imbalanced_data.ipynb

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   "source": [
    "# Imbalanced Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "# We'll start by importing the usual suspects\n",
    "import pandas as pd #for data manipulation\n",
    "import numpy as np #for numerical computations\n",
    "import matplotlib.pyplot as plt #for visualizations\n",
    "import seaborn as sns # his will run on top of matplotlib to give us some visulalizations\n",
    "#Let's use this magic line so that we can see the visualizations within the Notebook\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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    "colab_type": "code",
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   "outputs": [],
   "source": [
    "# loading in the dataset as a data frame\n",
    "df = pd.read_csv(\"default-rate.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>default</th>\n",
       "      <th>student</th>\n",
       "      <th>balance</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>729.526495</td>\n",
       "      <td>44361.62507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>817.180407</td>\n",
       "      <td>12106.13470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1073.549164</td>\n",
       "      <td>31767.13895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>529.250605</td>\n",
       "      <td>35704.49394</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>785.655883</td>\n",
       "      <td>38463.49588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "  default student      balance       income\n",
       "1      No      No   729.526495  44361.62507\n",
       "2      No     Yes   817.180407  12106.13470\n",
       "3      No      No  1073.549164  31767.13895\n",
       "4      No      No   529.250605  35704.49394\n",
       "5      No      No   785.655883  38463.49588"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the first records of the dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>balance</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>835.374886</td>\n",
       "      <td>33516.981876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>483.714985</td>\n",
       "      <td>13336.639563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>771.967729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>481.731105</td>\n",
       "      <td>21340.462905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>823.636973</td>\n",
       "      <td>34552.644800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1166.308387</td>\n",
       "      <td>43807.729275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2654.322576</td>\n",
       "      <td>73554.233500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "            balance        income\n",
       "count  10000.000000  10000.000000\n",
       "mean     835.374886  33516.981876\n",
       "std      483.714985  13336.639563\n",
       "min        0.000000    771.967729\n",
       "25%      481.731105  21340.462905\n",
       "50%      823.636973  34552.644800\n",
       "75%     1166.308387  43807.729275\n",
       "max     2654.322576  73554.233500"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a glimpse of the dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RG9PQmsBXEC3"
   },
   "source": [
    "Our dataset has 10000 records and four columns. The median balance is 823.6 and the maximum is 2654.32. The Median income is 34552.64 while the maximum is 73554.23. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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    {
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     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10000 entries, 1 to 10000\n",
      "Data columns (total 4 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   default  10000 non-null  object \n",
      " 1   student  10000 non-null  object \n",
      " 2   balance  10000 non-null  float64\n",
      " 3   income   10000 non-null  float64\n",
      "dtypes: float64(2), object(2)\n",
      "memory usage: 390.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# Get some more info on the dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hD9eY-mRXEC-"
   },
   "source": [
    "We can clearly see that our dataset has no null values, otherwise we'd have to deal with them first. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9QbdzCRAXEC_"
   },
   "source": [
    "## Visualizing the default classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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   "outputs": [],
   "source": [
    "defaulters = df.groupby('default').count().reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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    "id": "6cEEynpvXEDF",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>default</th>\n",
       "      <th>student</th>\n",
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       "      <th>income</th>\n",
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       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>9667</td>\n",
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       "      <td>9667</td>\n",
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       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
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      "text/plain": [
       "  default  student  balance  income\n",
       "0      No     9667     9667    9667\n",
       "1     Yes      333      333     333"
      ]
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   "source": [
    "defaulters"
   ]
  },
  {
   "cell_type": "code",
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     "text": [
      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
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    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='default', ylabel='count'>"
      ]
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     "execution_count": 10,
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     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df[\"default\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oxX4vqsjXEDS"
   },
   "source": [
    "From the above visualization we can see that most the data points in the daatset fall NO default class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ALS-IcxOXEDS"
   },
   "source": [
    "#### Visualization of Students"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "colab_type": "code",
    "id": "foNwd8-jXEDT",
    "outputId": "7e874edf-ae98-40a9-b3ff-7a771566016c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='student', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['student'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GBQXMIugXEDX"
   },
   "source": [
    "We see that most of the datapoints are not students"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7cKf14HMXEDY"
   },
   "source": [
    "Next, we can Seaborn's pairplot to get a quick visualization of the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 404
    },
    "colab_type": "code",
    "id": "QhjzGQWdXEDZ",
    "outputId": "cb318537-bbb7-44a0-fdc4-d5a150d2c0a6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1d266dd38e0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MA7TvCPIXEDe"
   },
   "source": [
    "### Distribution of the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5aa34CQRXEDf"
   },
   "source": [
    "let's check the distribution of the data using histogram and boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "IyqUcd1QXEDg",
    "outputId": "67adf1f9-9923-4ffc-c260-73810a87425f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='balance'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=df[\"balance\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8b4_kgm8XEDk"
   },
   "outputs": [],
   "source": [
    "q1, q3= np.percentile(df['income'],[25,75])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7yD5xnkhXEDo"
   },
   "outputs": [],
   "source": [
    "q_1, q_3= np.percentile(df['balance'],[25,75])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "pNeoUewnXEDu",
    "outputId": "04dd1a1f-27b5-41cb-b2da-06babda2c440"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21340.462905, 43807.729275)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1, q3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-ooKcriHXEDz"
   },
   "outputs": [],
   "source": [
    "iqr1 = q3 - q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "vqOzfKimXED4",
    "outputId": "95cac124-afb8-4f64-acd9-1f0887010d77"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22467.266369999998"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iqr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "B0I3MkN7XED-",
    "outputId": "7d2f94d0-af12-484c-e9b4-a4dd7bcf8737"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(481.73110505, 1166.30838675)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q_1, q_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "71jYLbLmXEEC"
   },
   "outputs": [],
   "source": [
    "qr2 = q_3 - q_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "MNJqzvXeXEEG",
    "outputId": "48fd30b4-29e2-4ce2-8e51-5019daf26c7d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "684.5772817"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DzKocvApXEEK"
   },
   "outputs": [],
   "source": [
    "lower_bound = q1 - (1.5 * q1)\n",
    "upper_bound = q3 + (1.5 * q3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "jFiEw9U-XEEP",
    "outputId": "6d6997ef-5870-41e6-d48d-ac0144bd4e70"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-10670.2314525, 109519.3231875)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_bound, upper_bound"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-X1D8c0XXEEU"
   },
   "outputs": [],
   "source": [
    "lower_bound_1 = q_1 - (1.5 * q_1)\n",
    "upper_bound_1 = q_3 + (1.5 * q_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "j-FEk1YyXEEZ",
    "outputId": "a894e82a-9e5d-45b7-eeb6-514a10d3a398"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-240.865552525, 2915.770966875)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_bound_1, upper_bound_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "uL2ZqFQDXEEh",
    "outputId": "1832f166-75eb-45c7-a00f-2a42f613767d"
   },
   "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>default</th>\n",
       "      <th>student</th>\n",
       "      <th>balance</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [default, student, balance, income]\n",
       "Index: []"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['balance'] > 2915.7709668749999]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "OLwG_zOEXEEm",
    "outputId": "d2939b3c-70ee-4ae1-e399-45ceac6ab3ab"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2654.322576"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['balance'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "G-ilZLkDXEEq",
    "outputId": "831e9ce7-65f9-409b-b456-f3efe97b47a9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='income'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=df[\"income\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "7GT8dRm7XEEt",
    "outputId": "9e890429-360f-4ed3-d96f-9ed145363b1b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='balance', ylabel='Density'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df[\"balance\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "TqGgn5QkXEEx",
    "outputId": "b6ae9c3a-5063-4798-81fd-9b26661c0714"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='income', ylabel='Density'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df[\"income\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DbJbr1m1XEE1"
   },
   "source": [
    "#### What's the balance for the two classes ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "I7uK8CtqXEE2",
    "outputId": "4692febc-3b2f-4652-bab5-4c1f90c7cf06"
   },
   "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>Default</th>\n",
       "      <th>student</th>\n",
       "      <th>Balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>5099855.481650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2671868.751861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>345756.477949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>236268.144690</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Default student        Balance\n",
       "0      No      No 5099855.481650\n",
       "1      No     Yes 2671868.751861\n",
       "2     Yes      No  345756.477949\n",
       "3     Yes     Yes  236268.144690"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('float_format', '{:f}'.format) # in order to see the full figure instead of e raised to \n",
    "default = df.groupby(['default','student'])['balance'].sum().reset_index() # Group by student and sum the balance and reset the index\n",
    "default.columns = ['Default','student','Balance'] # define the columns for the new data frame\n",
    "default = default.sort_values(by='Balance',ascending=False) # sort by values and arrange from the largest\n",
    "default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "gzYR129EXEE6",
    "outputId": "74557f45-3040-4247-e844-ac517a30f6b8"
   },
   "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>default</th>\n",
       "      <th>balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>7771724.233510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>582024.622639</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  default        balance\n",
       "0      No 7771724.233510\n",
       "1     Yes  582024.622639"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('default')['balance'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "Z0zMm2TlXEE9",
    "outputId": "6fc72274-4ab8-4e58-ea4e-945726d0614c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Default', ylabel='Balance'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=default, x='Default', y='Balance',hue='student')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "km3Iz1ChXEFB"
   },
   "source": [
    "We see that the total balance the people who haven't defaulted is 7771724 and for those who have defaulted is 582024. We also see that most of the people who haven't defaulted and have a high balance are **No**t students"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1E5qN-hjXEFC"
   },
   "source": [
    "#### What's the income for the two classes ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "SqmoYGCyXEFD",
    "outputId": "1d2dfe9f-e862-42d3-a048-7e25773e5e3c"
   },
   "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>Default</th>\n",
       "      <th>Student</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>273955578.919497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>50528553.847708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>8368760.438320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2316925.554049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Default Student           Income\n",
       "0      No      No 273955578.919497\n",
       "1      No     Yes  50528553.847708\n",
       "2     Yes      No   8368760.438320\n",
       "3     Yes     Yes   2316925.554049"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('float_format', '{:f}'.format) # in order to see the full figure instead of e raised to \n",
    "default_income = df.groupby(['default','student'])['income'].sum().reset_index() # Group by student and sum the balance and reset the index\n",
    "default_income.columns = ['Default','Student','Income'] # define the columns for the new data frame\n",
    "default_income = default_income.sort_values(by='Income',ascending=False) # sort by values and arrange from the largest\n",
    "default_income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "gzbKjbbVXEFG",
    "outputId": "9c983391-210e-4c49-fc37-499b01b33203"
   },
   "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>default</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>324484132.767205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>10685685.992369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  default           income\n",
       "0      No 324484132.767205\n",
       "1     Yes  10685685.992369"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('default')['income'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "S7-X28GCXEFJ",
    "outputId": "cda8d129-bc35-4365-8243-c963c874aef1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Default', ylabel='Income'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=default_income, x='Default', y='Income', hue='Student')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0SKpv_OuXEFN"
   },
   "source": [
    "The total income for the people who have defaulted is 324484132 and for 10685686 for those who haven't defaulted.Most of those who have the highest income and haven't defaulted are **No**t student."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dzgXUWihXEFO"
   },
   "source": [
    "#### Who has the highest amount of balance. Is it students or non-students?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "hyv2GUB7XEFP",
    "outputId": "49b4c621-354f-44e0-88f4-5d0b931b17aa"
   },
   "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>Student</th>\n",
       "      <th>Balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>5445611.959599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>2908136.896551</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Student        Balance\n",
       "0      No 5445611.959599\n",
       "1     Yes 2908136.896551"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('float_format', '{:f}'.format) # in order to see the full figure instead of e raised to \n",
    "loan_amount = df.groupby('student')['balance'].sum().reset_index() # Group by student and sum the balance and reset the index\n",
    "loan_amount.columns = ['Student','Balance'] # define the columns for the new data frame\n",
    "loan_amount = loan_amount.sort_values(by='Balance',ascending=False) # sort by values and arrange from the largest\n",
    "loan_amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "ah1oTYDFXEFV",
    "outputId": "429013e3-2245-4b10-c930-215881a09996"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Student', ylabel='Balance'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ELkzyLeANW3vagtu7LvLYvAB/XlWf3GRhMs3g07XmPQS4O0Zj4xa8mpXkwM0+HehQ4BbgHmDPBct/nORpSXYCXrZg+aVseiXOeecCb0qyR7eefZI8cRvjbL5OqXduwatlewB/23160IPADQx217wa+FqSH3b74d/N4NOGbgXWda+DwZU3P5vkZOCL829aVecleRpwWXfl243AaxlssT+a04C/T3Iv8Dz3w2sUvJqkJDXKXTSS1CgDL0mNMvCS1CgDL0mNMvCS1CgDL0mNMvCS1Kj/BxUbhM3lnrfDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=loan_amount, x='Student', y='Balance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "D9FYOkYZXEFZ"
   },
   "source": [
    "Surprisingly the people with the most balance are not students."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZykAu2B3XEFZ"
   },
   "source": [
    "#### Who has the highest amount of income. Is it students or non-students?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wT2oABY5XEFa"
   },
   "source": [
    "Let's now perform a similar operation on the income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "j46cJ6kVXEFa",
    "outputId": "b0cbf03a-f153-42c0-eff3-f72628a3721a"
   },
   "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>Student</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>282324339.357817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>52845479.401757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Student           Income\n",
       "0      No 282324339.357817\n",
       "1     Yes  52845479.401757"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('float_format', '{:f}'.format) # in order to see the full figure instead of e raised to \n",
    "income_amount = df.groupby('student')['income'].sum().reset_index() # Group by student and sum the income and reset the index\n",
    "income_amount.columns = ['Student','Income'] # define the columns for the new data frame\n",
    "income_amount = income_amount.sort_values(by='Income',ascending=False) # sort by values and arrange from the largest\n",
    "income_amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "eJHdMwDbXEFj",
    "outputId": "545b58a6-52cc-4759-cd7b-d26cfbd2ef7b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Student', ylabel='Income'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=income_amount, x='Student', y='Income')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pXn6XErAXEFq"
   },
   "source": [
    "The people with the most income are not students yet they are the people with the most balance.This could be because the people with most income also have the biggest borrowing muscle because they are more credit worth. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rRTp0C9jXEFr"
   },
   "source": [
    "#### Relationship between the income and balance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "PN9vWOpIXEFr",
    "outputId": "a6577731-e8e1-4054-ff2b-d4aba86619dc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1d26749bf10>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(data = df, x = df['income'], y = df['balance'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "g6lh9wzYXEFu",
    "outputId": "1438fa80-b57d-406c-8a3a-37adabf1e3ed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.1522434005865672"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['income'].corr(df['balance'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "-chSbrO6XEFy",
    "outputId": "e79b03bc-7e41-428c-a58b-155d1837a466"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.1522434005865672, 6.380440176576551e-53)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats  as stats\n",
    "stats.pearsonr(df['income'], df['balance'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cOqUyugcXEF5"
   },
   "source": [
    "We can see that there is a negative correlation of about 15% between the income and the balance. This means that as income increases the balance decreases. The 2-tailed p-value being less that 0.05 also suggest the same thing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "L_eYm0_BXEF6"
   },
   "source": [
    "### Average Income of Defaulters and Non defaulters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "1_fZDgYhXEF7",
    "outputId": "2e957f15-1b9a-477a-ef9f-b96b6b0a719e"
   },
   "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>Default</th>\n",
       "      <th>Mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>33566.166625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>32089.147124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Default         Mean\n",
       "0      No 33566.166625\n",
       "1     Yes 32089.147124"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_income = df.groupby('default')['income'].mean().reset_index() # Group by student and sum the income and reset the index\n",
    "average_income.columns = ['Default','Mean'] # define the columns for the new data frame\n",
    "average_income = average_income.sort_values(by='Mean',ascending=False) # sort by values and arrange from the largest\n",
    "average_income"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "v2-odGRzXEGA"
   },
   "source": [
    "### Relationship Between Defaulting and Being a Student"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FZYrV6zbXEGA"
   },
   "source": [
    "H0:There is no statistically significant relationship between defaulting and being a student.\n",
    "\n",
    "H1:There is a statistically significant relationship between defaulting and being a student."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "CMLSeoN7XEGB",
    "outputId": "44af0c9a-457e-40c1-c1e1-1419c2b74e47"
   },
   "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>student</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>6850</td>\n",
       "      <td>2817</td>\n",
       "      <td>9667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>206</td>\n",
       "      <td>127</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>7056</td>\n",
       "      <td>2944</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "student    No   Yes    All\n",
       "default                   \n",
       "No       6850  2817   9667\n",
       "Yes       206   127    333\n",
       "All      7056  2944  10000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contingency_table = pd.crosstab(\n",
    "    df['default'],\n",
    "    df['student'],\n",
    "    margins = True\n",
    ")\n",
    "contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CnVQnXQCXEGG"
   },
   "outputs": [],
   "source": [
    "from scipy.stats import chi2_contingency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "6NVXH078XEGJ",
    "outputId": "c03dc6b5-4b9a-4f79-b44f-894302427d16"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6850, 2817],\n",
       "       [ 206,  127]], dtype=int64)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_obs = np.array([contingency_table.iloc[0][0:2].values,contingency_table.iloc[1][0:2].values])\n",
    "f_obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2cEYS2_PXEGM"
   },
   "outputs": [],
   "source": [
    "chi2, p, dof, ex = chi2_contingency(f_obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "h_SOdCYyXEGP",
    "outputId": "d65e5a44-0cea-4573-bc93-d143cefdcedd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12.1166885136231, 0.0004997256756210486)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi2, p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "p67L04zTXEGT"
   },
   "source": [
    "Our p-value < 00.05 we therefore reject the null hypothesis and conclude that there is an relationship between the default rate and being a student."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ygpzhISwXEGU"
   },
   "source": [
    "###  Income of both defaulters and non defaulters are the same\n",
    "Ho The mean income of defaulters are  are equal.\n",
    "\n",
    "H1 The mean income of defaulters are  not are equal.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WsffHbGKXEGX"
   },
   "outputs": [],
   "source": [
    "from scipy.stats import ttest_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Wc2eild6XEGZ"
   },
   "outputs": [],
   "source": [
    "defaulters = df[df['default'] == 'Yes']['income']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bK83NAMpXEGe"
   },
   "outputs": [],
   "source": [
    "non_defaulters = df[df['default'] == 'No']['income']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "qWdwA6KjXEGh",
    "outputId": "fcd275f8-32ce-4d1d-bcfb-a51423b0eebc"
   },
   "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>default</th>\n",
       "      <th>student</th>\n",
       "      <th>balance</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1486.998122</td>\n",
       "      <td>17854.397030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2205.799521</td>\n",
       "      <td>14271.492250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1774.694223</td>\n",
       "      <td>20359.506090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1889.599190</td>\n",
       "      <td>48956.171590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1899.390626</td>\n",
       "      <td>20655.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9913</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>2148.898454</td>\n",
       "      <td>44309.917170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9922</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1627.898323</td>\n",
       "      <td>17546.997020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9950</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1750.253150</td>\n",
       "      <td>51578.940160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9952</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1515.606239</td>\n",
       "      <td>48688.512090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9979</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>2202.462395</td>\n",
       "      <td>47287.257110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>333 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     default student     balance       income\n",
       "137      Yes     Yes 1486.998122 17854.397030\n",
       "174      Yes     Yes 2205.799521 14271.492250\n",
       "202      Yes     Yes 1774.694223 20359.506090\n",
       "207      Yes      No 1889.599190 48956.171590\n",
       "210      Yes     Yes 1899.390626 20655.200000\n",
       "...      ...     ...         ...          ...\n",
       "9913     Yes      No 2148.898454 44309.917170\n",
       "9922     Yes     Yes 1627.898323 17546.997020\n",
       "9950     Yes      No 1750.253150 51578.940160\n",
       "9952     Yes      No 1515.606239 48688.512090\n",
       "9979     Yes      No 2202.462395 47287.257110\n",
       "\n",
       "[333 rows x 4 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['default'] == 'Yes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "U0ZxfVTBXEGq",
    "outputId": "b894e5db-68bb-4b55-9a06-fdbf028bb71f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-1.987338454885802, pvalue=0.046912142143412355)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(defaulters,non_defaulters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "57OL8gFXXEGu"
   },
   "source": [
    "Since our p value < 0.05 we reject the null hypothesis and conclude that the mean of the defaulters and non-defaulters are not equal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8hlxnP4pXEGv"
   },
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7SDVn9UOXEGw"
   },
   "source": [
    "I would like to see the ratio of the balance and the income."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Qz0XmHPXXEGw"
   },
   "outputs": [],
   "source": [
    "def divide(x):\n",
    "    balance,income = x[0],x[1]\n",
    "    if balance != 0:\n",
    "        return (income / balance)\n",
    "    return 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5JAXZKK6XEG2"
   },
   "outputs": [],
   "source": [
    "df['bal_income_ratio'] = df[['balance','income']].apply(divide,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "pNyTgyTWXEG9",
    "outputId": "1c17afaa-92cb-4f3b-c384-029d2f2ae145"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>default</th>\n",
       "      <th>student</th>\n",
       "      <th>balance</th>\n",
       "      <th>income</th>\n",
       "      <th>bal_income_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>729.526495</td>\n",
       "      <td>44361.625070</td>\n",
       "      <td>60.808792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>817.180407</td>\n",
       "      <td>12106.134700</td>\n",
       "      <td>14.814519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1073.549164</td>\n",
       "      <td>31767.138950</td>\n",
       "      <td>29.590763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>529.250605</td>\n",
       "      <td>35704.493940</td>\n",
       "      <td>67.462358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>785.655883</td>\n",
       "      <td>38463.495880</td>\n",
       "      <td>48.957179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  default student     balance       income  bal_income_ratio\n",
       "1      No      No  729.526495 44361.625070         60.808792\n",
       "2      No     Yes  817.180407 12106.134700         14.814519\n",
       "3      No      No 1073.549164 31767.138950         29.590763\n",
       "4      No      No  529.250605 35704.493940         67.462358\n",
       "5      No      No  785.655883 38463.495880         48.957179"
      ]
     },
     "execution_count": 55,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oU4mzWcqXEHC"
   },
   "source": [
    "## Develop a model to predict the probability of defaulting.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gVR_h2vjXEHD"
   },
   "source": [
    "We'll start by importing train_test_split that we'll use to split our dataset into a training and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8eD3119tXEHD"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qYVJCHz0XEHH"
   },
   "source": [
    "Next, we convert the default and student column into dummy variables because our model will only take numerical figures. We also drop the first dummy variable so that we don't fall into the dummy variable trap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xv8OdT3nXEHI"
   },
   "outputs": [],
   "source": [
    "feats = ['default','student']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RwwVw1LBXEHL"
   },
   "outputs": [],
   "source": [
    "df_final = pd.get_dummies(df,columns=feats,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "j0gIb3NRXEHO",
    "outputId": "cda5c68d-8e1a-4d5d-cedb-fab7ac7411e7"
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>balance</th>\n",
       "      <th>income</th>\n",
       "      <th>bal_income_ratio</th>\n",
       "      <th>default_Yes</th>\n",
       "      <th>student_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>729.526495</td>\n",
       "      <td>44361.625070</td>\n",
       "      <td>60.808792</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>817.180407</td>\n",
       "      <td>12106.134700</td>\n",
       "      <td>14.814519</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1073.549164</td>\n",
       "      <td>31767.138950</td>\n",
       "      <td>29.590763</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>529.250605</td>\n",
       "      <td>35704.493940</td>\n",
       "      <td>67.462358</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>785.655883</td>\n",
       "      <td>38463.495880</td>\n",
       "      <td>48.957179</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      balance       income  bal_income_ratio  default_Yes  student_Yes\n",
       "1  729.526495 44361.625070         60.808792            0            0\n",
       "2  817.180407 12106.134700         14.814519            0            1\n",
       "3 1073.549164 31767.138950         29.590763            0            0\n",
       "4  529.250605 35704.493940         67.462358            0            0\n",
       "5  785.655883 38463.495880         48.957179            0            0"
      ]
     },
     "execution_count": 59,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "colab_type": "code",
    "id": "NBiw1LTsXEHR",
    "outputId": "157d0b0d-35f5-4147-83e2-dd4aeaff1537"
   },
   "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>balance</th>\n",
       "      <th>income</th>\n",
       "      <th>bal_income_ratio</th>\n",
       "      <th>default_Yes</th>\n",
       "      <th>student_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>835.374886</td>\n",
       "      <td>33516.981876</td>\n",
       "      <td>261.887044</td>\n",
       "      <td>0.033300</td>\n",
       "      <td>0.294400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>483.714985</td>\n",
       "      <td>13336.639563</td>\n",
       "      <td>15973.871274</td>\n",
       "      <td>0.179428</td>\n",
       "      <td>0.455795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>771.967729</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>481.731105</td>\n",
       "      <td>21340.462905</td>\n",
       "      <td>20.499508</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>823.636973</td>\n",
       "      <td>34552.644800</td>\n",
       "      <td>37.139498</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1166.308387</td>\n",
       "      <td>43807.729275</td>\n",
       "      <td>64.729941</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2654.322576</td>\n",
       "      <td>73554.233500</td>\n",
       "      <td>1594781.106671</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           balance       income  bal_income_ratio  default_Yes  student_Yes\n",
       "count 10000.000000 10000.000000      10000.000000 10000.000000 10000.000000\n",
       "mean    835.374886 33516.981876        261.887044     0.033300     0.294400\n",
       "std     483.714985 13336.639563      15973.871274     0.179428     0.455795\n",
       "min       0.000000   771.967729          0.000000     0.000000     0.000000\n",
       "25%     481.731105 21340.462905         20.499508     0.000000     0.000000\n",
       "50%     823.636973 34552.644800         37.139498     0.000000     0.000000\n",
       "75%    1166.308387 43807.729275         64.729941     0.000000     1.000000\n",
       "max    2654.322576 73554.233500    1594781.106671     1.000000     1.000000"
      ]
     },
     "execution_count": 60,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gZCqoEJ6XEHT"
   },
   "source": [
    "At this stage we split the dataset into a test and training set,we'll use 70% for training and 30% for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4aFrisLyXEHU"
   },
   "outputs": [],
   "source": [
    "X = df_final.drop(['default_Yes'],axis=1)\n",
    "y = df_final['default_Yes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fViJZJQMXEHX"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3ecHBjdUXEHa"
   },
   "source": [
    "We then scale our dataset since our machine learning model will expect data that has been scaled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lnYKonKPXEHa"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wjbbmte4XEHg"
   },
   "outputs": [],
   "source": [
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "u-ezD28YXEHp"
   },
   "source": [
    "We shall then import a few classifiers and test them then pick the one that performs best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5tSJlC4uXEHq"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "d8yyQZtjXEHu"
   },
   "source": [
    "Let's now fit the different classifiers to our dataset and compute its scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 952
    },
    "colab_type": "code",
    "id": "tCe-e3P0XEHv",
    "outputId": "760763bd-8985-44f3-c073-3925337c9183"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.7s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The score of RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
      "                       criterion='gini', max_depth=None, max_features='auto',\n",
      "                       max_leaf_nodes=None, max_samples=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=1, min_samples_split=2,\n",
      "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
      "                       n_jobs=None, oob_score=False, random_state=None,\n",
      "                       verbose=1, warm_start=False) is 0.9683333333333334 \n",
      "The score of LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
      "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
      "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=1,\n",
      "                   warm_start=False) is 0.971 \n",
      "      Iter       Train Loss   Remaining Time \n",
      "         1           0.2202            1.33s\n",
      "         2           0.2063            1.32s\n",
      "         3           0.1956            1.31s\n",
      "         4           0.1877            1.29s\n",
      "         5           0.1810            1.28s\n",
      "         6           0.1753            1.27s\n",
      "         7           0.1708            1.25s\n",
      "         8           0.1665            1.25s\n",
      "         9           0.1633            1.22s\n",
      "        10           0.1604            1.20s\n",
      "        20           0.1435            0.94s\n",
      "        30           0.1353            0.76s\n",
      "        40           0.1227            0.63s\n",
      "        50           0.1167            0.51s\n",
      "        60           0.1120            0.41s\n",
      "        70           0.1047            0.30s\n",
      "        80           0.1029            0.20s\n",
      "        90           0.1005            0.10s\n",
      "       100           0.0967            0.00s\n",
      "The score of GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
      "                           learning_rate=0.1, loss='deviance', max_depth=3,\n",
      "                           max_features=None, max_leaf_nodes=None,\n",
      "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                           min_samples_leaf=1, min_samples_split=2,\n",
      "                           min_weight_fraction_leaf=0.0, n_estimators=100,\n",
      "                           n_iter_no_change=None, presort='deprecated',\n",
      "                           random_state=None, subsample=1.0, tol=0.0001,\n",
      "                           validation_fraction=0.1, verbose=True,\n",
      "                           warm_start=False) is 0.9706666666666667 \n",
      "The score of DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
      "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=1, min_samples_split=2,\n",
      "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
      "                       random_state=None, splitter='best') is 0.957 \n"
     ]
    }
   ],
   "source": [
    "classifiers = [RandomForestClassifier(verbose=1), LogisticRegression(verbose=1), GradientBoostingClassifier(verbose=True),DecisionTreeClassifier()]\n",
    "\n",
    "for classifier in classifiers:\n",
    "    classifier.fit(X_train, y_train)\n",
    "    print(\"The score of {} is {} \".format(classifier, classifier.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8SUldo4AXEHz"
   },
   "source": [
    "We notice that we are getting the highest score from the Logistic Regression. We'll proceed to use it and test its parameters using a Grid Search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "T5FdbO0IXEHz"
   },
   "outputs": [],
   "source": [
    "parameters = {\n",
    "    'C': [1.0,2.0,3.0],\n",
    "    'fit_intercept': [True,False],\n",
    "    'warm_start': [True,False]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "80ZnCUvSXEH2"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 306
    },
    "colab_type": "code",
    "id": "Q98UA1VTXEH7",
    "outputId": "1c0216db-d255-4049-bc16-a1852b5bb2f4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 12 candidates, totalling 120 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 120 out of 120 | elapsed:    2.8s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score=nan,\n",
       "             estimator=LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
       "                                          fit_intercept=True,\n",
       "                                          intercept_scaling=1, l1_ratio=None,\n",
       "                                          max_iter=100, multi_class='auto',\n",
       "                                          n_jobs=None, penalty='l2',\n",
       "                                          random_state=None, solver='lbfgs',\n",
       "                                          tol=0.0001, verbose=0,\n",
       "                                          warm_start=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'C': [1.0, 2.0, 3.0], 'fit_intercept': [True, False],\n",
       "                         'warm_start': [True, False]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=1)"
      ]
     },
     "execution_count": 69,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = GridSearchCV(LogisticRegression(), parameters, verbose=1, cv=10)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5bPdCt0_XEH-"
   },
   "source": [
    "Now'll check the best parameters and use that to fit to our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "_zZuobOIXEH_",
    "outputId": "279e540f-ad15-4b10-94cf-8971b9ef8230"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1.0, 'fit_intercept': True, 'warm_start': True}"
      ]
     },
     "execution_count": 70,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PSKhI-VIXEIC"
   },
   "outputs": [],
   "source": [
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "weights = compute_class_weight('balanced', y.unique(), y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "ZwTfsCdmXEIE",
    "outputId": "c579b122-2605-4c74-eb74-e04725e920ea"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=uint8)"
      ]
     },
     "execution_count": 72,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "dIp_3woRXEII",
    "outputId": "687b6ebd-8086-4047-9aaa-be4dfd120855"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.51722354, 15.01501502])"
      ]
     },
     "execution_count": 73,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "u5NV2foLXEIL"
   },
   "outputs": [],
   "source": [
    "class_weights = {\n",
    "    0:0.51722354,\n",
    "    1:15.01501502\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SAkxRWuIXEIN"
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "7ScMbdeVXEIU",
    "outputId": "04c2fbf8-7a63-42ee-f309-c7b85a512665"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.5       , 14.51501502])"
      ]
     },
     "execution_count": 76,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "9667 / (2 * np.bincount(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TLiZmABoXEIX"
   },
   "outputs": [],
   "source": [
    "lr = LogisticRegression(C=3.0, fit_intercept=True, warm_start = True, class_weight=class_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "8gH_D-icXEIb",
    "outputId": "a99d9575-3fee-4325-a33d-38e5695e139c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8673333333333333"
      ]
     },
     "execution_count": 78,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train, y_train)\n",
    "lr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "61r1gFhwXEIf"
   },
   "source": [
    "We can now use the predict method to make predictions on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4bSPx363XEIg"
   },
   "outputs": [],
   "source": [
    "predictions = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "0qGU3IBnXEIj",
    "outputId": "1ef8ed0b-5a5a-4aeb-8901-a2df199a3dbc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=uint8)"
      ]
     },
     "execution_count": 80,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Zb1Bqe6ZXEIm"
   },
   "source": [
    "We can also predict to get the class probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XerDwYXDXEIm"
   },
   "outputs": [],
   "source": [
    "predict_probabilities = lr.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "colab_type": "code",
    "id": "PfRgp0dgXEIp",
    "outputId": "e2a305fc-92d1-43b3-c691-52b1e0944c35"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.94143605, 0.05856395],\n",
       "       [0.7713903 , 0.2286097 ],\n",
       "       [0.9965554 , 0.0034446 ],\n",
       "       ...,\n",
       "       [0.89997152, 0.10002848],\n",
       "       [0.79516746, 0.20483254],\n",
       "       [0.75456391, 0.24543609]])"
      ]
     },
     "execution_count": 82,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_probabilities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tBmUL7T9XEIt"
   },
   "source": [
    "## Provide documentation on the model comparisons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eqNQWE4KXEIt"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "LWmz1l2QXEIw",
    "outputId": "ce956e77-b442-4296-f47a-9d17f1a936a4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2502  387]\n",
      " [  11  100]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7Cj7_ufKXEIz"
   },
   "source": [
    "From the 3000 test data points, the model predicted 2890 + 27  as correctly and predicted 18+65 incorrectly. 2890 are referred to at the True Positives, 18 are the False Positives, 65 are False Negatives and 27 are the True Negatives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "VRuuO6rLXEIz",
    "outputId": "3cc1ee3e-0ab4-4392-8582-b22a9183be02"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.87      0.93      2889\n",
      "           1       0.21      0.90      0.33       111\n",
      "\n",
      "    accuracy                           0.87      3000\n",
      "   macro avg       0.60      0.88      0.63      3000\n",
      "weighted avg       0.97      0.87      0.90      3000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xeyq-BfTXEI2"
   },
   "source": [
    "From the classification report we can see that performs better in predicting 'Not Defaulted'(98%) because most of the dataset values are 'Not Defaulted' (60%). From the recall we can tell that 99% of the times the model predicted that Not Defaulted when the actual value was not defaulted. For 29% of the times it predicted that a person had defaulted when they had actually defaulted. For 99% of the time the model predicted defaulted and that was the case. For 39% of the times the model predicted not defaulted and it was the case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 421
    },
    "colab_type": "code",
    "id": "wtBABg0bXEI7",
    "outputId": "7b41260c-75be-48d5-ad2f-5c3ad3b8b9a6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f32022cd780>"
      ]
     },
     "execution_count": 86,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "\n",
    "predictions = lr.predict(X_test)\n",
    "fpr, tpr, thresholds = roc_curve(y_test, predictions, pos_label=1)\n",
    "\n",
    "auc = roc_auc_score(y_test, predictions)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(fpr, tpr,label='AUC')\n",
    "plt.plot([0, 1], [0, 1], color='red', linestyle='--', label='Random')\n",
    "plt.title('AUC:{}'.format(auc))\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 105
    },
    "colab_type": "code",
    "id": "WhHcCcMDXEJB",
    "outputId": "35158b86-e84b-484e-9932-974904345553"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
      "  \"(https://pypi.org/project/six/).\", FutureWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from imblearn.under_sampling import NearMiss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mv5V2aH6XEJE"
   },
   "outputs": [],
   "source": [
    "underSample = NearMiss(0.3,random_state=1545)  # Try different ratios and compare results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 139
    },
    "colab_type": "code",
    "id": "Gep38iPlXEJH",
    "outputId": "16b0a56f-94a1-4c44-fba5-b74da0b58002"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "X_resampled,y_resampled = underSample.fit_resample(X.values,y.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "36v6SYzCXEJO",
    "outputId": "da1e3d64-e79d-4316-f974-b650f183c31e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1110\n",
       "1     333\n",
       "dtype: int64"
      ]
     },
     "execution_count": 120,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(y_resampled).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XZY1CCx8XEJQ"
   },
   "outputs": [],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(X_resampled,y_resampled)\n",
    "predictions2 = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "d5rek0yKYBH4",
    "outputId": "98e7626d-3cf2-43b7-ae6b-c4f9d2d19c76"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 953 1936]\n",
      " [  45   66]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "6CiKH2riYNFQ",
    "outputId": "31a0d3ac-0cb0-4e69-81ff-66b10bb0cb29"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.33      0.49      2889\n",
      "           1       0.03      0.59      0.06       111\n",
      "\n",
      "    accuracy                           0.34      3000\n",
      "   macro avg       0.49      0.46      0.28      3000\n",
      "weighted avg       0.92      0.34      0.47      3000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hxTRsXzbYPqg"
   },
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "d7sVj5KnYefR"
   },
   "outputs": [],
   "source": [
    "smote = SMOTE(0.8) # Try different ratios and compare results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "id": "cDBN-fcDYlB8",
    "outputId": "448eb162-6dc3-4d7d-c53a-2b09db12c45d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "X_resampled,y_resampled = smote.fit_resample(X.values,y.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "Yi_hVZ7zaBpn",
    "outputId": "e261634f-eb1e-4bb9-eeb5-dd57a05851e7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    9667\n",
       "1    7733\n",
       "dtype: int64"
      ]
     },
     "execution_count": 127,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(y_resampled).value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1iWBfcGcYrY_"
   },
   "outputs": [],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(X_resampled,y_resampled)\n",
    "predictions3 = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "_FHCU7BtYtZM",
    "outputId": "d54b5339-d3c1-4059-89b9-d02e742ef589"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.55      0.70      2889\n",
      "           1       0.06      0.75      0.11       111\n",
      "\n",
      "    accuracy                           0.56      3000\n",
      "   macro avg       0.52      0.65      0.41      3000\n",
      "weighted avg       0.95      0.56      0.68      3000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "id": "r_LbMgv3ZCX6",
    "outputId": "9a74b9e6-8cb4-4e42-de05-f267d84e35ba"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "rus = RandomUnderSampler(random_state=0)\n",
    "X_resampled, y_resampled = rus.fit_resample(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "KW95QK95lhro",
    "outputId": "8e04c2ef-ec0a-4c47-eee3-20d304fa1332"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    333\n",
       "0    333\n",
       "dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(y_resampled).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UZfN8jqJlkmP"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
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    "DbJbr1m1XEE1",
    "1E5qN-hjXEFC",
    "dzgXUWihXEFO",
    "ZykAu2B3XEFZ",
    "rRTp0C9jXEFr",
    "L_eYm0_BXEF6",
    "v2-odGRzXEGA",
    "ygpzhISwXEGU"
   ],
   "name": "imbalanced data.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
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