4JSQm5BnZ+pipad5RroSEhDwjaF8jOzsbc2bPwps3UZg1e66oo14evjXh6PqhSlT2P+cnJSEGRiYf/ninJMXBwOjzFRnV1NRgbp1bgc3OyQPRr8Nx8ciaPMnXkS0z8DDoPHqN3wpjM5vCOhSFMDHUh7qaGmITkuTa45OSYf6FZOpUwG1MX7sTc4d0RwWvEp/tq0y8/WrAuVje10hSQgyMTS1l7SlJsXl+1c/P2cObcOrAOgyauBb2Tp8/D9o6erArUgxvIyP+ZfTfR9nyP8Gt+IciENlZWQByR8BMzT6MGiclxucZDfu3Nq1egMAblzFp9kqYWyjHF2tjI0OoqakhLj5Brj0hIRGmJvlPizPL97M0Eerq6jAyNMTziBeIehONCdNmyR5/n5zVbdoKm1Yvg52tbeEeyHdQsmwNjHLL+75JzvO+icsz+pOf80c34syhtej/21rYFfC+iXv7Go/uXUP3EYu/LfjvwLNsDYz4qEJjdnbueyY5MQZG/+J8XDi6EWcPrUXf39bBroj8+Ti6YwFqNu0J38oNAQC2RYojPuY1zh1ax+RLBQmCaha+EAuTLyXUq1cvLFq0CK9evULt2rXh6OiYb79x48ZhxIgRcm0vXn79VIT39PT0oKf3YW2NIAgwNTVF0O0guLrmlrPNysrC/Xv30L17jwL34+7ugeCgILRo0VLWFnT7Njw8/79y4e8Tr9evX2H2nLkwMhL3l11tXX1o635I/gRBgIGxBZ48uAo759wvl9nZmXgedhP12o78/3YuCMjJzpTb95GtMxASeAa9xm2GmaVDoRyDImlqaMC9qCOu3w9DjXKlZO3X74WhWlnvArc7cTUQ09fsxMxBXfCTb0lFhFpodHT1ofPJa8TIxAJhdwPgWDT39Z+dnYUnIYFo2nHYZ/d15vBGnNy3BgPGr0IR1y+fh6ysTLx59RSuHmW+2FeRdPX0oasnf05MTM1xL/gmnF1zvwhmZ2Uh9H4Qfumaf/XTryUIAjatXoBbARcxYfYKWNnYfdP+CpOmpiaKu7kiMPgOfqr8YfpoYPAdVK6Qd8QbADzdSyDgxk25tltBwSju5goNDQ0UcbDH2mWL5R7fuG0H0lLTMKBPT1hayE+JVlb5vW8MTSwQdi8ADh+/b0Jvockvwz+7r3NHNuD0gTXoO241irh6FdjvxsUDMDA2g6dv1cI5iEJU0Pl4dO8q7J3fn49MhIfeQqNfRhS0GwDA+SMbcPbgavQeuwaOLnnPR1ZmGiQS+WX/EjV1fgknUgAmX0qoY8eOGDVqFNauXYstW7YU2E9bWzvPFENt7dhvfn6JRIJmzVtg925/2Nnbwc7OHrt3+UNbWxvVqteQ9Vvw+3yYm5uj2z8JWdNmzfHrmFHYs2c3KlashGvXAhAcHIR58xfItklLS8Pr1x8SxKg3UQgPD4ehoSGsrKxyq3fNmoHwJ08weco05ORIEReXW57c0NAQmgVUO1MkiUSCKvW64OKRNTC3doKFjRMuHF4DTS0dlKrUWNZvz+pfYWRqjXptc/9IXjyyBvZFS8LMqghysrMQducSgq4cRtOuk2TbHN48DXevHUOnYcugraOP5ITcUtQ6eobQ1NJR7IF+g44NqmPSym3wKFoEPsWcsf/cVUTFxqNVrdz1bcv8jyA6PhHT+ncCkJt4TV61DaM6t4SXmzNi/hk109HShIGermjH8W9JJBJUb9gJpw6sg6WtEyxtiuDUgbXQ1NaB308f1lRsWfYbTMys0LTDMAC5Uw2P7VqGrkPmwtzKHkkJuWs+tHX0oK2T+wPJgS2/w8uvGkwtbJGSGIeT+9YgPe0dKlRrlicOZSKRSFC/aTsc2rMZNnYOsLFzxKHdm6GlrYPK1T6U+16xcCrMzC3R/p+ELDsrCy9f5BZqyc7ORlzsWzx/+gg6Orqwscv9YWrjyt9x9dIpjBw/F7q6ekj4Z32Znp4+tLTFf9+0at4Ucxf+geJurvD0KIFjJ04j+m0MmjSsBwBYt2krYmLjMHbkUABA4wb1cOjon1i5dgMa1q+DkNAwnDh9Fr+Nzv0s0dLSQlFn+WuYGejnfmn/uD0tLQ2vIj+sp4t88wZPnj6DoYEBrK0soWwkEgmqNeiMM4fWwtK2CCxtnHDm4FpoaemgTJUP75vtK8bB2NQKjf9JyM4e3oDje5ai86B5MLPM/30D5BbyuXHxIMpVbQZ1deX/+iORSPBz/c44e2gtLGxy/9acO7QGWlo6cmuzdq4YB2MzKzRsn3s+zh9ZjxN7lqLjoHkwtbRD0j9/R3LPR+7rxLNMdZw9tAYmFrawcXDDq+ehuPTnZpSr3kLxB0qkYpT/00cFGRkZoVWrVjh27BiaN28uSgytW7dBZkYGVixf9s9Flt0xfcYsuRGyt2+jIVH7MO/c09MTv44dh61bNmPb1i2wsbXFr2PHwd39w5TGx48fYdzYX2X3161dAwCoVbs2RowYhZiYt7h+Lbcc7uBB8r+Gz54zFz4+paAMfm7UC1mZGTi8eRrSU5Pg4OKD7mPWyY2QJcZGyv2ymJmRisObpyEx7g00tXRgaVsUbfrOhU/FhrI+N875AwDWzZJfu9Oq9yyU+fm/80exbqUySEx5h3UHTiImIRGuDrb4Y3Rf2FrmTi+LSUhCVOyHaVX7z11FTo4UczftxdxNe2XtjX8ujyn9Oio8/sJQu1kPZGVmYPe6GUh9lwRnN28MHL9a7pft+JhIubUbf53ahezsLKxfKP+rdoPW/dGwbe77ISHuDTb98SveJcXDwMgMzsV8MGLmdphZKs9oT0GatOqEzMwMbFz5O96lJMO1uCfGTVssN0IW+/YN1D5638THxeC3oR/eD8cO7MCxAzvg4eWLibNz1widOZ57nbPpvw2Ue76+QyegWm3xCwjUqPoTkpKTsc1/N+Li4uHsVASzpkyAtVXu1Mi4+HhEv/1wzS9bG2vMnDIBK9dtxOFjx2FuboaBfXqiapX/r2R82ONwjPptouz+qnUbAQB1a9XAmOFDCuHICl/NJj2QlZmOvRtmIO1dEpxcfdDvtzX5vG8+vEaunPZHTnYWNi2WHx2r16o/6rf+8Jp4dD8A8TGRqPAfSjBqNOmJrMwM7N84Pfei064+6D1urfz5iI2U+1t89Z/zseWT81Gn5QDU++d8NO86Hif3LMH+jdORkhgHY1MrVKzVBnVa9lfMgZFSYcENxZII7yeKk1KpU6cOPDw8sGTJki93/siT8K8r5a1KgmOcvtxJhdRTL/iaSqooQLOm2CEoHXPdFLFDUCpWksKrxvijuJdcTOwQlEqOoLwl68XSpCx/3/+vaNijcC/38f/4c0PByxF+VHxnKJm4uDicOnUK586dw7Jly8QOh4iIiIh+YBz5UiwmX0qmTJkyiI+Px9y5c1GixH+n2hsREREREX0eky8l8/z5c7FDICIiIiIVIWWVS4VS+3IXIiIiIiIi+lZMvoiIiIiIiBSA0w6JiIiIiFQUC24oFke+iIiIiIiIFIAjX0REREREKkqQsuCGInHki4iIiIiISAGYfBERERERESkApx0SEREREakoFtxQLI58ERERERERKQBHvoiIiIiIVJQgsOCGInHki4iIiIiISAE48kVEREREpKKkXPOlUBz5IiIiIiIiUgAmX0RERERERArAaYdERERERCpKkLLghiJx5IuIiIiIiEgBOPJFRERERKSieJFlxeLIFxERERERkQIw+SIiIiIiIlIATjskIiIiIlJRgsCCG4rEkS8iIiIiIiIF4MgXEREREZGKYsENxeLIFxERERER/TBmzpyJypUrQ09PDyYmJl+1jSAImDJlCuzs7KCrq4vq1avjwYMHcn0yMjIwePBgWFhYQF9fH02bNsXLly//r9iYfBERERERqShBKhXt9r1kZmaiTZs26N+//1dvM2/ePCxcuBDLli3DzZs3YWNjgzp16iA5OVnWZ9iwYThw4AD8/f1x+fJlpKSkoHHjxsjJyfnq5+G0QyIiIiIi+mFMnToVALBp06av6i8IAhYvXozx48ejZcuWAIDNmzfD2toaO3bsQN++fZGYmIj169dj69atqF27NgBg27ZtcHR0xJkzZ1CvXr2vei6OfBERERERkcJlZGQgKSlJ7paRkaHwOJ49e4aoqCjUrVtX1qatrY1q1arh6tWrAIDAwEBkZWXJ9bGzs4OXl5esz9fgyNcPxs21qNghICMjA7Nnz8a4ceOgra0tdjhwcxU7AmU7J/VFfn7lOh91v9xFIZTpnABmIj8/z0d+lOmcOIr67LmU6XwoA56PvHhOvs7lI9VEe+4pU6bIRqnemzx5MqZMmaLQOKKiogAA1tbWcu3W1tb4+++/ZX20tLRgamqap8/77b8GR76o0GVkZGDq1Kmi/HKhrHhO5PF85MVzIo/nIy+eE3k8H/J4PvLiOVF+48aNQ2Jiotxt3Lhx+fadMmUKJBLJZ2+3bt36pngkEoncfUEQ8rR96mv6fIwjX0REREREpHDa2tpfPSo5aNAgtG/f/rN9nJ2d/1UcNjY2AHJHt2xtbWXt0dHRstEwGxsbZGZmIj4+Xm70Kzo6GpUrV/7q52LyRURERERESs3CwgIWFhbfZd9FixaFjY0NTp8+DV9fXwC5FRMvXryIuXPnAgDKli0LTU1NnD59Gm3btgUAREZG4v79+5g3b95XPxeTLyIiIiIi+mFEREQgLi4OERERyMnJQXBwMADAzc0NBgYGAAB3d3fMnj0bLVq0gEQiwbBhwzBr1iwUK1YMxYoVw6xZs6Cnp4cOHToAAIyNjdGzZ0+MHDkS5ubmMDMzw6hRo+Dt7S2rfvg1mHxRodPW1sbkyZO5uPUjPCfyeD7y4jmRx/ORF8+JPJ4PeTwfefGcqK5JkyZh8+bNsvvvR7POnz+P6tWrAwDCwsKQmJgo6zNmzBikpaVhwIABiI+PR4UKFXDq1CkYGhrK+ixatAgaGhpo27Yt0tLSUKtWLWzatAnq6upfHZtEEAThG4+PiIiIiIiIvoDVDomIiIiIiBSAyRcREREREZECMPkiIiIiIiJSACZfRERERERECsDki4iIiIj+87p164ZLly6JHQbRZzH5okL15MkTnDx5EmlpaQAAFtMkKlh4eDgmTJiAX375BdHR0QCAEydO4MGDByJHJo7169fn256dnY1x48YpOBrlpuqfrdHR0bh//z7u3r0rdyPVlpycjLp168qu0fTq1SuxQyLKg8kXFYrY2FjUrl0bxYsXR8OGDREZGQkA6NWrF0aOHClydMojISFB7BBEc/v2bdy7d092/9ChQ2jevDl+++03ZGZmihiZOC5evAhvb29cv34d+/fvR0pKCgDg7t27mDx5ssjRiWPkyJFo1aoV4uLiZG0PHz5E+fLlsXv3bhEjE0fnzp1lr4uPPX/+HFWrVhUhIvEFBgbCy8sLtra28PHxQenSpeHr6yv7L6m2ffv24dWrVxg0aBD27NkDZ2dnNGjQAHv37kVWVpbY4REBYPJFhWT48OHQ0NBAREQE9PT0ZO3t2rXDiRMnRIxMPHPnzsWuXbtk99u2bQtzc3PY29vjzp07IkYmjr59++LRo0cAgKdPn6J9+/bQ09PDnj17MGbMGJGjU7yxY8dixowZOH36NLS0tGTtNWrUQEBAgIiRiScoKAhv3ryBt7c3Tp8+jeXLl6NMmTLw8vJCcHCw2OEpXEhICLy9vXHlyhVZ2+bNm1GqVClYW1uLGJl4unfvjuLFi+Pq1at4+vQpnj17JvdfVfHpiN/nbqrG3NwcQ4cORVBQEG7cuAE3Nzd07twZdnZ2GD58OB4/fix2iKTqBKJCYG1tLQQHBwuCIAgGBgZCeHi4IAiC8PTpU0FfX1/M0ERTtGhR4cqVK4IgCMKpU6cEExMT4eTJk0LPnj2FOnXqiByd4hkZGQlPnjwRBEEQ5syZI9StW1cQBEG4fPmy4ODgIGZootDX1xeePn0qCIL8e+bZs2eCtra2mKGJKicnRxgyZIigpqYmaGpqCjt37hQ7JNFkZWUJv/76q6ClpSWMGzdOaN26tWBgYCCsX79e7NBEY2BgIDx+/FjsMEQnkUgENTU12X8/d1NVr1+/FubMmSMUL15c0NfXF7p06SLUqVNH0NDQEBYuXCh2eKTCNMRO/ujH8O7dO7kRr/diYmKgra0tQkTii4yMhKOjIwDg6NGjaNu2LerWrQtnZ2dUqFBB5OgUTxAESKVSAMCZM2fQuHFjAICjoyNiYmLEDE0UJiYmiIyMRNGiReXag4KCYG9vL1JU4jt69Ch27tyJypUrIywsDGvXrkXVqlVhZ2cndmgKp6GhgTlz5kBbWxvTp0+HhoYGLl68iEqVKokdmmhq1aqFO3fuwM3NTexQRPXs2TPZv4OCgjBq1CiMHj1a9toICAjAggULMG/ePLFCFEVWVhYOHz6MjRs34tSpU/Dx8cHw4cPRsWNHGBoaAgD8/f3Rv39/DB8+XORoSVUx+aJCUbVqVWzZsgXTp08HAEgkEkilUsyfPx81atQQOTpxmJqa4sWLF3B0dMSJEycwY8YMALlJSE5OjsjRKZ6fnx9mzJiB2rVr4+LFi1i5ciWA3C8RqjiFqkOHDvj111+xZ88e2fvlypUrGDVqFLp06SJ2eKLo27cvNm/ejBkzZmDkyJF48+YNevToAW9vb6xcuRJt27YVO0SFysrKwtixY7F8+XKMGzcOly9fRosWLbBhwwY0bNhQ7PBEsW7dOnTt2hX379+Hl5cXNDU15R5v2rSpSJEplpOTk+zfbdq0wZIlS+ReEz4+PnB0dMTEiRPRvHlzESIUh62tLaRSKX755RfcuHEDpUuXztOnXr16MDExUXhsRDJiD73Rj+HBgweCpaWlUL9+fUFLS0to3bq14OHhIVhbW8ummqmagQMHCk5OTkLt2rUFc3NzITk5WRAEQfD39xd8fX1Fjk7x7ty5I3h5eQlGRkbClClTZO2DBg0SfvnlFxEjE0dmZqbQoUMH2dQhTU1NQU1NTejUqZOQnZ0tdniiKFmypGz68seWLVumktOXfXx8BDc3NyEgIEAQBEGQSqXCnDlzBG1tbaF///4iRyeOQ4cOCUZGRoJEIslzU9Updjo6OkJISEie9pCQEEFHR0eEiMSzZcsWIS0tTewwiD5LIggqXq+WCk1UVBRWrlyJwMBASKVSlClTBgMHDoStra3YoYkiKysLf/zxB168eIFu3brJKnEtXrwYBgYG6NWrl8gRKof09HSoq6vn+QVbVTx9+hS3b9+GVCqFr68vihUrJnZIosnIyChwmnJYWBhKlCih4IjE1bNnTyxZsgT6+vpy7cHBwejUqRPu378vUmTicXZ2RuPGjTFx4kSVHDHPT5kyZeDh4YH169dDR0cHQO57qUePHggNDcXt27dFjpCIPsbki+g7effuXZ4vTUTvTZs2DaNGjcqzVjItLQ3z58/HpEmTRIqM/gs+l6j+yAwNDREcHAxXV1exQ1EaN27cQJMmTSCVSlGqVCkAwJ07dyCRSHD06FGUL19e5AiJ6GNMvqhQFFTOViKRQEdHB0WKFFG5LwoGBgZo27YtevTogZ9++knscESnpqYGiURS4OOqtg5OXV0dkZGRsLKykmuPjY2FlZWVypwPX1/fz74uPqYKv+AnJSV9dV8jI6PvGIly6tq1K37++WfOHPhEamoqtm3bhocPH0IQBHh6eqJDhw78AZBICbHgBhWK0qVLy75Avc/nP/5CpampiXbt2mH16tWyaRE/up07d2LTpk2oVasWnJyc0KNHD3Tp0kUlq7YBwIEDB+TuZ2VlISgoCJs3b8bUqVNFiko8giDkm3TcuXMHZmZmIkQkjo+LAaSnp2PFihXw9PSUVW27du0aHjx4gAEDBogUoWKZmJh8dTKqKgn6x4oXLy4rPuLt7Z1nuvKQIUNEikxcenp66NOnj9hhENFX4MgXFYpDhw7h119/xejRo1G+fHkIgoCbN29iwYIFmDx5MrKzszF27Fi0a9cOv//+u9jhKlRsbCy2bNmCTZs2ISQkBPXq1UOPHj3QtGlTaGjw948dO3Zg165dOHTokNihKISpqSkkEgkSExNhZGQk90U7JycHKSkp6NevH5YvXy5ilOLo1asXbG1tZVVT35s8eTJevHiBDRs2iBSZ4ly8eFH27+fPn2Ps2LHo1q2bXAnxzZs3Y/bs2ejatatYYYrm00szfEwikajUhZY/tnXrVqxevRpPnz5FQEAAnJycsGjRIri4uKBZs2Zih0dEH2HyRYWifPnymD59OurVqyfXfvLkSUycOBE3btzAwYMHMXLkSISHh4sUpfiWLl2K0aNHIzMzExYWFujXrx/Gjh2b7zXSVEV4eDh8fHzw7t07sUNRiM2bN0MQBPTo0QOLFy+GsbGx7DEtLS04Ozur7HWcjI2NcevWrTxFRx4/fgw/Pz8kJiaKFJk4atWqhV69euGXX36Ra9+xYwfWrFmDCxcuiBMYKZWVK1di0qRJGDZsGGbMmIEHDx7AxcUFmzZtwubNm3H+/HmxQySij/BndyoU9+7dk7vuyHtOTk64d+8egNypiZGRkYoOTXRRUVHYsmULNm7ciIiICLRu3Ro9e/bE69evMWfOHFy7dg2nTp0SO0xRpKWlYenSpXBwcBA7FIV5P1pRtGhRVK5cWWWrPOZHV1cXly9fzpN8Xb58WWWmK38sICAAq1atytPu5+fHNU8ks3TpUqxduxbNmzfHnDlzZO1+fn4YNWqUiJERUX6YfFGhcHd3x5w5c7BmzRpoaWkByF3TM2fOHLi7uwMAXr16pVKlgffv34+NGzfi5MmT8PT0xMCBA9GpUye5izuWLl1aVoL+R/d+ut17giAgOTkZenp62LZtm4iRiaNatWqyf6elpSErK0vucVUspjBs2DD0798fgYGBqFixIoDcNV8bNmxQyeqPjo6OWLVqFRYsWCDXvnr1ajg6OooUlXhCQ0Nx7do1VKpUCe7u7nj48CH++OMPZGRkoFOnTqhZs6bYIYri2bNn+f4d0dbWVpkZBUT/JUy+qFAsX74cTZs2hYODA3x8fCCRSHD37l3k5OTg6NGjAHKvZ6Qqi+YBoHv37mjfvj2uXLmCcuXK5dvHxcUF48ePV3Bk4li8eLHcfTU1NVhaWqJChQowNTUVJygRpaamYsyYMdi9ezdiY2PzPK6KxRTGjh0LFxcX/PHHH9ixYwcAwMPDA5s2bULbtm1Fjk7xFi1ahFatWuHkyZNyyWh4eDj27dsncnSKdeLECTRr1gwGBgZITU3FgQMH0KVLF5QqVQqCIKBevXo4efKkSiZgRYsWRXBwcJ7ZJ8ePH4enp6dIURFRQbjmiwpNSkoKtm3bhkePHkEQBLi7u6NDhw4wNDQUOzRRpKamqvRaLvq8gQMH4vz585g2bRq6dOmC5cuX49WrV1i9ejXmzJmDjh07ih0iKYEXL15g5cqVciXE+/Xrp3IjX5UrV0bNmjUxY8YM+Pv7Y8CAAejfvz9mzpwJABg/fjxu3rypklO4N27ciIkTJ2LBggXo2bMn1q1bh/DwcMyePRvr1q1D+/btxQ6RiD7C5IsKVUhICCIiIpCZmSnX3rRpU5EiUg6cVpYrISEB69evR2hoKCQSCTw9PdGjRw+5ohOqokiRItiyZQuqV68OIyMj3L59G25ubti6dSt27tyJP//8U+wQiZSGsbExAgMD4ebmBqlUCm1tbVy/fh1lypQBANy/fx+1a9dGVFSUyJGKY+3atZgxYwZevHgBALC3t8eUKVPQs2dPkSMjok8x+aJC8fTpU7Ro0QL37t2DRCLJcw0jVZxC9e7dO/z666+cVvaPW7duoV69etDV1ZVdjuDWrVtIS0vDqVOnZF+iVIWBgQEePHgAJycnODg4YP/+/ShfvjyePXsGb29vpKSkiB2iQpiZmeHRo0ewsLDIsy7wU3FxcQqMTBx3796Fl5cX1NTUCrx4/Xs+Pj4Kikp8HydfAGBoaIg7d+7AxcUFAPD333/D3d0daWlpYoYpupiYGEil0jwXbyci5cE1X1Qohg4diqJFi+LMmTNwcXHB9evXERcXh5EjR6rcdb3eGzNmDM6fP48VK1bkO61M1QwfPhxNmzbF2rVrZdc3y87ORq9evTBs2DBcunRJ5AgVy8XFBc+fP4eTkxM8PT2xe/dulC9fHkeOHJEryvKjW7RokWxq8qfrAlVR6dKlERUVBSsrK9nF6/P7jVQikajUDzjOzs548uSJLPkKCAhAkSJFZI+/ePECtra2YoUnqilTpqB79+5wcnKChYWF2OEQ0Rdw5IsKhYWFBc6dOwcfHx8YGxvjxo0bKFGiBM6dO4eRI0ciKChI7BAVjtPK5Onq6iIoKEhW/fK9kJAQ+Pn5ITU1VaTIxLFo0SKoq6tjyJAhOH/+PBo1aoScnBxkZ2dj4cKFGDp0qNghkgj+/vtvFClSBBKJBH///fdn++Z3eY8f1apVq+Do6IhGjRrl+/j48ePx5s0brFu3TsGRia9s2bK4c+cOqlWrhp49e6Jly5YqeWkGov8KJl9UKExNTREYGAgXFxe4urpi3bp1qFGjBsLDw+Ht7a1yX6wBTiv7lLW1NbZu3Yq6devKtZ88eRJdunTBmzdvRIpMOURERODWrVtwdXVFqVKlxA6HiP5D7t69i40bN2LHjh3IzMxE+/bt0aNHjwIr7RKReNTEDoB+DF5eXrL1CRUqVMC8efNw5coVTJs2TTYnX9W8n1YGQDatDIDKTSt7r127dujZsyd27dqFFy9e4OXLl/D390evXr3wyy+/iB2eQmVlZaFGjRp49OiRrK1IkSJo2bIlE6981K5dW2U/R/ITGRmJiIgIscNQuOzsbGhoaOD+/ftih6J0fHx8sGjRIrx69QobNmzAq1evUKVKFXh7e+OPP/5AYmKi2CES0T+45osKxYQJE2QXc5wxYwYaN26Mn3/+Gebm5ti1a5fI0Ymje/fusqkg48aNQ6NGjbB06VLZtDJV8/vvv0MikaBLly7Izs4GAGhqaqJ///4qtwZOU1MT9+/f/2xxCfqgRYsWiImJETsMpVGzZk08evRIpdZ8AYCGhgacnJxU7rj/H1KpFJmZmcjIyIAgCDAzM8PKlSsxceJErF27Fu3atRM7RCKVx2mH9N3ExcV9sXqZKuG0slypqakIDw+HIAhwc3NT2WuhjRw5EpqamiqXeNLnLVmyBH369IGOjg4iIiLg6OiY5zP05s2bSE1NRbVq1USKUjwbN27Enj17sG3bNpiZmYkdjtIIDAzExo0bsXPnTmhra6NLly7o1auXrEDJggULMG/ePJWf3k2kDJh8ERGJYPDgwdiyZQvc3Nzg5+cHfX19ucdVcXT0vSdPniA8PBxVq1aFrq5unktX/Mg0NDTw+vVrWFlZQV1dHZGRkSwb/hFfX188efIEWVlZcHJyyvO+uX37tkiRicfHxwehoaGoW7cuevfujSZNmkBdXV2uz9u3b2FtbQ2pVCpSlET0HqcdEhWiJUuWfHXfIUOGfMdIlEPLli2xadMmGBkZoWXLlp/tu3//fgVFpRzu378vu7bZx2u/AKhMovGp2NhYtGvXDufOnYNEIsHjx4/h4uKCXr16wcTEBAsWLBA7xO/Ozs4O+/btQ8OGDSEIAl6+fIn09PR8+35cal1VNG/eXOwQlE6bNm3Qo0cP2NvbF9jH0tKSiReRkuDIF1EhKlq0qNz9t2/fIjU1VVZgIyEhAXp6erCyssLTp09FiFCxunfvjiVLlsDQ0BDdu3f/bN+NGzcqKCpSVl26dEF0dDTWrVsHDw8P2UV0T506heHDh+PBgwdih/jdrVmzBoMHD5ati8zP+5FArn2iT73/SqeqP+AQ/Rcw+SL6Tnbs2IEVK1Zg/fr1KFGiBAAgLCwMvXv3Rt++fdGxY0eRIyRSLjY2Njh58iRKlSoFQ0NDWfKlapdnSE5Oxt9//w0fHx+cOXMG5ubm+fZT5bWjgYGBCA0NhUQigaenJ3x9fcUOSVRbtmzB/Pnz8fjxYwBA8eLFMXr0aHTu3FnkyIjoU5x2SPSdTJw4EXv37pUlXgBQokQJLFq0CK1bt1a55Gvq1Kno1KkTXF1dxQ5Fqa1YsQIxMTGYNGmS2KEo3Lt37/ItwBITEwNtbW0RIhKHoaEhvLy8sHHjRlSpUkWljv1LoqOj0b59e1y4cAEmJiYQBAGJiYmoUaMG/P39YWlpKXaICrdw4UJMnDgRgwYNQpUqVSAIAq5cuYJ+/fohJiYGw4cPFztEIvoIr/NF9J1ERkYiKysrT3tOTo5KVpzat28fihcvjooVK2LZsmV4+/at2CEppX379mHTpk1ihyGKqlWrYsuWLbL7EokEUqkU8+fPR40aNUSMTBxdu3aFtrY2MjMz8fLlS0RERMjdVNHgwYORlJSEBw8eIC4uDvHx8bh//z6SkpJUYh1tfpYuXYqVK1di7ty5aNq0KZo1a4Z58+ZhxYoV/9c6ZCJSDE47JPpOmjRpgoiICKxfvx5ly5aFRCLBrVu30Lt3bzg6OuLw4cNih6hwDx48wPbt2+Hv74+XL1+idu3a6NSpE5o3b65SJeezsrJQokQJHD16FJ6enmKHozRCQkJQvXp1lC1bFufOnUPTpk1lX7KvXLmicqOmjx8/Ro8ePXD16lW5dlVe82VsbIwzZ86gXLlycu03btxA3bp1kZCQIE5gItLR0cH9+/dlZeXfe/z4Mby9vQss2EJE4uDIF9F3smHDBtjb26N8+fLQ0dGBtrY2ypcvD1tbW6xbt07s8ERRsmRJzJo1C0+fPsX58+dRtGhRDBs2DDY2NmKHplCamprIyMjgovhPeHp64u7duyhfvjzq1KmDd+/eoWXLlggKClK5xAsAunXrBjU1NRw9ehSBgYG4ffs2bt++jaCgIJUsqQ7kXkRYU1MzT7umpqbKVvNzc3PD7t2787Tv2rULxYoVEyEiIvocjnwRfWePHz9GaGgosrOz4eXlheLFi4sdklIIDg7Gtm3b4O/vj9jYWKSlpYkdkkLNmTMHDx8+xLp166ChweW3lJe+vj4CAwPh7u4udihKo1mzZkhISMDOnTthZ2cHAHj16hU6duwIU1NTHDhwQOQIFW/fvn1o164dateujSpVqkAikeDy5cs4e/Ysdu/ejRYtWogdIhF9hMkX0Xe0fv16LFq0SFaBqlixYhg2bBh69eolcmTiePbsGXbs2IHt27fj0aNHqFq1Kjp06IA2bdrA2NhY7PAUqkWLFjh79iwMDAzg7e2d52KxqnbdMyD3cgMGBgZo06aNXPuePXuQmpqKrl27ihSZOMqVK4dFixbhp59+EjsUpfHixQs0a9YM9+/fh6OjIyQSCSIiIuDt7Y1Dhw7BwcFB7BBFERgYiEWLFiE0NBSCIMDT0xMjR45U+SqQRMqIyRfRdzJx4kQsWrQIgwcPRqVKlQAAAQEBWLZsGYYOHYoZM2aIHKFiVapUCTdu3IC3tzc6duyIDh06fPaioD86XvcsrxIlSmDVqlV5imtcvHgRffr0QVhYmEiRiePcuXOYMGECZs2aBW9v7zzT7YyMjESKTHynT5/Gw4cPZYlG7dq1xQ6JiOirMPki+k4sLCywdOlS/PLLL3LtO3fuxODBgxETEyNSZOL47bff0LFjR5QsWVLsUEhJ6ejo4OHDh3B2dpZrf/78OTw8PFRuaqqaWu6y7E/XBqpywQ3KlZSU9NV9VTlJJ1JGXGhA9J3k5OTAz88vT3vZsmWRnZ0tQkTimjVrFgAgMzMTz549g6urK9c6kRwrKyvcvXs3T/J1586dAi80/CM7f/682CEopbNnz8qm2EkkEri7u2PYsGEqNfplYmLyxYI9TNKJlBO/+RB9J506dcLKlSuxcOFCufY1a9ao3AWWASAtLQ2DBg3C5s2bAQCPHj2Ci4sLhgwZAjs7O4wdO1bkCBVv79692L17NyIiIpCZmSn3mCpWs2vfvj2GDBkCQ0NDVK1aFUDulMOhQ4eiffv2IkeneNWqVRM7BKWzbNkyDB8+HK1bt8bQoUMBANeuXUPDhg2xcOFCDBo0SOQIFYOJOdF/F6cdEn0ngwcPxpYtW+Do6IiKFSsCyP2S8OLFC3Tp0kVu/canCdqPaOjQobhy5QoWL16M+vXr4+7du3BxccHhw4cxefJkBAUFiR2iQi1ZsgTjx49H165dsXbtWnTv3h3h4eG4efMmBg4ciJkzZ4odosJlZmaic+fO2LNnj2xUVCqVokuXLli1ahW0tLREjlDxEhISsH79etkoj6enJ3r06KFyBWres7e3x7hx4/IkWcuXL8fMmTPx+vVrkSIjIvo6TL6IvpNPiwYURCKR4Ny5c985GvE5OTlh165dqFixIgwNDXHnzh24uLjgyZMnKFOmzP+1huFH4O7ujsmTJ+OXX36ROx+TJk1CXFwcli1bJnaIonn06BHu3LkDXV1deHt7w8nJSeyQRHHr1i3Uq1cPurq6KF++PARBwK1bt5CWloZTp06hTJkyYoeocIaGhggKCsr3gsK+vr5ISUkRKTJxxcfHyyXpHh4e6N69O8zMzMQOjYg+weSLiBRCT08P9+/fh4uLi1yycefOHVStWhWJiYlih6hQenp6CA0NhZOTE6ysrHD69GmUKlUKjx8/RsWKFREbGyt2iCSyn3/+GW5ubli7dq1sJDA7Oxu9evXC06dPcenSJZEjVLyOHTuidOnSGD16tFz777//jsDAQOzcuVOkyMRz8eJFNG3aFMbGxrJ1xoGBgUhISMDhw4c5fZVIyXDNFxEpRLly5XDs2DEMHjwYwIcKbmvXrpWV4lclNjY2iI2NhZOTE5ycnHDt2jWUKlUKz549gyr9JjZixAhMnz4d+vr6GDFixGf7qsL03I/dunVLLvECAA0NDYwZMybfYj4/qiVLlsj+7eHhgZkzZ+LChQuyz41r167hypUrGDlypFghimrgwIFo164dVq5cCXV1dQC5BZ8GDBiAgQMH4v79+yJHSEQfY/JFRAoxe/Zs1K9fHyEhIcjOzsYff/yBBw8eICAgABcvXhQ7PIWrWbMmjhw5gjJlyqBnz54YPnw49u7di1u3bqFly5Zih6cwQUFByMrKApBbZKSgCm5fquz2IzIyMkJERATc3d3l2l+8eAFDQ0ORolK8RYsWyd03NTVFSEgIQkJCZG0mJibYsGEDJkyYoOjwRBceHo59+/bJEi8AUFdXx4gRI7BlyxYRIyOi/DD5IiKFqFy5Mq5evYr58+fD1dVVtmYlICAA3t7eYoencGvWrIFUKgUA9OvXD2ZmZrh8+TKaNGmCfv36iRyd4nxcte3ChQviBaKE2rVrh549e+L3339H5cqVIZFIcPnyZYwePTrP9QN/ZM+ePRM7BKVWpkwZhIaGokSJEnLtoaGhKF26tDhBEVGBuOaLiL67rKws9OnTBxMnToSLi4vY4ZASys7Oho6ODoKDg+Hl5SV2OEohMzMTo0ePxqpVq2TXBtTU1ET//v0xZ84caGtrixwhKYNdu3ZhzJgxGDx4sFxl3eXLl2POnDnw8PCQ9fXx8RErTCL6B5MvIlIIExMT3L59m8nXP4oWLYpOnTqhY8eOeaaVqSpXV1fs378fpUqVEjsUpZKamorw8HAIggA3Nzfo6emJHZJCcV3g56mpqX32cYlEwgsuEykRTjskIoVo0aIFDh48+MUvT6pi8ODB2LlzJ2bOnAlfX1907twZ7dq1g62trdihiWbChAkYN24ctm3bxhLZABITE5GTkwMzMzO5qblxcXHQ0NCAkZGRiNEpDtcFfh6nZRL9t3Dki4gUYubMmfj9999Rq1YtlC1bFvr6+nKPDxkyRKTIxPXo0SNs374d/v7+ePr0KWrUqIFOnTqhS5cuYoemcL6+vnjy5AmysrLg5OSU5zVy+/ZtkSITR4MGDdCkSRMMGDBArn3VqlU4fPgw/vzzT5EiIyKif4vJFxEpRNGiRQt8TCKR4OnTpwqMRjldu3YN/fv3x927d1VyetDUqVM/+/jkyZMVFIlyMDMzw5UrV+TW7ADAw4cPUaVKFZW7FhzXBRbs1atXuHLlCqKjo2WFfN5T1R+2iJQVpx0SkUJ8PDXm/W8+qjpN6FM3btzAjh07sGvXLiQmJqJ169ZihyQKVUuuviQjI0NWaONjWVlZSEtLEyEicWloaMDJyUklf5j4nI0bN6Jfv37Q0tKCubm53OeqRCJh8kWkZD6/SpOIqBCtX78eXl5e0NHRgY6ODry8vLBu3TqxwxLFo0ePMHnyZBQrVgxVqlRBSEgI5syZgzdv3mDXrl1ihyeqW7duYevWrdi2bRsCAwPFDkc05cqVw5o1a/K0r1q1CmXLlhUhIvG9XxcYFxcndihKY9KkSZg0aRISExPx/PlzPHv2THbjjAIi5cNph0SkEBMnTsSiRYswePBgVKpUCQAQEBCAZcuWYejQoZgxY4bIESqWmpoa/Pz80KFDB7Rv3x42NjZihyS6ly9f4pdffsGVK1dgYmICAEhISEDlypWxc+dOODo6ihuggl25cgW1a9dGuXLlUKtWLQDA2bNncfPmTZw6dQo///yzyBEqHtcF5mVubo4bN27A1dVV7FCI6Csw+SIihbCwsMDSpUvzXBx2586dGDx4MGJiYkSKTByPHj1C8eLFxQ5DqdStWxdJSUnYvHmz7IKxYWFh6NGjB/T19XHq1CmRI1S84OBgzJ8/H8HBwdDV1YWPjw/GjRuHYsWKiR2aKLguMK8xY8bAzMwMY8eOFTsUIvoKTL6ISCFMTU1x48aNPF8aHz16hPLlyyMhIUGcwEhp6Orq4urVq/D19ZVrv337NqpUqaKS65yIviQnJweNGzdGWloavL29oampKfe4Kl77jEiZseAGESlEp06dsHLlyjxfBNasWYOOHTuKFJXy6dq1K168eIFz586JHYrCFSlSRHY9p49lZ2fD3t5ehIiIlN+sWbNw8uRJ2WjxpwU3iEi5MPkiIoVZv349Tp06hYoVKwLILa3+4sULdOnSRe7iy6r8S629vT3U1FSzFtK8efMwePBgLF++HGXLloVEIsGtW7cwdOhQ/P7772KHpzRq166Np0+fqmQxBTU1tc8mFKpYCXHhwoXYsGEDunXrJnYoRPQVOO2QiBSiRo0aX9VPIpGo5KgP5U5NTU1NRXZ2NjQ0cn8bfP/vTwsrqHK1u2XLliE2NlYl1zcdOnRI7n5WVhaCgoKwefNmTJ06FT179hQpMvHY2Njgr7/+Utl1gET/NUy+iIhExuue5dq8efNX9+3atet3jIT+a95fJ+/T5EwVzJ49G5GRkViyZInYoRDRV2DyRUQkkvXr12PRokV4/PgxAKBYsWIYNmwYevXqJXJkpIySkpJw7tw5lChRAh4eHmKHo1TCw8Ph4+ODd+/eiR2KwrVo0QLnzp2Dubk5SpYsmafgxv79+0WKjIjywzVfREQiKOi6Z8OHD8fz589V7rpnlFfbtm1RtWpVDBo0CGlpafDz88Pz588hCAL8/f3RqlUrsUNUCmlpaVi6dCkcHBzEDkUUJiYmaNmypdhhENFX4sgXEZEIeN2zr6eqBSZsbGxw8uRJlCpVCjt27MDkyZNx584dbN68GWvWrEFQUJDYISqcqamp3PRcQRCQnJwMXV1dbN++HU2bNhUxOiKiL+PIFxGRCHJycuDn55envWzZssjOzhYhIuXVvHlzxMbGih2GwiUmJsLMzAwAcOLECbRq1Qp6enpo1KgRRo8eLXJ04li0aJFc8qWmpgZLS0tUqFABpqamIkYmruzsbFy4cAHh4eHo0KEDDA0N8fr1axgZGcHAwEDs8IjoI0y+iIhEwOuefb1BgwaJHYIoHB0dERAQADMzM5w4cQL+/v4AgPj4eOjo6IgcnTi6deuGv/76C6tWrcLTp0+xd+9e2NvbY+vWrShatCh++uknsUNUuL///hv169dHREQEMjIyUKdOHRgaGmLevHlIT0/HqlWrxA6RiD6imheTISJSAuvXr4eXlxd69eqFXr16wcvLC2vXroWamhpGjBghu6mqpKQkHDx4EKGhoWKHIophw4ahY8eOcHBwgJ2dHapXrw4AuHTpEry9vcUNTiT79u1DvXr1oKenh6CgIGRkZAAAkpOTMWvWLJGjE8fQoUPh5+eH+Ph46OrqytpbtGiBs2fPihgZEeWHa76IiETA657l9WmBiVKlSql8gYlbt27hxYsXqFOnjmz62LFjx2BiYoIqVaqIHJ3i+fr6Yvjw4ejSpQsMDQ1x584duLi4IDg4GPXr10dUVJTYISqchYUFrly5ghIlSsidk+fPn8PT0xOpqalih0hEH+G0QyIiEZw/f17sEJTOpUuXMH78eADAgQMHIAgCEhISsHnzZsyYMUMlky8/P788awMbNWokUjTiCwsLQ9WqVfO0GxkZISEhQfEBKQGpVIqcnJw87S9fvoShoaEIERHR5zD5IiIipcACE/JycnKwadMmnD17FtHR0ZBKpXKPq8qI6MdsbW3x5MkTODs7y7VfvnwZLi4u4gQlsjp16mDx4sVYs2YNgNzR8pSUFEyePBkNGzYUOToi+hSTLyIiEaSnp2Pp0qU4f/58vl+sb9++LVJk4mGBCXlDhw7Fpk2b0KhRI3h5eclV+VNVffv2xdChQ7FhwwZIJBK8fv0aAQEBGDVqFCZNmiR2eKJYtGgRatSoAU9PT6Snp6NDhw54/PgxLCwssHPnTrHDI6JPMPkiIhJBjx49cPr0abRu3Rrly5fnF2t8KDBhYGAAJycnlS8w4e/vj927d3P04iNjxoxBYmIiatSogfT0dFStWhXaJjv4+AAABpJJREFU2toYNWqUylbFtLOzQ3BwMPz9/REYGAipVIqePXuiY8eOcgU4iEg5sOAGEZEIjI2N8eeff6pk0YTPYYGJD+zs7HDhwgUUL15c7FCUTmpqKkJCQiCVSuHp6clrWRHRfwaTLyIiEXh6esLf3x8+Pj5ih0JKasGCBXj69CmWLVvGkVH6v0VGRiIrKwtFihQROxQi+giTLyIiERw/fhxLlizBqlWr4OTkJHY4SoEFJuS1aNEC58+fh5mZGUqWLAlNTU25x/fv3y9SZPRf4OHhgUePHuVbCZGIxMM1X0REIvDz80N6ejpcXFygp6eX54t1XFycSJGJhwUm5JmYmKBFixZih0FKLDs7GzNnzkSPHj3g6Ogo99iWLVt4jS8iJcSRLyIiEdSuXRsRERHo2bMnrK2t8yQaXbt2FSky8VhYWGDLli0sMEH0fzAwMMD9+/fzlN8nIuXEkS8iIhFcvXoVAQEBKFWqlNihKA0tLS24ubmJHYbSefv2LcLCwiCRSFC8eHFYWlqKHRIpkdq1a+PChQvo1q2b2KEQ0Vdg8kVEJAJ3d3ekpaWJHYZSGTlyJP744w8WmPjHu3fvMHjwYGzZskW2/k1dXR1dunTB0qVLoaenJ3KEpAwaNGiAcePG4f79+yhbtiz09fXlHm/atKlIkRFRfjjtkIhIBKdOncLUqVMxc+ZMeHt751nzZWRkJFJk4mGBCXl9+/bFmTNnsGzZMlmZ/cuXL2PIkCGoU6cOVq5cKXKEpAzU1NQKfEwikbDgBpGSYfJFRCSCj78wfTzKIwiCyn5h6t69+2cf37hxo4IiUQ4WFhbYu3ev7GLT750/fx5t27bF27dvxQmMiIj+NU47JCISwfnz58UOQemoWnL1JampqbC2ts7TbmVlxSp2JOfs2bP5XqJBIpFg/fr1IkZGRJ8qeKyaiIi+m2rVqkFNTQ1r167F2LFj4ebmhmrVqiEiIgLq6upihyeqt2/f4vLly7hy5YpKj+5UqlQJkydPRnp6uqwtLS0NU6dORaVKlUSMjJTJ1KlTUbduXZw9exYxMTGIj4+X3VTxkhVEyo4jX0REIti3bx86d+6Mjh07IigoCBkZGQCA5ORkzJo1C3/++afIESoeC0zIW7x4MRo0aAAHBweUKlUKEokEwcHB0NbWxqlTp8QOj5TEqlWrsGnTJnTu3FnsUIjoK3Dki4hIBDNmzMCqVauwdu1aucISlStXxu3bt0WMTDwjRozAxYsXceTIESQkJCAhIQGHDh3CxYsXMXLkSLHDUzhvb288fvwYs2fPRunSpeHj44M5c+bgyZMnKFmypNjhkZLIzMxE5cqVxQ6DiL4SC24QEYlAT08PISEhcHZ2hqGhIe7cuQMXFxc8ffoUnp6eclPNVAULTMibPXs2rK2t0aNHD7n2DRs24O3bt/j1119FioyUya+//goDAwNMnDhR7FCI6Ctw2iERkQhsbW3x5MkTODs7y7VfvnwZLi4u4gQlMhaYkLd69Wrs2LEjT3vJkiXRvn17Jl8EAEhPT8eaNWtw5swZ+Pj45LlEw8KFC0WKjIjyw+SLiEgEffv2xdChQ7FhwwZIJBK8fv0aAQEBGDVqFCZNmiR2eKJ4X2Biy5Yt0NHRAaDaBSaioqJga2ubp93S0hKRkZEiRETK6O7duyhdujQA4P79+3KP8WLlRMqHyRcRkQjGjBmDxMRE1KhRA+np6ahatSq0tbUxatQoDBo0SOzwRMECE/IcHR1x5coVFC1aVK79ypUrsLOzEykqUja8bAXRfwvXfBERiSg1NRUhISGQSqXw9PSEgYGB2CGJKi0tDdu2bcPDhw8hCAI8PT3RsWNH6Orqih2aws2dOxfz58/H/PnzUbNmTQC513MaM2YMRo4ciXHjxokcIRER/b+YfBERkVJggQl5giBg7NixWLJkCTIzMwEAOjo6+PXXX1V2aioR0X8dky8iIlIKzs7O2LFjR56y2devX0f79u3x7NkzkSITV0pKCkJDQ6Grq4tixYpBW1tb7JCIiOhf4povIiJSCiwwkT8DAwOUK1dO7DCIiKgQ8CLLRESkFN4XmPgUC0wQEdGPgiNfRESkFHr16oVhw4YhKysr3wITRERE/3Vc80VEREqBBSaIiOhHx+SLiIiUCgtMEBHRj4rJFxERERERkQKw4AbR/9qvYwEAAACAQf7Ws9hVFgEAwEC+AAAABvIFAAAwkC8AAICBfAEAAAzkCwAAYCBfAAAAA/kCAAAYBMkT2A8UGLh5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "\n",
    "corr = df.corr(method=\"spearman\")\n",
    "\n",
    "mask = np.triu(np.ones_like(corr))\n",
    "\n",
    "sns.heatmap(\n",
    "    corr, vmin=-1, vmax=1, annot=True, square=True, cmap=\"coolwarm\",\n",
    "    mask=mask\n",
    ")\n",
    "\n",
    "ax.set_title('Spearman Correlation Coefficient')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age -- > SpearmanrResult(correlation=-0.011860718745247863, pvalue=0.016078711774224395)\n",
      "pdays -- > SpearmanrResult(correlation=-0.3249417994437113, pvalue=0.0)\n",
      "previous -- > SpearmanrResult(correlation=0.20091403160833127, pvalue=0.0)\n",
      "emp.var.rate -- > SpearmanrResult(correlation=-0.24747966943978417, pvalue=0.0)\n",
      "cons.price.idx -- > SpearmanrResult(correlation=-0.12218710346528146, pvalue=9.495550217905643e-137)\n",
      "cons.conf.idx -- > SpearmanrResult(correlation=0.04122858641488948, pvalue=5.733767718816937e-17)\n",
      "euribor3m -- > SpearmanrResult(correlation=-0.26682159876160344, pvalue=0.0)\n",
      "nr.employed -- > SpearmanrResult(correlation=-0.28395704046492, pvalue=0.0)\n",
      "y -- > SpearmanrResult(correlation=1.0, pvalue=0.0)\n"
     ]
    }
   ],
   "source": [
    "df_num = df.select_dtypes(include=['int', 'float'])\n",
    "\n",
    "\n",
    "for col in df_num.columns:\n",
    "    \n",
    "    corr_spearman = stats.spearmanr(df_num[col], df_num['y'])\n",
    "    \n",
    "    print(f'{col} -- > {corr_spearman}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Through the analysis of Spearman's correlation coefficients, we can conclude that we draw the same conclusions as we drew from Pearson's correlation coefficients. The variables are very similarly related in both correlation coefficients. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check target dtype\n",
    "\n",
    "df['y'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalDtype(categories=[0, 1], ordered=False)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transform `y` into dtype `category`\n",
    "\n",
    "df['y'] = df['y'].astype('category')\n",
    "df['y'].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3.3.1.3. Exclude Numeric Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the analyzes made above -**Pearson Correlation**, **Spearman Correlation** - the variables likely to be excluded from our dataset are:\n",
    "- `euribor3m`\n",
    "- `emp.var.rate`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop variables `emp.var.rate` and `euribor3m`\n",
    "\n",
    "df.drop(columns = ['emp.var.rate', 'euribor3m'], axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 3.3.2. Categorical variables: Chi-square test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['job', 'marital', 'education', 'default', 'housing', 'loan', 'poutcome', 'y']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat = df.select_dtypes(include=['object', 'category'])\n",
    "\n",
    "df_cat.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " y                 0     1\n",
      "job                      \n",
      "admin.         9070  1352\n",
      "blue-collar    8616   638\n",
      "entrepreneur   1332   124\n",
      "housemaid       954   106\n",
      "management     2596   328\n",
      "retired        1286   434\n",
      "self-employed  1272   149\n",
      "services       3646   323\n",
      "student         600   275\n",
      "technician     6013   730\n",
      "unemployed      870   144\n",
      "unknown         293    37\n",
      "Chi: 961.24 | Feature: job | p-value: 4.189763287563623e-199\n",
      "\n",
      " y             0     1\n",
      "marital              \n",
      "divorced   4136   476\n",
      "married   22396  2532\n",
      "single     9948  1620\n",
      "unknown      68    12\n",
      "Chi: 122.66 | Feature: marital | p-value: 2.068014648442211e-26\n",
      "\n",
      " y                        0     1\n",
      "education                       \n",
      "basic.4y              3748   428\n",
      "basic.6y              2104   188\n",
      "basic.9y              5572   473\n",
      "high.school           8484  1031\n",
      "illiterate              14     4\n",
      "professional.course   4648   595\n",
      "university.degree    10498  1670\n",
      "unknown               1480   251\n",
      "Chi: 193.11 | Feature: education | p-value: 3.3051890144025054e-38\n",
      "\n",
      " y            0     1\n",
      "default             \n",
      "no       28391  4197\n",
      "unknown   8154   443\n",
      "yes          3     0\n",
      "Chi: 406.58 | Feature: default | p-value: 5.1619579513916376e-89\n",
      "\n",
      " y            0     1\n",
      "housing             \n",
      "no       16596  2026\n",
      "unknown    883   107\n",
      "yes      19069  2507\n",
      "Chi: 5.68 | Feature: housing | p-value: 0.05829447669453452\n",
      "\n",
      " y            0     1\n",
      "loan                \n",
      "no       30100  3850\n",
      "unknown    883   107\n",
      "yes       5565   683\n",
      "Chi: 1.09 | Feature: loan | p-value: 0.5786752870441754\n",
      "\n",
      " y                0     1\n",
      "poutcome                \n",
      "failure       3647   605\n",
      "nonexistent  32422  3141\n",
      "success        479   894\n",
      "Chi: 4230.52 | Feature: poutcome | p-value: 0.0\n",
      "\n",
      " y      0     1\n",
      "y             \n",
      "0  36548     0\n",
      "1      0  4640\n",
      "Chi: 41178.0 | Feature: y | p-value: 0.0\n"
     ]
    }
   ],
   "source": [
    "for col in df_cat.columns:\n",
    "    table = pd.crosstab(df_cat[col], df['y'])\n",
    "    print('\\n', table)\n",
    "    chi, pval, _, expected_table = stats.chi2_contingency(table)\n",
    "    print('Chi:', round(chi,2),'| Feature:', col, '| p-value:', pval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Taking into account the p-value values, I would exclude from my models the variables: `loan`, `housing`. Because I want variables with a pvalue equal to or less than **0.05**, and the pvalue of the `housing` variable is `0.05829447669453452`, which is risky to stick with it, because it's approximately **0.06**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop variables `loan` and `housing`\n",
    "\n",
    "df.drop(columns = ['loan', 'housing'], axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 3.4. Outiliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check distribution of variables in our dataset\n",
    "\n",
    "fig = plt.figure(figsize = (20, 10))\n",
    "\n",
    "df.hist(bins=20)\n",
    "\n",
    "plt.suptitle(\"Histogram of Features\")\n",
    "plt.xlabel(\"Values\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "\n",
    "plt.subplots_adjust(hspace=2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_num = df.select_dtypes(include=['int', 'float'])\n",
    "\n",
    "\n",
    "for col in df_num.columns:\n",
    "    \n",
    "    skewness = stats.skew(df_num[col])\n",
    "    kurtosis = stats.kurtosis(df_num[col])\n",
    "    \n",
    "    if (-2 < skewness < 2) and kurtosis > 2:\n",
    "        print(f'Variable `{col}` has a skewness of {skewness} and a kurtosis of {kurtosis}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> This condition allows us to know if we have any variable with normal distribution according to kurtosis and skwness, and we can conclude that we do not have any variable with a normal distribution, as we can also see in the histograms above.\n",
    "\n",
    "> We can then use the IQR to identify outliers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#IQR\n",
    "def possible_outliers(x):\n",
    "    \n",
    "    percentile25 = x.quantile(0.25)\n",
    "    percentile75 = x.quantile(0.75)\n",
    "     \n",
    "    iqr = percentile75 - percentile25\n",
    "     \n",
    "     \n",
    "    upper_lim = percentile75 + 1.5*iqr\n",
    "    lower_lim = percentile25 - 1.5*iqr\n",
    "    \n",
    "    r = (x < lower_lim) | (x > upper_lim)\n",
    "    \n",
    "    return r.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable 'age' has 469 outliers.\n",
      "Variable 'pdays' has 1515 outliers.\n",
      "Variable 'previous' has 5625 outliers.\n",
      "Variable 'cons.conf.idx' has 447 outliers.\n"
     ]
    }
   ],
   "source": [
    "for col in df_num.columns:\n",
    "    num_outliers = possible_outliers(df_num[col])\n",
    "    \n",
    "    if num_outliers > 0:\n",
    "        print(f\"Variable '{col}' has {num_outliers} outliers.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4.1. Visualize outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### a) `age`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age - Outliers')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vizualizar outliers\n",
    "#age\n",
    "\n",
    "fig, ax = plt.figsize=(8, 20)\n",
    "    \n",
    "ax = \\\n",
    "sns.boxplot(\n",
    "    x=\"y\", \n",
    "    y=\"age\", \n",
    "    data=df\n",
    ")\n",
    "\n",
    "labels = [\"No\", \"Yes\"]\n",
    "ax.set_xticklabels(labels) # rename each boxplot\n",
    "ax.set(xlabel=None); # remove x label (target)\n",
    "\n",
    "# Title\n",
    "ax.set_title('Age - Outliers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### b) `pdays`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Pdays - Outliers')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vizualize outliers\n",
    "#pdays\n",
    "\n",
    "fig, ax = plt.figsize=(8, 20)\n",
    "    \n",
    "ax = \\\n",
    "sns.boxplot(\n",
    "    x=\"y\", \n",
    "    y=\"pdays\", \n",
    "    data=df\n",
    ")\n",
    "\n",
    "labels = [\"No\", \"Yes\"]\n",
    "ax.set_xticklabels(labels) # rename each boxplot\n",
    "ax.set(xlabel=None); # remove x label (target)\n",
    "\n",
    "# Title\n",
    "ax.set_title('Pdays - Outliers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of outliers in `pdays`: 3.68 %\n"
     ]
    }
   ],
   "source": [
    "print('Percentage of outliers in `pdays`:', str(round(possible_outliers(df['pdays'])/df['pdays'].count()*100,2)) + ' %')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### c) `previous`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Previous - Outliers')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vizualize outliers\n",
    "#previous\n",
    "\n",
    "fig, ax = plt.figsize=(8, 20)\n",
    "    \n",
    "ax = \\\n",
    "sns.boxplot(\n",
    "    x=\"y\", \n",
    "    y=\"previous\", \n",
    "    data=df\n",
    ")\n",
    "\n",
    "labels = [\"No\", \"Yes\"]\n",
    "ax.set_xticklabels(labels) # rename each boxplot\n",
    "ax.set(xlabel=None); # remove x label (target)\n",
    "\n",
    "# Title\n",
    "ax.set_title('Previous - Outliers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### d) `cons.conf.idx`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Consumer confidence index - Outliers')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vizualize outliers\n",
    "#cons.conf.idx\n",
    "\n",
    "fig, ax = plt.figsize=(8, 20)\n",
    "    \n",
    "ax = \\\n",
    "sns.boxplot(\n",
    "    x=\"y\", \n",
    "    y=\"cons.conf.idx\", \n",
    "    data=df\n",
    ")\n",
    "\n",
    "#labels\n",
    "labels = [\"No\", \"Yes\"]\n",
    "ax.set_xticklabels(labels) # rename each boxplot\n",
    "ax.set(xlabel=None); # remove x label (target)\n",
    "\n",
    "# Title\n",
    "ax.set_title('Consumer confidence index - Outliers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4.2. Handle outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To handle the outliers, we will use the binning - equal depth technique, with the exception of the `pdays` variable, where we will probably eliminate the outliers. But before dealing with outliers, let's divide the dataset between training and testing, as it is recommended before applying techniques to treat outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split dataset into train and test\n",
    "\n",
    "train_set, test_set = train_test_split(df, test_size = 0.2, random_state = seed)\n",
    "\n",
    "X_train = train_set.drop(['y'], axis = 1)\n",
    "y_train = train_set['y'].astype('category')\n",
    "\n",
    "X_test = test_set.drop(['y'], axis = 1)\n",
    "y_test = test_set['y'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalDtype(categories=[0, 1], ordered=False)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To check the dtype of the target variable, if our code above worked\n",
    "\n",
    "y_train.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a) `age`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "disc_quantile_estimator_age = KBinsDiscretizer(n_bins=[3], encode='ordinal', strategy='quantile')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train['age'] = disc_quantile_estimator_age.fit_transform(X_train[['age']])\n",
    "\n",
    "X_test['age'] = disc_quantile_estimator_age.transform(X_test[['age']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b) `pdays`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As there are not many outliers, due to the size of our dataset, we will exclude them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers in `pdays` = 41188\n"
     ]
    }
   ],
   "source": [
    "mask_outliers_pdays = df['pdays' ]< 1000\n",
    "\n",
    "print('Number of outliers in `pdays` =', mask_outliers_pdays.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[~mask_outliers_pdays]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### c) `previous`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this variable, we will not touch the outliers. Because there is a possibility of losing information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### d) `cons.conf.idx`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "disc_quantile_estimator_consconfidx = KBinsDiscretizer(n_bins=[3], encode='ordinal', strategy='quantile')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train['cons.conf.idx'] = disc_quantile_estimator_consconfidx.fit_transform(X_train[['cons.conf.idx']])\n",
    "\n",
    "X_test['cons.conf.idx'] = disc_quantile_estimator_consconfidx.transform(X_test[['cons.conf.idx']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 3.5. Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values in our dataset: \n",
      " age               0.0\n",
      "job               0.0\n",
      "marital           0.0\n",
      "education         0.0\n",
      "default           0.0\n",
      "pdays             0.0\n",
      "previous          0.0\n",
      "poutcome          0.0\n",
      "cons.price.idx    0.0\n",
      "cons.conf.idx     0.0\n",
      "nr.employed       0.0\n",
      "y                 0.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('Missing values in our dataset:', '\\n', df.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> As we can see above, we don't have missing values in our dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 3.6. Handle categorical variables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['job', 'marital', 'education', 'default', 'poutcome'], dtype='object')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recheck categorical variables\n",
    "\n",
    "X_train_cat = X_train.select_dtypes(include = ['object', 'category'])\n",
    "\n",
    "X_train_cat.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### a) `job`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "admin.           8328\n",
       "blue-collar      7439\n",
       "technician       5352\n",
       "services         3212\n",
       "management       2310\n",
       "retired          1363\n",
       "self-employed    1153\n",
       "entrepreneur     1145\n",
       "housemaid         867\n",
       "unemployed        798\n",
       "student           721\n",
       "unknown           262\n",
       "Name: job, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.job.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['blue-collar', 'admin.', 'retired', 'housemaid', 'management',\n",
       "       'technician', 'entrepreneur', 'services', 'student', 'unemployed',\n",
       "       'self-employed', 'unknown'], dtype=object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train['job'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_0</th>\n",
       "      <th>job_1</th>\n",
       "      <th>job_2</th>\n",
       "      <th>job_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12556</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35451</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30592</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17914</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3315</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6265</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11284</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38158</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15795</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32950 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_0  job_1  job_2  job_3\n",
       "12556      0      0      0      1\n",
       "35451      0      0      1      0\n",
       "30592      0      0      1      1\n",
       "17914      0      1      0      0\n",
       "3315       0      0      1      0\n",
       "...      ...    ...    ...    ...\n",
       "6265       0      0      1      1\n",
       "11284      0      1      0      1\n",
       "38158      0      0      1      0\n",
       "860        0      1      0      1\n",
       "15795      0      0      1      0\n",
       "\n",
       "[32950 rows x 4 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Due to the nature of the variable, as well as context, it makes sense to use a binary encoder technique on this variable\n",
    "# It seems that there is no inherent order or hierarchy among the categories\n",
    "\n",
    "binary_encoder_job = BinaryEncoder(drop_invariant=True)\n",
    "\n",
    "#encode\n",
    "\n",
    "X_train_job_encoded = binary_encoder_job.fit_transform(X_train[['job']])\n",
    "X_test_job_encoded = binary_encoder_job.transform(X_test[['job']])\n",
    "\n",
    "X_train_job_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>job_0</th>\n",
       "      <th>job_1</th>\n",
       "      <th>job_2</th>\n",
       "      <th>job_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12556</th>\n",
       "      <td>1.0</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.9y</td>\n",
       "      <td>unknown</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>93.918</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35451</th>\n",
       "      <td>0.0</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>92.893</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30592</th>\n",
       "      <td>2.0</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>92.893</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17914</th>\n",
       "      <td>1.0</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>divorced</td>\n",
       "      <td>basic.9y</td>\n",
       "      <td>no</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>93.918</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3315</th>\n",
       "      <td>1.0</td>\n",
       "      <td>admin.</td>\n",
       "      <td>single</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>93.994</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6265</th>\n",
       "      <td>2.0</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>unknown</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>93.994</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11284</th>\n",
       "      <td>1.0</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>94.465</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38158</th>\n",
       "      <td>1.0</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>success</td>\n",
       "      <td>92.431</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5017.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>1.0</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>93.994</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15795</th>\n",
       "      <td>0.0</td>\n",
       "      <td>admin.</td>\n",
       "      <td>single</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>93.918</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32950 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          job   marital            education  default  pdays  \\\n",
       "12556  1.0  blue-collar   married             basic.9y  unknown    999   \n",
       "35451  0.0       admin.   married    university.degree       no    999   \n",
       "30592  2.0      retired   married             basic.4y       no    999   \n",
       "17914  1.0    housemaid  divorced             basic.9y       no    999   \n",
       "3315   1.0       admin.    single          high.school  unknown    999   \n",
       "...    ...          ...       ...                  ...      ...    ...   \n",
       "6265   2.0      retired   married  professional.course  unknown    999   \n",
       "11284  1.0   management   married    university.degree       no    999   \n",
       "38158  1.0       admin.   married          high.school       no      4   \n",
       "860    1.0   management   married    university.degree       no    999   \n",
       "15795  0.0       admin.    single    university.degree       no    999   \n",
       "\n",
       "       previous     poutcome  cons.price.idx  cons.conf.idx  nr.employed  \\\n",
       "12556         0  nonexistent          93.918            1.0       5228.1   \n",
       "35451         0  nonexistent          92.893            0.0       5099.1   \n",
       "30592         1      failure          92.893            0.0       5099.1   \n",
       "17914         0  nonexistent          93.918            1.0       5228.1   \n",
       "3315          0  nonexistent          93.994            2.0       5191.0   \n",
       "...         ...          ...             ...            ...          ...   \n",
       "6265          0  nonexistent          93.994            2.0       5191.0   \n",
       "11284         0  nonexistent          94.465            1.0       5228.1   \n",
       "38158         1      success          92.431            2.0       5017.5   \n",
       "860           0  nonexistent          93.994            2.0       5191.0   \n",
       "15795         0  nonexistent          93.918            1.0       5228.1   \n",
       "\n",
       "       job_0  job_1  job_2  job_3  \n",
       "12556      0      0      0      1  \n",
       "35451      0      0      1      0  \n",
       "30592      0      0      1      1  \n",
       "17914      0      1      0      0  \n",
       "3315       0      0      1      0  \n",
       "...      ...    ...    ...    ...  \n",
       "6265       0      0      1      1  \n",
       "11284      0      1      0      1  \n",
       "38158      0      0      1      0  \n",
       "860        0      1      0      1  \n",
       "15795      0      0      1      0  \n",
       "\n",
       "[32950 rows x 15 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concat\n",
    "\n",
    "X_train = pd.concat([X_train, X_train_job_encoded], axis=1)\n",
    "X_test = pd.concat([X_test, X_test_job_encoded], axis=1)\n",
    "\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop na variável `job`\n",
    "\n",
    "X_train.drop('job', axis = 1, inplace = True)\n",
    "\n",
    "X_test.drop('job', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### b) `marital`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>marital_married</th>\n",
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       "      <th>marital_single</th>\n",
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       "<p>32950 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "       marital_married  marital_divorced  marital_single  marital_unknown\n",
       "12556                1                 0               0                0\n",
       "35451                1                 0               0                0\n",
       "30592                1                 0               0                0\n",
       "17914                0                 1               0                0\n",
       "3315                 0                 0               1                0\n",
       "...                ...               ...             ...              ...\n",
       "6265                 1                 0               0                0\n",
       "11284                1                 0               0                0\n",
       "38158                1                 0               0                0\n",
       "860                  1                 0               0                0\n",
       "15795                0                 0               1                0\n",
       "\n",
       "[32950 rows x 4 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Taking into account the context of the `marital` variable, let's apply the OHE technique\n",
    "# It seems that there is no inherent order or hierarchy among the categories\n",
    "\n",
    "ohe_encoder_marital = OHE(use_cat_names=True)\n",
    "\n",
    "X_train_marital_encoded = ohe_encoder_marital.fit_transform(X_train[['marital']])\n",
    "X_test_marital_encoded = ohe_encoder_marital.transform(X_test[['marital']])\n",
    "\n",
    "X_train_marital_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "married     19823\n",
       "single       9333\n",
       "divorced     3733\n",
       "unknown        61\n",
       "Name: marital, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train['marital'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "        marital  marital_married  marital_divorced  marital_single  \\\n",
       "12556   married                1                 0               0   \n",
       "35451   married                1                 0               0   \n",
       "30592   married                1                 0               0   \n",
       "17914  divorced                0                 1               0   \n",
       "3315     single                0                 0               1   \n",
       "...         ...              ...               ...             ...   \n",
       "6265    married                1                 0               0   \n",
       "11284   married                1                 0               0   \n",
       "38158   married                1                 0               0   \n",
       "860     married                1                 0               0   \n",
       "15795    single                0                 0               1   \n",
       "\n",
       "       marital_unknown  \n",
       "12556                0  \n",
       "35451                0  \n",
       "30592                0  \n",
       "17914                0  \n",
       "3315                 0  \n",
       "...                ...  \n",
       "6265                 0  \n",
       "11284                0  \n",
       "38158                0  \n",
       "860                  0  \n",
       "15795                0  \n",
       "\n",
       "[32950 rows x 5 columns]"
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     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marital = pd.concat([X_train[['marital']], X_train_marital_encoded ], axis=1)\n",
    "marital"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32950 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age   marital            education  default  pdays  previous  \\\n",
       "12556  1.0   married             basic.9y  unknown    999         0   \n",
       "35451  0.0   married    university.degree       no    999         0   \n",
       "30592  2.0   married             basic.4y       no    999         1   \n",
       "17914  1.0  divorced             basic.9y       no    999         0   \n",
       "3315   1.0    single          high.school  unknown    999         0   \n",
       "...    ...       ...                  ...      ...    ...       ...   \n",
       "6265   2.0   married  professional.course  unknown    999         0   \n",
       "11284  1.0   married    university.degree       no    999         0   \n",
       "38158  1.0   married          high.school       no      4         1   \n",
       "860    1.0   married    university.degree       no    999         0   \n",
       "15795  0.0    single    university.degree       no    999         0   \n",
       "\n",
       "          poutcome  cons.price.idx  cons.conf.idx  nr.employed  job_0  job_1  \\\n",
       "12556  nonexistent          93.918            1.0       5228.1      0      0   \n",
       "35451  nonexistent          92.893            0.0       5099.1      0      0   \n",
       "30592      failure          92.893            0.0       5099.1      0      0   \n",
       "17914  nonexistent          93.918            1.0       5228.1      0      1   \n",
       "3315   nonexistent          93.994            2.0       5191.0      0      0   \n",
       "...            ...             ...            ...          ...    ...    ...   \n",
       "6265   nonexistent          93.994            2.0       5191.0      0      0   \n",
       "11284  nonexistent          94.465            1.0       5228.1      0      1   \n",
       "38158      success          92.431            2.0       5017.5      0      0   \n",
       "860    nonexistent          93.994            2.0       5191.0      0      1   \n",
       "15795  nonexistent          93.918            1.0       5228.1      0      0   \n",
       "\n",
       "       job_2  job_3  marital_married  marital_divorced  marital_single  \\\n",
       "12556      0      1                1                 0               0   \n",
       "35451      1      0                1                 0               0   \n",
       "30592      1      1                1                 0               0   \n",
       "17914      0      0                0                 1               0   \n",
       "3315       1      0                0                 0               1   \n",
       "...      ...    ...              ...               ...             ...   \n",
       "6265       1      1                1                 0               0   \n",
       "11284      0      1                1                 0               0   \n",
       "38158      1      0                1                 0               0   \n",
       "860        0      1                1                 0               0   \n",
       "15795      1      0                0                 0               1   \n",
       "\n",
       "       marital_unknown  \n",
       "12556                0  \n",
       "35451                0  \n",
       "30592                0  \n",
       "17914                0  \n",
       "3315                 0  \n",
       "...                ...  \n",
       "6265                 0  \n",
       "11284                0  \n",
       "38158                0  \n",
       "860                  0  \n",
       "15795                0  \n",
       "\n",
       "[32950 rows x 18 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = pd.concat([X_train, X_train_marital_encoded], axis=1)\n",
    "X_test = pd.concat([X_test, X_test_marital_encoded], axis=1)\n",
    "\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop na variável `marital`\n",
    "\n",
    "X_train.drop('marital', axis = 1, inplace = True)\n",
    "\n",
    "X_test.drop('marital', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### c) `education`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>education</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>university.degree</th>\n",
       "      <td>9750</td>\n",
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       "    <tr>\n",
       "      <th>high.school</th>\n",
       "      <td>7614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>basic.9y</th>\n",
       "      <td>4808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>professional.course</th>\n",
       "      <td>4176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>basic.4y</th>\n",
       "      <td>3338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>basic.6y</th>\n",
       "      <td>1852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>1399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>illiterate</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     education\n",
       "university.degree         9750\n",
       "high.school               7614\n",
       "basic.9y                  4808\n",
       "professional.course       4176\n",
       "basic.4y                  3338\n",
       "basic.6y                  1852\n",
       "unknown                   1399\n",
       "illiterate                  13"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train['education'].value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>education_basic.9y</th>\n",
       "      <th>education_university.degree</th>\n",
       "      <th>education_basic.4y</th>\n",
       "      <th>education_high.school</th>\n",
       "      <th>education_professional.course</th>\n",
       "      <th>education_basic.6y</th>\n",
       "      <th>education_unknown</th>\n",
       "      <th>education_illiterate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12556</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>17914</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3315</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       education_basic.9y  education_university.degree  education_basic.4y  \\\n",
       "12556                   1                            0                   0   \n",
       "35451                   0                            1                   0   \n",
       "30592                   0                            0                   1   \n",
       "17914                   1                            0                   0   \n",
       "3315                    0                            0                   0   \n",
       "\n",
       "       education_high.school  education_professional.course  \\\n",
       "12556                      0                              0   \n",
       "35451                      0                              0   \n",
       "30592                      0                              0   \n",
       "17914                      0                              0   \n",
       "3315                       1                              0   \n",
       "\n",
       "       education_basic.6y  education_unknown  education_illiterate  \n",
       "12556                   0                  0                     0  \n",
       "35451                   0                  0                     0  \n",
       "30592                   0                  0                     0  \n",
       "17914                   0                  0                     0  \n",
       "3315                    0                  0                     0  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Due to the nature of the variable, as well as context, it makes sense to use OHE on this variable\n",
    "# althought it seems that there is an inherent order or hierarchy among the categories, by applying OHE our models have a better performace than using ordinal encoding \n",
    "\n",
    "ohe_encoder_education = OHE(use_cat_names=True)\n",
    "\n",
    "#encode\n",
    "\n",
    "X_train_education_encoded = ohe_encoder_education.fit_transform(X_train[['education']])\n",
    "X_test_education_encoded = ohe_encoder_education.transform(X_test[['education']])\n",
    "\n",
    "X_train_education_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
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       "      <td>93.994</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15795</th>\n",
       "      <td>0.0</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>93.918</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32950 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age            education  default  pdays  previous     poutcome  \\\n",
       "12556  1.0             basic.9y  unknown    999         0  nonexistent   \n",
       "35451  0.0    university.degree       no    999         0  nonexistent   \n",
       "30592  2.0             basic.4y       no    999         1      failure   \n",
       "17914  1.0             basic.9y       no    999         0  nonexistent   \n",
       "3315   1.0          high.school  unknown    999         0  nonexistent   \n",
       "...    ...                  ...      ...    ...       ...          ...   \n",
       "6265   2.0  professional.course  unknown    999         0  nonexistent   \n",
       "11284  1.0    university.degree       no    999         0  nonexistent   \n",
       "38158  1.0          high.school       no      4         1      success   \n",
       "860    1.0    university.degree       no    999         0  nonexistent   \n",
       "15795  0.0    university.degree       no    999         0  nonexistent   \n",
       "\n",
       "       cons.price.idx  cons.conf.idx  nr.employed  job_0  ...  marital_single  \\\n",
       "12556          93.918            1.0       5228.1      0  ...               0   \n",
       "35451          92.893            0.0       5099.1      0  ...               0   \n",
       "30592          92.893            0.0       5099.1      0  ...               0   \n",
       "17914          93.918            1.0       5228.1      0  ...               0   \n",
       "3315           93.994            2.0       5191.0      0  ...               1   \n",
       "...               ...            ...          ...    ...  ...             ...   \n",
       "6265           93.994            2.0       5191.0      0  ...               0   \n",
       "11284          94.465            1.0       5228.1      0  ...               0   \n",
       "38158          92.431            2.0       5017.5      0  ...               0   \n",
       "860            93.994            2.0       5191.0      0  ...               0   \n",
       "15795          93.918            1.0       5228.1      0  ...               1   \n",
       "\n",
       "       marital_unknown  education_basic.9y  education_university.degree  \\\n",
       "12556                0                   1                            0   \n",
       "35451                0                   0                            1   \n",
       "30592                0                   0                            0   \n",
       "17914                0                   1                            0   \n",
       "3315                 0                   0                            0   \n",
       "...                ...                 ...                          ...   \n",
       "6265                 0                   0                            0   \n",
       "11284                0                   0                            1   \n",
       "38158                0                   0                            0   \n",
       "860                  0                   0                            1   \n",
       "15795                0                   0                            1   \n",
       "\n",
       "       education_basic.4y  education_high.school  \\\n",
       "12556                   0                      0   \n",
       "35451                   0                      0   \n",
       "30592                   1                      0   \n",
       "17914                   0                      0   \n",
       "3315                    0                      1   \n",
       "...                   ...                    ...   \n",
       "6265                    0                      0   \n",
       "11284                   0                      0   \n",
       "38158                   0                      1   \n",
       "860                     0                      0   \n",
       "15795                   0                      0   \n",
       "\n",
       "       education_professional.course  education_basic.6y  education_unknown  \\\n",
       "12556                              0                   0                  0   \n",
       "35451                              0                   0                  0   \n",
       "30592                              0                   0                  0   \n",
       "17914                              0                   0                  0   \n",
       "3315                               0                   0                  0   \n",
       "...                              ...                 ...                ...   \n",
       "6265                               1                   0                  0   \n",
       "11284                              0                   0                  0   \n",
       "38158                              0                   0                  0   \n",
       "860                                0                   0                  0   \n",
       "15795                              0                   0                  0   \n",
       "\n",
       "       education_illiterate  \n",
       "12556                     0  \n",
       "35451                     0  \n",
       "30592                     0  \n",
       "17914                     0  \n",
       "3315                      0  \n",
       "...                     ...  \n",
       "6265                      0  \n",
       "11284                     0  \n",
       "38158                     0  \n",
       "860                       0  \n",
       "15795                     0  \n",
       "\n",
       "[32950 rows x 25 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concat\n",
    "\n",
    "X_train = pd.concat([X_train, X_train_education_encoded], axis=1)\n",
    "X_test = pd.concat([X_test, X_test_education_encoded], axis=1)\n",
    "\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop `education`\n",
    "\n",
    "X_train.drop('education', axis=1, inplace=True)\n",
    "X_test.drop('education', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### d) `default`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>no</th>\n",
       "      <td>26090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>6857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yes</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         default\n",
       "no         26090\n",
       "unknown     6857\n",
       "yes            3"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train['default'].value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['unknown', 'no', 'yes'], dtype=object)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train['default'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>default_unknown</th>\n",
       "      <th>default_no</th>\n",
       "      <th>default_yes</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12556</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>35451</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>30592</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17914</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3315</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "       default_unknown  default_no  default_yes\n",
       "12556                1           0            0\n",
       "35451                0           1            0\n",
       "30592                0           1            0\n",
       "17914                0           1            0\n",
       "3315                 1           0            0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Due to the nature of the variable, as well as context, it makes sense to use an OHE on this variable\n",
    "# It seems that there is no inherent order or hierarchy among the categories\n",
    "\n",
    "ohe_encoder_default = OHE(use_cat_names=True)\n",
    "\n",
    "#encode\n",
    "\n",
    "X_train_default_encoded = ohe_encoder_default.fit_transform(X_train[['default']])\n",
    "X_test_default_encoded = ohe_encoder_default.transform(X_test[['default']])\n",
    "\n",
    "X_train_default_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>age</th>\n",
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       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
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       "      <th>cons.conf.idx</th>\n",
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       "      <th>job_1</th>\n",
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       "      <th>default_yes</th>\n",
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       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15795</th>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>93.918</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32950 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  default  pdays  previous     poutcome  cons.price.idx  \\\n",
       "12556  1.0  unknown    999         0  nonexistent          93.918   \n",
       "35451  0.0       no    999         0  nonexistent          92.893   \n",
       "30592  2.0       no    999         1      failure          92.893   \n",
       "17914  1.0       no    999         0  nonexistent          93.918   \n",
       "3315   1.0  unknown    999         0  nonexistent          93.994   \n",
       "...    ...      ...    ...       ...          ...             ...   \n",
       "6265   2.0  unknown    999         0  nonexistent          93.994   \n",
       "11284  1.0       no    999         0  nonexistent          94.465   \n",
       "38158  1.0       no      4         1      success          92.431   \n",
       "860    1.0       no    999         0  nonexistent          93.994   \n",
       "15795  0.0       no    999         0  nonexistent          93.918   \n",
       "\n",
       "       cons.conf.idx  nr.employed  job_0  job_1  ...  \\\n",
       "12556            1.0       5228.1      0      0  ...   \n",
       "35451            0.0       5099.1      0      0  ...   \n",
       "30592            0.0       5099.1      0      0  ...   \n",
       "17914            1.0       5228.1      0      1  ...   \n",
       "3315             2.0       5191.0      0      0  ...   \n",
       "...              ...          ...    ...    ...  ...   \n",
       "6265             2.0       5191.0      0      0  ...   \n",
       "11284            1.0       5228.1      0      1  ...   \n",
       "38158            2.0       5017.5      0      0  ...   \n",
       "860              2.0       5191.0      0      1  ...   \n",
       "15795            1.0       5228.1      0      0  ...   \n",
       "\n",
       "       education_university.degree  education_basic.4y  education_high.school  \\\n",
       "12556                            0                   0                      0   \n",
       "35451                            1                   0                      0   \n",
       "30592                            0                   1                      0   \n",
       "17914                            0                   0                      0   \n",
       "3315                             0                   0                      1   \n",
       "...                            ...                 ...                    ...   \n",
       "6265                             0                   0                      0   \n",
       "11284                            1                   0                      0   \n",
       "38158                            0                   0                      1   \n",
       "860                              1                   0                      0   \n",
       "15795                            1                   0                      0   \n",
       "\n",
       "       education_professional.course  education_basic.6y  education_unknown  \\\n",
       "12556                              0                   0                  0   \n",
       "35451                              0                   0                  0   \n",
       "30592                              0                   0                  0   \n",
       "17914                              0                   0                  0   \n",
       "3315                               0                   0                  0   \n",
       "...                              ...                 ...                ...   \n",
       "6265                               1                   0                  0   \n",
       "11284                              0                   0                  0   \n",
       "38158                              0                   0                  0   \n",
       "860                                0                   0                  0   \n",
       "15795                              0                   0                  0   \n",
       "\n",
       "       education_illiterate  default_unknown  default_no  default_yes  \n",
       "12556                     0                1           0            0  \n",
       "35451                     0                0           1            0  \n",
       "30592                     0                0           1            0  \n",
       "17914                     0                0           1            0  \n",
       "3315                      0                1           0            0  \n",
       "...                     ...              ...         ...          ...  \n",
       "6265                      0                1           0            0  \n",
       "11284                     0                0           1            0  \n",
       "38158                     0                0           1            0  \n",
       "860                       0                0           1            0  \n",
       "15795                     0                0           1            0  \n",
       "\n",
       "[32950 rows x 27 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concat\n",
    "\n",
    "X_train = pd.concat([X_train, X_train_default_encoded], axis=1)\n",
    "X_test = pd.concat([X_test, X_test_default_encoded], axis=1)\n",
    "\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop `default`\n",
    "\n",
    "X_train.drop('default', axis=1, inplace=True)\n",
    "X_test.drop('default', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### e) `poutcome`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>poutcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>nonexistent</th>\n",
       "      <td>28437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>failure</th>\n",
       "      <td>3423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>success</th>\n",
       "      <td>1090</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             poutcome\n",
       "nonexistent     28437\n",
       "failure          3423\n",
       "success          1090"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train['poutcome'].value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>poutcome_nonexistent</th>\n",
       "      <th>poutcome_failure</th>\n",
       "      <th>poutcome_success</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12556</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35451</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30592</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17914</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3315</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       poutcome_nonexistent  poutcome_failure  poutcome_success\n",
       "12556                     1                 0                 0\n",
       "35451                     1                 0                 0\n",
       "30592                     0                 1                 0\n",
       "17914                     1                 0                 0\n",
       "3315                      1                 0                 0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Due to the nature of the variable, as well as context, it makes sense to use an OHE on this variable\n",
    "# It seems that there is no inherent order or hierarchy among the categories\n",
    "\n",
    "ohe_encoder_poutcome = OHE(use_cat_names=True)\n",
    "\n",
    "#encode\n",
    "\n",
    "X_train_poutcome_encoded = ohe_encoder_poutcome.fit_transform(X_train[['poutcome']])\n",
    "X_test_poutcome_encoded = ohe_encoder_poutcome.transform(X_test[['poutcome']])\n",
    "\n",
    "X_train_poutcome_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>job_0</th>\n",
       "      <th>job_1</th>\n",
       "      <th>job_2</th>\n",
       "      <th>...</th>\n",
       "      <th>education_professional.course</th>\n",
       "      <th>education_basic.6y</th>\n",
       "      <th>education_unknown</th>\n",
       "      <th>education_illiterate</th>\n",
       "      <th>default_unknown</th>\n",
       "      <th>default_no</th>\n",
       "      <th>default_yes</th>\n",
       "      <th>poutcome_nonexistent</th>\n",
       "      <th>poutcome_failure</th>\n",
       "      <th>poutcome_success</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12556</th>\n",
       "      <td>1.0</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>93.918</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>35451</th>\n",
       "      <td>0.0</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>92.893</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>30592</th>\n",
       "      <td>2.0</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>92.893</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17914</th>\n",
       "      <td>1.0</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>93.918</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3315</th>\n",
       "      <td>1.0</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>93.994</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  pdays  previous     poutcome  cons.price.idx  cons.conf.idx  \\\n",
       "12556  1.0    999         0  nonexistent          93.918            1.0   \n",
       "35451  0.0    999         0  nonexistent          92.893            0.0   \n",
       "30592  2.0    999         1      failure          92.893            0.0   \n",
       "17914  1.0    999         0  nonexistent          93.918            1.0   \n",
       "3315   1.0    999         0  nonexistent          93.994            2.0   \n",
       "\n",
       "       nr.employed  job_0  job_1  job_2  ...  education_professional.course  \\\n",
       "12556       5228.1      0      0      0  ...                              0   \n",
       "35451       5099.1      0      0      1  ...                              0   \n",
       "30592       5099.1      0      0      1  ...                              0   \n",
       "17914       5228.1      0      1      0  ...                              0   \n",
       "3315        5191.0      0      0      1  ...                              0   \n",
       "\n",
       "       education_basic.6y  education_unknown  education_illiterate  \\\n",
       "12556                   0                  0                     0   \n",
       "35451                   0                  0                     0   \n",
       "30592                   0                  0                     0   \n",
       "17914                   0                  0                     0   \n",
       "3315                    0                  0                     0   \n",
       "\n",
       "       default_unknown  default_no  default_yes  poutcome_nonexistent  \\\n",
       "12556                1           0            0                     1   \n",
       "35451                0           1            0                     1   \n",
       "30592                0           1            0                     0   \n",
       "17914                0           1            0                     1   \n",
       "3315                 1           0            0                     1   \n",
       "\n",
       "       poutcome_failure  poutcome_success  \n",
       "12556                 0                 0  \n",
       "35451                 0                 0  \n",
       "30592                 1                 0  \n",
       "17914                 0                 0  \n",
       "3315                  0                 0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concat\n",
    "\n",
    "X_train = pd.concat([X_train, X_train_poutcome_encoded], axis=1)\n",
    "X_test = pd.concat([X_test, X_test_poutcome_encoded], axis=1)\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop `poutcome`\n",
    "\n",
    "X_train.drop('poutcome', axis=1, inplace=True)\n",
    "X_test.drop('poutcome', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.7. Selection of variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the above analysis, we conclude that the most relevant variables to decide whether a customer is more likely to adhere to the term deposit are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age',\n",
       " 'pdays',\n",
       " 'previous',\n",
       " 'cons.price.idx',\n",
       " 'cons.conf.idx',\n",
       " 'nr.employed',\n",
       " 'job_0',\n",
       " 'job_1',\n",
       " 'job_2',\n",
       " 'job_3',\n",
       " 'marital_married',\n",
       " 'marital_divorced',\n",
       " 'marital_single',\n",
       " 'marital_unknown',\n",
       " 'education_basic.9y',\n",
       " 'education_university.degree',\n",
       " 'education_basic.4y',\n",
       " 'education_high.school',\n",
       " 'education_professional.course',\n",
       " 'education_basic.6y',\n",
       " 'education_unknown',\n",
       " 'education_illiterate',\n",
       " 'default_unknown',\n",
       " 'default_no',\n",
       " 'default_yes',\n",
       " 'poutcome_nonexistent',\n",
       " 'poutcome_failure',\n",
       " 'poutcome_success']"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>job_0</th>\n",
       "      <th>job_1</th>\n",
       "      <th>job_2</th>\n",
       "      <th>job_3</th>\n",
       "      <th>...</th>\n",
       "      <th>education_professional.course</th>\n",
       "      <th>education_basic.6y</th>\n",
       "      <th>education_unknown</th>\n",
       "      <th>education_illiterate</th>\n",
       "      <th>default_unknown</th>\n",
       "      <th>default_no</th>\n",
       "      <th>default_yes</th>\n",
       "      <th>poutcome_nonexistent</th>\n",
       "      <th>poutcome_failure</th>\n",
       "      <th>poutcome_success</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12556</th>\n",
       "      <td>1.0</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>93.918</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35451</th>\n",
       "      <td>0.0</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>92.893</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30592</th>\n",
       "      <td>2.0</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>92.893</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17914</th>\n",
       "      <td>1.0</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>93.918</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3315</th>\n",
       "      <td>1.0</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>93.994</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  pdays  previous  cons.price.idx  cons.conf.idx  nr.employed  \\\n",
       "12556  1.0    999         0          93.918            1.0       5228.1   \n",
       "35451  0.0    999         0          92.893            0.0       5099.1   \n",
       "30592  2.0    999         1          92.893            0.0       5099.1   \n",
       "17914  1.0    999         0          93.918            1.0       5228.1   \n",
       "3315   1.0    999         0          93.994            2.0       5191.0   \n",
       "\n",
       "       job_0  job_1  job_2  job_3  ...  education_professional.course  \\\n",
       "12556      0      0      0      1  ...                              0   \n",
       "35451      0      0      1      0  ...                              0   \n",
       "30592      0      0      1      1  ...                              0   \n",
       "17914      0      1      0      0  ...                              0   \n",
       "3315       0      0      1      0  ...                              0   \n",
       "\n",
       "       education_basic.6y  education_unknown  education_illiterate  \\\n",
       "12556                   0                  0                     0   \n",
       "35451                   0                  0                     0   \n",
       "30592                   0                  0                     0   \n",
       "17914                   0                  0                     0   \n",
       "3315                    0                  0                     0   \n",
       "\n",
       "       default_unknown  default_no  default_yes  poutcome_nonexistent  \\\n",
       "12556                1           0            0                     1   \n",
       "35451                0           1            0                     1   \n",
       "30592                0           1            0                     0   \n",
       "17914                0           1            0                     1   \n",
       "3315                 1           0            0                     1   \n",
       "\n",
       "       poutcome_failure  poutcome_success  \n",
       "12556                 0                 0  \n",
       "35451                 0                 0  \n",
       "30592                 1                 0  \n",
       "17914                 0                 0  \n",
       "3315                  0                 0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataset final \n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 4. Which algorithm led to the best results?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since our problem is a classification problem, let's test the models:\n",
    "- Logistic Regression\n",
    "- Decision Trees\n",
    "- Random Forest\n",
    "\n",
    "In this project, we will not use the Neural Network and SVM models, as they are very complex models, intended for datasets and problems that are also complex. For example, SVM is widely used for problems such as *\"handwriting recognition, intrusion detection, face detection, email classification, gene classification, and in web pages\"*, and Neural Networks for *\"Image and video data, Natural language processing data, Time-series data, Audio data, Graph and network data*. With this, I intend to show how complex and powerful these two models are, too powerful for our problem. As some professors mentioned during the course \"a cannon to kill an ant \", so I decided not to use them for this model, sticking with the simplest and most traditional models.\n",
    "\n",
    "KNN also decided not to use it, because it is a more suitable model for smaller datasets than the one we are working with, as it is a *slow learner* (it does not need to be trained). Why is KNN a **slow learner**? Because the algorithm calculates the Euclidean distance between the test point and all training points, stores the distances in a list and sorts, chooses the first k points and finally assigns the class to the test point based on the majority of the present class. For this reason, we will not use it in this project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### a) Normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a new normalized dataset, because we're going to test some models that don't need a normalized dataset, as is the case of the Random Forest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "\n",
    "features_names = X_train.columns\n",
    "\n",
    "X_train_scaler = scaler.fit_transform(X_train)\n",
    "X_train_scaler = pd.DataFrame(X_train, columns = features_names)\n",
    "\n",
    "X_test_scaler = scaler.transform(X_test)\n",
    "X_test_scaler = pd.DataFrame(X_test, columns = features_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### b) Total Cost Function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$totalCost = 500∗FP+2000∗FN$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### c) Baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we will calculate the baseline, that is, the cost we have without any model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds_all_bad = np.ones(y_test.shape) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of True Negatives: 0\n",
      "Number of True Positives: 935\n",
      "Number of False Negatives: 0\n",
      "Number of False Positives: 7303\n"
     ]
    }
   ],
   "source": [
    "tn, fp, fn, tp = confusion_matrix(y_test, y_preds_all_bad).ravel()\n",
    "\n",
    "print('Number of True Negatives:', tn)\n",
    "print('Number of True Positives:', tp)\n",
    "print('Number of False Negatives:', fn)\n",
    "print('Number of False Positives:', fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Cost: 6828305\n"
     ]
    }
   ],
   "source": [
    "print('Total Cost:', fp*935)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11349842194707453"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_preds_all_bad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> If all customers were **bad candidates** (they didn't sign up for term credit), the bank would have a cost of **6 828 305 EUR**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of True Negatives: 7303\n",
      "Number of True Positives: 0\n",
      "Number of False Negatives: 935\n",
      "Number of False Positives: 0\n"
     ]
    }
   ],
   "source": [
    "y_preds_all_good = np.zeros(y_test.shape) \n",
    "\n",
    "tn, fp, fn, tp = confusion_matrix(y_test, y_preds_all_good).ravel()\n",
    "\n",
    "print('Number of True Negatives:', tn)\n",
    "print('Number of True Positives:', tp)\n",
    "print('Number of False Negatives:', fn)\n",
    "print('Number of False Positives:', fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Cost: 2805000\n"
     ]
    }
   ],
   "source": [
    "print('Total Cost:', fn*3000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> If all customers were **good candidates** (subscribed to term credit), the bank would have a cost of **2 805 000 EUR** - our baseline."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### d) Evaluation Metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "def total_cost(y_test, y_preds, threshold = 0.5):\n",
    "    \n",
    "    tn, fp, fn, tp = confusion_matrix(y_test == 1, y_preds > threshold).ravel()\n",
    "    \n",
    "    cost_fn = fn*2000\n",
    "    cost_fp = fp*500\n",
    "    \n",
    "    return cost_fn + cost_fp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "def min_cost_threshold(y_test, y_preds, x_limits=None):\n",
    "    \n",
    "    costs = {}\n",
    "    \n",
    "    for threshold in np.arange(0, 1.1, 0.1):\n",
    "        \n",
    "        costs[round(threshold, 1)] = total_cost(y_test, y_preds, threshold = threshold)\n",
    "        \n",
    "    plt.plot(list(costs.keys()), list(costs.values()))\n",
    "    plt.ylabel('Cost')\n",
    "    plt.xlabel('Threshold')\n",
    "    \n",
    "    if x_limits:\n",
    "        plt.xlim(x_limits)\n",
    "        \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### e) Target distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_train.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have an unbalanced target variable. This information is important for the choice of metrics for model evaluation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1. Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 18.6 s, sys: 4.17 s, total: 22.8 s\n",
      "Wall time: 3.16 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "lr = LogisticRegression(max_iter = 500, solver = 'lbfgs', random_state = seed, class_weight = 'balanced' )\n",
    "\n",
    "parameters = {'C':[0.001, 0.01, 0.1, 1, 10, 100]}\n",
    "\n",
    "clf_lr = GridSearchCV(lr, parameters, cv = 5).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8067492109735372"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean accuracy\n",
    "\n",
    "clf_lr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds = clf_lr.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auc: 0.74823561630595\n"
     ]
    }
   ],
   "source": [
    "#AUC\n",
    "\n",
    "print('auc:', roc_auc_score(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_cost_threshold(y_test, y_preds, x_limits=([0.2,0.8]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Cost: 1382500 EUR\n"
     ]
    }
   ],
   "source": [
    "print('Total Cost:', total_cost(y_test, y_preds, threshold = 0.5), 'EUR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> With the **Logistic Regression** model the minimum cost for a threshold of 0.5 is **1 382 500 EUR** (AUC-score of 0.7482), which is better than our baseline.  \n",
    "\n",
    "> **Note:** I tried both normalized and non-normalized datasets and got the same result every time. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2. Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.21 s, sys: 169 ms, total: 1.38 s\n",
      "Wall time: 780 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "trees = tree.DecisionTreeClassifier(random_state = seed,  class_weight = 'balanced')\n",
    "\n",
    "parameters = {'max_depth':[3, 6], 'min_samples_split': [2, 4, 10]}\n",
    "\n",
    "clf_tree = GridSearchCV(trees, parameters, cv = 5).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight='balanced', max_depth=6, random_state=42)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_tree.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8466860888565185"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds = clf_tree.predict_proba(X_test)[:,1]\n",
    "\n",
    "clf_tree.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auc: 0.7745903265891023\n"
     ]
    }
   ],
   "source": [
    "# AUC\n",
    "\n",
    "print('auc:', roc_auc_score(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_cost_threshold(y_test, y_preds, x_limits=([0.2,0.8]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Cost: 1234500 EUR\n"
     ]
    }
   ],
   "source": [
    "print('Total Cost:', total_cost(y_test, y_preds, threshold = 0.5), 'EUR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> With the **Decision Tree** model the minimum cost for a threshold of 0.5 is **1 234 500 EUR** (AUC-score of 0.7745), which is better than our baseline, and the Logistic Regression model ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3. Random Forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 26s, sys: 902 ms, total: 1min 27s\n",
      "Wall time: 1min 27s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "rf = RandomForestClassifier(random_state = seed,  class_weight = 'balanced').fit(X_train, y_train)\n",
    "\n",
    "parameters = {'n_estimators':[10, 100, 300, 1000]}\n",
    "\n",
    "clf_rf = GridSearchCV(rf, parameters, cv = 5).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8316338917212915"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds = clf_rf.predict_proba(X_test)[:,1]\n",
    "\n",
    "clf_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auc: 0.6881781056938727\n"
     ]
    }
   ],
   "source": [
    "#AUC\n",
    "\n",
    "print('auc:', roc_auc_score(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_cost_threshold(y_test, y_preds, x_limits=([0.2,0.8]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Cost: 1487000 EUR\n"
     ]
    }
   ],
   "source": [
    "print('Total Cost:', total_cost(y_test, y_preds, threshold = 0.5), 'EUR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> With the **Random Forests** model the minimum cost for a threshold of 0.5 is **1 487 000** (AUC-score: 0.6881), which is better than our baseline, however, worse than the Logistic Regression and Decision Tree models. The **Decision Tree** model was the model that led to the best result, with a **total cost of 1 238 500 EUR** and an **AUC-score of 0.7482** (best total cost, and best AUC- score)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3.1. Feature Importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos verificar quais as variáveis mais relevantes para avaliar se o cliente tem propensão para ser um bom candidato à campanha de marketing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "importances = clf_tree.best_estimator_.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>nr.employed</td>\n",
       "      <td>0.820662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cons.price.idx</td>\n",
       "      <td>0.102657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pdays</td>\n",
       "      <td>0.031615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>default_no</td>\n",
       "      <td>0.011455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>poutcome_failure</td>\n",
       "      <td>0.008947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>job_3</td>\n",
       "      <td>0.004096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>previous</td>\n",
       "      <td>0.003733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>0.003640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>education_basic.4y</td>\n",
       "      <td>0.002723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>education_university.degree</td>\n",
       "      <td>0.002377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>education_basic.6y</td>\n",
       "      <td>0.001438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>job_1</td>\n",
       "      <td>0.001284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>marital_single</td>\n",
       "      <td>0.001239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>marital_unknown</td>\n",
       "      <td>0.000975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>education_professional.course</td>\n",
       "      <td>0.000702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>marital_divorced</td>\n",
       "      <td>0.000696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>default_unknown</td>\n",
       "      <td>0.000540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>job_2</td>\n",
       "      <td>0.000477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>education_basic.9y</td>\n",
       "      <td>0.000214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>poutcome_success</td>\n",
       "      <td>0.000193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cons.conf.idx</td>\n",
       "      <td>0.000172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>poutcome_nonexistent</td>\n",
       "      <td>0.000164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>education_high.school</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>education_unknown</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>education_illiterate</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>marital_married</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>job_0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>default_yes</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          feature  importance\n",
       "5                     nr.employed    0.820662\n",
       "3                  cons.price.idx    0.102657\n",
       "1                           pdays    0.031615\n",
       "23                     default_no    0.011455\n",
       "26               poutcome_failure    0.008947\n",
       "9                           job_3    0.004096\n",
       "2                        previous    0.003733\n",
       "0                             age    0.003640\n",
       "16             education_basic.4y    0.002723\n",
       "15    education_university.degree    0.002377\n",
       "19             education_basic.6y    0.001438\n",
       "7                           job_1    0.001284\n",
       "12                 marital_single    0.001239\n",
       "13                marital_unknown    0.000975\n",
       "18  education_professional.course    0.000702\n",
       "11               marital_divorced    0.000696\n",
       "22                default_unknown    0.000540\n",
       "8                           job_2    0.000477\n",
       "14             education_basic.9y    0.000214\n",
       "27               poutcome_success    0.000193\n",
       "4                   cons.conf.idx    0.000172\n",
       "25           poutcome_nonexistent    0.000164\n",
       "17          education_high.school    0.000000\n",
       "20              education_unknown    0.000000\n",
       "21           education_illiterate    0.000000\n",
       "10                marital_married    0.000000\n",
       "6                           job_0    0.000000\n",
       "24                    default_yes    0.000000"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature importances\n",
    "\n",
    "feature_importances = pd.DataFrame({'feature': X_train.columns, 'importance': importances})\n",
    "feature_importances = feature_importances.sort_values(by='importance', ascending=False)\n",
    "feature_importances\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Para melhor visualizarmos os nossos dados \n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.title('Feature Importances')\n",
    "sns.barplot(x='importance', y='feature', data=feature_importances, palette='viridis')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, the variable with a significant impact on the target variable, and therefore more relevant to decide whether or not the customer is a good candidate for the marketing campaign is (with a significant difference from the other variables) the variable `nr. employed`, followed by the variables `cons.price.idx` and `pdays`.\n",
    "\n",
    "- The variable `nr.employed` refers to the number of employees in a given economy/sector and is usually measured as a quarterly indicator. This variable is an indicator of the level of employment in a given economy/sector.\n",
    "\n",
    "- The `cons.price.idx` variable refers to the consumer price index (CPI). It is a monthly indicator that measures changes in the prices of a basket of goods and services consumed by households in a given country or region. The CPI is a key indicator that provides information about the level of inflation and deflation in an economy, as well as consumer purchasing power.\n",
    "\n",
    "- The `pdays` variable refers to the number of days that have passed since the customer was last contacted for a previous marketing campaign."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 5. Which/What evaluation metrics did you use to compare the performance of the various models? Why?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As our target variable is unbalanced, the most indicated metric to be used would be the AUC. However, the objective of the work is to find out which model makes the best prediction so that the cost for the Awesome Bank is as low as possible, so for that we used the **Total Cost**.\n",
    "\n",
    "However, it is also the **Decision Tree model that has the highest AUC-score**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 6. What is the cost that the bank has without any model?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without any model, the Awesome Bank would have a cost of **2 805 000 EUR** (if all customers were bad candidates)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 7. What is the cost that the bank will have with your model?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With our model, the Awesome Bank has a total cost of **1 234 500 EUR**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    " # 8. Final considerations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We applied a normalized dataset to this model, even knowing that the models used as a rule are not used with normalized datasets, but just out of curiosity I applied a normalized dataset, obtaining the same result normalizing our dataset.\n",
    "\n",
    "- The Decision Tree and Random Forest models have a better performance without outliers (with the outliers treated) regarding the **Total Cost**, however the Logistic Regression model has a worse performance. As far as **AUC_score** is concerned, the Decision Tree model performs better without outliers than with outliers, but on the contrary the Logistic Regression and Random Forest models perform worse without outliers. \n",
    "\n",
    "- It is interesting that Random forest has a better total cost without outliers, however worse performance in terms of AUC-score.\n",
    "\n",
    "- It is also quite interesting that the importance of the variables also changes, with the treatment of the variables. Both the order and the weight. Here we also tried not to handle the outliers of the `cons.conf.idx` variable, but we didn't get a better result.\n",
    "\n",
    "**With outerers:**\n",
    "- **Logistic Regression:** Total Cost: 1 329 000 EUR | AUC-score: 0.7527\n",
    "- **Decision Tree:** Total cost: 1 238 500 EUR | AUC-score: 0.7716\n",
    "- **Random Forests:** Total Cost: 1 526 500 EUR | AUC-score: 0.7158\n",
    "\n",
    "**No outfitters:**\n",
    "- **Logistic Regression:** Total Cost: 1 382 500 EUR | AUC-score: 0.7482\n",
    "- **Decision Tree:** Total cost: 1 234 500 EUR | AUC-score: 0.7745\n",
    "- **Random Forests:** Total Cost: 1 487 000 EUR | AUC-score: 0.6881\n",
    "\n",
    "\n",
    "\n",
    "- In like manner, it would be more appropriate to apply ordinal encoding to the variable `education`, however by applying One-hot-encoding the tree models have a better performance.\n",
    "\n",
    "`education` **- Ordinal Encoding:**\n",
    "- **Logistic Regression:** Total Cost: 1 396 500 EUR | AUC-score: 0.7516\n",
    "- **Decision Tree:** Total cost: 1 236 500 EUR | AUC-score: 0.7735\n",
    "- **Random Forests:** Total Cost: 1 491 500 EUR | AUC-score: 0.6872\n",
    "\n",
    "We used the dataset without outliers, or with the outliers treated, because it was the one that gave us the best result."
   ]
  },
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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