notebooks/instructions.ipynb

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    "# Loan predictions\n",
    "\n",
    "## Problem Statement\n",
    "\n",
    "We want to automate the loan eligibility process based on customer details that are provided as online application forms are being filled. You can find the dataset [here](https://drive.google.com/file/d/1h_jl9xqqqHflI5PsuiQd_soNYxzFfjKw/view?usp=sharing). These details concern the customer's Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and other things as well. \n",
    "\n",
    "|Variable| Description|\n",
    "|: ------------- |:-------------|\n",
    "|Loan_ID| Unique Loan ID|\n",
    "|Gender| Male/ Female|\n",
    "|Married| Applicant married (Y/N)|\n",
    "|Dependents| Number of dependents|\n",
    "|Education| Applicant Education (Graduate/ Under Graduate)|\n",
    "|Self_Employed| Self employed (Y/N)|\n",
    "|ApplicantIncome| Applicant income|\n",
    "|CoapplicantIncome| Coapplicant income|\n",
    "|LoanAmount| Loan amount in thousands|\n",
    "|Loan_Amount_Term| Term of loan in months|\n",
    "|Credit_History| credit history meets guidelines|\n",
    "|Property_Area| Urban/ Semi Urban/ Rural|\n",
    "|Loan_Status| Loan approved (Y/N)\n",
    "\n",
    "\n",
    "\n",
    "### Explore the problem in following stages:\n",
    "\n",
    "1. Hypothesis Generation – understanding the problem better by brainstorming possible factors that can impact the outcome\n",
    "2. Data Exploration – looking at categorical and continuous feature summaries and making inferences about the data.\n",
    "3. Data Cleaning – imputing missing values in the data and checking for outliers\n",
    "4. Feature Engineering – modifying existing variables and creating new ones for analysis\n",
    "5. Model Building – making predictive models on the data"
   ]
  },
  {
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   "source": [
    "## 1. Hypothesis Generation\n",
    "\n",
    "Generating a hypothesis is a major step in the process of analyzing data. This involves understanding the problem and formulating a meaningful hypothesis about what could potentially have a good impact on the outcome. This is done BEFORE looking at the data, and we end up creating a laundry list of the different analyses which we can potentially perform if data is available.\n",
    "\n",
    "#### Possible hypotheses\n",
    "Which applicants are more likely to get a loan\n",
    "\n",
    "1. Applicants having a credit history \n",
    "2. Applicants with higher applicant and co-applicant incomes\n",
    "3. Applicants with higher education level\n",
    "4. Properties in urban areas with high growth perspectives\n",
    "\n",
    "Do more brainstorming and create some hypotheses of your own. Remember that the data might not be sufficient to test all of these, but forming these enables a better understanding of the problem.\n",
    "\n",
    "1.\tMarried applicants have a better chance of getting a loan.\n",
    "2.\tAre male applicants more likely to get a loan?\n",
    "3.\tApplicants with a credit score are more likely to get a loan.\n",
    "4.\tApplicants with co-applicant are more likely to get a loan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Exploration\n",
    "Let's do some basic data exploration here and come up with some inferences about the data. Go ahead and try to figure out some irregularities and address them in the next section. "
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         NaN             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Rural           N  \n",
       "2             1.0         Urban           Y  \n",
       "3             1.0         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df = pd.read_csv(\"../data/data.csv\") \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the key challenges in any data set are missing values. Lets start by checking which columns contain missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Loan_ID            614 non-null    object \n",
      " 1   Gender             601 non-null    object \n",
      " 2   Married            611 non-null    object \n",
      " 3   Dependents         599 non-null    object \n",
      " 4   Education          614 non-null    object \n",
      " 5   Self_Employed      582 non-null    object \n",
      " 6   ApplicantIncome    614 non-null    int64  \n",
      " 7   CoapplicantIncome  614 non-null    float64\n",
      " 8   LoanAmount         592 non-null    float64\n",
      " 9   Loan_Amount_Term   600 non-null    float64\n",
      " 10  Credit_History     564 non-null    float64\n",
      " 11  Property_Area      614 non-null    object \n",
      " 12  Loan_Status        614 non-null    object \n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 62.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               13\n",
       "Married               3\n",
       "Dependents           15\n",
       "Education             0\n",
       "Self_Employed        32\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           22\n",
       "Loan_Amount_Term     14\n",
       "Credit_History       50\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at some basic statistics for numerical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false,
    "jupyter": {
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>592.000000</td>\n",
       "      <td>600.00000</td>\n",
       "      <td>564.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5403.459283</td>\n",
       "      <td>1621.245798</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>342.00000</td>\n",
       "      <td>0.842199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6109.041673</td>\n",
       "      <td>2926.248369</td>\n",
       "      <td>85.587325</td>\n",
       "      <td>65.12041</td>\n",
       "      <td>0.364878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2877.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3812.500000</td>\n",
       "      <td>1188.500000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5795.000000</td>\n",
       "      <td>2297.250000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81000.000000</td>\n",
       "      <td>41667.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>480.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       614.000000         614.000000  592.000000         600.00000   \n",
       "mean       5403.459283        1621.245798  146.412162         342.00000   \n",
       "std        6109.041673        2926.248369   85.587325          65.12041   \n",
       "min         150.000000           0.000000    9.000000          12.00000   \n",
       "25%        2877.500000           0.000000  100.000000         360.00000   \n",
       "50%        3812.500000        1188.500000  128.000000         360.00000   \n",
       "75%        5795.000000        2297.250000  168.000000         360.00000   \n",
       "max       81000.000000       41667.000000  700.000000         480.00000   \n",
       "\n",
       "       Credit_History  \n",
       "count      564.000000  \n",
       "mean         0.842199  \n",
       "std          0.364878  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. How many applicants have a `Credit_History`? (`Credit_History` has value 1 for those who have a credit history and 0 otherwise)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>percentage</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>475</td>\n",
       "      <td>84.219858</td>\n",
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       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>89</td>\n",
       "      <td>15.780142</td>\n",
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      "text/plain": [
       "     count  percentage\n",
       "1.0    475   84.219858\n",
       "0.0     89   15.780142"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of applicants with credit history\n",
    "count = df['Credit_History'].value_counts()\n",
    "percentage = count / count.sum() * 100\n",
    "credit_history = pd.concat ([count,percentage], axis = 1)\n",
    "credit_history.columns = ['count', 'percentage']\n",
    "credit_history"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Aproximately the 84% of the applicants have credit history.**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Is the `ApplicantIncome` distribution in line with your expectation? Similarly, what about `CoapplicantIncome`?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      614.000000\n",
       "mean      5403.459283\n",
       "std       6109.041673\n",
       "min        150.000000\n",
       "25%       2877.500000\n",
       "50%       3812.500000\n",
       "75%       5795.000000\n",
       "max      81000.000000\n",
       "Name: ApplicantIncome, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ApplicantIncome'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      614.000000\n",
       "mean      1621.245798\n",
       "std       2926.248369\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%       1188.500000\n",
       "75%       2297.250000\n",
       "max      41667.000000\n",
       "Name: CoapplicantIncome, dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CoapplicantIncome'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.histplot(data = df, x='ApplicantIncome')\n",
    "ax.set(xlabel=\"Applicant Income\", ylabel=\"Count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.histplot(data = df, x='CoapplicantIncome')\n",
    "ax.set(xlabel=\"Co-applicant Income\", ylabel=\"Count\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Majority of applicants do not have a co-applicant. That is why the majority has zero value on the column coapplicant Income.**\n",
    "\n",
    "- **For the Applicant income there are some values outside the majority (outliers). The distribution is right tailed.**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Tip: Can you see a possible skewness in the data by comparing the mean to the median, i.e. the 50% figure of a feature.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_applicant = df['ApplicantIncome'].mean()\n",
    "median_applicant = df['ApplicantIncome'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean for applicant income: 5403.459283387622\n",
      "Median for applicant income: 3812.5\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean for applicant income:\", mean_applicant)\n",
    "print(\"Median for applicant income:\", median_applicant)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **The data is skewed and the distribution is right tailed.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's discuss nominal (categorical) variable. Look at the number of unique values in each of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>614</td>\n",
       "      <td>601</td>\n",
       "      <td>611</td>\n",
       "      <td>599</td>\n",
       "      <td>614</td>\n",
       "      <td>582</td>\n",
       "      <td>614</td>\n",
       "      <td>614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>614</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>489</td>\n",
       "      <td>398</td>\n",
       "      <td>345</td>\n",
       "      <td>480</td>\n",
       "      <td>500</td>\n",
       "      <td>233</td>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Loan_ID Gender Married Dependents Education Self_Employed  \\\n",
       "count        614    601     611        599       614           582   \n",
       "unique       614      2       2          4         2             2   \n",
       "top     LP001002   Male     Yes          0  Graduate            No   \n",
       "freq           1    489     398        345       480           500   \n",
       "\n",
       "       Property_Area Loan_Status  \n",
       "count            614         614  \n",
       "unique             3           2  \n",
       "top        Semiurban           Y  \n",
       "freq             233         422  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(exclude='number')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Male', 'Female', nan], dtype=object)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'Yes', nan], dtype=object)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Married'].unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0', '1', '2', '3+', nan], dtype=object)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Dependents'].unique()\t\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Graduate', 'Not Graduate'], dtype=object)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Education'].unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'Yes', nan], dtype=object)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Self_Employed'].unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Urban', 'Rural', 'Semiurban'], dtype=object)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Property_Area'].unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Y', 'N'], dtype=object)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Loan_Status'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explore further using the frequency of different categories in each nominal variable. Exclude the ID obvious reasons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      489\n",
       "Female    112\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    398\n",
       "No     213\n",
       "Name: Married, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Married'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     345\n",
       "1     102\n",
       "2     101\n",
       "3+     51\n",
       "Name: Dependents, dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Dependents'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Graduate        480\n",
       "Not Graduate    134\n",
       "Name: Education, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Education'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     500\n",
       "Yes     82\n",
       "Name: Self_Employed, dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Self_Employed'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Semiurban    233\n",
       "Urban        202\n",
       "Rural        179\n",
       "Name: Property_Area, dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Property_Area'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    422\n",
       "N    192\n",
       "Name: Loan_Status, dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Loan_Status'].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution analysis\n",
    "\n",
    "Study distribution of various variables. Plot the histogram of ApplicantIncome, try different number of bins.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Graphs ApplicantIncome\n",
    "fig, ax = plt.subplots(2, 2, figsize=(15, 10), sharey=True)\n",
    "fig.suptitle('Applicant Income in different bins')\n",
    "\n",
    "sns.histplot(x='ApplicantIncome', data=df, bins=10, ax=ax[0, 0])\n",
    "ax[0, 0].set(xlabel=\"Applicant Income\", ylabel=\"Count\")\n",
    "ax[0, 0].set_title('Income Histogram 10 bins')\n",
    "\n",
    "sns.histplot(x='ApplicantIncome', data=df, bins=15, ax=ax[0, 1])\n",
    "ax[0, 1].set(xlabel=\"Applicant Income\", ylabel=\"Count\")\n",
    "ax[0, 1].set_title('Income Histogram 15 bins')\n",
    "\n",
    "sns.histplot(x='ApplicantIncome', data=df, bins=20, ax=ax[1, 0])\n",
    "ax[1, 0].set(xlabel=\"Applicant Income\", ylabel=\"Count\")\n",
    "ax[1, 0].set_title('Income Histogram 20 bins')\n",
    "\n",
    "sns.histplot(x='ApplicantIncome', data=df, bins=30, ax=ax[1, 1])\n",
    "ax[1, 1].set(xlabel=\"Applicant Income\", ylabel=\"Count\")\n",
    "ax[1, 1].set_title('Income Histogram 30 bins')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Look at box plots to understand the distributions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['ApplicantIncome'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.boxplot(df['ApplicantIncome'])\n",
    "ax.set_title('Applicant Income')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the distribution of income segregated  by `Education`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Education', ylabel='ApplicantIncome'>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#violin plot to show data distribution in both categories.\n",
    "sns.violinplot(x=\"Education\", y=\"ApplicantIncome\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Education and Income Kernel')"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(x=\"ApplicantIncome\", data=df, hue=\"Education\")\n",
    "plt.title(\"Education and Income Kernel\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the histogram and boxplot of LoanAmount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAHC2Ag47o0ePVkFBgf74xz8qJSVFLpcrFHUBAAAERcBh56233tIbb7yhPn36hKIeAACAoAr4OjstWrRQy5YtQ1ELAABA0AUcdv7zP/9Tjz32mI4ePRqKegAAAIIq4MNYTz31lL755hslJSUpPT1dMTExPuuLioqCVhwAAMDZCjjsDBkyJARlAAAAhEbAYWfq1KmhqAMAACAkAp6zAwAAUJ8EvGenurpaTz/9tP7nf/5He/fuVVVVlc/6AwcOBK04AACAsxXwnp3HH39cs2bN0rBhw1ReXq4JEyZo6NChcrvdysnJCUGJAAAAZy7gsPPKK69owYIFeuihhxQdHa3hw4frxRdf1GOPPaZPP/00FDUCAACcsYDDTmlpqTp16iRJatq0qcrLyyVJgwcP1htvvBHc6gAAAM5SwGGnbdu22r9/vyTpwgsv1Jo1ayRJGzduVFxcXHCrAwAAOEsBh52bb75Za9eulSSNHz9ejz76qDIzM3XHHXdo1KhRQS8QAADgbAR8Ntb06dOdn2+55RalpaXp448/1oUXXqgbb7wxqMUBAACcrYDDzq/17NlTPXv2DEYtAAAAQRfwYay8vDwtWrSoVvuiRYs0Y8aMoBQFAAAQLAGHnRdeeEEXXXRRrfZLLrlEzz//fFCKAgAACJYzOvU8JSWlVvv555/vnKUFAAAQKQIOOzUTkn/t448/VmpqalCKAgAACJaAJyiPHj1a2dnZOn78uK666ipJ0tq1azVx4kQ9+OCDQS8QAADgbAQcdiZOnKgDBw5o3Lhxzk1AGzVqpEmTJmny5MlBLxAAAOBsBBx2XC6XZsyYoUcffVQ7d+5U48aNlZmZydWTAQBARDrj6+w0bdpUPXr0CGYtAAAAQedX2Bk6dKheeuklNWvWTEOHDj1l35UrVwalMAAAgGDwK+wkJibK5XI5PwMAANQXfoWdxYsXn/RnAACASBfwdXYAAADqE7/27HTt2tU5jHU6RUVFZ1UQAABAMPkVdoYMGRLiMgAAAELDr7AzderUUNcBAAAQEmd8nZ1NmzZp586dcrlcuvjii9WtW7dg1gUAABAUAYedkpISDR8+XB9//LGaN28uSTp06JB69+6tV199VWlpacGuEQAA4IwFfDbWqFGjdPz4ce3cuVMHDhzQgQMHtHPnThljdNddd4WiRgAAgDMW8J6dDz/8UOvXr1eHDh2ctg4dOmjOnDnq06dPUIsDTqbmBrSn43a7FR19xkdqAQCWCPgvQbt27XT8+PFa7R6PR23atAlKUcDJeKs9kjtKCQkJfvVPSmmjkr3fEXgAoIEL+K/AzJkzdd999+nZZ59Vt27d5HK5tGnTJo0fP15/+ctfQlEjcIIxkrdaNz+dr6jY2FN29XqOa+X4AfJ6veeoOABApAo47Nx55506evSoevbs6fyP2ePxKDo6WqNGjdKoUaOcvgcOHAhepcD/546OUVR0TLjLAADUEwGHndmzZ4egDAAAgNAIOOyMHDkyFHUAAACExBnN3KyurtaqVat8Lip40003MREUAABEnIDTybZt23TTTTeptLTUOf38yy+/1Pnnn6/Vq1erU6dOQS8SOFP+nKbOKeoAYLeALyo4evRoXXLJJSopKVFRUZGKiopUXFyszp07609/+lMoagQC9svT1OPi4k65tG2XLo/HE+6SAQAhEvB/Zz///HNt2rRJLVq0cNpatGihadOmqUePHkEtDjhjfp6mzinqAGC/gPfsdOjQQT/88EOt9rKyMl144YVBKQoIlprT1Ota3JzCDgDWCzjs5Obm6v7779ff/vY3lZSUqKSkRH/729+UnZ2tGTNmqKKiwlkAAADCLeDDWIMHD5YkDRs2TC6XS5JkjJEk3XDDDc5jl8ul6urqYNUJAABwRgIOO++//36d64qKinTZZZedVUEAAADBFHDY6devn8/j8vJyvfLKK3rxxRf1+eefszcHAABElIDn7NR47733dPvttyslJUVz5szR9ddfr02bNgWzNgAAgLMW0J6dkpISvfTSS1q0aJGOHDmiYcOG6fjx41qxYoU6duwYqhoBAADOmN97dq6//np17NhRO3bs0Jw5c/T9999rzpw5oawNAADgrPm9Z2fNmjW6//77dffddyszMzOUNQEAAASN33t2PvzwQx0+fFjdu3dXz549NXfuXP3jH/8IZW215OXlyeVyKTs722kzxignJ0epqalq3Lix+vfvr+3bt5/TugAAQOTyO+z06tVLCxYs0P79+zVmzBgtX75cbdq0kdfrVX5+vg4fPhzKOrVx40bNnz9fnTt39mmfOXOmZs2apblz52rjxo1KTk7WNddcE/J6AABA/RDw2Vjx8fEaNWqUPvroI23dulUPPvigpk+frtatW+vGG28MRY36+eefddttt2nBggU+9+Qyxmj27NmaMmWKhg4dqqysLC1ZskRHjx7VsmXLQlILAACoX8741HPpxH2yZs6cqZKSEr366qvBqqmWe+65R4MGDdKAAQN82nfv3q3S0lINHDjQaYuLi1O/fv20fv36Ol+vsrLS57YW3NoCAAB7BXxRwZOJiorSkCFDNGTIkGC8nI/ly5erqKhIGzdurLWutLRUkpSUlOTTnpSUpD179tT5mnl5eXr88ceDWygAAIhIZ7VnJ9SKi4s1fvx4LV26VI0aNaqzX809umrU3JurLpMnT1Z5ebmzFBcXB61mAAAQWYKyZydUCgsLVVZWpm7dujlt1dXVWrdunebOnatdu3ZJOrGHJyUlxelTVlZWa2/PL8XFxSkuLi50hQMAgIgR0Xt2rr76am3dulVbtmxxlu7du+u2227Tli1b1L59eyUnJys/P995TlVVlQoKCtS7d+8wVg4AACJFRO/ZSUhIUFZWlk9bkyZN1KpVK6c9Oztbubm5yszMVGZmpnJzcxUfH68RI0aEo2QAABBhIjrs+GPixIk6duyYxo0bp4MHD6pnz55as2aNEhISwl0aAACIAPUu7HzwwQc+j10ul3JycpSTkxOWegAAQGSL6Dk7AAAAZ4uwAwAArEbYAQAAVqt3c3aAUKiqqvKrn9vtVnQ0/2wAoD7hWxsNmrfaI7mj/D57LymljUr2fkfgAYB6hG9sNGzGSN5q3fx0vqJiY0/Z1es5rpXjB8jr9Z6j4gAAwUDYASS5o2MUFR0T7jIAACHABGUAAGA1wg4AALAaYQcAAFiNOTuQJHk8Hr8m3vp7ijYAAJGCsAN5PB61bZeuH/bv8/s5xpgQVgQAQPAQdiCv16sf9u/T0Gfelfs0ZyR5/nlUrz94nUTWAQDUE4QdOPw5/drL6dkAgHqGCcoAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKtxnR3L+XMbCG4BAQCwGWHHYoHeBoJbQAAAbETYsZi/t4HgFhAAAJsRdhqA090GgltAAABsRtgBAuTPHCe3263oaP55AUAk4NsY8JO32iO5o5SQkHDavkkpbVSy9zsCDwBEAL6JAX8ZI3mrdfPT+YqKja2zm9dzXCvHDzjtWXAAgHODsAME6HRzoAAAkYWLCgIAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAq0V02MnLy1OPHj2UkJCg1q1ba8iQIdq1a5dPH2OMcnJylJqaqsaNG6t///7avn17mCoGAACRJqLDTkFBge655x59+umnys/Pl8fj0cCBA3XkyBGnz8yZMzVr1izNnTtXGzduVHJysq655hodPnw4jJUDAIBIER3uAk7l7bff9nm8ePFitW7dWoWFhbriiitkjNHs2bM1ZcoUDR06VJK0ZMkSJSUladmyZRozZkw4ygYAABEkovfs/Fp5ebkkqWXLlpKk3bt3q7S0VAMHDnT6xMXFqV+/flq/fn2dr1NZWamKigqfpT7xeDyqqqryawEAoKGrN2HHGKMJEyaob9++ysrKkiSVlpZKkpKSknz6JiUlOetOJi8vT4mJic6SlpYWusKDzOPxqG27dMXFxZ12SUhIkHRi7AAAaKgi+jDWL91777364osv9NFHH9Va53K5fB4bY2q1/dLkyZM1YcIE53FFRUW9CTxer1c/7N+noc+8K3d0zCn7ev55VK8/eJ1E1gEANGD1Iuzcd999Wr16tdatW6e2bds67cnJyZJO7OFJSUlx2svKymrt7fmlmj0f9Zk7OkZRpwk73tOsBwCgIYjow1jGGN17771auXKl3nvvPWVkZPisz8jIUHJysvLz8522qqoqFRQUqHfv3ue6XAAAEIEies/OPffco2XLlunvf/+7EhISnHk4iYmJaty4sVwul7Kzs5Wbm6vMzExlZmYqNzdX8fHxGjFiRJirBwAAkSCiw868efMkSf379/dpX7x4se68805J0sSJE3Xs2DGNGzdOBw8eVM+ePbVmzRpnci4AAGjYIjrs+HMWkcvlUk5OjnJyckJfEAAAqHcies4OAADA2SLsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsFp0uAsAEHwej0der/e0/dxut6Kj+RoAYDe+5QDLeDwetW2Xrh/27ztt36SUNirZ+x2BB4DV+IYDLOP1evXD/n0a+sy7ckfH1N3Pc1wrxw/waw8QANRnhB0gRKqqqvzqF6pDSe7oGEWdIuwAQENB2AGCzFvtkdxRSkhI8Ks/h5IAILT4dgWCzRjJW62bn85XVGzsKbtyKAkAQo+wA4QIh5EAIDJwnR0AAGA1wg4AALAaYQcAAFiNsAMAAKzGBGWgnvD3FhD+Xt8HABoKwg5QDwRyC4gaxpgQVgQA9QdhB6gH/L0FhCR5/nlUrz94nUTWAQBJhB2gXvHn2j1eru0DAD6YoAwAAKxG2AEAAFYj7AAAAKsxZyeC+HNqMacVAwAQGMJOhAj01GJOKwYAwD+EnQjh76nFnFYMAEBgCDsR5nSnFnNaMQAAgSHsABHgdHOxQjlXy9/Xdrvdio7mKwNA/cM3FxBG3mqP5I5SQkKCX/2DOVcr0PdOSmmjkr3fEXgA1Dt8awHhZIzkrdbNT+crKja2zm4hmavl53tLktdzXCvHD/DrRqQAEGkIO0AECOdcLX9uQREIf+/OLnFoDMC5wbcMgKAJ9BIKHBoDcC7wDQMgaAK5OzuHxgCcK4QdAEEX7ENjAHA2CDsh5u/8BW4DgfogFKfI+/Mc5vaEj7/fYfyOEMn4ZIZQoPMXJG4DgcgUilPkA3lN5vaERyDfYfyOEMn4VIZQIPMXuA0EIlooTpH38zWZ2xM+/n6H8TtCpCPsnAP+zF/gNhCoD0JxijzzeyIfvyPUd4QdAFZhjkn4cOsRRCo+bQCswRyT8ODWI4h0fNIAWIM5JmHCrUcQ4Qg7AKzj7xyTYB92aei3ymioc3s4dBr5GHUADU4oDrtwq4yGiUOn9QMjDqDhCcFhF26V0TBx6LR+IOwAaLBCcdiloR7Kaej4vUc2wg4A+CEUt8rwR0OfBxRsts2vCffno76Mpzts7xxkzz33nDIyMtSoUSN169ZNH374YbhLAmCBX87viYuLq3Opmf8TzFu+1MwHOdX7/nJp2y5dHo8naO9vm0DGsz6MZbg/H/VpPCM/tvrhtddeU3Z2tp577jn16dNHL7zwgq677jrt2LFD7dq1C3d5AOqzUNwqw0/MAwou2+bXhPvzUZ/G04qwM2vWLN11110aPXq0JGn27Nl65513NG/ePOXl5YW5OgA2CMWtMoL13giMbeMZ7u0J9/v7o96HnaqqKhUWFurhhx/2aR84cKDWr19/0udUVlaqsrLSeVxeXi5JqqioCHptklT58yG5o05zI9DKo05fT8wp/vfoZz9ek9e05TW91cclST/++KNiT3PmlL//5sK97f5uUyDfIYGMk79CMZ6hqNNf/m5PKD5z4fz9hPv9a967oqIi6L/zmr/bpz18bOq5ffv2GUnm448/9mmfNm2a+e1vf3vS50ydOtXoxM5mFhYWFhYWlnq+FBcXnzIr1Ps9OzVcLpfPY2NMrbYakydP1oQJE5zHXq9XBw4cUKtWrep8zulUVFQoLS1NxcXFatas2Rm9Rn3HGDAGEmNQg3FgDCTGQArtGBhjdPjwYaWmpp6yX70PO+edd56ioqJUWlrq015WVqakpKSTPqdmdvgvNW/ePCj1NGvWrMF+oGswBoyBxBjUYBwYA4kxkEI3BomJiaftU+9PPY+NjVW3bt2Un5/v056fn6/evXuHqSoAABAp6v2eHUmaMGGC/vjHP6p79+7q1auX5s+fr71792rs2LHhLg0AAISZFWHn1ltv1U8//aQnnnhC+/fvV1ZWlt58801dcMEF56yGuLg4TZ06tdbhsYaEMWAMJMagBuPAGEiMgRQZY+AyJoiX+wQAAIgw9X7ODgAAwKkQdgAAgNUIOwAAwGqEHQAAYDXCThA899xzysjIUKNGjdStWzd9+OGH4S4paNatW6cbbrhBqampcrlcev31133WG2OUk5Oj1NRUNW7cWP3799f27dt9+lRWVuq+++7TeeedpyZNmujGG29USUnJOdyKs5OXl6cePXooISFBrVu31pAhQ7Rr1y6fPraPw7x589S5c2fnomC9evXSW2+95ay3fftPJi8vTy6XS9nZ2U5bQxiHnJwcuVwunyU5OdlZ3xDGQJL27dun22+/Xa1atVJ8fLwuvfRSFRYWOuttH4f09PRanwOXy6V77rlHUgRu/9ndmQrLly83MTExZsGCBWbHjh1m/PjxpkmTJmbPnj3hLi0o3nzzTTNlyhSzYsUKI8msWrXKZ/306dNNQkKCWbFihdm6dau59dZbTUpKiqmoqHD6jB071rRp08bk5+eboqIic+WVV5ouXboYj8dzjrfmzFx77bVm8eLFZtu2bWbLli1m0KBBpl27dubnn392+tg+DqtXrzZvvPGG2bVrl9m1a5d55JFHTExMjNm2bZsxxv7t/7UNGzaY9PR007lzZzN+/HinvSGMw9SpU80ll1xi9u/f7yxlZWXO+oYwBgcOHDAXXHCBufPOO81nn31mdu/ebd59913z9ddfO31sH4eysjKfz0B+fr6RZN5//31jTORtP2HnLF1++eVm7NixPm0XXXSRefjhh8NUUej8Oux4vV6TnJxspk+f7rT985//NImJieb55583xhhz6NAhExMTY5YvX+702bdvn3G73ebtt98+Z7UHU1lZmZFkCgoKjDENdxxatGhhXnzxxQa3/YcPHzaZmZkmPz/f9OvXzwk7DWUcpk6darp06XLSdQ1lDCZNmmT69u1b5/qGMg6/NH78ePOb3/zGeL3eiNx+DmOdhaqqKhUWFmrgwIE+7QMHDtT69evDVNW5s3v3bpWWlvpsf1xcnPr16+dsf2FhoY4fP+7TJzU1VVlZWfV2jMrLyyVJLVu2lNTwxqG6ulrLly/XkSNH1KtXrwa3/ffcc48GDRqkAQMG+LQ3pHH46quvlJqaqoyMDP3hD3/Qt99+K6nhjMHq1avVvXt3/f73v1fr1q3VtWtXLViwwFnfUMahRlVVlZYuXapRo0bJ5XJF5PYTds7Cjz/+qOrq6lo3HE1KSqp1Y1Ib1Wzjqba/tLRUsbGxatGiRZ196hNjjCZMmKC+ffsqKytLUsMZh61bt6pp06aKi4vT2LFjtWrVKnXs2LHBbL8kLV++XEVFRcrLy6u1rqGMQ8+ePfXyyy/rnXfe0YIFC1RaWqrevXvrp59+ajBj8O2332revHnKzMzUO++8o7Fjx+r+++/Xyy+/LKnhfBZqvP766zp06JDuvPNOSZG5/VbcLiLcXC6Xz2NjTK02m53J9tfXMbr33nv1xRdf6KOPPqq1zvZx6NChg7Zs2aJDhw5pxYoVGjlypAoKCpz1tm9/cXGxxo8frzVr1qhRo0Z19rN9HK677jrn506dOqlXr176zW9+oyVLluhf/uVfJNk/Bl6vV927d1dubq4kqWvXrtq+fbvmzZunO+64w+ln+zjUWLhwoa677jqlpqb6tEfS9rNn5yycd955ioqKqpVCy8rKaiVaG9WcgXGq7U9OTlZVVZUOHjxYZ5/64r777tPq1av1/vvvq23btk57QxmH2NhYXXjhherevbvy8vLUpUsXPfPMMw1m+wsLC1VWVqZu3bopOjpa0dHRKigo0F//+ldFR0c722H7OPxakyZN1KlTJ3311VcN5rOQkpKijh07+rRdfPHF2rt3r6SG850gSXv27NG7776r0aNHO22RuP2EnbMQGxurbt26KT8/36c9Pz9fvXv3DlNV505GRoaSk5N9tr+qqkoFBQXO9nfr1k0xMTE+ffbv369t27bVmzEyxujee+/VypUr9d577ykjI8NnfUMZh18zxqiysrLBbP/VV1+trVu3asuWLc7SvXt33XbbbdqyZYvat2/fIMbh1yorK7Vz506lpKQ0mM9Cnz59al1+4ssvv3RuPt1QxkGSFi9erNatW2vQoEFOW0Ruf9CnPDcwNaeeL1y40OzYscNkZ2ebJk2amO+++y7cpQXF4cOHzebNm83mzZuNJDNr1iyzefNm59T66dOnm8TERLNy5UqzdetWM3z48JOeXti2bVvz7rvvmqKiInPVVVfVm9MrjTHm7rvvNomJieaDDz7wOdXy6NGjTh/bx2Hy5Mlm3bp1Zvfu3eaLL74wjzzyiHG73WbNmjXGGPu3vy6/PBvLmIYxDg8++KD54IMPzLfffms+/fRTM3jwYJOQkOB85zWEMdiwYYOJjo4206ZNM1999ZV55ZVXTHx8vFm6dKnTpyGMQ3V1tWnXrp2ZNGlSrXWRtv2EnSB49tlnzQUXXGBiY2PNZZdd5pySbIP333/fSKq1jBw50hhz4hTLqVOnmuTkZBMXF2euuOIKs3XrVp/XOHbsmLn33ntNy5YtTePGjc3gwYPN3r17w7A1Z+Zk2y/JLF682Olj+ziMGjXK+Yyff/755uqrr3aCjjH2b39dfh12GsI41FwvJSYmxqSmppqhQ4ea7du3O+sbwhgYY8z//u//mqysLBMXF2cuuugiM3/+fJ/1DWEc3nnnHSPJ7Nq1q9a6SNt+lzHGBH9/EQAAQGRgzg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDoCA3HnnnRoyZEi4y6hTbm6uoqKiNH369HCXEjCXy6XXX3893GUA1iHsALDK4sWLNXHiRC1atCjcpQCIEIQdAEFVUFCgyy+/XHFxcUpJSdHDDz8sj8fjrH/77bfVt29fNW/eXK1atdLgwYP1zTffOOu/++47uVwurVy5UldeeaXi4+PVpUsXffLJJ36997Fjx/TEE0/oyJEjWrdunc/6nJwcXXrppVq0aJHatWunpk2b6u6771Z1dbVmzpyp5ORktW7dWtOmTfN53t69e3XTTTepadOmatasmYYNG6YffvjBWX+yvV3Z2dnq37+/87h///66//77NXHiRLVs2VLJycnKyclx1qenp0uSbr75ZrlcLucxgLNH2AEQNPv27dP111+vHj166PPPP9e8efO0cOFC/dd//ZfT58iRI5owYYI2btyotWvXyu126+abb5bX6/V5rSlTpuihhx7Sli1b9Nvf/lbDhw/3CU0ns3DhQg0fPlwxMTEaPny4Fi5cWKvPN998o7feektvv/22Xn31VS1atEiDBg1SSUmJCgoKNGPGDP35z3/Wp59+KkkyxmjIkCE6cOCACgoKlJ+fr2+++Ua33nprwOOzZMkSNWnSRJ999plmzpypJ554Qvn5+ZKkjRs3SjqxZ2r//v3OYwBBEJLbiwKw1siRI81NN9100nWPPPKI6dChg/F6vU7bs88+a5o2bWqqq6tP+pyysjIjybkj8u7du40k8+KLLzp9tm/fbiSZnTt31llXeXm5iY+PN1u2bDHGGLN582YTHx9vysvLnT5Tp0418fHxpqKiwmm79tprTXp6uk99HTp0MHl5ecYYY9asWWOioqJ87sZcU8+GDRvqHJPx48ebfv36OY/79etn+vbt69OnR48eZtKkSc5jSWbVqlV1biOAM8OeHQBBs3PnTvXq1Usul8tp69Onj37++WeVlJRIOrFnZcSIEWrfvr2aNWumjIwMSScOFf1S586dnZ9TUlIkSWVlZXW+97Jly9S+fXt16dJFknTppZeqffv2Wr58uU+/9PR0JSQkOI+TkpLUsWNHud1un7aa99q5c6fS0tKUlpbmrO/YsaOaN2+unTt3+jEqJ9+mmu061TYBCA7CDoCgMcb4BJ2aNklO+w033KCffvpJCxYs0GeffabPPvtMklRVVeXzvJiYGOfnmuf++lDXLy1atEjbt29XdHS0s2zfvr3Woaxfvm7Na5+srea9TrZNv253u93OdtY4fvx4reec6n0AhE50uAsAYI+OHTtqxYoVPkFg/fr1SkhIUJs2bfTTTz9p586deuGFF/Sv//qvkqSPPvrorN9369at2rRpkz744AO1bNnSaT906JCuuOIKbdu2TVlZWWe8TXv37lVxcbGzd2fHjh0qLy/XxRdfLEk6//zztW3bNp/nbdmypVa4OZ2YmBhVV1efUZ0A6saeHQABKy8v15YtW3yWvXv3aty4cSouLtZ9992n//u//9Pf//53TZ06VRMmTJDb7VaLFi3UqlUrzZ8/X19//bXee+89TZgw4azrWbhwoS6//HJdccUVysrKcpa+ffuqV69eJ52o7K8BAwaoc+fOuu2221RUVKQNGzbojjvuUL9+/dS9e3dJ0lVXXaVNmzbp5Zdf1ldffaWpU6fWCj/+SE9P19q1a1VaWqqDBw+ecc0AfBF2AATsgw8+UNeuXX2Wxx57TG3atNGbb76pDRs2qEuXLho7dqzuuusu/fnPf5Z04nDP8uXLVVhYqKysLD3wwAN68sknz6qWqqoqLV26VL/73e9Ouv53v/udli5dWuswmb9qLvTXokULXXHFFRowYIDat2+v1157zelz7bXX6tFHH9XEiRPVo0cPHT58WHfccUfA7/XUU08pPz9faWlp6tq16xnVC6A2l/n1gWYAAACLsGcHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKv9P4GGaBNlIiVFAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.histplot(data = df, x='LoanAmount')\n",
    "ax.set(xlabel=\"Loan Amount\", ylabel=\"Applicants\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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okN/v946ioqLJlA3AcpFIRB0dHeP2dXR0KBKJTHFFAEybVECprq7Wn/zJn6isrExr167Vv/zLv0iSXnzxRW+M4zhpr3Fdd0zbaNcas2fPHsViMe/o6uqaTNkALLd48WJlZWWN25eVlaXFixdPcUUATLuhx4znzZunsrIyvfPOO97TPKNnQnp7e71ZlWAwqOHhYfX39084Zjw5OTnKz89POwDMHD/96U8nvFE+lUrppz/96RRXBMC0GwooyWRS//u//6tbb71VS5YsUTAYVFtbm9c/PDyskydPqrKyUpJUXl6u2bNnp43p6enR2bNnvTEAMk9FRYXmz58/bt/8+fNVUVExxRUBMC17MoN37dqlL3/5yyouLlZvb6/+9m//VvF4XJs2bZLjOAqHw6qvr1dJSYlKSkpUX1+vuXPnqq6uTpLk9/u1efNm7dy5UwUFBVq4cKF27drlXTICkJkcx1EoFNLbb789pi8UCl3zMjGAmWdSAaW7u1tf+9rXdOnSJS1atEi///u/r9dff1233367JGn37t0aGhrS1q1b1d/fr4qKCp04cUJ5eXnezzhw4ICys7O1YcMGDQ0Nac2aNTp69OiE158BzHyRSGTccCJJb7/9tiKRiPc+AyAzOO40fH4vHo/L7/crFotxPwowA7iuq927d4/7JM/KlSv11FNPMYsCzACT+fxmLx4AxjmOo2XLlo3bd+eddxJOgAxEQAFgXCqV0ne/+91x+7773e+yFQaQgQgoAIx79dVXr/qY8auvvjrFFQEwjYACwLiampoJb5TPzs5WTU3NFFcEwDQCCgDjsrKy9Jd/+Zfj9u3evZun/IAMREABYIUvfvGL3u7oV9xyyy1pm4sCyBwEFADWOHToUNr3//AP/2CoEgCmEVAAWCMQCGj16tWSpNWrV191jy4AMxsLtQEAgCnBQm0AAGBaI6AAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUABYpb29XbW1tWpvbzddCgCDCCgArJFIJNTY2KiLFy+qsbFRiUTCdEkADCGgALBGU1OT+vr6JEl9fX1qbm42XBEAUwgoAKzQ3d2t5uZmXdlg3XVdNTc3q7u723BlAEwgoAAwznVdHTx4cML2K6EFQOYgoAAwLhKJqKOjQ6lUKq09lUqpo6NDkUjEUGUATCGgADCuuLhYK1asUFZWVlp7VlaWVq5cqeLiYkOVATCFgALAOMdxtH379gnbHccxUBUAkwgoAKywePFi1dXVeWHEcRzV1dXptttuM1wZABMIKACssXHjRhUUFEiSCgsLVVdXZ7giAKYQUABYw+fzaceOHQoEAnrsscfk8/lMlwTAkGzTBQDAh1VWVqqystJ0GQAMYwYFAABYh4ACAACsQ0ABAADWIaAAsEp7e7tqa2vV3t5uuhQABhFQAFgjkUiosbFRFy9eVGNjoxKJhOmSABhCQAFgjaamJl26dEmSdOnSJTU3NxuuCIApNxRQGhoa5DiOwuGw1+a6rvbu3atQKKTc3FytXr1a586dS3tdMpnUtm3bVFhYqHnz5mn9+vVsqQ5kuO7ubjU1NaW1NTU18d4AZKjrDigdHR167rnntHTp0rT2/fv3q7GxUYcOHVJHR4eCwaDWrVungYEBb0w4HFZLS4uOHTumU6dOaXBwUDU1NWN2MgWQGVzX1cGDB+W6blr7yMjIuO0AZr7rCiiDg4PauHGjnn/+eS1YsMBrd11XzzzzjJ544gndf//9Ki0t1Ysvvqhf//rX3lRtLBbTkSNH9PTTT2vt2rW688479fLLL+vMmTN67bXXbs5ZAZhWIpGIOjo6xgQR13XV0dGhSCRiqDIAplxXQHn00Uf1pS99SWvXrk1rP3/+vKLRqKqqqry2nJwcrVq1yrsjv7OzU5cvX04bEwqFVFpaOuFd+8lkUvF4PO0AMHMUFRUpPz9/3L78/HwVFRVNcUUATJt0QDl27Jj++7//Ww0NDWP6otGoJCkQCKS1BwIBry8ajWrOnDlpMy+jx4zW0NAgv9/vHbxZATNLV1fXhH94xONxdXV1TXFFAEybVEDp6urS9u3b9fLLL191E68r26Vf4brumLbRrjZmz549isVi3sGbFTCzFBcXa8WKFWPeAxzH0cqVK1VcXGyoMgCmTCqgdHZ2qre3V+Xl5crOzlZ2drZOnjypv//7v1d2drY3czJ6JqS3t9frCwaDGh4eVn9//4RjRsvJyVF+fn7aAWDmcBxH27dv16xZ6W9JWVlZ2r59+zX/wAEw80wqoKxZs0ZnzpzRm2++6R3Lly/Xxo0b9eabb+p3fud3FAwG1dbW5r1meHhYJ0+e9HYnLS8v1+zZs9PG9PT06OzZs+xgCmSwxYsXq66uzgsjjuOorq5Ot912m+HKAJiQPZnBeXl5Ki0tTWubN2+eCgoKvPZwOKz6+nqVlJSopKRE9fX1mjt3rurq6iRJfr9fmzdv1s6dO1VQUKCFCxdq165dKisrG3PTLYDMsnHjRrW2turSpUsqLCz03jcAZJ5JBZSPYvfu3RoaGtLWrVvV39+viooKnThxQnl5ed6YAwcOKDs7Wxs2bNDQ0JDWrFmjo0ePKisr62aXA2Aa8fl8qq6uVlNTk774xS9e9V43ADOb407DFZDi8bj8fr9isRj3owAzSCKR0AMPPODNoFzrhnwA08tkPr/ZiweANZqamtTX1ydJ6uvrYy8eIIMRUABYobu7W83Nzd5qsq7rqrm5mb14gAxFQAFg3JW9eEZGRtLaU6kUe/EAGYqAAsA49uIBMBoBBYBx7MUDYDQCCgDj2IsHwGgEFADGFRcXq6ysbNy+pUuXshcPkIEIKACsxg2yQGYioAAwLhKJ6MyZM+P2nTlzhptkgQxEQAFgXHFxsVasWDFm12LHcbRy5Uou8QAZiIACwDjHcbR9+/Zx+7Zv3z4muACY+QgoAKzlOA73oAAZioACwLgrK8nOmpX+luQ4DivJAhmKgALAuCsryaZSqbT2VCrFSrJAhiKgADBuoptkZ82axU2yQIYioAAw7spNsqMv5YyMjHCTLJChCCgArBCNRsdt7+npmeJKANiAgALAuJGREe3bt2/cvn379mlkZGSKKwJgGgEFgHGnT5++6maBp0+fnuKKAJhGQAFgXEVFhfLz88ft8/v9qqiomOKKAJhGQAFg3KxZs/TNb35z3L4nn3xyzPooAGY+fusBWGH58uUqKytLa1u6dKmWLVtmqCIAJhFQAFjjW9/6lvdIseM4E944C2DmI6AAsIbP55PP5xvzNYDMQ0ABYI2mpiYlEglJUiKRUHNzs+GKAJhCQAFghe7ubjU3N3urybquq+bmZnV3dxuuDIAJBBQAxl3ZzXi8pe7ZzRjITAQUAMZd2c149IqxIyMj7GYMZCgCCgDjiouLxzxifMXSpUvZzRjIQAQUAFbj8g6QmQgoAIyLRCI6c+bMuH1nzpzhEg+QgQgoAIwrKiqacC+e/Px8FRUVTXFFAEwjoAAwrqur66q7GXd1dU1xRQBMI6AAMI6bZAGMRkABYIVkMjlu+5WVZQFkFgIKAOPeffddvf322+P2vf3223r33XenuCIAphFQABh3rUeJedQYyDwEFAAAYJ1JBZTDhw9r6dKlys/PV35+vu666y61trZ6/a7rau/evQqFQsrNzdXq1at17ty5tJ+RTCa1bds2FRYWat68eVq/fj2bgQEZznGcG+oHMPNMKqAsXrxY3/72t/XGG2/ojTfe0Be+8AX98R//sRdC9u/fr8bGRh06dEgdHR0KBoNat26dBgYGvJ8RDofV0tKiY8eO6dSpUxocHFRNTY1SqdTNPTMA00ZxcbHmz58/bt/8+fN5igfIQI57gxd3Fy5cqO985zt6+OGHFQqFFA6H9fjjj0v6YLYkEAjoqaee0pYtWxSLxbRo0SK99NJLqq2tlSRduHBBRUVFOn78uO65556P9G/G43H5/X7FYrEJF3cCMH28++672rRp04T9L774om6//fYprAjAx2Eyn9/XfQ9KKpXSsWPH9P777+uuu+7S+fPnFY1GVVVV5Y3JycnRqlWr1N7eLknq7OzU5cuX08aEQiGVlpZ6Y8aTTCYVj8fTDgAzByvJAhht0gHlzJkzmj9/vnJycvTII4+opaVFn/70pxWNRiVJgUAgbXwgEPD6otGo5syZowULFkw4ZjwNDQ3y+/3ewZsVMLOwkiyA0SYdUD71qU/pzTff1Ouvv64///M/16ZNm/TWW295/aNvZnNd95o3uF1rzJ49exSLxbyDNytgZmEGBcBokw4oc+bM0e/+7u9q+fLlamho0Gc/+1kdPHhQwWBQksbMhPT29nqzKsFgUMPDw+rv759wzHhycnK8J4euHABmDmZQAIx2w+uguK6rZDKpJUuWKBgMqq2tzesbHh7WyZMnVVlZKUkqLy/X7Nmz08b09PTo7Nmz3hgAmaeoqOiqT/EwgwJknuzJDP7rv/5rVVdXq6ioSAMDAzp27Jj+4z/+Qz/60Y/kOI7C4bDq6+tVUlKikpIS1dfXa+7cuaqrq5Mk+f1+bd68WTt37lRBQYEWLlyoXbt2qaysTGvXrv1YThCA/SKRiAYHB8ftGxwcVCQS0Sc/+cmpLQqAUZMKKBcvXtSDDz6onp4e+f1+LV26VD/60Y+0bt06SdLu3bs1NDSkrVu3qr+/XxUVFTpx4oTy8vK8n3HgwAFlZ2drw4YNGhoa0po1a3T06FFlZWXd3DMDAADT1g2vg2IC66AAM8vIyIjWr18/7izK/Pnz9corr2jWLHbmAKa7KVkHBQBulq6urqte4uEmWSDzEFAAGFdcXKzf+73fG7fvU5/6FEvdAxmIgALAONd1deHChXH7fvWrX2kaXokGcIMIKACMO3369FUv8Zw+fXqKKwJgGgEFgHHLly+/oX4AMw8BBYBxx48fv6F+ADMPAQWAcTU1NROuhZSdna2ampoprgiAaZNaqA2YiVzXVSKRMF1GxguHw3r66afHbR8eHjZQET7M5/Ndc+NX4GZioTZkvKGhIVVXV5suA7Baa2urcnNzTZeBaY6F2gAAwLTGJR5kPJ/Pp9bWVtNlQFIikdB9990nSbr77rv1jW98w3BFuMLn85kuARmGgIKM5zgOU9cW+sY3vsH/C5DBuMQDAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsM6mA0tDQoBUrVigvL0+33HKL7r33Xv385z9PG+O6rvbu3atQKKTc3FytXr1a586dSxuTTCa1bds2FRYWat68eVq/fr26u7tv/GwAAMCMMKmAcvLkST366KN6/fXX1dbWpt/85jeqqqrS+++/743Zv3+/GhsbdejQIXV0dCgYDGrdunUaGBjwxoTDYbW0tOjYsWM6deqUBgcHVVNTo1QqdfPODAAATFuO67ru9b74//7v/3TLLbfo5MmT+sM//EO5rqtQKKRwOKzHH39c0gezJYFAQE899ZS2bNmiWCymRYsW6aWXXlJtba0k6cKFCyoqKtLx48d1zz33XPPfjcfj8vv9isViys/Pv97yAVhmaGhI1dXVkqTW1lbl5uYargjAzTSZz+8bugclFotJkhYuXChJOn/+vKLRqKqqqrwxOTk5WrVqldrb2yVJnZ2dunz5ctqYUCik0tJSbwwAAMhs2df7Qtd1tWPHDv3BH/yBSktLJUnRaFSSFAgE0sYGAgG9++673pg5c+ZowYIFY8Zcef1oyWRSyWTS+z4ej19v2QAAYBq47hmUr3/96/qf//kfff/73x/T5zhO2veu645pG+1qYxoaGuT3+72jqKjoessGAADTwHUFlG3btumVV17Rv//7v2vx4sVeezAYlKQxMyG9vb3erEowGNTw8LD6+/snHDPanj17FIvFvKOrq+t6ygYAANPEpAKK67r6+te/rn/6p3/Sv/3bv2nJkiVp/UuWLFEwGFRbW5vXNjw8rJMnT6qyslKSVF5ertmzZ6eN6enp0dmzZ70xo+Xk5Cg/Pz/tAAAAM9ek7kF59NFH1dzcrH/+539WXl6eN1Pi9/uVm5srx3EUDodVX1+vkpISlZSUqL6+XnPnzlVdXZ03dvPmzdq5c6cKCgq0cOFC7dq1S2VlZVq7du3NP0MAADDtTCqgHD58WJK0evXqtPYXXnhBDz30kCRp9+7dGhoa0tatW9Xf36+KigqdOHFCeXl53vgDBw4oOztbGzZs0NDQkNasWaOjR48qKyvrxs4GAADMCDe0DooprIMCzEysgwLMbFO2DgoAAMDHgYACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGCd697NGDfGdV0lEgnTZQBW+fDvBL8fwPh8Pt81N+CdCQgohiQSCW9BKgBj3XfffaZLAKyUKYsYcokHAABYhxkUCwx+7mtyZ/FfAch1pZHffPD1rGwpA6axgY/CGfmN5r/5fdNlTCk+FS3gzsqWsmabLgOwxBzTBQDWmXab5t0EXOIBAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWCfbdAGZynXd336TumyuEACA/T70OZH2+TGDEVAMSSaT3td5/++YwUoAANNJMpnU3LlzTZfxseMSDwAAsA4zKIbk5OR4Xw989qtS1myD1QAArJa67M22f/jzYyYjoBjiOM5vv8maTUABAHwkaZ8fMxiXeAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArDPpgPKf//mf+vKXv6xQKCTHcfTDH/4wrd91Xe3du1ehUEi5ublavXq1zp07lzYmmUxq27ZtKiws1Lx587R+/Xp1d3ff0IkAAICZY9IB5f3339dnP/tZHTp0aNz+/fv3q7GxUYcOHVJHR4eCwaDWrVungYEBb0w4HFZLS4uOHTumU6dOaXBwUDU1NUqlUtd/JgAAYMaY9EJt1dXVqq6uHrfPdV0988wzeuKJJ3T//fdLkl588UUFAgE1Nzdry5YtisViOnLkiF566SWtXbtWkvTyyy+rqKhIr732mu65554bOJ3pyRn5jTJj6yfgGlxXGvnNB1/PypYyZEEq4FqcK78XGeSmriR7/vx5RaNRVVVVeW05OTlatWqV2tvbtWXLFnV2dury5ctpY0KhkEpLS9Xe3j5uQEkmk2mb68Xj8ZtZtnHz3/y+6RIAALDKTb1JNhqNSpICgUBaeyAQ8Pqi0ajmzJmjBQsWTDhmtIaGBvn9fu8oKiq6mWUDAADLfCx78YzeJ8B13WvuHXC1MXv27NGOHTu87+Px+LQPKT6fT62trabLAKySSCR03333SZJaWlrk8/kMVwTYJ1N+L25qQAkGg5I+mCW59dZbvfbe3l5vViUYDGp4eFj9/f1psyi9vb2qrKwc9+fm5OTMuN0bHcdRbm6u6TIAa/l8Pn5HgAx2Uy/xLFmyRMFgUG1tbV7b8PCwTp486YWP8vJyzZ49O21MT0+Pzp49O2FAAQAAmWXSMyiDg4P6xS9+4X1//vx5vfnmm1q4cKGKi4sVDodVX1+vkpISlZSUqL6+XnPnzlVdXZ0kye/3a/Pmzdq5c6cKCgq0cOFC7dq1S2VlZd5TPQAAILNNOqC88cYb+qM/+iPv+yv3hmzatElHjx7V7t27NTQ0pK1bt6q/v18VFRU6ceKE8vLyvNccOHBA2dnZ2rBhg4aGhrRmzRodPXpUWVlZN+GUAADAdOe4rjvtluCIx+Py+/2KxWLKz883XQ6Am2RoaMhbZ6m1tZV7UIAZZjKf3+zFAwAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrZJsuADDNdV0lEgnTZUBK+3/g/8QuPp9PjuOYLgMZhICCjJdIJFRdXW26DIxy3333mS4BH9La2qrc3FzTZSCDcIkHAABYx+gMyrPPPqvvfOc76unp0Wc+8xk988wz+vznP2+yJGQgn8+n1tZW02VAH1xuSyaTkqScnBwuKVjE5/OZLgEZxlhA+cEPfqBwOKxnn31Wd999t/7xH/9R1dXVeuutt1RcXGyqLGQgx3GYurbI3LlzTZcAwAKO67quiX+4oqJCy5Yt0+HDh722O+64Q/fee68aGhqu+tp4PC6/369YLKb8/PyPu1QAAHATTObz28g9KMPDw+rs7FRVVVVae1VVldrb28eMTyaTisfjaQcAAJi5jASUS5cuKZVKKRAIpLUHAgFFo9Ex4xsaGuT3+72jqKhoqkoFAAAGGH2KZ/QNcK7rjntT3J49exSLxbyjq6trqkoEAAAGGLlJtrCwUFlZWWNmS3p7e8fMqkgf3M2fk5MzVeUBAADDjMygzJkzR+Xl5Wpra0trb2trU2VlpYmSAACARYw9Zrxjxw49+OCDWr58ue666y4999xzikQieuSRR0yVBAAALGEsoNTW1qqvr0/79u1TT0+PSktLdfz4cd1+++2mSgIAAJYwtg7KjWAdFAAAph/r10EBAAC4GgIKAACwDgEFAABYh4ACAACsY+wpnhtx5b5e9uQBAGD6uPK5/VGez5mWAWVgYECS2JMHAIBpaGBgQH6//6pjpuVjxiMjI7pw4YLy8vLG3bsHwPQVj8dVVFSkrq4ulhEAZhjXdTUwMKBQKKRZs65+l8m0DCgAZi7WOQIgcZMsAACwEAEFAABYh4ACwCo5OTl68sknlZOTY7oUAAZxDwoAALAOMygAAMA6BBQAAGAdAgoAALAOAQUAAFiHgALAKs8++6yWLFkin8+n8vJy/eQnPzFdEgADCCgArPGDH/xA4XBYTzzxhH72s5/p85//vKqrqxWJREyXBmCK8ZgxAGtUVFRo2bJlOnz4sNd2xx136N5771VDQ4PBygBMNWZQAFhheHhYnZ2dqqqqSmuvqqpSe3u7oaoAmEJAAWCFS5cuKZVKKRAIpLUHAgFFo1FDVQEwhYACwCqO46R977rumDYAMx8BBYAVCgsLlZWVNWa2pLe3d8ysCoCZj4ACwApz5sxReXm52tra0trb2tpUWVlpqCoApmSbLgAArtixY4cefPBBLV++XHfddZeee+45RSIRPfLII6ZLAzDFCCgArFFbW6u+vj7t27dPPT09Ki0t1fHjx3X77bebLg3AFGMdFAAAYB3uQQEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOv8fRuWi0nM/MPUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['LoanAmount'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There might be some extreme values. Both `ApplicantIncome` and `LoanAmount` require some amount of data munging. `LoanAmount` has missing and well as extreme values values, while `ApplicantIncome` has a few extreme values, which demand deeper understanding. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical variable analysis\n",
    "\n",
    "Try to understand categorical variables in more details using `pandas.DataFrame.pivot_table` and some visualizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Loan_ID            614 non-null    object \n",
      " 1   Gender             601 non-null    object \n",
      " 2   Married            611 non-null    object \n",
      " 3   Dependents         599 non-null    object \n",
      " 4   Education          614 non-null    object \n",
      " 5   Self_Employed      582 non-null    object \n",
      " 6   ApplicantIncome    614 non-null    int64  \n",
      " 7   CoapplicantIncome  614 non-null    float64\n",
      " 8   LoanAmount         592 non-null    float64\n",
      " 9   Loan_Amount_Term   600 non-null    float64\n",
      " 10  Credit_History     564 non-null    float64\n",
      " 11  Property_Area      614 non-null    object \n",
      " 12  Loan_Status        614 non-null    object \n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 62.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>Gender</th>\n",
       "      <th>NoApproved</th>\n",
       "      <th>Approved</th>\n",
       "      <th>Total</th>\n",
       "      <th>%NoApproved</th>\n",
       "      <th>%Approved</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>36</td>\n",
       "      <td>73</td>\n",
       "      <td>109</td>\n",
       "      <td>33.027523</td>\n",
       "      <td>66.972477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>140</td>\n",
       "      <td>330</td>\n",
       "      <td>470</td>\n",
       "      <td>29.787234</td>\n",
       "      <td>70.212766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Total</td>\n",
       "      <td>176</td>\n",
       "      <td>403</td>\n",
       "      <td>579</td>\n",
       "      <td>30.397237</td>\n",
       "      <td>69.602763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  NoApproved  Approved  Total  %NoApproved  %Approved\n",
       "0  Female          36        73    109    33.027523  66.972477\n",
       "1    Male         140       330    470    29.787234  70.212766\n",
       "2   Total         176       403    579    30.397237  69.602763"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Aprovals by Gender\n",
    "df_approvals_gender = pd.pivot_table(df, values='LoanAmount', index=['Gender'], columns=['Loan_Status'], aggfunc=['count'], fill_value=None, margins=True, margins_name='Total').reset_index()\n",
    "df_approvals_gender.columns =['Gender','NoApproved','Approved','Total']\n",
    "df_approvals_gender['%NoApproved'] = df_approvals_gender['NoApproved'] / df_approvals_gender['Total']* 100\n",
    "df_approvals_gender['%Approved']   = df_approvals_gender['Approved'] / df_approvals_gender['Total']* 100\n",
    "df_approvals_gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>Education</th>\n",
       "      <th>NoApproved</th>\n",
       "      <th>Approved</th>\n",
       "      <th>Total</th>\n",
       "      <th>%NoApproved</th>\n",
       "      <th>%Approved</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Graduate</td>\n",
       "      <td>134</td>\n",
       "      <td>331</td>\n",
       "      <td>465</td>\n",
       "      <td>28.817204</td>\n",
       "      <td>71.182796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>47</td>\n",
       "      <td>80</td>\n",
       "      <td>127</td>\n",
       "      <td>37.007874</td>\n",
       "      <td>62.992126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Total</td>\n",
       "      <td>181</td>\n",
       "      <td>411</td>\n",
       "      <td>592</td>\n",
       "      <td>30.574324</td>\n",
       "      <td>69.425676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Education  NoApproved  Approved  Total  %NoApproved  %Approved\n",
       "0      Graduate         134       331    465    28.817204  71.182796\n",
       "1  Not Graduate          47        80    127    37.007874  62.992126\n",
       "2         Total         181       411    592    30.574324  69.425676"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Approvals by Education \n",
    "df_approvals_education = pd.pivot_table(df, values='LoanAmount', index=['Education'], columns=['Loan_Status'], aggfunc=['count'], fill_value=None, margins=True, margins_name='Total').reset_index()\n",
    "df_approvals_education .columns =['Education','NoApproved','Approved','Total']\n",
    "df_approvals_education['%NoApproved'] = df_approvals_education['NoApproved'] / df_approvals_education['Total']* 100\n",
    "df_approvals_education['%Approved']   = df_approvals_education['Approved'] / df_approvals_education['Total']* 100\n",
    "df_approvals_education "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>Married</th>\n",
       "      <th>NoApproved</th>\n",
       "      <th>Approved</th>\n",
       "      <th>Total</th>\n",
       "      <th>%NoApproved</th>\n",
       "      <th>%Approved</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>75</td>\n",
       "      <td>131</td>\n",
       "      <td>206</td>\n",
       "      <td>36.407767</td>\n",
       "      <td>63.592233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>106</td>\n",
       "      <td>278</td>\n",
       "      <td>384</td>\n",
       "      <td>27.604167</td>\n",
       "      <td>72.395833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Total</td>\n",
       "      <td>181</td>\n",
       "      <td>409</td>\n",
       "      <td>590</td>\n",
       "      <td>30.677966</td>\n",
       "      <td>69.322034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Married  NoApproved  Approved  Total  %NoApproved  %Approved\n",
       "0      No          75       131    206    36.407767  63.592233\n",
       "1     Yes         106       278    384    27.604167  72.395833\n",
       "2   Total         181       409    590    30.677966  69.322034"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Approvals by Married\n",
    "df_approvals_marital_status = pd.pivot_table(df, values='LoanAmount', index=['Married'], columns=['Loan_Status'], aggfunc=['count'], fill_value=None, margins=True, margins_name='Total').reset_index()\n",
    "df_approvals_marital_status.columns =['Married','NoApproved','Approved','Total']\n",
    "df_approvals_marital_status['%NoApproved'] = df_approvals_marital_status['NoApproved'] / df_approvals_marital_status['Total']* 100\n",
    "df_approvals_marital_status['%Approved']   = df_approvals_marital_status['Approved'] / df_approvals_marital_status['Total']* 100\n",
    "df_approvals_marital_status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>Dependents</th>\n",
       "      <th>NoApproved</th>\n",
       "      <th>Approved</th>\n",
       "      <th>Total</th>\n",
       "      <th>%NoApproved</th>\n",
       "      <th>%Approved</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "      <td>233</td>\n",
       "      <td>334</td>\n",
       "      <td>30.239521</td>\n",
       "      <td>69.760479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>63</td>\n",
       "      <td>98</td>\n",
       "      <td>35.714286</td>\n",
       "      <td>64.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>75</td>\n",
       "      <td>98</td>\n",
       "      <td>23.469388</td>\n",
       "      <td>76.530612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3+</td>\n",
       "      <td>16</td>\n",
       "      <td>33</td>\n",
       "      <td>49</td>\n",
       "      <td>32.653061</td>\n",
       "      <td>67.346939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Total</td>\n",
       "      <td>175</td>\n",
       "      <td>404</td>\n",
       "      <td>579</td>\n",
       "      <td>30.224525</td>\n",
       "      <td>69.775475</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Dependents  NoApproved  Approved  Total  %NoApproved  %Approved\n",
       "0          0         101       233    334    30.239521  69.760479\n",
       "1          1          35        63     98    35.714286  64.285714\n",
       "2          2          23        75     98    23.469388  76.530612\n",
       "3         3+          16        33     49    32.653061  67.346939\n",
       "4      Total         175       404    579    30.224525  69.775475"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Approvals by Dependents\n",
    "df_approvals_Dependents = pd.pivot_table(df, values='LoanAmount', index=['Dependents'], columns=['Loan_Status'], aggfunc=['count'], fill_value=None, margins=True, margins_name='Total').reset_index()\n",
    "df_approvals_Dependents.columns =['Dependents','NoApproved','Approved','Total']\n",
    "df_approvals_Dependents['%NoApproved'] = df_approvals_Dependents['NoApproved'] / df_approvals_Dependents['Total']* 100\n",
    "df_approvals_Dependents['%Approved']   = df_approvals_Dependents['Approved'] / df_approvals_Dependents['Total']* 100\n",
    "df_approvals_Dependents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>Self_Employed</th>\n",
       "      <th>NoApproved</th>\n",
       "      <th>Approved</th>\n",
       "      <th>Total</th>\n",
       "      <th>%NoApproved</th>\n",
       "      <th>%Approved</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>148</td>\n",
       "      <td>334</td>\n",
       "      <td>482</td>\n",
       "      <td>30.705394</td>\n",
       "      <td>69.294606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>25</td>\n",
       "      <td>54</td>\n",
       "      <td>79</td>\n",
       "      <td>31.645570</td>\n",
       "      <td>68.354430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Total</td>\n",
       "      <td>173</td>\n",
       "      <td>388</td>\n",
       "      <td>561</td>\n",
       "      <td>30.837790</td>\n",
       "      <td>69.162210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Self_Employed  NoApproved  Approved  Total  %NoApproved  %Approved\n",
       "0            No         148       334    482    30.705394  69.294606\n",
       "1           Yes          25        54     79    31.645570  68.354430\n",
       "2         Total         173       388    561    30.837790  69.162210"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Approvals by Self_Employed\n",
    "df_approvals_self_employed = pd.pivot_table(df, values='LoanAmount', index=['Self_Employed'], columns=['Loan_Status'], aggfunc=['count'], fill_value=None, margins=True, margins_name='Total').reset_index()\n",
    "df_approvals_self_employed.columns =['Self_Employed','NoApproved','Approved','Total']\n",
    "df_approvals_self_employed['%NoApproved'] = df_approvals_self_employed['NoApproved'] / df_approvals_self_employed['Total']* 100\n",
    "df_approvals_self_employed['%Approved']   = df_approvals_self_employed['Approved'] / df_approvals_self_employed['Total']* 100\n",
    "df_approvals_self_employed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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>PropertyArea</th>\n",
       "      <th>NoApproved</th>\n",
       "      <th>Approved</th>\n",
       "      <th>Total</th>\n",
       "      <th>%NoApproved</th>\n",
       "      <th>%Approved</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rural</td>\n",
       "      <td>66</td>\n",
       "      <td>107</td>\n",
       "      <td>173</td>\n",
       "      <td>38.150289</td>\n",
       "      <td>61.849711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Semiurban</td>\n",
       "      <td>53</td>\n",
       "      <td>175</td>\n",
       "      <td>228</td>\n",
       "      <td>23.245614</td>\n",
       "      <td>76.754386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Urban</td>\n",
       "      <td>62</td>\n",
       "      <td>129</td>\n",
       "      <td>191</td>\n",
       "      <td>32.460733</td>\n",
       "      <td>67.539267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Total</td>\n",
       "      <td>181</td>\n",
       "      <td>411</td>\n",
       "      <td>592</td>\n",
       "      <td>30.574324</td>\n",
       "      <td>69.425676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PropertyArea  NoApproved  Approved  Total  %NoApproved  %Approved\n",
       "0        Rural          66       107    173    38.150289  61.849711\n",
       "1    Semiurban          53       175    228    23.245614  76.754386\n",
       "2        Urban          62       129    191    32.460733  67.539267\n",
       "3        Total         181       411    592    30.574324  69.425676"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Approvals by Property_Area\n",
    "df_approvals_self_property_area = pd.pivot_table(df, values='LoanAmount', index=['Property_Area'], columns=['Loan_Status'], aggfunc=['count'], fill_value=None, margins=True, margins_name='Total').reset_index()\n",
    "df_approvals_self_property_area.columns =['PropertyArea','NoApproved','Approved','Total']\n",
    "df_approvals_self_property_area['%NoApproved'] = df_approvals_self_property_area['NoApproved'] / df_approvals_self_property_area['Total']* 100\n",
    "df_approvals_self_property_area['%Approved']   = df_approvals_self_property_area['Approved'] / df_approvals_self_property_area['Total']* 100\n",
    "df_approvals_self_property_area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x2000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Graph all the comparissons with categorical variables\n",
    "\n",
    "fig, ax = plt.subplots(3, 2, figsize=(10, 20), sharey=True)\n",
    "fig.suptitle('Categorical variables approval rate')\n",
    "df_approvals_education = df_approvals_education.loc[df_approvals_education.Education !='Total']\n",
    "df_approvals_gender = df_approvals_gender.loc[df_approvals_gender.Gender !='Total']\n",
    "df_approvals_marital_status = df_approvals_marital_status.loc[df_approvals_marital_status.Married !='Total']\n",
    "df_approvals_Dependents = df_approvals_Dependents.loc[df_approvals_Dependents.Dependents !='Total']\n",
    "df_approvals_self_employed = df_approvals_self_employed.loc[df_approvals_self_employed.Self_Employed !='Total']\n",
    "df_approvals_self_property_area = df_approvals_self_property_area.loc[df_approvals_self_property_area.PropertyArea !='Total']\n",
    "\n",
    "sns.barplot(x='Education', y='%Approved', data=df_approvals_education,ax=ax[0,0])\n",
    "ax[0, 0].set(ylabel=\"% Approval\")\n",
    "ax[0, 0].set_title('Approval by Education')\n",
    "\n",
    "sns.barplot(x='Gender', y='%Approved', data=df_approvals_gender,ax=ax[0,1])\n",
    "ax[0, 1].set(ylabel=\"% Approval\")\n",
    "ax[0, 1].set_title('Approval by Gender')\n",
    "\n",
    "sns.barplot(x='Married', y='%Approved', data=df_approvals_marital_status,ax=ax[1,0])\n",
    "ax[1, 0].set(ylabel=\"% Approval\")\n",
    "ax[1, 0].set_title('Approval by Marital Status')\n",
    "\n",
    "sns.barplot(x='Dependents', y='%Approved', data=df_approvals_Dependents,ax=ax[1,1])\n",
    "ax[1, 1].set(ylabel=\"% Approval\")\n",
    "ax[1, 1].set_title('Approval by Dependents')\n",
    "\n",
    "sns.barplot(x='Self_Employed', y='%Approved', data=df_approvals_self_employed,ax=ax[2,0])\n",
    "ax[2, 0].set(ylabel=\"% Approval\")\n",
    "ax[2, 0].set_title('Approval by Self Employment')\n",
    "\n",
    "sns.barplot(x='PropertyArea', y='%Approved', data=df_approvals_self_property_area,ax=ax[2,1])\n",
    "ax[2, 1].set(ylabel=\"% Approval\")\n",
    "ax[2, 1].set_title('Approval by Property Area')\n",
    "#sns.histplot(x='ApplicantIncome', data=df, bins=10, ax=ax[0, 0])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data Cleaning\n",
    "\n",
    "This step typically involves imputing missing values and treating outliers. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputing Missing Values\n",
    "\n",
    "Missing values may not always be NaNs. For instance, the `Loan_Amount_Term` might be 0, which does not make sense.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Impute missing values for all columns. Use the values which you find most meaningful (mean, mode, median, zero.... maybe different mean values for different groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               13\n",
       "Married               3\n",
       "Dependents           15\n",
       "Education             0\n",
       "Self_Employed        32\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           22\n",
       "Loan_Amount_Term     14\n",
       "Credit_History       50\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     500\n",
       "Yes     82\n",
       "Name: Self_Employed, dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Self_Employed'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "#Fill Nan values for categorical variables\n",
    "df['Gender'] = df['Gender'].fillna(df['Gender'].mode().values[0])\n",
    "df['Married'] = df['Married'].fillna('No') #If the status for married is empty, it's possible that the applicant is single or another not married status.\n",
    "df['Dependents'] = df['Dependents'].fillna(0) #If the applicant didn't fill dependants the most logic option is that they don't have\n",
    "df['Self_Employed'] = df['Self_Employed'].fillna(df['Self_Employed'].mode().values[0])\n",
    "df['Credit_History'] = df['Credit_History'].fillna(0)#I considered that the best option is not credit history for nan values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender                0\n",
       "Married               0\n",
       "Dependents            0\n",
       "Education             0\n",
       "Self_Employed         0\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           22\n",
       "Loan_Amount_Term     14\n",
       "Credit_History        0\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360.0    512\n",
       "180.0     44\n",
       "480.0     15\n",
       "300.0     13\n",
       "240.0      4\n",
       "84.0       4\n",
       "120.0      3\n",
       "60.0       2\n",
       "36.0       2\n",
       "12.0       1\n",
       "Name: Loan_Amount_Term, dtype: int64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Loan_Amount_Term'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "342.0"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Loan_Amount_Term'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    360.0\n",
       "Name: Loan_Amount_Term, dtype: float64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Loan_Amount_Term'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Loan_Amount_Term'] = df['Loan_Amount_Term'].fillna(df['Loan_Amount_Term'].mean())#Decided to use the mean based on the data distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['LoanAmount'].median()\n",
    "df['LoanAmount'] = df['LoanAmount'].fillna(df['LoanAmount'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID              0\n",
       "Gender               0\n",
       "Married              0\n",
       "Dependents           0\n",
       "Education            0\n",
       "Self_Employed        0\n",
       "ApplicantIncome      0\n",
       "CoapplicantIncome    0\n",
       "LoanAmount           0\n",
       "Loan_Amount_Term     0\n",
       "Credit_History       0\n",
       "Property_Area        0\n",
       "Loan_Status          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extreme values\n",
    "Try a log transformation to get rid of the extreme values in `LoanAmount`. Plot the histogram before and after the transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>LoanAmountLog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.107210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "      <td>2.107210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.819544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.079181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.149219</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0       128.0             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  LoanAmountLog  \n",
       "0             1.0         Urban           Y       2.107210  \n",
       "1             1.0         Rural           N       2.107210  \n",
       "2             1.0         Urban           Y       1.819544  \n",
       "3             1.0         Urban           Y       2.079181  \n",
       "4             1.0         Urban           Y       2.149219  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['LoanAmountLog'] = np.log10(df['LoanAmount'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Graphs ApplicantIncome\n",
    "fig, ax = plt.subplots(1, 2, figsize=(12, 5))#, sharey=True)\n",
    "fig.suptitle('Comparison Before and After Log Transformation')\n",
    "\n",
    "sns.histplot(x='LoanAmount', data=df, bins=30, ax=ax[0])\n",
    "ax[0].set(xlabel=\"Loan Amount\", ylabel=\"Count\")\n",
    "ax[0].set_title('Before Log Tranformation')\n",
    "\n",
    "sns.histplot(x='LoanAmountLog', data=df, bins=30, ax=ax[1])\n",
    "ax[1].set(xlabel=\"Loan Amount Log\", ylabel=\"Count\")\n",
    "ax[1].set_title('After Log Transformation')\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine both incomes as total income and take a log transformation of the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>LoanAmountLog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.107210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "      <td>2.107210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.819544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.079181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.149219</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>LP002978</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.851258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>LP002979</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.602060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>LP002983</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.403121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>LP002984</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.271842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>LP002990</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "      <td>2.123852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Loan_ID  Gender Married Dependents     Education Self_Employed  \\\n",
       "0    LP001002    Male      No          0      Graduate            No   \n",
       "1    LP001003    Male     Yes          1      Graduate            No   \n",
       "2    LP001005    Male     Yes          0      Graduate           Yes   \n",
       "3    LP001006    Male     Yes          0  Not Graduate            No   \n",
       "4    LP001008    Male      No          0      Graduate            No   \n",
       "..        ...     ...     ...        ...           ...           ...   \n",
       "609  LP002978  Female      No          0      Graduate            No   \n",
       "610  LP002979    Male     Yes         3+      Graduate            No   \n",
       "611  LP002983    Male     Yes          1      Graduate            No   \n",
       "612  LP002984    Male     Yes          2      Graduate            No   \n",
       "613  LP002990  Female      No          0      Graduate           Yes   \n",
       "\n",
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0               5849                0.0       128.0             360.0   \n",
       "1               4583             1508.0       128.0             360.0   \n",
       "2               3000                0.0        66.0             360.0   \n",
       "3               2583             2358.0       120.0             360.0   \n",
       "4               6000                0.0       141.0             360.0   \n",
       "..               ...                ...         ...               ...   \n",
       "609             2900                0.0        71.0             360.0   \n",
       "610             4106                0.0        40.0             180.0   \n",
       "611             8072              240.0       253.0             360.0   \n",
       "612             7583                0.0       187.0             360.0   \n",
       "613             4583                0.0       133.0             360.0   \n",
       "\n",
       "     Credit_History Property_Area Loan_Status  LoanAmountLog  \n",
       "0               1.0         Urban           Y       2.107210  \n",
       "1               1.0         Rural           N       2.107210  \n",
       "2               1.0         Urban           Y       1.819544  \n",
       "3               1.0         Urban           Y       2.079181  \n",
       "4               1.0         Urban           Y       2.149219  \n",
       "..              ...           ...         ...            ...  \n",
       "609             1.0         Rural           Y       1.851258  \n",
       "610             1.0         Rural           Y       1.602060  \n",
       "611             1.0         Urban           Y       2.403121  \n",
       "612             1.0         Urban           Y       2.271842  \n",
       "613             0.0     Semiurban           N       2.123852  \n",
       "\n",
       "[614 rows x 14 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>LoanAmountLog</th>\n",
       "      <th>Income_CombinedLog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.107210</td>\n",
       "      <td>3.767082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "      <td>2.107210</td>\n",
       "      <td>3.784689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.819544</td>\n",
       "      <td>3.477121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.079181</td>\n",
       "      <td>3.693815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.149219</td>\n",
       "      <td>3.778151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0       128.0             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  LoanAmountLog  Income_CombinedLog  \n",
       "0             1.0         Urban           Y       2.107210            3.767082  \n",
       "1             1.0         Rural           N       2.107210            3.784689  \n",
       "2             1.0         Urban           Y       1.819544            3.477121  \n",
       "3             1.0         Urban           Y       2.079181            3.693815  \n",
       "4             1.0         Urban           Y       2.149219            3.778151  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Income_CombinedLog'] = np.log10(df['ApplicantIncome'] + df['CoapplicantIncome'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UAqCapaWluTQuKChIoaGhbq4GANzD6TCTmJio4ODgcl8pLV26VD/++KOmTZvmtuIAuEdh7mlJNg0fPtyl8b6+fjp0KI1AA8ArOR1m3n33XSUlJZVr79y5sx599FHCDOCFSgryJRl1HzZNzdtFODU2L+uEdiydpZycHMIMAK/kdJjJzs5WSEhIufbmzZvb/8oJgHfybxGqpqEdPF0GALiV03/NdHHB7+W2bNmiVq1auaUoAACAynL6ysyYMWMUFxenkpIS3XPPPZKkDRs2aOrUqXr22WfdXiAAAMDVOB1mpk6dqjNnzmj8+PH2m0w2bNhQ06ZN04wZM9xeIAAAwNU4HWZsNpvmzp2rF198UWlpafL19VV4eDi//gsAADzC5d+Z8ff312233ebOWgAAAJxWqTAzePBgLV++XIGBgRo8ePBV+3766aduKQwAAKAyKhVmGjVqJJvNZv9vAAAAb1GpMLNs2bIK/xsAAMDTnP6dGQAAAG9SqSszkZGR9q+ZriU1NbVKBQEAADijUmFm0KBB1VwGAACAayoVZmbOnFnddQAAALjE5d+Z2bVrl9LS0mSz2dSxY0fdeuut7qwLAACgUpwOMydPntTQoUO1ZcsWNW7cWJL0008/qVevXlq5cqXatGnj7hoBAACuyOm/Zho9erRKSkqUlpamM2fO6MyZM0pLS5MxRk8++WR11AgAAHBFTl+Z+frrr7V161Z16NDB3tahQwctXLhQ0dHRbi0OAADgWpy+MhMaGqqSkpJy7aWlpfrFL37hlqIAAAAqy+kwM2/ePE2cOFG7du2SMUbSz4uBJ02apNdff93tBQIAAFyN018zjRo1SgUFBYqKilK9ej8PLy0tVb169TR69GiNHj3a3vfMmTPuqxQAAKACToeZBQsWVEMZAAAArnE6zIwcObI66gAAAHCJSzeavHDhgj755BO98sormj17tlavXq3S0tIqFZKYmCibzaa4uDh7mzFG8fHxatWqlXx9fdW7d28dOHCgSq8DAABqFqevzHz//fd66KGHlJ2dbf/z7MOHD6t58+Zau3atunbt6nQRO3fu1JIlS9StWzeH9nnz5mn+/Plavny5brrpJs2ePVt9+/ZVenq6AgICnH4dAABQ8zh9ZWbMmDHq3LmzTp48qdTUVKWmpiozM1PdunXT2LFjnS7g3Llzeuyxx/Tee++pSZMm9nZjjBYsWKDnn39egwcPVpcuXbRixQoVFBQoKSnJ6dcBAAA1k9NhZt++fUpMTHQIHk2aNNGrr76qvXv3Ol3AhAkT9MADD+jee+91aD9+/Liys7PVr18/e5uPj49iYmK0devWK+6vqKhIeXl5DhsAAKi5nA4zHTp00L/+9a9y7adOnVL79u2d2teqVauUmpqqxMTEcs9lZ2dLkoKDgx3ag4OD7c9VJDExUY0aNbJv3CsKAICazekwk5CQoNjYWH3yySc6efKkTp48qU8++URxcXGaO3dupa+IZGZmatKkSfrwww/VsGHDK/az2WwOj40x5douNWPGDOXm5tq3zMxM5yYIAAAsxekFwAMGDJAkDRkyxB4qLv4S8MCBA+2PbTabLly4cMX97N69W6dOndKtt95qb7tw4YI2b96sRYsWKT09XdLPV2hCQkLsfU6dOlXuas2lfHx85OPj4+y0AACARTkdZjZu3HjF51JTU3XLLbdUaj99+vTR/v37HdqeeOIJRUREaNq0abrxxhvVsmVLpaSkKDIyUpJUXFysTZs2ae7cuc6WDQAAaiinw0xMTIzD49zcXH300Ud6//33tW/fvqtejblUQECAunTp4tB2ww03qFmzZvb2uLg4JSQkKDw8XOHh4UpISJCfn5+GDRvmbNkAAKCGcjrMXPT3v/9dS5cu1aeffqq2bdvqN7/5jT744AN31qapU6eqsLBQ48eP19mzZxUVFaXk5GR+YwYAANg5FWZOnjyp5cuXa+nSpTp//ryGDBmikpISrV69Wp06dapyMV999ZXDY5vNpvj4eMXHx1d53wAAoGaq9F8z3X///erUqZMOHjyohQsX6ocfftDChQurszYAAIBrqvSVmeTkZMXGxuqpp55SeHh4ddYEAABQaZW+MvP1118rPz9fPXr0UFRUlBYtWqQff/yxOmsDAAC4pkqHmZ49e+q9995TVlaWxo0bp1WrVukXv/iFysrKlJKSovz8/OqsEwAAoEJO/wKwn5+fRo8erW+++Ub79+/Xs88+qzlz5qhFixZ68MEHq6NGAACAK3I6zFyqQ4cOmjdvnk6ePKmVK1e6qyYAAIBKq1KYuahu3boaNGiQ1q5d647dAQAAVJpbwgwAAICnuPwLwABql7S0NJfHBgUFKTQ01I3VAMD/R5gBcFWFuacl2TR8+HCX9+Hr66dDh9IINACqBWEGwFWVFORLMuo+bJqat4twenxe1gntWDpLOTk5hBkA1YIwA6BS/FuEqmloB0+XAQDlEGZw3WVkZCgnJ8elsVVZtwEAqJkIM7iuMjIyFBHRUYWFBVXaT0lRsZsqAgBYHWEG11VOTo4KCwsUNXqmAkPCnB6ftX+bvl+7RKWlpe4vDgBgSYQZeERgSJhL6y/ysk64vxgAgKXxo3kAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDS6nm6AAC1Q1pamkvjgoKCFBoa6uZqANQkhBkA1aow97Qkm4YPH+7SeF9fPx06lEagAXBFhBkA1aqkIF+SUfdh09S8XYRTY/OyTmjH0lnKyckhzAC4IsIMgOvCv0WomoZ28HQZAGogwgwAr+fqehuJNTdAbUCYAeC1qrreRmLNDVAbEGYAeK2qrLeRWHMD1BaEGQBej/U2AK6GH80DAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACW5tEwk5iYqNtuu00BAQFq0aKFBg0apPT0dIc+xhjFx8erVatW8vX1Ve/evXXgwAEPVQwAALyNR8PMpk2bNGHCBG3fvl0pKSkqLS1Vv379dP78eXufefPmaf78+Vq0aJF27typli1bqm/fvsrPz/dg5QAAwFvU8+SLf/HFFw6Ply1bphYtWmj37t26++67ZYzRggUL9Pzzz2vw4MGSpBUrVig4OFhJSUkaN26cJ8oGAABexKvWzOTm5kqSmjZtKkk6fvy4srOz1a9fP3sfHx8fxcTEaOvWrRXuo6ioSHl5eQ4bAACoubwmzBhjNHnyZN15553q0qWLJCk7O1uSFBwc7NA3ODjY/tzlEhMT1ahRI/vWpk2b6i0cAAB4lNeEmaefflrfffedVq5cWe45m83m8NgYU67tohkzZig3N9e+ZWZmVku9AADAO3h0zcxFEydO1Nq1a7V582a1bt3a3t6yZUtJP1+hCQkJsbefOnWq3NWai3x8fOTj41O9BQMAAK/h0Sszxhg9/fTT+vTTT/X3v/9d7dq1c3i+Xbt2atmypVJSUuxtxcXF2rRpk3r16nW9ywUAAF7Io1dmJkyYoKSkJP31r39VQECAfR1Mo0aN5OvrK5vNpri4OCUkJCg8PFzh4eFKSEiQn5+fhg0b5snSAQCAl/BomFm8eLEkqXfv3g7ty5Yt06hRoyRJU6dOVWFhocaPH6+zZ88qKipKycnJCggIuM7VAgAAb+TRMGOMuWYfm82m+Ph4xcfHV39BAADAcrzmr5kAAABcQZgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACW5hU3msT1l5GRoZycHJfHBwUFKTQ01I0VAQDgGsJMLZSRkaGIiI4qLCxweR++vn46dCiNQAMA8DjCTC2Uk5OjwsICRY2eqcCQMKfH52Wd0I6ls5STk0OYAQB4HGGmFgsMCVPT0A6eLgMAgCphATAAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0fjQPAKpJVe6Bxv3PgMojzABANajqPdC4/xlQeYQZAKgGVbkHGvc/A5xDmAGAasQ90IDqR5gBUOOlpaW5NK6oqEg+Pj7X9TUBOI8wA6DGKsw9Lcmm4cOHu7YDm00ypko1lBQVV2k8gGsjzACosUoK8iUZdR82Tc3bRTg1Nmv/Nn2/dolLYy8dX1pa6vRYAM4hzACo8fxbhDq9biUv64TLYy8dD6D68aN5AADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0rjRJFyWlpZ2XcYAAHA1hBk4rTD3tCSbhg8f7vI+SoqK3VcQAKBWI8zAaSUF+ZKMug+bpubtIpwam7V/m75fu0SlpaXVUxwAoNYhzMBl/i1C1TS0g1Nj8rJOVE8xAIBaiwXAAADA0ggzAADA0ggzAADA0ggzAADA0lgAbGEZGRnKyclxehy/9QIAqEkIMxaVkZGhiIiOKiwscHkf/NYLAKAmIMxYVE5OjgoLCxQ1eqYCQ8KcGstvvQAAahLCjMUFhoTxWy8AgFqNMAMAXqoq69uCgoIUGhrqxmoA70WYAQAv4477n/n6+unQoTQCDWoFwgwAeJmq3P9M+vmr5B1LZyknJ4cwg1qBMAMAXsqV+58BtRFhBgBqKNbcoLYgzABADcOaG9Q2hBkAqGFYc4PahjADADUUa25QWxBmAAAVcnXNjSfX27h6z7qLWCtkTYQZAICDqq658dR6G3fcs461QtZEmAEAOKjKmhtPrrepyj3rJNYKWRlhBgBQIauuuXHlnnWwNsKMB1Xlu92q/H4EAODKrLhWqLYjzHiIO77blaSSomI3VQQAtZtV1wqBMOMxVf1uN2v/Nn2/dolKS0vdXxwA1EJWXSsEwozHufrdbl7WCfcXAwCw7Fqh2owwU0WurnthzQuAmqwq57iioiL5+Phc19d0F0/dD6uqv6/j6jGXvGOtkCXCzB//+Ee99tprysrKUufOnbVgwQLdddddni7LLeteWPMCoCZxx32hZLNJxrg83BPnVU/eD8stazCrcMy9Ya2Q14eZP//5z4qLi9Mf//hHRUdH691339V//Md/6ODBgx5PglVZ98KaFwA1UVXvC3Xx3OjKeE+eVz15Pyx3rcG08lohrw8z8+fP15NPPqkxY8ZIkhYsWKD169dr8eLFSkxM9HB1P3Nl3QtrXgDUZK6uO7l4bnRlvDecVz253qaqazCtvFbIq8NMcXGxdu/erenTpzu09+vXT1u3bq1wTFFRkYqKiuyPc3NzJUl5eXlur+/cuXOSpDP/SFdpUaFTY/Oy/iFJyv3nEdWvZ3P6tasynte+/q9d1fG8Nq9tlfG8touvnZ0hSdq9e7f935bKSk9Pl+Tav0VSFef9f3WfO3fO7f/OXtyfqczXX8aL/fOf/zSSzJYtWxzaX331VXPTTTdVOGbmzJlGEhsbGxsbG1sN2DIzM6+ZF7z6ysxFNptjUjTGlGu7aMaMGZo8ebL9cVlZmc6cOaNmzZpdcYw3ysvLU5s2bZSZmanAwEBPl3Pd1fb5SxyD2j5/iWNQ2+cv1e5jYIxRfn6+WrVqdc2+Xh1mgoKCVLduXWVnZzu0nzp1SsHBwRWO8fHxKffnZY0bN66uEqtdYGBgrfsAX6q2z1/iGNT2+Uscg9o+f6n2HoNGjRpVql+daq6jSho0aKBbb71VKSkpDu0pKSnq1auXh6oCAADexKuvzEjS5MmTNWLECPXo0UM9e/bUkiVLlJGRod///veeLg0AAHgBrw8zjzzyiE6fPq2XX35ZWVlZ6tKliz7//HO1bdvW06VVKx8fH82cOdPlX2S0uto+f4ljUNvnL3EMavv8JY5BZdmMqcLPLAIAAHiYV6+ZAQAAuBbCDAAAsDTCDAAAsDTCDAAAsDTCzHWwePFidevWzf6jRz179tS6deuu2P/TTz9V37591bx5c3v/9evXO/RZvny5bDZbue3f//53dU/Hac7O/6uvvqpwbocOHXLot3r1anXq1Ek+Pj7q1KmT1qxZU91TcYmz8x81alSF8+/cubO9j5Xe/4okJibKZrMpLi7uqv02bdqkW2+9VQ0bNtSNN96od955p1wfq3wOLlWZ+de088ClKjP/mnYeuFxljkFtOBe4C2HmOmjdurXmzJmjXbt2adeuXbrnnnv00EMP6cCBAxX237x5s/r27avPP/9cu3fv1q9+9SsNHDhQe/bscegXGBiorKwsh61hw4bXY0pOcXb+F6WnpzvMLTw83P7ctm3b9Mgjj2jEiBHat2+fRowYoSFDhmjHjh3VPR2nOTv/t956y2HemZmZatq0qX7729869LPK+3+5nTt3asmSJerWrdtV+x0/flz333+/7rrrLu3Zs0d/+MMfFBsbq9WrV9v7WOlzcFFl51/TzgMXVXb+F9WU88ClKnsMavq5wK2qfDdIuKRJkybm/fffr3T/Tp06mVmzZtkfL1u2zDRq1KgaKrs+rjb/jRs3Gknm7NmzVxw/ZMgQc9999zm09e/f3zz66KPuLLPaOPP+r1mzxthsNnPixAl7m1Xf//z8fBMeHm5SUlJMTEyMmTRp0hX7Tp061URERDi0jRs3ztxxxx32x1b7HDgz/4pY/TzgzPxr6nmgKp+BmnQucDeuzFxnFy5c0KpVq3T+/Hn17NmzUmPKysqUn5+vpk2bOrSfO3dObdu2VevWrTVgwIBy/4/NGzkz/8jISIWEhKhPnz7auHGjw3Pbtm1Tv379HNr69++vrVu3ur1md3Ll/f/ggw907733lvuhSCu+/xMmTNADDzyge++995p9r/Qe79q1SyUlJVft462fA2fmf7macB5wZf417TxQlc9ATToXuJvX/wJwTbF//3717NlT//73v+Xv7681a9aoU6dOlRr7xhtv6Pz58xoyZIi9LSIiQsuXL1fXrl2Vl5ent956S9HR0dq3b5/DZVhv4cz8Q0JCtGTJEt16660qKirSn/70J/Xp00dfffWV7r77bklSdnZ2uZuNBgcHl7spqbdw9f3PysrSunXrlJSU5NButfdfklatWqXU1FTt3LmzUv2v9B6XlpYqJydHISEhlvocODv/y1n9PODs/GvieaAqn4GadC6oFp6+NFRbFBUVmSNHjpidO3ea6dOnm6CgIHPgwIFrjktKSjJ+fn4mJSXlqv0uXLhgbr75ZjNx4kR3lexWrs7/ogEDBpiBAwfaH9evX98kJSU59Pnwww+Nj4+P22p2J1fnn5CQYJo1a2aKioqu2s/b3/+MjAzTokULs3fvXnvbtS6xh4eHm4SEBIe2b775xkgyWVlZxhjrfA5cmf+lrH4eqOr8L7LyeaCqx6CmnAuqC2HGQ/r06WPGjh171T6rVq0yvr6+5m9/+1ul9jlmzJhy3x97q8rM/1KzZ892WD/Rpk0bM3/+fIc+8+fPN6GhoW6rsTpVZv5lZWWmffv2Ji4urlL79Ob3f82aNUaSqVu3rn2TZGw2m6lbt64pLS0tN+auu+4ysbGxDm2ffvqpqVevnikuLjbGWOdz4Mr8L6oJ54GqzP9SVj4PVOUY1KRzQXVhzYyHGGNUVFR0xedXrlypUaNGKSkpSQ888ECl9rd3716FhIS4s8xqc635X27Pnj0Oc+vZs6dSUlIc+iQnJ6tXr15uq7E6VWb+mzZt0v/+7//qySefrNT+vPn979Onj/bv36+9e/fatx49euixxx7T3r17Vbdu3XJjrvQe9+jRQ/Xr179qH2/7HLgyf6nmnAdcnf/lrHweqMoxqEnngmrjwSBVa8yYMcNs3rzZHD9+3Hz33XfmD3/4g6lTp45JTk42xhgzffp0M2LECHv/pKQkU69ePfP222+brKws+/bTTz/Z+8THx5svvvjCHD161OzZs8c88cQTpl69embHjh3XfX7X4uz833zzTbNmzRpz+PBh8/3335vp06cbSWb16tX2Plu2bDF169Y1c+bMMWlpaWbOnDmmXr16Zvv27dd9ftfi7PwvGj58uImKiqpwn1Z6/6/k8kvslx+HY8eOGT8/P/PMM8+YgwcPmg8++MDUr1/ffPLJJ/Y+VvocXO5a869p54HLXWv+Ne08UJFrHYOLavq5wB0IM9fB6NGjTdu2bU2DBg1M8+bNTZ8+fez/kBljzMiRI01MTIz9cUxMjJFUbhs5cqS9T1xcnAkNDbXvs1+/fmbr1q3XcVaV5+z8586da375y1+ahg0bmiZNmpg777zTfPbZZ+X2+9///d+mQ4cOpn79+iYiIsLhJOdNnJ2/Mcb89NNPxtfX1yxZsqTCfVrp/b+Sy0/kFR2Hr776ykRGRpoGDRqYsLAws3jx4nL7scrn4HLXmn9NOw9c7lrzr2nngYpU5n8DteFc4A42Y4zxzDUhAACAqmPNDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDIBaoXfv3oqLi7tqn7CwMC1YsMAragFQeYQZwIuMGjVKgwYN8nQZ1aa4uFjz5s3TzTffLD8/PwUFBSk6OlrLli1TSUmJp8vTzp07NXbsWE+XoeXLl6tx48aeLgOwjHqeLgBA7VBcXKz+/ftr3759euWVVxQdHa3AwEBt375dr7/+uiIjI9W9e3eP1ti8eXOPvj4A13BlBvBivXv3VmxsrKZOnaqmTZuqZcuWio+Pd+jz008/aezYsQoODlbDhg3VpUsX/e1vf7M/v3r1anXu3Fk+Pj4KCwvTG2+84TA+LCxMs2fP1uOPPy5/f3+1bdtWf/3rX/Xjjz/qoYcekr+/v7p27apdu3Y5jNu6davuvvtu+fr6qk2bNoqNjdX58+evOJcFCxZo8+bN2rBhgyZMmKDu3bvrxhtv1LBhw7Rjxw6Fh4dLkoqKihQbG6sWLVqoYcOGuvPOO7Vz5077fr766ivZbDatX79ekZGR8vX11T333KNTp05p3bp16tixowIDAzV06FAVFBQ41FBaWqqnn35ajRs3VrNmzfTCCy/o0tvTXf41k81m0/vvv69f//rX8vPzU3h4uNauXeuwz4MHD+r++++Xv7+/goODNWLECOXk5NifP3/+vP3YhoSElDv+rsjIyLC/N4GBgRoyZIj+9a9/OfSZPXu2WrRooYCAAI0ZM0bTp0/3eFgEqo2Hb3QJ4BIjR440Dz30kP1xTEyMCQwMNPHx8ebw4cNmxYoVxmaz2e+6feHCBXPHHXeYzp07m+TkZHP06FHzP//zP+bzzz83xhiza9cuU6dOHfPyyy+b9PR0s2zZMuPr62uWLVtmf422bduapk2bmnfeecccPnzYPPXUUyYgIMDcd9995uOPPzbp6elm0KBBpmPHjqasrMwYY8x3331n/P39zZtvvmkOHz5stmzZYiIjI82oUaOuOLdu3bqZfv36XfMYxMbGmlatWpnPP//cHDhwwIwcOdI0adLEnD592hhjzMaNG40kc8cdd5hvvvnGpKammvbt25uYmBjTr18/k5qaajZv3myaNWtm5syZ43As/f39zaRJk8yhQ4fMhx9+aPz8/BzuRty2bVvz5ptv2h9LMq1btzZJSUnmyJEjJjY21vj7+9tr+eGHH0xQUJCZMWOGSUtLM6mpqaZv377mV7/6lX0fTz31lGndurVJTk423333nRkwYIC9jitZtmyZadSoUYXPlZWVmcjISHPnnXeaXbt2me3bt5tbbrnF4W7LH374oWnYsKFZunSpSU9PN7NmzTKBgYHm5ptvvubxB6yIMAN4kYrCzJ133unQ57bbbjPTpk0zxhizfv16U6dOHZOenl7h/oYNG2b69u3r0Pbcc8+ZTp062R+3bdvWDB8+3P44KyvLSDIvvviivW3btm1GksnKyjLGGDNixAgzduxYh/1+/fXXpk6dOqawsLDCWnx9fU1sbOyVpm6MMebcuXOmfv365qOPPrK3FRcXm1atWpl58+YZY/5/mPnyyy/tfRITE40kc/ToUXvbuHHjTP/+/e2PY2JiHAKZMcZMmzbNdOzY0eFYXB5mXnjhBYf6bDabWbdunTHGmBdffLFcQMvMzDSSTHp6usnPzzcNGjQwq1atsj9/+vRp4+vr63KYSU5ONnXr1jUZGRn2tgMHDhhJ5ttvvzXGGBMVFWUmTJjgMC46OpowgxqLr5kAL9etWzeHxyEhITp16pQkae/evWrdurVuuummCsempaUpOjraoS06OlpHjhzRhQsXKnyN4OBgSVLXrl3LtV183d27d2v58uXy9/e3b/3791dZWZmOHz9eYS3GGNlstqvO9ejRoyopKXGouX79+rr99tuVlpbm0Pfymv38/HTjjTc6tF2s96I77rjDoYaePXuWOxaXu/R1brjhBgUEBDgch40bNzoch4iICPtcjh49quLiYvXs2dO+j6ZNm6pDhw5XPQ5Xk5aWpjZt2qhNmzb2tk6dOqlx48b2Y5Senq7bb7/dYdzlj4GahAXAgJerX7++w2ObzaaysjJJkq+v71XHVhQgzCVrRCp6jYv9K2q7+LplZWUaN26cYmNjy+0rNDS0wlpuuummcoGkonovfb2rzePy+q52nKriavstKyvTwIEDNXfu3HLjQkJCdOTIkSq//uWuFAovb6/M+w7UFFyZASysW7duOnnypA4fPlzh8506ddI333zj0LZ161bddNNNqlu3rsuve8stt+jAgQNq3759ua1BgwYVjhk2bJi+/PJL7dmzp9xzpaWlOn/+vH38pTWXlJRo165d6tixo8v1XrR9+/Zyj8PDw10+FhePQ1hYWLnjcMMNN6h9+/aqX7++w+uePXv2iu9XZXTq1EkZGRnKzMy0tx08eFC5ubn2Y9ShQwd9++23DuMuX8AN1CSEGcDCYmJidPfdd+s3v/mNUlJSdPz4ca1bt05ffPGFJOnZZ5/Vhg0b9Morr+jw4cNasWKFFi1apClTplTpdadNm6Zt27ZpwoQJ2rt3r44cOaK1a9dq4sSJVxwTFxen6Oho9enTR2+//bb27dunY8eO6eOPP1ZUVJSOHDmiG264QU899ZSee+45ffHFFzp48KB+97vfqaCgQE8++WSVapakzMxMTZ48Wenp6Vq5cqUWLlyoSZMmuby/CRMm6MyZMxo6dKi+/fZbHTt2TMnJyRo9erQuXLggf39/Pfnkk3ruuee0YcMGff/99xo1apTq1Ln2qffChQvau3evw3bw4EHde++96tatmx577DGlpqbq22+/1eOPP66YmBj16NFDkjRx4kR98MEHWrFihY4cOaLZs2fru+++u+bXfIBV8TUTYHGrV6/WlClTNHToUPvVjTlz5kj6+crBxx9/rJdeekmvvPKKQkJC9PLLL2vUqFFVes1u3bpp06ZNev7553XXXXfJGKNf/vKXeuSRR644xsfHRykpKXrzzTf17rvvasqUKfLz81PHjh0VGxurLl26SJLmzJmjsrIyjRgxQvn5+erRo4fWr1+vJk2aVKlmSXr88cdVWFio22+/XXXr1tXEiROr9CN5rVq10pYtWzRt2jT1799fRUVFatu2re677z57YHnttdd07tw5PfjggwoICNCzzz6r3Nzca+773LlzioyMdGhr27atTpw4ob/85S+aOHGi7r77btWpU0f33XefFi5caO/32GOP6dixY5oyZYr+/e9/a8iQIRo1alS5qzVATWEzfJEKADVe37591bJlS/3pT3/ydCmA23FlBgBqmIKCAr3zzjvq37+/6tatq5UrV+rLL79USkqKp0sDqgVXZgCghiksLNTAgQOVmpqqoqIidejQQS+88IIGDx7s6dKAakGYAQAAlsZfMwEAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEv7f+Fm0wCAeD52AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.histplot(data = df, x='Income_CombinedLog')\n",
    "ax.set(xlabel=\"Income Combined Log\", ylabel=\"Applicants\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>LoanAmountLog</th>\n",
       "      <th>Income_CombinedLog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.107210</td>\n",
       "      <td>3.767082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "      <td>2.107210</td>\n",
       "      <td>3.784689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.819544</td>\n",
       "      <td>3.477121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.079181</td>\n",
       "      <td>3.693815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.149219</td>\n",
       "      <td>3.778151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0       128.0             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  LoanAmountLog  Income_CombinedLog  \n",
       "0             1.0         Urban           Y       2.107210            3.767082  \n",
       "1             1.0         Rural           N       2.107210            3.784689  \n",
       "2             1.0         Urban           Y       1.819544            3.477121  \n",
       "3             1.0         Urban           Y       2.079181            3.693815  \n",
       "4             1.0         Urban           Y       2.149219            3.778151  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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>Loan_ID</th>\n",
       "      <th>Married</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>LoanAmountLog</th>\n",
       "      <th>...</th>\n",
       "      <th>dependents_0</th>\n",
       "      <th>dependents_1</th>\n",
       "      <th>dependents_2</th>\n",
       "      <th>dependents_3+</th>\n",
       "      <th>is_graduated</th>\n",
       "      <th>is_selfemployed</th>\n",
       "      <th>property_Semiurban</th>\n",
       "      <th>property_Urban</th>\n",
       "      <th>is_approved</th>\n",
       "      <th>has_credit_1.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>No</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.107210</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2.107210</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.819544</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.079181</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>No</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.149219</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Married     Education Self_Employed  ApplicantIncome  \\\n",
       "0  LP001002      No      Graduate            No             5849   \n",
       "1  LP001003     Yes      Graduate            No             4583   \n",
       "2  LP001005     Yes      Graduate           Yes             3000   \n",
       "3  LP001006     Yes  Not Graduate            No             2583   \n",
       "4  LP001008      No      Graduate            No             6000   \n",
       "\n",
       "   CoapplicantIncome  LoanAmount  Loan_Amount_Term Loan_Status  LoanAmountLog  \\\n",
       "0                0.0       128.0             360.0           Y       2.107210   \n",
       "1             1508.0       128.0             360.0           N       2.107210   \n",
       "2                0.0        66.0             360.0           Y       1.819544   \n",
       "3             2358.0       120.0             360.0           Y       2.079181   \n",
       "4                0.0       141.0             360.0           Y       2.149219   \n",
       "\n",
       "   ...  dependents_0  dependents_1  dependents_2  dependents_3+  is_graduated  \\\n",
       "0  ...             1             0             0              0             1   \n",
       "1  ...             0             1             0              0             1   \n",
       "2  ...             1             0             0              0             1   \n",
       "3  ...             1             0             0              0             0   \n",
       "4  ...             1             0             0              0             1   \n",
       "\n",
       "   is_selfemployed  property_Semiurban  property_Urban  is_approved  \\\n",
       "0                0                   0               1            1   \n",
       "1                0                   0               0            0   \n",
       "2                1                   0               1            1   \n",
       "3                0                   0               1            1   \n",
       "4                0                   0               1            1   \n",
       "\n",
       "   has_credit_1.0  \n",
       "0               1  \n",
       "1               1  \n",
       "2               1  \n",
       "3               1  \n",
       "4               1  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Transform variables for model\n",
    "df = pd.get_dummies(df, columns=['Gender'], prefix='is', prefix_sep='_', drop_first=True)\n",
    "#df = pd.get_dummies(df, columns=['Married'], prefix='is_married',  drop_first=True)\n",
    "df['is_married'] = np.where(df['Married'] == 'Yes', 1, 0)\n",
    "df = pd.get_dummies(df, columns=['Dependents'], prefix='dependents',  drop_first=True)\n",
    "#df = pd.get_dummies(df, columns=['Education'], prefix='is',  drop_first=True)\n",
    "df['is_graduated'] = np.where(df['Education'] == 'Graduate', 1, 0)\n",
    "#df = pd.get_dummies(df, columns=['Self_Employed'], prefix='is_self_employed',  drop_first=True)\n",
    "df['is_selfemployed'] = np.where(df['Self_Employed'] == 'Yes', 1, 0)\n",
    "df = pd.get_dummies(df, columns = ['Property_Area'], prefix='property', drop_first=True)\n",
    "df['is_approved'] = np.where(df['Loan_Status'] == 'Y', 1, 0)\n",
    "df = pd.get_dummies(df, columns=['Credit_History'], prefix='has_credit',  drop_first=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>LoanAmountLog</th>\n",
       "      <th>Income_CombinedLog</th>\n",
       "      <th>is_Male</th>\n",
       "      <th>is_married</th>\n",
       "      <th>dependents_0</th>\n",
       "      <th>dependents_1</th>\n",
       "      <th>dependents_2</th>\n",
       "      <th>dependents_3+</th>\n",
       "      <th>is_graduated</th>\n",
       "      <th>is_selfemployed</th>\n",
       "      <th>property_Semiurban</th>\n",
       "      <th>property_Urban</th>\n",
       "      <th>is_approved</th>\n",
       "      <th>has_credit_1.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>2.107210</td>\n",
       "      <td>3.767082</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>2.107210</td>\n",
       "      <td>3.784689</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.819544</td>\n",
       "      <td>3.477121</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>2.079181</td>\n",
       "      <td>3.693815</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>2.149219</td>\n",
       "      <td>3.778151</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LoanAmount  Loan_Amount_Term  LoanAmountLog  Income_CombinedLog  is_Male  \\\n",
       "0       128.0             360.0       2.107210            3.767082        1   \n",
       "1       128.0             360.0       2.107210            3.784689        1   \n",
       "2        66.0             360.0       1.819544            3.477121        1   \n",
       "3       120.0             360.0       2.079181            3.693815        1   \n",
       "4       141.0             360.0       2.149219            3.778151        1   \n",
       "\n",
       "   is_married  dependents_0  dependents_1  dependents_2  dependents_3+  \\\n",
       "0           0             1             0             0              0   \n",
       "1           1             0             1             0              0   \n",
       "2           1             1             0             0              0   \n",
       "3           1             1             0             0              0   \n",
       "4           0             1             0             0              0   \n",
       "\n",
       "   is_graduated  is_selfemployed  property_Semiurban  property_Urban  \\\n",
       "0             1                0                   0               1   \n",
       "1             1                0                   0               0   \n",
       "2             1                1                   0               1   \n",
       "3             0                0                   0               1   \n",
       "4             1                0                   0               1   \n",
       "\n",
       "   is_approved  has_credit_1.0  \n",
       "0            1               1  \n",
       "1            0               1  \n",
       "2            1               1  \n",
       "3            1               1  \n",
       "4            1               1  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_model = df.drop(columns=['Loan_ID','Married','Education','Self_Employed','ApplicantIncome','CoapplicantIncome','Loan_Status']) \n",
    "df_model.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Building a Predictive Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Prepare data for variables\n",
    "tsize = 0.2\n",
    "rstate = 25\n",
    "X = df_model.drop(['is_approved'], axis=1)\n",
    "y = df_model['is_approved']\n",
    "\n",
    "# Initialize the StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Scale the selected columns\n",
    "X_scaled = scaler.fit_transform(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=tsize, random_state=rstate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predictions\n",
    "y_pred = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 73.17%\n",
      "Precision: 70.97%\n",
      "Recall: 67.01%\n",
      "F1 score: 67.86%\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred, average='macro')\n",
    "recall = recall_score(y_test, y_pred, average='macro')\n",
    "f1 = f1_score(y_test, y_pred, average='macro')\n",
    "\n",
    "print(\"Accuracy: {:.2f}%\".format(accuracy * 100))\n",
    "print(\"Precision: {:.2f}%\".format(precision * 100))\n",
    "print(\"Recall: {:.2f}%\".format(recall * 100))\n",
    "print(\"F1 score: {:.2f}%\".format(f1 * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try paramater grid search to improve the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "#Define Hyper parameters\n",
    "param_grid = {\n",
    "    'n_estimators': [10, 50, 200],\n",
    "    'max_depth': [None, 5, 10],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best hyperparameters:  {'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "Best accuracy score: 75.76%\n"
     ]
    }
   ],
   "source": [
    "# Create a GridSearchCV object and fit it to the training data\n",
    "grid_search = GridSearchCV(rfc, param_grid=param_grid, cv=5)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best hyperparameters and the corresponding accuracy score\n",
    "print(\"Best hyperparameters: \", grid_search.best_params_)\n",
    "print(\"Best accuracy score: {:.2f}%\".format(grid_search.best_score_ * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "## 5. Using Pipeline\n",
    "If you didn't use pipelines before, transform your data prep, feat. engineering and modeling steps into Pipeline. It will be helpful for deployment.\n",
    "\n",
    "The goal here is to create the pipeline that will take one row of our dataset and predict the probability of being granted a loan.\n",
    "\n",
    "`pipeline.predict(x)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline, FeatureUnion\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.pipeline import Pipeline, FeatureUnion\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.preprocessing import PowerTransformer\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>LP002978</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>LP002979</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>LP002983</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>LP002984</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>LP002990</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Loan_ID  Gender Married Dependents     Education Self_Employed  \\\n",
       "0    LP001002    Male      No          0      Graduate            No   \n",
       "1    LP001003    Male     Yes          1      Graduate            No   \n",
       "2    LP001005    Male     Yes          0      Graduate           Yes   \n",
       "3    LP001006    Male     Yes          0  Not Graduate            No   \n",
       "4    LP001008    Male      No          0      Graduate            No   \n",
       "..        ...     ...     ...        ...           ...           ...   \n",
       "609  LP002978  Female      No          0      Graduate            No   \n",
       "610  LP002979    Male     Yes         3+      Graduate            No   \n",
       "611  LP002983    Male     Yes          1      Graduate            No   \n",
       "612  LP002984    Male     Yes          2      Graduate            No   \n",
       "613  LP002990  Female      No          0      Graduate           Yes   \n",
       "\n",
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0               5849                0.0         NaN             360.0   \n",
       "1               4583             1508.0       128.0             360.0   \n",
       "2               3000                0.0        66.0             360.0   \n",
       "3               2583             2358.0       120.0             360.0   \n",
       "4               6000                0.0       141.0             360.0   \n",
       "..               ...                ...         ...               ...   \n",
       "609             2900                0.0        71.0             360.0   \n",
       "610             4106                0.0        40.0             180.0   \n",
       "611             8072              240.0       253.0             360.0   \n",
       "612             7583                0.0       187.0             360.0   \n",
       "613             4583                0.0       133.0             360.0   \n",
       "\n",
       "     Credit_History Property_Area Loan_Status  \n",
       "0               1.0         Urban           Y  \n",
       "1               1.0         Rural           N  \n",
       "2               1.0         Urban           Y  \n",
       "3               1.0         Urban           Y  \n",
       "4               1.0         Urban           Y  \n",
       "..              ...           ...         ...  \n",
       "609             1.0         Rural           Y  \n",
       "610             1.0         Rural           Y  \n",
       "611             1.0         Urban           Y  \n",
       "612             1.0         Urban           Y  \n",
       "613             0.0     Semiurban           N  \n",
       "\n",
       "[614 rows x 13 columns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_p = pd.read_csv(\"../data/data.csv\")\n",
    "df_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_p = df_p.drop('Loan_ID',axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_transform = Pipeline([('impute_mean', SimpleImputer(strategy='mean')), \n",
    "                              ('scaling', StandardScaler())])\n",
    "categorical_transform = Pipeline([('impute_mode', SimpleImputer(strategy='most_frequent')), \n",
    "                                  ('one-hot-encode', OneHotEncoder(sparse=False))])\n",
    "amount_transform = Pipeline([(\"impute_mean\", SimpleImputer(strategy='mean')),(\"log_transform\", PowerTransformer())])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_processing = ColumnTransformer([('numeric',numeric_transform,['Loan_Amount_Term']),\n",
    "                                     ('categorical',categorical_transform,['Gender','Married','Credit_History','Dependents','Education','Self_Employed','Credit_History','Property_Area']),\n",
    "                                     #('sumcolumns',sum_columnsp,['ApplicantIncome','CoapplicantIncome']),\n",
    "                                     #('columndroper', column_dropper,['ApplicantIncome','CoapplicantIncome']),\n",
    "                                     ('amount', amount_transform,['ApplicantIncome','CoapplicantIncome','LoanAmount'] )])\n",
    "#columns_processing = ColumnTransformer([('sumcolumns',sum_columnsp,['ApplicantIncome','CoapplicantIncome']),\n",
    "#                                        ('columndroper', column_dropper,['ApplicantIncome','CoapplicantIncome'])])                                     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_union = FeatureUnion([('pca', PCA(n_components=2)), \n",
    "                              ('select_best', SelectKBest(k=3))])\n",
    "model = RandomForestClassifier(criterion='gini',max_depth=None, min_samples_leaf = 4, min_samples_split=2,n_estimators=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline(steps=[('preprocessing', data_processing),\n",
    "                            ('scaling', StandardScaler()),\n",
    "                           ('features', feature_union),\n",
    "                           ('classifier', model)])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_p.drop('Loan_Status', axis=1)\n",
    "y = df['Loan_Status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsize = 0.2\n",
    "rstate = 25\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=tsize, random_state=rstate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;numeric&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;impute_mean&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaling&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;Loan_Amount_Term&#x27;]),\n",
       "                                                 (&#x27;categorical&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;impute_mode&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;one-hot-encode&#x27;,\n",
       "                                                                   OneHotEncoder(sparse=False))]),\n",
       "                                                  [&#x27;Gender&#x27;, &#x27;Married&#x27;,\n",
       "                                                   &#x27;Credit_History&#x27;,...\n",
       "                                                  Pipeline(steps=[(&#x27;impute_mean&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;log_transform&#x27;,\n",
       "                                                                   PowerTransformer())]),\n",
       "                                                  [&#x27;ApplicantIncome&#x27;,\n",
       "                                                   &#x27;CoapplicantIncome&#x27;,\n",
       "                                                   &#x27;LoanAmount&#x27;])])),\n",
       "                (&#x27;scaling&#x27;, StandardScaler()),\n",
       "                (&#x27;features&#x27;,\n",
       "                 FeatureUnion(transformer_list=[(&#x27;pca&#x27;, PCA(n_components=2)),\n",
       "                                                (&#x27;select_best&#x27;,\n",
       "                                                 SelectKBest(k=3))])),\n",
       "                (&#x27;classifier&#x27;,\n",
       "                 RandomForestClassifier(min_samples_leaf=4, n_estimators=10))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;numeric&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;impute_mean&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaling&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;Loan_Amount_Term&#x27;]),\n",
       "                                                 (&#x27;categorical&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;impute_mode&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;one-hot-encode&#x27;,\n",
       "                                                                   OneHotEncoder(sparse=False))]),\n",
       "                                                  [&#x27;Gender&#x27;, &#x27;Married&#x27;,\n",
       "                                                   &#x27;Credit_History&#x27;,...\n",
       "                                                  Pipeline(steps=[(&#x27;impute_mean&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;log_transform&#x27;,\n",
       "                                                                   PowerTransformer())]),\n",
       "                                                  [&#x27;ApplicantIncome&#x27;,\n",
       "                                                   &#x27;CoapplicantIncome&#x27;,\n",
       "                                                   &#x27;LoanAmount&#x27;])])),\n",
       "                (&#x27;scaling&#x27;, StandardScaler()),\n",
       "                (&#x27;features&#x27;,\n",
       "                 FeatureUnion(transformer_list=[(&#x27;pca&#x27;, PCA(n_components=2)),\n",
       "                                                (&#x27;select_best&#x27;,\n",
       "                                                 SelectKBest(k=3))])),\n",
       "                (&#x27;classifier&#x27;,\n",
       "                 RandomForestClassifier(min_samples_leaf=4, n_estimators=10))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessing: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;numeric&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;impute_mean&#x27;,\n",
       "                                                  SimpleImputer()),\n",
       "                                                 (&#x27;scaling&#x27;,\n",
       "                                                  StandardScaler())]),\n",
       "                                 [&#x27;Loan_Amount_Term&#x27;]),\n",
       "                                (&#x27;categorical&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;impute_mode&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;one-hot-encode&#x27;,\n",
       "                                                  OneHotEncoder(sparse=False))]),\n",
       "                                 [&#x27;Gender&#x27;, &#x27;Married&#x27;, &#x27;Credit_History&#x27;,\n",
       "                                  &#x27;Dependents&#x27;, &#x27;Education&#x27;, &#x27;Self_Employed&#x27;,\n",
       "                                  &#x27;Credit_History&#x27;, &#x27;Property_Area&#x27;]),\n",
       "                                (&#x27;amount&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;impute_mean&#x27;,\n",
       "                                                  SimpleImputer()),\n",
       "                                                 (&#x27;log_transform&#x27;,\n",
       "                                                  PowerTransformer())]),\n",
       "                                 [&#x27;ApplicantIncome&#x27;, &#x27;CoapplicantIncome&#x27;,\n",
       "                                  &#x27;LoanAmount&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numeric</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Loan_Amount_Term&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">categorical</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Gender&#x27;, &#x27;Married&#x27;, &#x27;Credit_History&#x27;, &#x27;Dependents&#x27;, &#x27;Education&#x27;, &#x27;Self_Employed&#x27;, &#x27;Credit_History&#x27;, &#x27;Property_Area&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(sparse=False)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">amount</label><div class=\"sk-toggleable__content\"><pre>[&#x27;ApplicantIncome&#x27;, &#x27;CoapplicantIncome&#x27;, &#x27;LoanAmount&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PowerTransformer</label><div class=\"sk-toggleable__content\"><pre>PowerTransformer()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">features: FeatureUnion</label><div class=\"sk-toggleable__content\"><pre>FeatureUnion(transformer_list=[(&#x27;pca&#x27;, PCA(n_components=2)),\n",
       "                               (&#x27;select_best&#x27;, SelectKBest(k=3))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>pca</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=2)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>select_best</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=3)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(min_samples_leaf=4, n_estimators=10)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessing',\n",
       "                 ColumnTransformer(transformers=[('numeric',\n",
       "                                                  Pipeline(steps=[('impute_mean',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('scaling',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['Loan_Amount_Term']),\n",
       "                                                 ('categorical',\n",
       "                                                  Pipeline(steps=[('impute_mode',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('one-hot-encode',\n",
       "                                                                   OneHotEncoder(sparse=False))]),\n",
       "                                                  ['Gender', 'Married',\n",
       "                                                   'Credit_History',...\n",
       "                                                  Pipeline(steps=[('impute_mean',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('log_transform',\n",
       "                                                                   PowerTransformer())]),\n",
       "                                                  ['ApplicantIncome',\n",
       "                                                   'CoapplicantIncome',\n",
       "                                                   'LoanAmount'])])),\n",
       "                ('scaling', StandardScaler()),\n",
       "                ('features',\n",
       "                 FeatureUnion(transformer_list=[('pca', PCA(n_components=2)),\n",
       "                                                ('select_best',\n",
       "                                                 SelectKBest(k=3))])),\n",
       "                ('classifier',\n",
       "                 RandomForestClassifier(min_samples_leaf=4, n_estimators=10))])"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 80.49%\n",
      "Precision: 86.34%\n",
      "Recall: 72.63%\n",
      "F1 score: 74.66%\n"
     ]
    }
   ],
   "source": [
    "\n",
    "y_pred = pipeline.predict(X_test)\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred, average='macro')\n",
    "recall = recall_score(y_test, y_pred, average='macro')\n",
    "f1 = f1_score(y_test, y_pred, average='macro')\n",
    "\n",
    "print(\"Accuracy: {:.2f}%\".format(acc * 100))\n",
    "print(\"Precision: {:.2f}%\".format(precision * 100))\n",
    "print(\"Recall: {:.2f}%\".format(recall * 100))\n",
    "print(\"F1 score: {:.2f}%\".format(f1 * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hyper parameter tuning\n",
    "#model = RandomForestClassifier()\n",
    "pipeline = Pipeline(steps=[('preprocessing', data_processing),\n",
    "                            ('scaling', StandardScaler()),\n",
    "                           #('features', feature_union),\n",
    "                           ('model', RandomForestClassifier())])\n",
    "param_grid = {\n",
    "    #'features__pca__n_components': [3, 5,None],\n",
    "    #'features__select_best__k': [1, 3, 6,None],\n",
    "    'model__n_estimators': [10, 50, 100],\n",
    "    'model__max_depth': [3, 5, None],\n",
    "    'model__criterion':['gini','log_loss'],\n",
    "    'model__min_samples_leaf' : [1,3,4], \n",
    "    'model__min_samples_split':[1,2,3,5] \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                                        ColumnTransformer(transformers=[(&#x27;numeric&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;impute_mean&#x27;,\n",
       "                                                                                          SimpleImputer()),\n",
       "                                                                                         (&#x27;scaling&#x27;,\n",
       "                                                                                          StandardScaler())]),\n",
       "                                                                         [&#x27;Loan_Amount_Term&#x27;]),\n",
       "                                                                        (&#x27;categorical&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;impute_mode&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;one-hot-encode&#x27;,\n",
       "                                                                                          OneHotEncoder(sparse=False))]),\n",
       "                                                                         [&#x27;Gender&#x27;...\n",
       "                                                                                          SimpleImputer()),\n",
       "                                                                                         (&#x27;log_transform&#x27;,\n",
       "                                                                                          PowerTransformer())]),\n",
       "                                                                         [&#x27;ApplicantIncome&#x27;,\n",
       "                                                                          &#x27;CoapplicantIncome&#x27;,\n",
       "                                                                          &#x27;LoanAmount&#x27;])])),\n",
       "                                       (&#x27;scaling&#x27;, StandardScaler()),\n",
       "                                       (&#x27;model&#x27;, RandomForestClassifier())]),\n",
       "             param_grid={&#x27;model__criterion&#x27;: [&#x27;gini&#x27;, &#x27;log_loss&#x27;],\n",
       "                         &#x27;model__max_depth&#x27;: [3, 5, None],\n",
       "                         &#x27;model__min_samples_leaf&#x27;: [1, 3, 4],\n",
       "                         &#x27;model__min_samples_split&#x27;: [1, 2, 3, 5],\n",
       "                         &#x27;model__n_estimators&#x27;: [10, 50, 100]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                                        ColumnTransformer(transformers=[(&#x27;numeric&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;impute_mean&#x27;,\n",
       "                                                                                          SimpleImputer()),\n",
       "                                                                                         (&#x27;scaling&#x27;,\n",
       "                                                                                          StandardScaler())]),\n",
       "                                                                         [&#x27;Loan_Amount_Term&#x27;]),\n",
       "                                                                        (&#x27;categorical&#x27;,\n",
       "                                                                         Pipeline(steps=[(&#x27;impute_mode&#x27;,\n",
       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                         (&#x27;one-hot-encode&#x27;,\n",
       "                                                                                          OneHotEncoder(sparse=False))]),\n",
       "                                                                         [&#x27;Gender&#x27;...\n",
       "                                                                                          SimpleImputer()),\n",
       "                                                                                         (&#x27;log_transform&#x27;,\n",
       "                                                                                          PowerTransformer())]),\n",
       "                                                                         [&#x27;ApplicantIncome&#x27;,\n",
       "                                                                          &#x27;CoapplicantIncome&#x27;,\n",
       "                                                                          &#x27;LoanAmount&#x27;])])),\n",
       "                                       (&#x27;scaling&#x27;, StandardScaler()),\n",
       "                                       (&#x27;model&#x27;, RandomForestClassifier())]),\n",
       "             param_grid={&#x27;model__criterion&#x27;: [&#x27;gini&#x27;, &#x27;log_loss&#x27;],\n",
       "                         &#x27;model__max_depth&#x27;: [3, 5, None],\n",
       "                         &#x27;model__min_samples_leaf&#x27;: [1, 3, 4],\n",
       "                         &#x27;model__min_samples_split&#x27;: [1, 2, 3, 5],\n",
       "                         &#x27;model__n_estimators&#x27;: [10, 50, 100]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;numeric&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;impute_mean&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaling&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;Loan_Amount_Term&#x27;]),\n",
       "                                                 (&#x27;categorical&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;impute_mode&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;one-hot-encode&#x27;,\n",
       "                                                                   OneHotEncoder(sparse=False))]),\n",
       "                                                  [&#x27;Gender&#x27;, &#x27;Married&#x27;,\n",
       "                                                   &#x27;Credit_History&#x27;,\n",
       "                                                   &#x27;Dependents&#x27;, &#x27;Education&#x27;,\n",
       "                                                   &#x27;Self_Employed&#x27;,\n",
       "                                                   &#x27;Credit_History&#x27;,\n",
       "                                                   &#x27;Property_Area&#x27;]),\n",
       "                                                 (&#x27;amount&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;impute_mean&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;log_transform&#x27;,\n",
       "                                                                   PowerTransformer())]),\n",
       "                                                  [&#x27;ApplicantIncome&#x27;,\n",
       "                                                   &#x27;CoapplicantIncome&#x27;,\n",
       "                                                   &#x27;LoanAmount&#x27;])])),\n",
       "                (&#x27;scaling&#x27;, StandardScaler()),\n",
       "                (&#x27;model&#x27;, RandomForestClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessing: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;numeric&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;impute_mean&#x27;,\n",
       "                                                  SimpleImputer()),\n",
       "                                                 (&#x27;scaling&#x27;,\n",
       "                                                  StandardScaler())]),\n",
       "                                 [&#x27;Loan_Amount_Term&#x27;]),\n",
       "                                (&#x27;categorical&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;impute_mode&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;one-hot-encode&#x27;,\n",
       "                                                  OneHotEncoder(sparse=False))]),\n",
       "                                 [&#x27;Gender&#x27;, &#x27;Married&#x27;, &#x27;Credit_History&#x27;,\n",
       "                                  &#x27;Dependents&#x27;, &#x27;Education&#x27;, &#x27;Self_Employed&#x27;,\n",
       "                                  &#x27;Credit_History&#x27;, &#x27;Property_Area&#x27;]),\n",
       "                                (&#x27;amount&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;impute_mean&#x27;,\n",
       "                                                  SimpleImputer()),\n",
       "                                                 (&#x27;log_transform&#x27;,\n",
       "                                                  PowerTransformer())]),\n",
       "                                 [&#x27;ApplicantIncome&#x27;, &#x27;CoapplicantIncome&#x27;,\n",
       "                                  &#x27;LoanAmount&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numeric</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Loan_Amount_Term&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">categorical</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Gender&#x27;, &#x27;Married&#x27;, &#x27;Credit_History&#x27;, &#x27;Dependents&#x27;, &#x27;Education&#x27;, &#x27;Self_Employed&#x27;, &#x27;Credit_History&#x27;, &#x27;Property_Area&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-43\" type=\"checkbox\" ><label for=\"sk-estimator-id-43\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-44\" type=\"checkbox\" ><label for=\"sk-estimator-id-44\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(sparse=False)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-45\" type=\"checkbox\" ><label for=\"sk-estimator-id-45\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">amount</label><div class=\"sk-toggleable__content\"><pre>[&#x27;ApplicantIncome&#x27;, &#x27;CoapplicantIncome&#x27;, &#x27;LoanAmount&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-46\" type=\"checkbox\" ><label for=\"sk-estimator-id-46\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-47\" type=\"checkbox\" ><label for=\"sk-estimator-id-47\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PowerTransformer</label><div class=\"sk-toggleable__content\"><pre>PowerTransformer()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-48\" type=\"checkbox\" ><label for=\"sk-estimator-id-48\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-49\" type=\"checkbox\" ><label for=\"sk-estimator-id-49\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('preprocessing',\n",
       "                                        ColumnTransformer(transformers=[('numeric',\n",
       "                                                                         Pipeline(steps=[('impute_mean',\n",
       "                                                                                          SimpleImputer()),\n",
       "                                                                                         ('scaling',\n",
       "                                                                                          StandardScaler())]),\n",
       "                                                                         ['Loan_Amount_Term']),\n",
       "                                                                        ('categorical',\n",
       "                                                                         Pipeline(steps=[('impute_mode',\n",
       "                                                                                          SimpleImputer(strategy='most_frequent')),\n",
       "                                                                                         ('one-hot-encode',\n",
       "                                                                                          OneHotEncoder(sparse=False))]),\n",
       "                                                                         ['Gender'...\n",
       "                                                                                          SimpleImputer()),\n",
       "                                                                                         ('log_transform',\n",
       "                                                                                          PowerTransformer())]),\n",
       "                                                                         ['ApplicantIncome',\n",
       "                                                                          'CoapplicantIncome',\n",
       "                                                                          'LoanAmount'])])),\n",
       "                                       ('scaling', StandardScaler()),\n",
       "                                       ('model', RandomForestClassifier())]),\n",
       "             param_grid={'model__criterion': ['gini', 'log_loss'],\n",
       "                         'model__max_depth': [3, 5, None],\n",
       "                         'model__min_samples_leaf': [1, 3, 4],\n",
       "                         'model__min_samples_split': [1, 2, 3, 5],\n",
       "                         'model__n_estimators': [10, 50, 100]})"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid = GridSearchCV(pipeline, param_grid=param_grid, cv=5) \n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best test set accuracy: 79.67479674796748\n",
      "Achieved with hyperparameters: {'model__criterion': 'log_loss', 'model__max_depth': None, 'model__min_samples_leaf': 3, 'model__min_samples_split': 3, 'model__n_estimators': 10}\n"
     ]
    }
   ],
   "source": [
    "best_hyperparams = grid.best_params_\n",
    "best_acc = grid.score(X_test, y_test)*100\n",
    "print(f'Best test set accuracy: {best_acc}\\nAchieved with hyperparameters: {best_hyperparams}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save Pickle\n",
    "\n",
    "import pickle\n",
    "model_columns = list(X.columns)\n",
    "with open('../data/model_columns.pkl', 'wb') as file:\n",
    "    pickle.dump(model_columns, file)\n",
    "    \n",
    "pickle.dump(pipeline, open('../data/classifier.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Deploy your model to cloud and test it with PostMan, BASH or Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "ename": "JSONDecodeError",
     "evalue": "Expecting value: line 1 column 1 (char 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mJSONDecodeError\u001b[0m                           Traceback (most recent call last)",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/THE_ONE/lib/python3.10/site-packages/requests/models.py:971\u001b[0m, in \u001b[0;36mResponse.json\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    970\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 971\u001b[0m     \u001b[39mreturn\u001b[39;00m complexjson\u001b[39m.\u001b[39;49mloads(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtext, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    972\u001b[0m \u001b[39mexcept\u001b[39;00m JSONDecodeError \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m    973\u001b[0m     \u001b[39m# Catch JSON-related errors and raise as requests.JSONDecodeError\u001b[39;00m\n\u001b[1;32m    974\u001b[0m     \u001b[39m# This aliases json.JSONDecodeError and simplejson.JSONDecodeError\u001b[39;00m\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/THE_ONE/lib/python3.10/json/__init__.py:346\u001b[0m, in \u001b[0;36mloads\u001b[0;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m    343\u001b[0m \u001b[39mif\u001b[39;00m (\u001b[39mcls\u001b[39m \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m object_hook \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m\n\u001b[1;32m    344\u001b[0m         parse_int \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m parse_float \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m\n\u001b[1;32m    345\u001b[0m         parse_constant \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m object_pairs_hook \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m kw):\n\u001b[0;32m--> 346\u001b[0m     \u001b[39mreturn\u001b[39;00m _default_decoder\u001b[39m.\u001b[39;49mdecode(s)\n\u001b[1;32m    347\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mcls\u001b[39m \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/THE_ONE/lib/python3.10/json/decoder.py:337\u001b[0m, in \u001b[0;36mJSONDecoder.decode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m    333\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Return the Python representation of ``s`` (a ``str`` instance\u001b[39;00m\n\u001b[1;32m    334\u001b[0m \u001b[39mcontaining a JSON document).\u001b[39;00m\n\u001b[1;32m    335\u001b[0m \n\u001b[1;32m    336\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 337\u001b[0m obj, end \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mraw_decode(s, idx\u001b[39m=\u001b[39;49m_w(s, \u001b[39m0\u001b[39;49m)\u001b[39m.\u001b[39;49mend())\n\u001b[1;32m    338\u001b[0m end \u001b[39m=\u001b[39m _w(s, end)\u001b[39m.\u001b[39mend()\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/THE_ONE/lib/python3.10/json/decoder.py:355\u001b[0m, in \u001b[0;36mJSONDecoder.raw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n\u001b[1;32m    354\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mStopIteration\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m--> 355\u001b[0m     \u001b[39mraise\u001b[39;00m JSONDecodeError(\u001b[39m\"\u001b[39m\u001b[39mExpecting value\u001b[39m\u001b[39m\"\u001b[39m, s, err\u001b[39m.\u001b[39mvalue) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39m\n\u001b[1;32m    356\u001b[0m \u001b[39mreturn\u001b[39;00m obj, end\n",
      "\u001b[0;31mJSONDecodeError\u001b[0m: Expecting value: line 1 column 1 (char 0)",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mJSONDecodeError\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[157], line 35\u001b[0m\n\u001b[1;32m     33\u001b[0m     \u001b[39mprint\u001b[39m(response\u001b[39m.\u001b[39mjson())\n\u001b[1;32m     34\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m---> 35\u001b[0m     \u001b[39mprint\u001b[39m(response\u001b[39m.\u001b[39;49mjson())\n\u001b[1;32m     36\u001b[0m     \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mrequest failed\u001b[39m\u001b[39m'\u001b[39m)\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/THE_ONE/lib/python3.10/site-packages/requests/models.py:975\u001b[0m, in \u001b[0;36mResponse.json\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    971\u001b[0m     \u001b[39mreturn\u001b[39;00m complexjson\u001b[39m.\u001b[39mloads(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtext, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m    972\u001b[0m \u001b[39mexcept\u001b[39;00m JSONDecodeError \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m    973\u001b[0m     \u001b[39m# Catch JSON-related errors and raise as requests.JSONDecodeError\u001b[39;00m\n\u001b[1;32m    974\u001b[0m     \u001b[39m# This aliases json.JSONDecodeError and simplejson.JSONDecodeError\u001b[39;00m\n\u001b[0;32m--> 975\u001b[0m     \u001b[39mraise\u001b[39;00m RequestsJSONDecodeError(e\u001b[39m.\u001b[39mmsg, e\u001b[39m.\u001b[39mdoc, e\u001b[39m.\u001b[39mpos)\n",
      "\u001b[0;31mJSONDecodeError\u001b[0m: Expecting value: line 1 column 1 (char 0)"
     ]
    }
   ],
   "source": [
    "## Test locally\n",
    "import requests as r\n",
    "import pandas as pd\n",
    "import json\n",
    "import json\n",
    "\n",
    "\n",
    "base_url = 'http://127.0.0.1:5000/' #base url local host\n",
    "\n",
    "json_data = {\n",
    "        \"Gender\": 'Female',\n",
    "        \"Married\": 'No',\n",
    "        \"Dependents\": '0',\n",
    "        \"Education\": 'Graduate',\n",
    "        \"Self_Employed\": 'No',\n",
    "        \"ApplicantIncome\": 1500,\n",
    "        \"CoapplicantIncome\": 2000,\n",
    "        \"LoanAmount\": 25,\n",
    "        \"Loan_Amount_Term\": 60,\n",
    "        \"Credit_History\": 1,\n",
    "        \"Property_Area\": 'Urban'\n",
    "        }\n",
    "\n",
    "# Get Response\n",
    "# response = r.get(base_url)\n",
    "response = r.post(base_url + \"predict\", json = json_data)\n",
    "\n",
    "\n",
    "if response.status_code == 200:\n",
    "    print('...')\n",
    "    print('request successful')\n",
    "    print('...')\n",
    "    print(response.json())\n",
    "else:\n",
    "    print(response.json())\n",
    "    print('request failed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...\n",
      "request successful\n",
      "...\n",
      "{'prediction': \"['Y']\"}\n"
     ]
    }
   ],
   "source": [
    "## Test locally\n",
    "import requests as r\n",
    "import pandas as pd\n",
    "import json\n",
    "import json\n",
    "\n",
    "\n",
    "base_url = 'http://127.0.0.1:5000/' #base url local host\n",
    "\n",
    "json_data = {\n",
    "        \"Gender\": 'Male',\n",
    "        \"Married\": 'Yes',\n",
    "        \"Dependents\": '1',\n",
    "        \"Education\": 'Graduate',\n",
    "        \"Self_Employed\": 'No',\n",
    "        \"ApplicantIncome\": 2500,\n",
    "        \"CoapplicantIncome\": 2600,\n",
    "        \"LoanAmount\": 28,\n",
    "        \"Loan_Amount_Term\": 120,\n",
    "        \"Credit_History\": 1,\n",
    "        \"Property_Area\": 'Rural'\n",
    "        }\n",
    "\n",
    "# Get Response\n",
    "# response = r.get(base_url)\n",
    "response = r.post(base_url + \"predict\", json = json_data)\n",
    "\n",
    "\n",
    "if response.status_code == 200:\n",
    "    print('...')\n",
    "    print('request successful')\n",
    "    print('...')\n",
    "    print(response.json())\n",
    "else:\n",
    "    print(response.json())\n",
    "    print('request failed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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