Credit Card Fraud Detection (Classification Model).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3f46d7a5",
   "metadata": {},
   "source": [
    "# Classification Model for Credit Card Fraud Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cebca654",
   "metadata": {},
   "source": [
    "In this Machine Learning Project, we will be analysing the dataset taken from www.kaggle.com related to details of credit card owners. This data consists of features like Gender, Income type, House type, marital status and many more. Our focus will be on analysing the data, getting the insights related to these features and there role in affecting the target, we will perform feature engineering, feature selection and develop a Classification model that will predict whether it is a Fraud or not based on the new data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffbe1658",
   "metadata": {},
   "source": [
    "## Importing the Important Libraries required"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3018c295",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from imblearn.combine import SMOTETomek\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import metrics\n",
    "\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4ca9a82",
   "metadata": {},
   "source": [
    "## Reading the 'credit_dataset.csv' Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cbf708c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading dataset as DataFrame by pandas\n",
    "credit_df = pd.read_csv('credit_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8a34e2fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>CAR</th>\n",
       "      <th>REALITY</th>\n",
       "      <th>NO_OF_CHILD</th>\n",
       "      <th>INCOME</th>\n",
       "      <th>INCOME_TYPE</th>\n",
       "      <th>EDUCATION_TYPE</th>\n",
       "      <th>FAMILY_TYPE</th>\n",
       "      <th>HOUSE_TYPE</th>\n",
       "      <th>FLAG_MOBIL</th>\n",
       "      <th>WORK_PHONE</th>\n",
       "      <th>PHONE</th>\n",
       "      <th>E_MAIL</th>\n",
       "      <th>FAMILY SIZE</th>\n",
       "      <th>BEGIN_MONTH</th>\n",
       "      <th>AGE</th>\n",
       "      <th>YEARS_EMPLOYED</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>House / apartment</td>\n",
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       "      <td>157500.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
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       "      <td>157500.0</td>\n",
       "      <td>Pensioner</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>1</td>\n",
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       "      <td>Secondary / secondary special</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
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       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>Rented apartment</td>\n",
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       "<p>25134 rows × 19 columns</p>\n",
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      ],
      "text/plain": [
       "            ID GENDER CAR REALITY  NO_OF_CHILD    INCOME  \\\n",
       "0      5008806      M   Y       Y            0  112500.0   \n",
       "1      5008808      F   N       Y            0  270000.0   \n",
       "2      5008809      F   N       Y            0  270000.0   \n",
       "3      5008810      F   N       Y            0  270000.0   \n",
       "4      5008811      F   N       Y            0  270000.0   \n",
       "...        ...    ...  ..     ...          ...       ...   \n",
       "25129  5149828      M   Y       Y            0  315000.0   \n",
       "25130  5149834      F   N       Y            0  157500.0   \n",
       "25131  5149838      F   N       Y            0  157500.0   \n",
       "25132  5150049      F   N       Y            0  283500.0   \n",
       "25133  5150337      M   N       Y            0  112500.0   \n",
       "\n",
       "                INCOME_TYPE                 EDUCATION_TYPE  \\\n",
       "0                   Working  Secondary / secondary special   \n",
       "1      Commercial associate  Secondary / secondary special   \n",
       "2      Commercial associate  Secondary / secondary special   \n",
       "3      Commercial associate  Secondary / secondary special   \n",
       "4      Commercial associate  Secondary / secondary special   \n",
       "...                     ...                            ...   \n",
       "25129               Working  Secondary / secondary special   \n",
       "25130  Commercial associate               Higher education   \n",
       "25131             Pensioner               Higher education   \n",
       "25132               Working  Secondary / secondary special   \n",
       "25133               Working  Secondary / secondary special   \n",
       "\n",
       "                FAMILY_TYPE         HOUSE_TYPE  FLAG_MOBIL  WORK_PHONE  PHONE  \\\n",
       "0                   Married  House / apartment           1           0      0   \n",
       "1      Single / not married  House / apartment           1           0      1   \n",
       "2      Single / not married  House / apartment           1           0      1   \n",
       "3      Single / not married  House / apartment           1           0      1   \n",
       "4      Single / not married  House / apartment           1           0      1   \n",
       "...                     ...                ...         ...         ...    ...   \n",
       "25129               Married  House / apartment           1           0      0   \n",
       "25130               Married  House / apartment           1           0      1   \n",
       "25131               Married  House / apartment           1           0      1   \n",
       "25132               Married  House / apartment           1           0      0   \n",
       "25133  Single / not married   Rented apartment           1           0      0   \n",
       "\n",
       "       E_MAIL  FAMILY SIZE  BEGIN_MONTH  AGE  YEARS_EMPLOYED  TARGET  \n",
       "0           0            2           29   59               3       0  \n",
       "1           1            1            4   52               8       0  \n",
       "2           1            1           26   52               8       0  \n",
       "3           1            1           26   52               8       0  \n",
       "4           1            1           38   52               8       0  \n",
       "...       ...          ...          ...  ...             ...     ...  \n",
       "25129       0            2           11   48               7       1  \n",
       "25130       1            2           23   34               4       1  \n",
       "25131       1            2           32   34               4       1  \n",
       "25132       0            2            9   49               2       1  \n",
       "25133       0            1           13   25               3       1  \n",
       "\n",
       "[25134 rows x 19 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# observing the data that we have\n",
    "credit_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64087bc2",
   "metadata": {},
   "source": [
    "It can be observed that in total we have 25134 rows and 19 columns(features) i.e id, gender, car, reality, no_of_child, income, income_type, education_type, family_type, house_type, flag_mobil, work_phone, phone, e_mail, family size, begin_month, age, years_employed, target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "641fb92b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25134 entries, 0 to 25133\n",
      "Data columns (total 19 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   ID              25134 non-null  int64  \n",
      " 1   GENDER          25134 non-null  object \n",
      " 2   CAR             25134 non-null  object \n",
      " 3   REALITY         25134 non-null  object \n",
      " 4   NO_OF_CHILD     25134 non-null  int64  \n",
      " 5   INCOME          25134 non-null  float64\n",
      " 6   INCOME_TYPE     25134 non-null  object \n",
      " 7   EDUCATION_TYPE  25134 non-null  object \n",
      " 8   FAMILY_TYPE     25134 non-null  object \n",
      " 9   HOUSE_TYPE      25134 non-null  object \n",
      " 10  FLAG_MOBIL      25134 non-null  int64  \n",
      " 11  WORK_PHONE      25134 non-null  int64  \n",
      " 12  PHONE           25134 non-null  int64  \n",
      " 13  E_MAIL          25134 non-null  int64  \n",
      " 14  FAMILY SIZE     25134 non-null  int64  \n",
      " 15  BEGIN_MONTH     25134 non-null  int64  \n",
      " 16  AGE             25134 non-null  int64  \n",
      " 17  YEARS_EMPLOYED  25134 non-null  int64  \n",
      " 18  TARGET          25134 non-null  int64  \n",
      "dtypes: float64(1), int64(11), object(7)\n",
      "memory usage: 3.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# understanding the features that we have in the dataset\n",
    "credit_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c93b302",
   "metadata": {},
   "source": [
    "We can observe that we are having numerical and categorical features both, we need to perform encoding on the categorical features inorder to use them in our machine learning model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ee454d1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                0\n",
       "GENDER            0\n",
       "CAR               0\n",
       "REALITY           0\n",
       "NO_OF_CHILD       0\n",
       "INCOME            0\n",
       "INCOME_TYPE       0\n",
       "EDUCATION_TYPE    0\n",
       "FAMILY_TYPE       0\n",
       "HOUSE_TYPE        0\n",
       "FLAG_MOBIL        0\n",
       "WORK_PHONE        0\n",
       "PHONE             0\n",
       "E_MAIL            0\n",
       "FAMILY SIZE       0\n",
       "BEGIN_MONTH       0\n",
       "AGE               0\n",
       "YEARS_EMPLOYED    0\n",
       "TARGET            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for Nan Values in features\n",
    "credit_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dea13d1",
   "metadata": {},
   "source": [
    "The features dont contain any missing values so we dont need to handle any 'Nan' values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5488270f",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0d5458ed",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\prank\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\seaborn\\axisgrid.py:392: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  fig, axes = plt.subplots(nrow, ncol, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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A3yQ5vqp2jLNuSRq21kMQrwM2VtWX5+hzBnB1VT1dVY8CG4CTx1KdNA8sWz7FokMOnfO1bPlU6zK1G62noZ0FXNX3/sIk5wDrgF+tqq8DS4Db+/pMd23fJclqYDXAscceO5KCpUnjw1Tnr2ZHwEmeB7wJ+GTXdDmwnN7wxGbgfXu7z6paU1UrqmrF4sWLh1WqJI1EyyGINwJ3V9XjAFX1eFXtqKpngA/x7DDDDHBM33ZLuzZJmtdaBvDZ9A0/JDmqb90q4L5u+XrgrCSLkiwDpoAvjq1KSRqRJmPASb4H+Gngl/qafz/JiUABj+1cV1X3J7kGeADYDlzgDAhJOy1bPsWmmek5+0zq/VKaBHBV/Qvwvbu0vWWO/pcAl4y6LknzzyAnISf1fimtp6FJ0gHLAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWqk9WPpJc1h245nWHTIoXP3mdDH7WjPDGBpgtWO7ay67PNz9pnUx+1ozxyCkKRGDGBJasQAlqRGHAOWNLGWLZ9i08z0nH3m80lIA1jSxNo0M82qS2+ds898PglpAEsaqkGmzgEcvWQpj258eAwVTS4DWNJQDTJ1DmDtRaeNoZrJ5kk4SWrEAJakRpoFcJLHktybZH2SdV3bC5PcnOTh7ucRXXuSXJpkQ5J7kry8Vd2SNCytj4B/sqpOrKoV3ft3A7dU1RRwS/ce4I3AVPdaDVw+9kolachaB/CuzgA+2i1/FHhzX/uV1XM7cHiSoxrUJ0lD0zKAC/hMkruSrO7ajqyqzd3yPwFHdstLgK/0bTvdtX2HJKuTrEuybuvWraOqW5KGouU0tFdX1UySFwM3J3mof2VVVZLamx1W1RpgDcCKFSv2altJGrdmR8BVNdP93AKsBU4GHt85tND93NJ1nwGO6dt8adcmSfNWkwBO8j1JXrBzGXg9cB9wPXBu1+1c4NPd8vXAOd1siFOAb/QNVUjSvNRqCOJIYG2SnTV8vKpuTHIncE2StwFfBs7s+t8AnA5sAJ4C3jr+kiVpuJoEcFU9AvzIbtq/BrxuN+0FXDCG0iRpbCZtGpokHTAMYElqxACWpEYMYElqxACWpEYMYElqxACWpEZ8JJGkJgZ5dtx8fuLxIAxgSU0M8uy4+fzE40E4BCFJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjXg/YKmzbPkUm2am5+xz9JKlPLrx4TFVpIXOAJY6m2amWXXprXP2WXvRaWOqRgcChyAkqZGxB3CSY5J8NskDSe5P8itd+28nmUmyvnud3rfNxUk2JPlSkjeMu2ZJGoUWQxDbgV+tqruTvAC4K8nN3bo/rKr39ndOcgJwFvCDwNHA3yQ5vqp2jLVqSRqysR8BV9Xmqrq7W/4m8CCwZI5NzgCurqqnq+pRYANw8ugrlaTRajoGnOQ44CTgjq7pwiT3JLkiyRFd2xLgK32bTTNLYCdZnWRdknVbt24dVdmSNBTNAjjJ84FrgXdW1ZPA5cBy4ERgM/C+vd1nVa2pqhVVtWLx4sXDLFcCYNuOZ1h0yKFzvrZt29a6TM0TTaahJXkuvfD9WFVdB1BVj/et/xDwl93bGeCYvs2Xdm3S2NWO7ay67PNz9vnE+a8eUzWa71rMggjwYeDBqnp/X/tRfd1WAfd1y9cDZyVZlGQZMAV8cVz1StKotDgCfhXwFuDeJOu7tt8Ezk5yIlDAY8AvAVTV/UmuAR6gN4PiAmdASFoIxh7AVfV5ILtZdcMc21wCXDKyoiSpAa+Ek6RGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGmjyUU9J47Xya85x9fJrz2BnA0gHApzlPJgNYBwSPADWJDGAdEDwC1CTyJJwkNWIAS1IjBrAkNWIAS1IjBrAkNeIsiL3kdCZJw2IA7yWnM0kalnkzBJFkZZIvJdmQ5N2t65Gk/TUvAjjJQcBlwBuBE4Czk5zQtipJ2j/zIoCBk4ENVfVIVf0bcDVwRuOaJGm/pKpa17BHSX4WWFlVv9i9fwvwY1V14S79VgOru7cvBb401kJ370XAV1sXsYtJrAkms65JrAkms65JrAkmo66vVtXKXRsX1Em4qloDrGldR78k66pqRes6+k1iTTCZdU1iTTCZdU1iTTC5dcH8GYKYAY7pe7+0a5OkeWu+BPCdwFSSZUmeB5wFXN+4JknaL/NiCKKqtie5ELgJOAi4oqrub1zWoCZqSKQziTXBZNY1iTXBZNY1iTXB5NY1P07CSdJCNF+GICRpwTGAJakRA3g/JHksyb1J1idZt5v1SXJpd/n0PUle3rWfmOQLSe7v2n++dU196w9LMp3kA5NQU5Jjk3wmyYNJHkhy3ITU9fvd/78Huz4ZU03f3/3uPJ3k13ZZN7LL9fe1riTHJPls9//u/iS/0rqmvvUHJfm7JH85rJr2WlX52scX8BjwojnWnw78NRDgFOCOrv14YKpbPhrYDBzesqa+9X8MfBz4QOs/p27d/wV+ult+PvDvWtcFvBL4W3onhA8CvgCcOqaaXgz8KHAJ8Gt97QcBG4GXAM8D/h44YYx/VrPVdRTw8m75BcA/DKuufa2pb/27ut/1vxzWn9PevjwCHq0zgCur53bg8CRHVdU/VNXDAFW1CdgCLG5ZE0CSVwBHAp8ZUy1z1tTd7+PgqroZoKq+VVVPta4LKOAQekG3CHgu8Pg4CqqqLVV1J7DrPU+bXq4/W11Vtbmq7u6Wvwk8CCxpWRNAkqXAfwD+bBy1zMYA3j8FfCbJXd1l0LtaAnyl7/00u/zyJTmZ3l/kjS1rSvIc4H3Ad31Va1UTvW8K/5zkuu6r4h+kd2OmpnVV1ReAz9L75rIZuKmqHhxTTbPZ4+9ao7q+rRs+Ogm4YwJq+iPgN4BnhlTLPpkX84An2KuraibJi4GbkzxUVbcNunF3NPW/gXOrali/CPta0zuAG6pqekjDmcOo6WDgNfT+0v4j8AngPODDLetK8n3AD9C7IpNu29dU1f9rVdMY7O/v+vOBa4F3VtWTLWtK8jPAlqq6K8mpQ6pln3gEvB+qaqb7uQVYS+9rYL9ZL6FOchjwV8BvdV9vW9f048CFSR4D3guck+T3Gtc0DazvvlZvB/4ceDlDsh91rQJu74ZEvkVvnPjHx1TTbEZ6uf5+1EWS59IL349V1XUTUNOrgDd1v+tXA6cl+T/DqmtvGMD7KMn3JHnBzmXg9cB9u3S7nl6QJckpwDeqanN6l1OvpTe++KlJqKmqfqGqjq2q4+gNQ1xZVft9Jn1/aqJ3CfrhSXaOj58GPLC/NQ2hrn8EfiLJwV24/AS9sc1x1DSbkV2uvz91pfd16sPAg1X1/mHUs781VdXFVbW0+10/C7i1qv7zsGrbGw5B7LsjgbXd1/WDgY9X1Y1Jzgeoqg8CN9A7k74BeAp4a7ftmcBrge9Ncl7Xdl5VrW9Y06jsc01VtaObPnRL9xf5LuBDresCPkXvH4N76Y1D3lhVfzGOmpL8e2AdcBjwTJJ30ptV8GRGd7n+PtcF/DDwFuDeJOu7/f1mVd3QqqYhDoHsNy9FlqRGHIKQpEYMYElqxACWpEYMYElqxACWpFkkuSLJliSDTrs7M8/eeOjje+zvLAipJ8m3qur53SWzDwIP0bvnwzeBP62qjzQsTw0keS3wLXrz4l+2h75TwDXAaVX19SQv7i4SmZXzgKXd21hVJwEkeQlwXZJU1f9qXJfGqKpuyy63QE2yHLiM3g20ngLeXlUPAW8HLquqr3fbzhm+4BCEtEdV9Qi9Wxde1LoWTYQ1wC9X1SvoXTX6p1378cDxSf42ye1JVu5pRx4BS4O5G/j+1kWore6mQq8EPtl306pF3c+DgSngVHr34rgtyQ9V1T/Ptj8DWBrM0G8Rp3npOcA/V9WJu1k3Te+m/duAR5P8A71AvnOunUnas5MYwg13NL9195F4NMnPwbcfW/Uj3eo/p3f0S5IX0RuSeGSu/RnA0h50J2HeC/xJ41I0ZkmuovfIqZem96zEtwG/ALwtyd8D9/Psk0duAr6W5AF6N+z/9ar62pz7dxqa1OM0NI2bASxJjTgEIUmNGMCS1IgBLEmNGMCS1IgBLEmNGMCS1IgBLEmN/H+ea1vncdjg7AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAYiklEQVR4nO3de5RdZ33e8e8Ti1sqXNv1VLZsJzJIiWpoIhLFmEtWDS4gG1pDSwwOwQ4LorSVWww0jU1KzSW0tOUWshR3CVAxXQ5GBRwE9rJxjIHSxcXCuL4THxS7liwsOeZmYJGO+fWPsyUfhpnRjDRn3jkz389aZ83ev73PPu+ey6Otd+/97lQVkqT593OtGyBJS5UBLEmNGMCS1IgBLEmNGMCS1Miy1g0Yhg0bNtQ111zTuhmStF8mKy7KI+AHH3ywdRMk6aAWZQBL0igwgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpkUU5GtpcGh8fp9frHZhfvXo1y5b5bZN0+EySg+j1emzcfBXLx1by8L772bLphaxdu7Z1syQtAgbwDCwfW8mRx61q3QxJi4x9wJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUyNACOMlJSW5IckeS25O8tqu/OcnuJDd3r7MG3nNxkl6SbyR5wUB9Q1frJbloWG0+XOPj49x1110HXuPj462bJGkBG+atyOPAG6rqpiRPBL6W5Lpu2Xuq6p2DKyc5BXg58BRgJfCXSX6pW7wZeB6wC7gxyfaqumOIbT8kjhshaTaGFsBVtQfY001/P8mdwAnTvOVs4Iqq+jHw10l6wKndsl5V7QRIckW37oILYHDcCEkzNy99wElWAU8DvtKVLkhyS5KtSY7uaicA9w28bVdXm6o+8TM2JtmRZMe+ffvmehckac4NPYCTLAc+DlxYVd8DLgWeDKyjf4T8rrn4nKraUlXrq2r92NjYXGxSkoZqqMNRJnkM/fC9vKo+AVBVDwwsfz/w6W52N3DSwNtP7GpMU5ekkTXMqyACfBC4s6rePVA/fmC1lwC3ddPbgZcneVySk4E1wFeBG4E1SU5O8lj6J+q2D6vdkjRfhnkE/CzglcCtSW7uam8Ezk2yDijgHuD3Aarq9iTb6J9cGwc2VdUjAEkuAK4FjgC2VtXtQ2z3jAw+qsjHFEk6FMO8CuKLQCZZdPU073k78PZJ6ldP974W9l9yBni5maRD4mHbYVg+tnJG6/lgT0mTMQXmgTdoSJqMATxPvEFD0kQOxiNJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjSxr3YClbHx8nF6vd2B+9erVLFvmj0RaKvxrb6jX67Fx81UsH1vJw/vuZ8umF7J27drWzZI0T4bWBZHkpCQ3JLkjye1JXtvVj0lyXZK7u69Hd/UkeV+SXpJbkvzawLbO79a/O8n5w2pzC8vHVnLkcatYPraydVMkzbNh9gGPA2+oqlOA04BNSU4BLgKur6o1wPXdPMCZwJrutRG4FPqBDVwCPB04Fbhkf2hL0igbWgBX1Z6quqmb/j5wJ3ACcDZwWbfaZcCLu+mzgQ9X35eBo5IcD7wAuK6qHqqqbwPXARuG1W5Jmi/zchVEklXA04CvACuqak+36FvAim76BOC+gbft6mpT1SVppA09gJMsBz4OXFhV3xtcVlUF1Bx9zsYkO5Ls2Ldv31xsUpKGaqgBnOQx9MP38qr6RFd+oOtaoPu6t6vvBk4aePuJXW2q+k+pqi1Vtb6q1o+Njc3tjkjSEAzzKogAHwTurKp3DyzaDuy/kuF84JMD9fO6qyFOA77bdVVcCzw/ydHdybfndzVJGmnDvA74WcArgVuT3NzV3gi8A9iW5NXAvcA53bKrgbOAHvBD4FUAVfVQkrcBN3brvbWqHhpiuyVpXgwtgKvqi0CmWHzGJOsXsGmKbW0Fts5d6ySpPceCkKRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGlrVugH7W+Pg4vV7vwPzq1atZtswflbTY+Fe9APV6PTZuvorlYyt5eN/9bNn0QtauXdu6WZLmmAG8QC0fW8mRx61q3QxJQ2QfsCQ1YgBLUiMGsCQ1YgBLUiOehBshXp4mLS7+9Y4QL0+TFhcDeMR4eZq0eNgHLEmNGMCS1IgBLEmNGMCS1IgBLEmNGMCS1MjQAjjJ1iR7k9w2UHtzkt1Jbu5eZw0suzhJL8k3krxgoL6hq/WSXDSs9krSfBvmEfCHgA2T1N9TVeu619UASU4BXg48pXvPnyU5IskRwGbgTOAU4NxuXUkaeUO7EaOqvpBk1QxXPxu4oqp+DPx1kh5waresV1U7AZJc0a17x1y3V5LmW4s+4AuS3NJ1URzd1U4A7htYZ1dXm6r+M5JsTLIjyY59+/YNo92SNKdmFMBJnjWT2gxcCjwZWAfsAd51CNuYVFVtqar1VbV+bGxsrjYrSUMz0yPgP51hbVpV9UBVPVJVPwHez6PdDLuBkwZWPbGrTVWXpJE3bR9wkmcAzwTGkrx+YNGRwBGz/bAkx1fVnm72JcD+KyS2A3+e5N3ASmAN8FUgwJokJ9MP3pcDvz3bz5WkhehgJ+EeCyzv1nviQP17wEune2OSjwCnA8cm2QVcApyeZB1QwD3A7wNU1e1JttE/uTYObKqqR7rtXABcSz/wt1bV7TPfPUlauKYN4Kr6PPD5JB+qqntns+GqOneS8genWf/twNsnqV8NXD2bz5akUTDTy9Ael2QLsGrwPVX13GE0SpKWgpkG8P8E/hvwAeCR4TVHkpaOmQbweFVdOtSW6JD5rDhpNM30r/RTSf4VcCXw4/3FqnpoKK3SrPisOGk0zTSAz+++/sFArYAnzW1zdKh8Vpw0emYUwFV18rAbIklLzYwCOMl5k9Wr6sNz2xxJWjpm2gXxGwPTjwfOAG4CDGBJOkQz7YL414PzSY4CrhhGgyRpqTjU4Sh/ANgvLEmHYaZ9wJ+if9UD9Mdk+AfAtmE1SpKWgpn2Ab9zYHocuLeqdg2hPZK0ZMyoC6IblOcu+iOiHQ387TAbJUlLwUyfiHEO/fF5fws4B/hKkmmHo5QkTW+mXRB/BPxGVe0FSDIG/CXwsWE1TJIWu5leBfFz+8O38zezeK8kaRIzPQK+Jsm1wEe6+ZfhIOmSdFgO9ky41cCKqvqDJP8MeHa36EvA5cNunA6Pw1RKC9vB/hrfC1wMUFWfAD4BkOQfdsv+yRDbpsPkMJXSwnawAF5RVbdOLFbVrUlWDadJmksOUyktXAc7kXbUNMueMIftkKQl52ABvCPJ700sJnkN8LXhNEmSloaDdUFcCFyZ5BU8GrjrgccCLxliuyRp0Zs2gKvqAeCZSZ4DPLUrX1VVnx16yyRpkZvpeMA3ADcMuS2StKR4N5skNWIAS1IjBrAkNWIAS1IjBrAkNWIAS1IjBrAkNWIAS1IjBrAkNWIAS1IjBrAkNWIAS1IjBrAkNWIAS1IjBrAkNWIAS1IjBrAkNWIAS1IjBrAkNWIAS1IjBrAkNWIAS1IjQwvgJFuT7E1y20DtmCTXJbm7+3p0V0+S9yXpJbklya8NvOf8bv27k5w/rPYuJePj49x1110HXuPj462bJC1JwzwC/hCwYULtIuD6qloDXN/NA5wJrOleG4FLoR/YwCXA04FTgUv2h7YOXa/XY+Pmq3j9tq+zcfNV9Hq91k2SlqShBXBVfQF4aEL5bOCybvoy4MUD9Q9X35eBo5IcD7wAuK6qHqqqbwPX8bOhrkOwfGwlRx63iuVjK1s3RVqy5rsPeEVV7emmvwWs6KZPAO4bWG9XV5uq/jOSbEyyI8mOffv2zW2rJWkImp2Eq6oCag63t6Wq1lfV+rGxsbnarCQNzXwH8ANd1wLd171dfTdw0sB6J3a1qeqSNPLmO4C3A/uvZDgf+ORA/bzuaojTgO92XRXXAs9PcnR38u35XU2SRt6yYW04yUeA04Fjk+yifzXDO4BtSV4N3Auc061+NXAW0AN+CLwKoKoeSvI24MZuvbdW1cQTe5I0koYWwFV17hSLzphk3QI2TbGdrcDWOWyaJC0I3gknSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0M7ZlwGm3j4+P0er0D86tXr2bZMn9dpLnkX5Qm1ev12Lj5KpaPreThffezZdMLWbt2betmSYuKAawpLR9byZHHrWrdDGnRsg9YkhrxCLhjn6ek+WbCdOzzlDTfDOAB9nlKmk/2AUtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSI44HrFkZfHLIoT41xKePSH3+1mtW9j85BDjkp4b49BGpzwDWrC0fWzkn2/DpI1rq7AOWpEYMYElqpEkXRJJ7gO8DjwDjVbU+yTHAR4FVwD3AOVX17SQB/gQ4C/gh8LtVdVOLdmtynlSTDk3LI+DnVNW6qlrfzV8EXF9Va4Dru3mAM4E13WsjcOm8t1TT2n9S7fXbvs7GzVf9VBhLmtpC6oI4G7ism74MePFA/cPV92XgqCTHN2ifprH/pNpcnKCTlopWAVzAZ5J8LcnGrraiqvZ0098CVnTTJwD3Dbx3V1f7KUk2JtmRZMe+ffuG1W5JmjOtOuqeXVW7k/x94Lokdw0urKpKUrPZYFVtAbYArF+/flbvlaQWmhwBV9Xu7ute4ErgVOCB/V0L3de93eq7gZMG3n5iV5OkkTbvAZzk7yR54v5p4PnAbcB24PxutfOBT3bT24Hz0nca8N2BrgpJGlktuiBWAFf2ry5jGfDnVXVNkhuBbUleDdwLnNOtfzX9S9B69C9De9X8N1mS5t68B3BV7QR+dZL63wBnTFIvYNM8NE1zzOuDpen516ChcdAdaXoGsIbKQXekqS2kGzEkaUkxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhpxPGAtGT6hQwuNv31aMnxChxYaA1hLik/o0EJiAGvRsatBo8LfSi14sw1Uuxo0KgxgLRhTBe2hBKpdDRoFBrAWjOmC1kDVYmQAa0ExaLWUeCOGJDViAEtSIwawJDViH7BGltf7atT526qR5fW+GnUGsEaaV01olNkHLEmNeASsJe9gfcn2NWtY/C3SknewvmT7mjUsBrDEwfuSZ9PX7BGzZsrfCukQzeXgQVqaDGDpEDl4kA6XASwdBoNWh8PL0CSpEY+ApXkyVZ/xbE/aeZJv8fCnJs2TqfqMZ3vSzpN8i4cBLM2jqfqMJ6tPd6Q72/W1MPnTkRYoj4wXPwNYWsBme5XFfFyV4ZH23PG7JmlWPNKeOwawpFmzD3pu+N2RFrnZBOPhhOhUR8YG89RG5ruQZAPwJ8ARwAeq6h2NmySNhNkE4+F2L0x2ZDxXwTxXQb6Q/kEYiQBOcgSwGXgesAu4Mcn2qrqjbcuk0TDTYJxq3fn6/OmCebb/OMzFdoYd1iMRwMCpQK+qdgIkuQI4G5jTAH543/0Hvu7c+XcB2Llz50Hrs1l3IdcHLaX9Ptj3Y6G1ay7rE/dz/zpTfS/m4/N37tzJm/7H9Tzh6DF+9O19vO2VZ/CkJz1pyvWnMhfbmbiNyy/5vTk94ZiqmrONDUuSlwIbquo13fwrgadX1QUD62wENnazvwx84xA+6ljgwcNs7qhZivsM7vdS03q/H6yqDROLo3IEfFBVtQXYcjjbSLKjqtbPUZNGwlLcZ3C/W7djvi3U/R6V0dB2AycNzJ/Y1SRpZI1KAN8IrElycpLHAi8HtjdukyQdlpHogqiq8SQXANfSvwxta1XdPoSPOqwujBG1FPcZ3O+lZkHu90ichJOkxWhUuiAkadExgCWpEQOY/m3OSb6RpJfkotbtGZYkW5PsTXLbQO2YJNclubv7enTLNg5DkpOS3JDkjiS3J3ltV1/U+57k8Um+muT/dPv9lq5+cpKvdL/vH+1ObC8qSY5I8vUkn+7mF+Q+L/kAHrjN+UzgFODcJKe0bdXQfAiYeDH4RcD1VbUGuL6bX2zGgTdU1SnAacCm7me82Pf9x8Bzq+pXgXXAhiSnAf8ZeE9VrQa+Dby6XROH5rXAnQPzC3Kfl3wAM3Cbc1X9LbD/NudFp6q+ADw0oXw2cFk3fRnw4vls03yoqj1VdVM3/X36f5gnsMj3vfoe7mYf070KeC7wsa6+6PY7yYnAC4EPdPNhge6zAdz/Q7xvYH5XV1sqVlTVnm76W8CKlo0ZtiSrgKcBX2EJ7Hv3X/Gbgb3AdcA3ge9U1Xi3ymL8fX8v8O+An3Tzf48Fus8GsA6o/jWJi/a6xCTLgY8DF1bV9waXLdZ9r6pHqmod/btHTwUW9aMrkrwI2FtVX2vdlpkYiRsxhmyp3+b8QJLjq2pPkuPpHyktOkkeQz98L6+qT3TlJbHvAFX1nSQ3AM8AjkqyrDsiXGy/788C/mmSs4DHA0fSH0d8Qe6zR8De5rwdOL+bPh/4ZMO2DEXXB/hB4M6qevfAokW970nGkhzVTT+B/njadwI3AC/tVltU+11VF1fViVW1iv7f8mer6hUs0H32Tjig+9fyvTx6m/Pb27ZoOJJ8BDid/tB8DwCXAH8BbAN+AbgXOKeqJp6oG2lJng38L+BWHu0XfCP9fuBFu+9JfoX+Cacj6B9sbauqtyZ5Ev2TzccAXwd+p6p+3K6lw5HkdODfVtWLFuo+G8CS1IhdEJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJoX6ftikjMHar+V5JokjyS5eeB10cA6xyb5f0n+xYTt3ZPk1iS3JPl8kl8cWPZH3fCLt3Tbe/o07fpcNxTp/s/+WFd/c5JKsnpg3Qu72vpJ2vCZJMcN1I+d8DmPTfLebjjEu5N8MsmJSZ6X5EvdzSKDwyg+s2vD7gnfm6OSnJ7ku91630jyhe4WXI2aqvLla15ewFPp34n1eGA5cDfwZODhad7zL+nfRPH5CfV7gGO76bcA7++mnwF8CXhcN38ssHKa7X8OWD9J/c3ALcC/H6j9b+C2/etPaMN/BN43sT7w3nfSvxvviG7+VcBXgdC/QeA1Xf3CgX15M/0bCSa27XTg0wPz67rPPKP1z9jX7F4eAWveVNVtwKeAPwT+A/DhqvrmQd52LvAG4IRumMHJfIlHR7c6HniwurucqurBqrr/EJv8F3RDkyZ5MvBd4MEp1v0CsHqyBUl+nn7gvq6qHuna9d/pxusFXgdcnOQpwAX0vz8zVlU3A2/t3qsRYgBrvr0F+G36A+D/l672hAn/zX4Z9J9kARxfVV+lf8vwy6bY5gb6YQnwGeCkJH+V5M+S/KMZtOnygc/+rwP17wH3JXkq/XEFPjrNNl5E/1bnyawG/m9NGIEN2AE8pfpDYr6X/j8kf1w/fTv06wbadsM0n38Ti3yks8XI0dA0r6rqB0k+Sr/bYf+9+D+q/pCJE72MfvBC/7/pW4F3DSy/IckxwMPAm7rtP5zk14HfBJ4DfDTJRVX1oWma9Yqq2jHFsivoh+8LgDPoH8kOuiHJI3TdFdN8xsFsBt4xSTvfU1XvnMH7cxifrUY8AlYLP+HRQXGmcy7wu0nuoT9y2a8kWTOw/DnALwI30z+yBg6Mgfu5qrqE/n/L//lhtPXTwCuZ/AgW4DlVta6qzquq70yxjW8Cv5DkiRPqvw7c3rX5JxzeeMRP46cfwaMRYABrQUryS8DyqjqhqlZVf3jB/0Q/lA+o/viuFwLnpf+QzV+eENLr6I90dkiq6of0+2QPeYS8qvoB/VHJ3p3+MwhJch7w88BnD3W7+3Wjnr2J/lG0RohdEFoInpD+Y3P2uwb4EXDlhPU+Tr8f9q2DxeoPqP4RYBNwNfCn3Ti440AP2HiQz788yY+66Qer6h9P2P4VM9+VA25Jsv8ofxtwMf0rIf6qq98FvKSqDnbU+7okvzMw/+Lu628m+Tr9EN8L/Juquv4Q2qmGHI5SkhqxC0KSGrELQktCkiuBkyeU/7Cqrm3RHgnsgpCkZuyCkKRGDGBJasQAlqRGDGBJauT/Awg7Q2ElUQuAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in (features for features in credit_df.columns if features != 'TARGET'):\n",
    "    if credit_df[feature].dtype == 'O':\n",
    "        plt.figure()\n",
    "        g = sns.countplot(x = feature, data = credit_df)\n",
    "        g.set_xticklabels(g.get_xticklabels(), rotation = 40, ha = \"right\")\n",
    "    else:\n",
    "        plt.figure()\n",
    "        sns.displot(credit_df[feature])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e33350f",
   "metadata": {},
   "source": [
    "### Observing Correlation between features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8cdd9807",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a Heatmap\n",
    "\n",
    "corrmat = credit_df.corr()\n",
    "features = corrmat.index\n",
    "plt.figure(figsize = [20,20])\n",
    "\n",
    "g = sns.heatmap(credit_df[features].corr(),annot = True, cmap = \"RdYlGn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8c8aa6fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# observing the feature FLAG_MOBIL\n",
    "credit_df['FLAG_MOBIL'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "479a17d1",
   "metadata": {},
   "source": [
    "It can be clearly seen that the column FLAG_MOBIL has no correlation with any of the feature and is just a constant so we can simply drop this column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "23cea13b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# droping FLAG_MOBIL column from dataframe\n",
    "credit_df.drop('FLAG_MOBIL', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7c89b70",
   "metadata": {},
   "source": [
    "## Categorical Features Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9bc7f11",
   "metadata": {},
   "source": [
    "Separating the data into independent and dependent features as our target feature is a categorical feature, we dont have to affect it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "feb53016",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [c for c in credit_df.columns if c not in ['TARGET']]\n",
    "credit_x = credit_df[columns]\n",
    "credit_y = credit_df['TARGET']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f72cc677",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25134, 17)   (25134,)\n"
     ]
    }
   ],
   "source": [
    "print(credit_x.shape,' ',credit_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "19d6af7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['GENDER', 'CAR', 'REALITY', 'INCOME_TYPE', 'EDUCATION_TYPE', 'FAMILY_TYPE', 'HOUSE_TYPE']\n"
     ]
    }
   ],
   "source": [
    "# creating a list of categorical features\n",
    "cat_feat = [features for features in credit_x.columns if credit_x[features].dtype == 'O']\n",
    "print(cat_feat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "32cde2df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cardinality of GENDER : 2\n",
      "Unique values of GENDER : ['M' 'F']\n",
      "\n",
      "Cardinality of CAR : 2\n",
      "Unique values of CAR : ['Y' 'N']\n",
      "\n",
      "Cardinality of REALITY : 2\n",
      "Unique values of REALITY : ['Y' 'N']\n",
      "\n",
      "Cardinality of INCOME_TYPE : 5\n",
      "Unique values of INCOME_TYPE : ['Working' 'Commercial associate' 'State servant' 'Student' 'Pensioner']\n",
      "\n",
      "Cardinality of EDUCATION_TYPE : 5\n",
      "Unique values of EDUCATION_TYPE : ['Secondary / secondary special' 'Higher education' 'Incomplete higher'\n",
      " 'Lower secondary' 'Academic degree']\n",
      "\n",
      "Cardinality of FAMILY_TYPE : 5\n",
      "Unique values of FAMILY_TYPE : ['Married' 'Single / not married' 'Civil marriage' 'Separated' 'Widow']\n",
      "\n",
      "Cardinality of HOUSE_TYPE : 6\n",
      "Unique values of HOUSE_TYPE : ['House / apartment' 'Rented apartment' 'Municipal apartment'\n",
      " 'With parents' 'Co-op apartment' 'Office apartment']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# checking the cardinality of each categorical feature\n",
    "\n",
    "for feature in cat_feat:\n",
    "    print(\"Cardinality of {} : {}\".format(feature, credit_x[feature].nunique()))\n",
    "    print(\"Unique values of {} : {}\".format(feature, credit_x[feature].unique()))\n",
    "    print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a46dd35d",
   "metadata": {},
   "source": [
    "As it can be observed that the cardinality of the categorical features is not very high so we can encode them easily by One Hot Encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5013c75c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\prank\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\core\\frame.py:4908: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    }
   ],
   "source": [
    "# Encoding the categorical features \n",
    "for feature in cat_feat:\n",
    "    encoded = pd.get_dummies(credit_x[feature], prefix = feature, drop_first = True)\n",
    "    credit_x.drop(feature, axis = 1, inplace = True)\n",
    "    credit_x = pd.concat([credit_x, encoded], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "69522400",
   "metadata": {},
   "outputs": [
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       "        ID  NO_OF_CHILD    INCOME  WORK_PHONE  PHONE  E_MAIL  FAMILY SIZE  \\\n",
       "0  5008806            0  112500.0           0      0       0            2   \n",
       "1  5008808            0  270000.0           0      1       1            1   \n",
       "2  5008809            0  270000.0           0      1       1            1   \n",
       "3  5008810            0  270000.0           0      1       1            1   \n",
       "4  5008811            0  270000.0           0      1       1            1   \n",
       "\n",
       "   BEGIN_MONTH  AGE  YEARS_EMPLOYED  GENDER_M  CAR_Y  REALITY_Y  \\\n",
       "0           29   59               3         1      1          1   \n",
       "1            4   52               8         0      0          1   \n",
       "2           26   52               8         0      0          1   \n",
       "3           26   52               8         0      0          1   \n",
       "4           38   52               8         0      0          1   \n",
       "\n",
       "   INCOME_TYPE_Pensioner  INCOME_TYPE_State servant  INCOME_TYPE_Student  \\\n",
       "0                      0                          0                    0   \n",
       "1                      0                          0                    0   \n",
       "2                      0                          0                    0   \n",
       "3                      0                          0                    0   \n",
       "4                      0                          0                    0   \n",
       "\n",
       "   INCOME_TYPE_Working  EDUCATION_TYPE_Higher education  \\\n",
       "0                    1                                0   \n",
       "1                    0                                0   \n",
       "2                    0                                0   \n",
       "3                    0                                0   \n",
       "4                    0                                0   \n",
       "\n",
       "   EDUCATION_TYPE_Incomplete higher  EDUCATION_TYPE_Lower secondary  \\\n",
       "0                                 0                               0   \n",
       "1                                 0                               0   \n",
       "2                                 0                               0   \n",
       "3                                 0                               0   \n",
       "4                                 0                               0   \n",
       "\n",
       "   EDUCATION_TYPE_Secondary / secondary special  FAMILY_TYPE_Married  \\\n",
       "0                                             1                    1   \n",
       "1                                             1                    0   \n",
       "2                                             1                    0   \n",
       "3                                             1                    0   \n",
       "4                                             1                    0   \n",
       "\n",
       "   FAMILY_TYPE_Separated  FAMILY_TYPE_Single / not married  FAMILY_TYPE_Widow  \\\n",
       "0                      0                                 0                  0   \n",
       "1                      0                                 1                  0   \n",
       "2                      0                                 1                  0   \n",
       "3                      0                                 1                  0   \n",
       "4                      0                                 1                  0   \n",
       "\n",
       "   HOUSE_TYPE_House / apartment  HOUSE_TYPE_Municipal apartment  \\\n",
       "0                             1                               0   \n",
       "1                             1                               0   \n",
       "2                             1                               0   \n",
       "3                             1                               0   \n",
       "4                             1                               0   \n",
       "\n",
       "   HOUSE_TYPE_Office apartment  HOUSE_TYPE_Rented apartment  \\\n",
       "0                            0                            0   \n",
       "1                            0                            0   \n",
       "2                            0                            0   \n",
       "3                            0                            0   \n",
       "4                            0                            0   \n",
       "\n",
       "   HOUSE_TYPE_With parents  \n",
       "0                        0  \n",
       "1                        0  \n",
       "2                        0  \n",
       "3                        0  \n",
       "4                        0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# observing the data after encoding\n",
    "credit_x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "738691e9",
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>52</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>52</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>52</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   NO_OF_CHILD    INCOME  WORK_PHONE  PHONE  E_MAIL  FAMILY SIZE  BEGIN_MONTH  \\\n",
       "0            0  112500.0           0      0       0            2           29   \n",
       "1            0  270000.0           0      1       1            1            4   \n",
       "2            0  270000.0           0      1       1            1           26   \n",
       "3            0  270000.0           0      1       1            1           26   \n",
       "4            0  270000.0           0      1       1            1           38   \n",
       "\n",
       "   AGE  YEARS_EMPLOYED  GENDER_M  CAR_Y  REALITY_Y  INCOME_TYPE_Pensioner  \\\n",
       "0   59               3         1      1          1                      0   \n",
       "1   52               8         0      0          1                      0   \n",
       "2   52               8         0      0          1                      0   \n",
       "3   52               8         0      0          1                      0   \n",
       "4   52               8         0      0          1                      0   \n",
       "\n",
       "   INCOME_TYPE_State servant  INCOME_TYPE_Student  INCOME_TYPE_Working  \\\n",
       "0                          0                    0                    1   \n",
       "1                          0                    0                    0   \n",
       "2                          0                    0                    0   \n",
       "3                          0                    0                    0   \n",
       "4                          0                    0                    0   \n",
       "\n",
       "   EDUCATION_TYPE_Higher education  EDUCATION_TYPE_Incomplete higher  \\\n",
       "0                                0                                 0   \n",
       "1                                0                                 0   \n",
       "2                                0                                 0   \n",
       "3                                0                                 0   \n",
       "4                                0                                 0   \n",
       "\n",
       "   EDUCATION_TYPE_Lower secondary  \\\n",
       "0                               0   \n",
       "1                               0   \n",
       "2                               0   \n",
       "3                               0   \n",
       "4                               0   \n",
       "\n",
       "   EDUCATION_TYPE_Secondary / secondary special  FAMILY_TYPE_Married  \\\n",
       "0                                             1                    1   \n",
       "1                                             1                    0   \n",
       "2                                             1                    0   \n",
       "3                                             1                    0   \n",
       "4                                             1                    0   \n",
       "\n",
       "   FAMILY_TYPE_Separated  FAMILY_TYPE_Single / not married  FAMILY_TYPE_Widow  \\\n",
       "0                      0                                 0                  0   \n",
       "1                      0                                 1                  0   \n",
       "2                      0                                 1                  0   \n",
       "3                      0                                 1                  0   \n",
       "4                      0                                 1                  0   \n",
       "\n",
       "   HOUSE_TYPE_House / apartment  HOUSE_TYPE_Municipal apartment  \\\n",
       "0                             1                               0   \n",
       "1                             1                               0   \n",
       "2                             1                               0   \n",
       "3                             1                               0   \n",
       "4                             1                               0   \n",
       "\n",
       "   HOUSE_TYPE_Office apartment  HOUSE_TYPE_Rented apartment  \\\n",
       "0                            0                            0   \n",
       "1                            0                            0   \n",
       "2                            0                            0   \n",
       "3                            0                            0   \n",
       "4                            0                            0   \n",
       "\n",
       "   HOUSE_TYPE_With parents  \n",
       "0                        0  \n",
       "1                        0  \n",
       "2                        0  \n",
       "3                        0  \n",
       "4                        0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# droping the ID column as it is a unique id and wont affect the target\n",
    "credit_x.drop('ID', axis = 1, inplace = True)\n",
    "credit_x.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17f20ebe",
   "metadata": {},
   "source": [
    "## Checking if our data is balanced or imbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cf2d09db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 17,
     "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": [
    "Target = pd.value_counts(credit_y, sort = True)\n",
    "Target.plot(kind = 'bar', rot = 0)\n",
    "plt.title(\"Target Distribution\")\n",
    "plt.xlabel(\"Target\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6a69cb1",
   "metadata": {},
   "source": [
    "It can be clearly seen from the above bar chart that our data is immensely imbalanced, so any model made on such dataset will be definately biased. Such imbalanced dataset can be handled either by 'Undersampling' or 'Oversampling' technique.\n",
    "\n",
    "- Random Oversampling: Randomly duplicate examples in the minority class.\n",
    "- Random Undersampling: Randomly delete examples in the majority class.\n",
    "\n",
    "The major issue with undersampling is that we might miss the important records which can affect our model's prediction, so mostly oversampling is prefered.\n",
    "\n",
    "Some of the more widely used and implemented oversampling methods include:\n",
    "\n",
    "- Random Oversampling\n",
    "- Synthetic Minority Oversampling Technique (SMOTE)\n",
    "- Borderline-SMOTE\n",
    "- Borderline Oversampling with SVM\n",
    "- Adaptive Synthetic Sampling (ADASYN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2b8113e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we will be using Synthetic Minority Oversampling Technique (SMOTE) method\n",
    "\n",
    "smk = SMOTETomek()\n",
    "credit_x_res, credit_y_res = smk.fit_resample(credit_x, credit_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f00612b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25134, 29) (25134,)\n",
      "(49312, 29) (49312,)\n"
     ]
    }
   ],
   "source": [
    "# Observing the data before and after oversampling\n",
    "\n",
    "print(credit_x.shape,credit_y.shape)\n",
    "print(credit_x_res.shape,credit_y_res.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0884d79d",
   "metadata": {},
   "source": [
    "## Feature Scaling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "218377e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling the features by MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "scaled_features = scaler.fit_transform(credit_x_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c0b07ef5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NO_OF_CHILD</th>\n",
       "      <th>INCOME</th>\n",
       "      <th>WORK_PHONE</th>\n",
       "      <th>PHONE</th>\n",
       "      <th>E_MAIL</th>\n",
       "      <th>FAMILY SIZE</th>\n",
       "      <th>BEGIN_MONTH</th>\n",
       "      <th>AGE</th>\n",
       "      <th>YEARS_EMPLOYED</th>\n",
       "      <th>GENDER_M</th>\n",
       "      <th>CAR_Y</th>\n",
       "      <th>REALITY_Y</th>\n",
       "      <th>INCOME_TYPE_Pensioner</th>\n",
       "      <th>INCOME_TYPE_State servant</th>\n",
       "      <th>INCOME_TYPE_Student</th>\n",
       "      <th>INCOME_TYPE_Working</th>\n",
       "      <th>EDUCATION_TYPE_Higher education</th>\n",
       "      <th>EDUCATION_TYPE_Incomplete higher</th>\n",
       "      <th>EDUCATION_TYPE_Lower secondary</th>\n",
       "      <th>EDUCATION_TYPE_Secondary / secondary special</th>\n",
       "      <th>FAMILY_TYPE_Married</th>\n",
       "      <th>FAMILY_TYPE_Separated</th>\n",
       "      <th>FAMILY_TYPE_Single / not married</th>\n",
       "      <th>FAMILY_TYPE_Widow</th>\n",
       "      <th>HOUSE_TYPE_House / apartment</th>\n",
       "      <th>HOUSE_TYPE_Municipal apartment</th>\n",
       "      <th>HOUSE_TYPE_Office apartment</th>\n",
       "      <th>HOUSE_TYPE_Rented apartment</th>\n",
       "      <th>HOUSE_TYPE_With parents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.483333</td>\n",
       "      <td>0.826087</td>\n",
       "      <td>0.069767</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.156977</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.673913</td>\n",
       "      <td>0.186047</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.156977</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>0.673913</td>\n",
       "      <td>0.186047</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.156977</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>0.673913</td>\n",
       "      <td>0.186047</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.156977</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.673913</td>\n",
       "      <td>0.186047</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   NO_OF_CHILD    INCOME  WORK_PHONE  PHONE  E_MAIL  FAMILY SIZE  BEGIN_MONTH  \\\n",
       "0          0.0  0.055233         0.0    0.0     0.0     0.052632     0.483333   \n",
       "1          0.0  0.156977         0.0    1.0     1.0     0.000000     0.066667   \n",
       "2          0.0  0.156977         0.0    1.0     1.0     0.000000     0.433333   \n",
       "3          0.0  0.156977         0.0    1.0     1.0     0.000000     0.433333   \n",
       "4          0.0  0.156977         0.0    1.0     1.0     0.000000     0.633333   \n",
       "\n",
       "        AGE  YEARS_EMPLOYED  GENDER_M  CAR_Y  REALITY_Y  \\\n",
       "0  0.826087        0.069767       1.0    1.0        1.0   \n",
       "1  0.673913        0.186047       0.0    0.0        1.0   \n",
       "2  0.673913        0.186047       0.0    0.0        1.0   \n",
       "3  0.673913        0.186047       0.0    0.0        1.0   \n",
       "4  0.673913        0.186047       0.0    0.0        1.0   \n",
       "\n",
       "   INCOME_TYPE_Pensioner  INCOME_TYPE_State servant  INCOME_TYPE_Student  \\\n",
       "0                    0.0                        0.0                  0.0   \n",
       "1                    0.0                        0.0                  0.0   \n",
       "2                    0.0                        0.0                  0.0   \n",
       "3                    0.0                        0.0                  0.0   \n",
       "4                    0.0                        0.0                  0.0   \n",
       "\n",
       "   INCOME_TYPE_Working  EDUCATION_TYPE_Higher education  \\\n",
       "0                  1.0                              0.0   \n",
       "1                  0.0                              0.0   \n",
       "2                  0.0                              0.0   \n",
       "3                  0.0                              0.0   \n",
       "4                  0.0                              0.0   \n",
       "\n",
       "   EDUCATION_TYPE_Incomplete higher  EDUCATION_TYPE_Lower secondary  \\\n",
       "0                               0.0                             0.0   \n",
       "1                               0.0                             0.0   \n",
       "2                               0.0                             0.0   \n",
       "3                               0.0                             0.0   \n",
       "4                               0.0                             0.0   \n",
       "\n",
       "   EDUCATION_TYPE_Secondary / secondary special  FAMILY_TYPE_Married  \\\n",
       "0                                           1.0                  1.0   \n",
       "1                                           1.0                  0.0   \n",
       "2                                           1.0                  0.0   \n",
       "3                                           1.0                  0.0   \n",
       "4                                           1.0                  0.0   \n",
       "\n",
       "   FAMILY_TYPE_Separated  FAMILY_TYPE_Single / not married  FAMILY_TYPE_Widow  \\\n",
       "0                    0.0                               0.0                0.0   \n",
       "1                    0.0                               1.0                0.0   \n",
       "2                    0.0                               1.0                0.0   \n",
       "3                    0.0                               1.0                0.0   \n",
       "4                    0.0                               1.0                0.0   \n",
       "\n",
       "   HOUSE_TYPE_House / apartment  HOUSE_TYPE_Municipal apartment  \\\n",
       "0                           1.0                             0.0   \n",
       "1                           1.0                             0.0   \n",
       "2                           1.0                             0.0   \n",
       "3                           1.0                             0.0   \n",
       "4                           1.0                             0.0   \n",
       "\n",
       "   HOUSE_TYPE_Office apartment  HOUSE_TYPE_Rented apartment  \\\n",
       "0                          0.0                          0.0   \n",
       "1                          0.0                          0.0   \n",
       "2                          0.0                          0.0   \n",
       "3                          0.0                          0.0   \n",
       "4                          0.0                          0.0   \n",
       "\n",
       "   HOUSE_TYPE_With parents  \n",
       "0                      0.0  \n",
       "1                      0.0  \n",
       "2                      0.0  \n",
       "3                      0.0  \n",
       "4                      0.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# as MinMaxScaler returns a array so we are converting it to a DataFrame\n",
    "\n",
    "credit_x_res = pd.DataFrame(scaled_features,columns = credit_x.columns)\n",
    "credit_x_res.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce5bcb2f",
   "metadata": {},
   "source": [
    "## Train Test Split for model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a820796c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(29587, 29)   (29587,)\n",
      "(19725, 29)   (19725,)\n"
     ]
    }
   ],
   "source": [
    "# splitting data into train and test\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(credit_x_res, credit_y_res, test_size = 0.4)\n",
    "print(x_train.shape,' ',y_train.shape)\n",
    "print(x_test.shape,' ',y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "489a5eee",
   "metadata": {},
   "source": [
    "## Building our KNN Classification Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e840e61",
   "metadata": {},
   "source": [
    "## Identifying the best value of K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "38b87f32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# building KNN model for different values of k and evaluating the accuracy score and error rate\n",
    "accuracy_score = []\n",
    "error_rate = []\n",
    "\n",
    "k_vals = [10,20,30,40,50,60,70,80,90,100]\n",
    "\n",
    "for k in k_vals:\n",
    "    model = KNeighborsClassifier(n_neighbors = k,metric='euclidean', n_jobs = -1)\n",
    "    model.fit(x_train,y_train)\n",
    "    predictions = model.predict(x_test)\n",
    "    \n",
    "    # accuracy score\n",
    "    accuracy_score.append(metrics.accuracy_score(y_test, predictions))\n",
    "    # error rate\n",
    "    error_rate.append(np.mean(predictions != y_test))\n",
    "\n",
    "# K values vs accuracy score\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(k_vals, accuracy_score, color = 'blue',linestyle='dashed', \n",
    "         marker='o',markerfacecolor='red', markersize=10)\n",
    "plt.xlabel('K values')\n",
    "plt.ylabel('Accuracy Score')\n",
    "\n",
    "# K values vs error rate\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(k_vals, error_rate, color = 'blue',linestyle='dashed', \n",
    "         marker='o',markerfacecolor='red', markersize=10)\n",
    "plt.xlabel('K values')\n",
    "plt.ylabel('Error Rate')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e04aec5",
   "metadata": {},
   "source": [
    "You can vary the value K by comparing the accuracy scores and error rate that we are getting in the above graphs. We usually take the K value after which we tend to get a good accuracy and a low error rate because it is considered to be the best K at which our model performs well consistently.\n",
    "\n",
    "We can see that our error rate is increasing as we increase the K value.\n",
    "\n",
    "we can consider K = 80 as we are getting a good accuracy of >85% with a small error rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "81b34662",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(metric='euclidean', n_neighbors=80)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating the model for k=80\n",
    "model = KNeighborsClassifier(n_neighbors = 80,metric='euclidean')\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c0091fa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# making predictions for test data\n",
    "predictions = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b45f6461",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8645880861850443\n",
      "0.13541191381495565\n"
     ]
    }
   ],
   "source": [
    "# accuracy score and error rate for predictions made on test data\n",
    "print(metrics.accuracy_score(y_test, predictions))\n",
    "print(np.mean(predictions != y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e158b4d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8053, 1807],\n",
       "       [ 864, 9001]], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confusion matrix to evaluate the performance of the KNN model\n",
    "metrics.confusion_matrix(y_test, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "187e9eb2",
   "metadata": {},
   "source": [
    "Our model is giving good accuracy with a small error rate and from confusion matrix we can see that our model is performing well for the selected K."
   ]
  }
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