1-6-Try-Dropping-the-Columns-one-by-one.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('dataset/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36992, 25)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customer_id', 'Name', 'age', 'gender', 'security_no',\n",
       "       'region_category', 'membership_category', 'joining_date',\n",
       "       'joined_through_referral', 'referral_id', 'preferred_offer_types',\n",
       "       'medium_of_operation', 'internet_option', 'last_visit_time',\n",
       "       'days_since_last_login', 'avg_time_spent', 'avg_transaction_value',\n",
       "       'avg_frequency_login_days', 'points_in_wallet', 'used_special_discount',\n",
       "       'offer_application_preference', 'past_complaint', 'complaint_status',\n",
       "       'feedback', 'churn_risk_score'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _joined_through_ref_cleaner(row):\n",
    "    yesOrNo = 'Yes' if row[\"referral_id\"] != 'xxxxxxxx' else 'No'\n",
    "    row['joined_through_referral'] = yesOrNo if (row['joined_through_referral'] == '?') else row['joined_through_referral']\n",
    "    return row\n",
    "\n",
    "def joined_through_ref_cleaner(df):\n",
    "    df2 = df.apply(lambda row:_joined_through_ref_cleaner(row),axis=1)\n",
    "    return df2\n",
    "    \n",
    "def churn_risk_score_negative(df):\n",
    "    df.loc[(df[\"churn_risk_score\"] < 0) | (df[\"churn_risk_score\"] > 5)] = int(df[\"churn_risk_score\"].mean())\n",
    "    return df\n",
    "\n",
    "def joining_date_converter(df):\n",
    "    minDate = pd.to_datetime(df['joining_date']).min()\n",
    "#     print(minDate)\n",
    "    df[\"joining_date\"] = (pd.to_datetime(df[\"joining_date\"]) - minDate).astype('timedelta64[D]')\n",
    "    return df\n",
    "\n",
    "def last_visit_time_converter(df):\n",
    "    df['last_visit_time'] = pd.to_timedelta(df['last_visit_time'], unit='ns').dt.total_seconds().astype('int64')\n",
    "#     df[\"last_visit_time\"] = pd.to_datetime(df[\"last_visit_time\"]).astype('int64') // 10**9\n",
    "    return df\n",
    "\n",
    "def transform_data(df):\n",
    "    df1 = joined_through_ref_cleaner(df)\n",
    "    df2 = joining_date_converter(df1)\n",
    "    df3 = last_visit_time_converter(df2)\n",
    "    return df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = transform_data(df)\n",
    "# df = churn_risk_score_negative(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dropAllNA(df):\n",
    "    df[\"gender\"] = df[\"gender\"].replace(\"Unknown\",np.NaN)\n",
    "    df[\"avg_frequency_login_days\"] = df[\"avg_frequency_login_days\"].replace(\"Error\",np.NaN)\n",
    "#     df[\"avg_frequency_login_days\"] = df[\"avg_frequency_login_days\"].replace(\"-999\",50)\n",
    "    df[\"medium_of_operation\"] = df[\"medium_of_operation\"].replace(\"?\",np.NaN)\n",
    "    \n",
    "    df['gender'].fillna(df[\"gender\"].value_counts().index[0],inplace=True)\n",
    "    df['avg_frequency_login_days'].fillna(df[df[\"avg_frequency_login_days\"] != '-999']['avg_frequency_login_days'].astype('float64').mean(),inplace=True)\n",
    "    df['medium_of_operation'].fillna(df[\"medium_of_operation\"].value_counts().index[0],inplace=True)\n",
    "    df['region_category'].fillna(df[\"region_category\"].value_counts().index[0],inplace=True)\n",
    "    df['preferred_offer_types'].fillna(df[\"preferred_offer_types\"].value_counts().index[0],inplace=True)\n",
    "    \n",
    "    df['points_in_wallet'].fillna(df[\"points_in_wallet\"].astype('float64').mean(),inplace=True)\n",
    "    df['points_in_wallet'] = df['points_in_wallet'].astype('float64')\n",
    "    df['avg_frequency_login_days'] = df['avg_frequency_login_days'].astype(\"float64\")\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = dropAllNA(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                     0\n",
       "Name                            0\n",
       "age                             0\n",
       "gender                          0\n",
       "security_no                     0\n",
       "region_category                 0\n",
       "membership_category             0\n",
       "joining_date                    0\n",
       "joined_through_referral         0\n",
       "referral_id                     0\n",
       "preferred_offer_types           0\n",
       "medium_of_operation             0\n",
       "internet_option                 0\n",
       "last_visit_time                 0\n",
       "days_since_last_login           0\n",
       "avg_time_spent                  0\n",
       "avg_transaction_value           0\n",
       "avg_frequency_login_days        0\n",
       "points_in_wallet                0\n",
       "used_special_discount           0\n",
       "offer_application_preference    0\n",
       "past_complaint                  0\n",
       "complaint_status                0\n",
       "feedback                        0\n",
       "churn_risk_score                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.fillna(df.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                     0\n",
       "Name                            0\n",
       "age                             0\n",
       "gender                          0\n",
       "security_no                     0\n",
       "region_category                 0\n",
       "membership_category             0\n",
       "joining_date                    0\n",
       "joined_through_referral         0\n",
       "referral_id                     0\n",
       "preferred_offer_types           0\n",
       "medium_of_operation             0\n",
       "internet_option                 0\n",
       "last_visit_time                 0\n",
       "days_since_last_login           0\n",
       "avg_time_spent                  0\n",
       "avg_transaction_value           0\n",
       "avg_frequency_login_days        0\n",
       "points_in_wallet                0\n",
       "used_special_discount           0\n",
       "offer_application_preference    0\n",
       "past_complaint                  0\n",
       "complaint_status                0\n",
       "feedback                        0\n",
       "churn_risk_score                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36992, 25)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                     0\n",
       "Name                            0\n",
       "age                             0\n",
       "gender                          0\n",
       "security_no                     0\n",
       "region_category                 0\n",
       "membership_category             0\n",
       "joining_date                    0\n",
       "joined_through_referral         0\n",
       "referral_id                     0\n",
       "preferred_offer_types           0\n",
       "medium_of_operation             0\n",
       "internet_option                 0\n",
       "last_visit_time                 0\n",
       "days_since_last_login           0\n",
       "avg_time_spent                  0\n",
       "avg_transaction_value           0\n",
       "avg_frequency_login_days        0\n",
       "points_in_wallet                0\n",
       "used_special_discount           0\n",
       "offer_application_preference    0\n",
       "past_complaint                  0\n",
       "complaint_status                0\n",
       "feedback                        0\n",
       "churn_risk_score                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "label_encoder = preprocessing.LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                      object\n",
       "Name                             object\n",
       "age                               int64\n",
       "gender                           object\n",
       "security_no                      object\n",
       "region_category                  object\n",
       "membership_category              object\n",
       "joining_date                    float64\n",
       "joined_through_referral          object\n",
       "referral_id                      object\n",
       "preferred_offer_types            object\n",
       "medium_of_operation              object\n",
       "internet_option                  object\n",
       "last_visit_time                   int64\n",
       "days_since_last_login             int64\n",
       "avg_time_spent                  float64\n",
       "avg_transaction_value           float64\n",
       "avg_frequency_login_days        float64\n",
       "points_in_wallet                float64\n",
       "used_special_discount            object\n",
       "offer_application_preference     object\n",
       "past_complaint                   object\n",
       "complaint_status                 object\n",
       "feedback                         object\n",
       "churn_risk_score                  int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                      object\n",
       "Name                             object\n",
       "age                               int64\n",
       "gender                           object\n",
       "security_no                      object\n",
       "region_category                  object\n",
       "membership_category              object\n",
       "joining_date                    float64\n",
       "joined_through_referral          object\n",
       "referral_id                      object\n",
       "preferred_offer_types            object\n",
       "medium_of_operation              object\n",
       "internet_option                  object\n",
       "last_visit_time                   int64\n",
       "days_since_last_login             int64\n",
       "avg_time_spent                  float64\n",
       "avg_transaction_value           float64\n",
       "avg_frequency_login_days        float64\n",
       "points_in_wallet                float64\n",
       "used_special_discount            object\n",
       "offer_application_preference     object\n",
       "past_complaint                   object\n",
       "complaint_status                 object\n",
       "feedback                         object\n",
       "churn_risk_score                  int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.select_dtypes(include=\"object\")\n",
    "df2 = df2.astype(str).apply(label_encoder.fit_transform)\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>Name</th>\n",
       "      <th>gender</th>\n",
       "      <th>security_no</th>\n",
       "      <th>region_category</th>\n",
       "      <th>membership_category</th>\n",
       "      <th>joined_through_referral</th>\n",
       "      <th>referral_id</th>\n",
       "      <th>preferred_offer_types</th>\n",
       "      <th>medium_of_operation</th>\n",
       "      <th>internet_option</th>\n",
       "      <th>used_special_discount</th>\n",
       "      <th>offer_application_preference</th>\n",
       "      <th>past_complaint</th>\n",
       "      <th>complaint_status</th>\n",
       "      <th>feedback</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32243</td>\n",
       "      <td>28675</td>\n",
       "      <td>0</td>\n",
       "      <td>34811</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>11358</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7055</td>\n",
       "      <td>34951</td>\n",
       "      <td>0</td>\n",
       "      <td>5804</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2225</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5910</td>\n",
       "      <td>25315</td>\n",
       "      <td>0</td>\n",
       "      <td>1534</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>471</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32400</td>\n",
       "      <td>10462</td>\n",
       "      <td>1</td>\n",
       "      <td>32501</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5506</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6070</td>\n",
       "      <td>24704</td>\n",
       "      <td>0</td>\n",
       "      <td>29819</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>11358</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id   Name  gender  security_no  region_category  \\\n",
       "0        32243  28675       0        34811                2   \n",
       "1         7055  34951       0         5804                0   \n",
       "2         5910  25315       0         1534                1   \n",
       "3        32400  10462       1        32501                0   \n",
       "4         6070  24704       0        29819                0   \n",
       "\n",
       "   membership_category  joined_through_referral  referral_id  \\\n",
       "0                    3                        0        11358   \n",
       "1                    4                        1         2225   \n",
       "2                    2                        1          471   \n",
       "3                    2                        1         5506   \n",
       "4                    2                        0        11358   \n",
       "\n",
       "   preferred_offer_types  medium_of_operation  internet_option  \\\n",
       "0                      1                    1                2   \n",
       "1                      1                    1                1   \n",
       "2                      1                    1                2   \n",
       "3                      1                    1                1   \n",
       "4                      0                    2                1   \n",
       "\n",
       "   used_special_discount  offer_application_preference  past_complaint  \\\n",
       "0                      1                             1               0   \n",
       "1                      1                             0               1   \n",
       "2                      0                             1               1   \n",
       "3                      0                             1               1   \n",
       "4                      0                             1               1   \n",
       "\n",
       "   complaint_status  feedback  \n",
       "0                 1         4  \n",
       "1                 2         5  \n",
       "2                 3         3  \n",
       "3                 4         3  \n",
       "4                 2         3  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\MAYANK\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:2963: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self[k1] = value[k2]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = df2[['gender',\n",
    "       'region_category', 'membership_category', \n",
    "       'joined_through_referral', 'preferred_offer_types',\n",
    "       'medium_of_operation', 'internet_option',\n",
    "        'used_special_discount',\n",
    "       'offer_application_preference', 'past_complaint', 'complaint_status',\n",
    "       'feedback']]\n",
    "X[['points_in_wallet', 'age', 'avg_time_spent', 'avg_frequency_login_days','last_visit_time', 'joining_date', 'days_since_last_login', 'avg_transaction_value']] = df[['points_in_wallet', 'age', 'avg_time_spent','avg_frequency_login_days', 'last_visit_time', 'joining_date', 'days_since_last_login', 'avg_transaction_value']]\n",
    "y = df[\"churn_risk_score\"]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
    "# print(X.shape,y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>gender</th>\n",
       "      <th>region_category</th>\n",
       "      <th>membership_category</th>\n",
       "      <th>joined_through_referral</th>\n",
       "      <th>preferred_offer_types</th>\n",
       "      <th>medium_of_operation</th>\n",
       "      <th>internet_option</th>\n",
       "      <th>used_special_discount</th>\n",
       "      <th>offer_application_preference</th>\n",
       "      <th>past_complaint</th>\n",
       "      <th>complaint_status</th>\n",
       "      <th>feedback</th>\n",
       "      <th>points_in_wallet</th>\n",
       "      <th>age</th>\n",
       "      <th>avg_time_spent</th>\n",
       "      <th>avg_frequency_login_days</th>\n",
       "      <th>last_visit_time</th>\n",
       "      <th>joining_date</th>\n",
       "      <th>days_since_last_login</th>\n",
       "      <th>avg_transaction_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>781.750000</td>\n",
       "      <td>18</td>\n",
       "      <td>300.63</td>\n",
       "      <td>17.0</td>\n",
       "      <td>58082</td>\n",
       "      <td>959.0</td>\n",
       "      <td>17</td>\n",
       "      <td>53005.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>686.882199</td>\n",
       "      <td>32</td>\n",
       "      <td>306.34</td>\n",
       "      <td>10.0</td>\n",
       "      <td>45493</td>\n",
       "      <td>970.0</td>\n",
       "      <td>16</td>\n",
       "      <td>12838.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>500.690000</td>\n",
       "      <td>44</td>\n",
       "      <td>516.16</td>\n",
       "      <td>22.0</td>\n",
       "      <td>82401</td>\n",
       "      <td>680.0</td>\n",
       "      <td>14</td>\n",
       "      <td>21027.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>567.660000</td>\n",
       "      <td>37</td>\n",
       "      <td>53.27</td>\n",
       "      <td>6.0</td>\n",
       "      <td>57470</td>\n",
       "      <td>667.0</td>\n",
       "      <td>11</td>\n",
       "      <td>25239.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>663.060000</td>\n",
       "      <td>31</td>\n",
       "      <td>113.13</td>\n",
       "      <td>16.0</td>\n",
       "      <td>56804</td>\n",
       "      <td>985.0</td>\n",
       "      <td>20</td>\n",
       "      <td>24483.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  region_category  membership_category  joined_through_referral  \\\n",
       "0       0                2                    3                        0   \n",
       "1       0                0                    4                        1   \n",
       "2       0                1                    2                        1   \n",
       "3       1                0                    2                        1   \n",
       "4       0                0                    2                        0   \n",
       "\n",
       "   preferred_offer_types  medium_of_operation  internet_option  \\\n",
       "0                      1                    1                2   \n",
       "1                      1                    1                1   \n",
       "2                      1                    1                2   \n",
       "3                      1                    1                1   \n",
       "4                      0                    2                1   \n",
       "\n",
       "   used_special_discount  offer_application_preference  past_complaint  \\\n",
       "0                      1                             1               0   \n",
       "1                      1                             0               1   \n",
       "2                      0                             1               1   \n",
       "3                      0                             1               1   \n",
       "4                      0                             1               1   \n",
       "\n",
       "   complaint_status  feedback  points_in_wallet  age  avg_time_spent  \\\n",
       "0                 1         4        781.750000   18          300.63   \n",
       "1                 2         5        686.882199   32          306.34   \n",
       "2                 3         3        500.690000   44          516.16   \n",
       "3                 4         3        567.660000   37           53.27   \n",
       "4                 2         3        663.060000   31          113.13   \n",
       "\n",
       "   avg_frequency_login_days  last_visit_time  joining_date  \\\n",
       "0                      17.0            58082         959.0   \n",
       "1                      10.0            45493         970.0   \n",
       "2                      22.0            82401         680.0   \n",
       "3                       6.0            57470         667.0   \n",
       "4                      16.0            56804         985.0   \n",
       "\n",
       "   days_since_last_login  avg_transaction_value  \n",
       "0                     17               53005.25  \n",
       "1                     16               12838.38  \n",
       "2                     14               21027.00  \n",
       "3                     11               25239.56  \n",
       "4                     20               24483.66  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "model = RandomForestClassifier()\n",
    "# model = AdaBoostClassifier()\n",
    "# model = KNeighborsClassifier()\n",
    "# model = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6219734233041044"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score,mean_squared_error\n",
    "score = f1_score(y_test, y_pred,average=\"macro\")\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_df = pd.DataFrame({\n",
    "    \"churn_risk_score\":y_pred\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4, 3, 5, 2], dtype=int64)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df[\"churn_risk_score\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>Name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>security_no</th>\n",
       "      <th>region_category</th>\n",
       "      <th>membership_category</th>\n",
       "      <th>joining_date</th>\n",
       "      <th>joined_through_referral</th>\n",
       "      <th>referral_id</th>\n",
       "      <th>...</th>\n",
       "      <th>days_since_last_login</th>\n",
       "      <th>avg_time_spent</th>\n",
       "      <th>avg_transaction_value</th>\n",
       "      <th>avg_frequency_login_days</th>\n",
       "      <th>points_in_wallet</th>\n",
       "      <th>used_special_discount</th>\n",
       "      <th>offer_application_preference</th>\n",
       "      <th>past_complaint</th>\n",
       "      <th>complaint_status</th>\n",
       "      <th>feedback</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fffe43004900440031003700300030003400</td>\n",
       "      <td>Alethia Meints</td>\n",
       "      <td>50</td>\n",
       "      <td>F</td>\n",
       "      <td>OQJ1XAY</td>\n",
       "      <td>Village</td>\n",
       "      <td>Premium Membership</td>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>No</td>\n",
       "      <td>xxxxxxxx</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>386.26</td>\n",
       "      <td>40721.44</td>\n",
       "      <td>7.0</td>\n",
       "      <td>733.830000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Poor Product Quality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fffe43004900440031003900370037003300</td>\n",
       "      <td>Ming Lopez</td>\n",
       "      <td>41</td>\n",
       "      <td>M</td>\n",
       "      <td>OUQRPKO</td>\n",
       "      <td>Village</td>\n",
       "      <td>Gold Membership</td>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>No</td>\n",
       "      <td>xxxxxxxx</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>37.80</td>\n",
       "      <td>9644.40</td>\n",
       "      <td>9.0</td>\n",
       "      <td>726.000000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Poor Website</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fffe43004900440034003800360037003000</td>\n",
       "      <td>Carina Flannigan</td>\n",
       "      <td>31</td>\n",
       "      <td>F</td>\n",
       "      <td>02J2RE7</td>\n",
       "      <td>Town</td>\n",
       "      <td>Silver Membership</td>\n",
       "      <td>2017-03-03</td>\n",
       "      <td>No</td>\n",
       "      <td>xxxxxxxx</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>215.36</td>\n",
       "      <td>3693.25</td>\n",
       "      <td>21.0</td>\n",
       "      <td>713.780000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Solved in Follow-up</td>\n",
       "      <td>No reason specified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fffe43004900440036003200370033003400</td>\n",
       "      <td>Kyung Wanner</td>\n",
       "      <td>64</td>\n",
       "      <td>M</td>\n",
       "      <td>5YEQIF1</td>\n",
       "      <td>Town</td>\n",
       "      <td>Silver Membership</td>\n",
       "      <td>2017-08-18</td>\n",
       "      <td>Yes</td>\n",
       "      <td>CID8941</td>\n",
       "      <td>...</td>\n",
       "      <td>-999</td>\n",
       "      <td>44.57</td>\n",
       "      <td>36809.56</td>\n",
       "      <td>11.0</td>\n",
       "      <td>744.970000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No Information Available</td>\n",
       "      <td>Too many ads</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fffe43004900440035003000370031003900</td>\n",
       "      <td>Enola Gatto</td>\n",
       "      <td>16</td>\n",
       "      <td>F</td>\n",
       "      <td>100RYB5</td>\n",
       "      <td>Town</td>\n",
       "      <td>No Membership</td>\n",
       "      <td>2015-05-05</td>\n",
       "      <td>Yes</td>\n",
       "      <td>CID5690</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>349.88</td>\n",
       "      <td>40675.86</td>\n",
       "      <td>8.0</td>\n",
       "      <td>299.048351</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Solved in Follow-up</td>\n",
       "      <td>Poor Website</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            customer_id              Name  age gender  \\\n",
       "0  fffe43004900440031003700300030003400    Alethia Meints   50      F   \n",
       "1  fffe43004900440031003900370037003300        Ming Lopez   41      M   \n",
       "2  fffe43004900440034003800360037003000  Carina Flannigan   31      F   \n",
       "3  fffe43004900440036003200370033003400      Kyung Wanner   64      M   \n",
       "4  fffe43004900440035003000370031003900       Enola Gatto   16      F   \n",
       "\n",
       "  security_no region_category membership_category joining_date  \\\n",
       "0     OQJ1XAY         Village  Premium Membership   2015-11-02   \n",
       "1     OUQRPKO         Village     Gold Membership   2016-03-01   \n",
       "2     02J2RE7            Town   Silver Membership   2017-03-03   \n",
       "3     5YEQIF1            Town   Silver Membership   2017-08-18   \n",
       "4     100RYB5            Town       No Membership   2015-05-05   \n",
       "\n",
       "  joined_through_referral referral_id  ... days_since_last_login  \\\n",
       "0                      No    xxxxxxxx  ...                    12   \n",
       "1                      No    xxxxxxxx  ...                    11   \n",
       "2                      No    xxxxxxxx  ...                    18   \n",
       "3                     Yes     CID8941  ...                  -999   \n",
       "4                     Yes     CID5690  ...                     6   \n",
       "\n",
       "  avg_time_spent avg_transaction_value avg_frequency_login_days  \\\n",
       "0         386.26              40721.44                      7.0   \n",
       "1          37.80               9644.40                      9.0   \n",
       "2         215.36               3693.25                     21.0   \n",
       "3          44.57              36809.56                     11.0   \n",
       "4         349.88              40675.86                      8.0   \n",
       "\n",
       "   points_in_wallet  used_special_discount  offer_application_preference  \\\n",
       "0        733.830000                    Yes                            No   \n",
       "1        726.000000                    Yes                            No   \n",
       "2        713.780000                    Yes                            No   \n",
       "3        744.970000                    Yes                            No   \n",
       "4        299.048351                     No                           Yes   \n",
       "\n",
       "  past_complaint          complaint_status              feedback  \n",
       "0             No            Not Applicable  Poor Product Quality  \n",
       "1             No            Not Applicable          Poor Website  \n",
       "2            Yes       Solved in Follow-up   No reason specified  \n",
       "3            Yes  No Information Available          Too many ads  \n",
       "4            Yes       Solved in Follow-up          Poor Website  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_test_data = pd.read_csv(\"dataset/test.csv\")\n",
    "real_test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "real_test_data = transform_data(real_test_data)\n",
    "real_test_data = dropAllNA(real_test_data)\n",
    "real_test_data2 = real_test_data[['age', 'gender',\n",
    "       'region_category', 'membership_category', 'joining_date',\n",
    "       'joined_through_referral', 'preferred_offer_types',\n",
    "       'medium_of_operation', 'internet_option', 'last_visit_time',\n",
    "       'days_since_last_login', 'avg_time_spent', 'avg_transaction_value',\n",
    "       'avg_frequency_login_days', 'points_in_wallet', 'used_special_discount',\n",
    "       'offer_application_preference', 'past_complaint', 'complaint_status',\n",
    "       'feedback']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "real_test_data2 = real_test_data2.fillna(real_test_data2.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                               int64\n",
       "gender                           object\n",
       "region_category                  object\n",
       "membership_category              object\n",
       "joining_date                    float64\n",
       "joined_through_referral          object\n",
       "preferred_offer_types            object\n",
       "medium_of_operation              object\n",
       "internet_option                  object\n",
       "last_visit_time                   int64\n",
       "days_since_last_login             int64\n",
       "avg_time_spent                  float64\n",
       "avg_transaction_value           float64\n",
       "avg_frequency_login_days        float64\n",
       "points_in_wallet                float64\n",
       "used_special_discount            object\n",
       "offer_application_preference     object\n",
       "past_complaint                   object\n",
       "complaint_status                 object\n",
       "feedback                         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_test_data2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoded_str = real_test_data2.select_dtypes(include=\"object\")#.apply(label_encoder.fit_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_ip = encoded_str.astype(str).apply(label_encoder.fit_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>region_category</th>\n",
       "      <th>membership_category</th>\n",
       "      <th>joined_through_referral</th>\n",
       "      <th>preferred_offer_types</th>\n",
       "      <th>medium_of_operation</th>\n",
       "      <th>internet_option</th>\n",
       "      <th>used_special_discount</th>\n",
       "      <th>offer_application_preference</th>\n",
       "      <th>past_complaint</th>\n",
       "      <th>complaint_status</th>\n",
       "      <th>feedback</th>\n",
       "      <th>points_in_wallet</th>\n",
       "      <th>age</th>\n",
       "      <th>avg_time_spent</th>\n",
       "      <th>last_visit_time</th>\n",
       "      <th>joining_date</th>\n",
       "      <th>days_since_last_login</th>\n",
       "      <th>avg_transaction_value</th>\n",
       "      <th>avg_frequency_login_days</th>\n",
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       "      <td>425.0</td>\n",
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       "      <td>215.36</td>\n",
       "      <td>60039</td>\n",
       "      <td>792.0</td>\n",
       "      <td>18</td>\n",
       "      <td>3693.25</td>\n",
       "      <td>21.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>36809.56</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   gender  region_category  membership_category  joined_through_referral  \\\n",
       "0       0                2                    4                        0   \n",
       "1       1                2                    1                        0   \n",
       "2       0                1                    5                        0   \n",
       "3       1                1                    5                        1   \n",
       "4       0                1                    2                        1   \n",
       "\n",
       "   preferred_offer_types  medium_of_operation  internet_option  \\\n",
       "0                      2                    2                2   \n",
       "1                      2                    1                0   \n",
       "2                      1                    0                1   \n",
       "3                      0                    1                0   \n",
       "4                      2                    2                1   \n",
       "\n",
       "   used_special_discount  offer_application_preference  past_complaint  \\\n",
       "0                      1                             0               0   \n",
       "1                      1                             0               0   \n",
       "2                      1                             0               1   \n",
       "3                      1                             0               1   \n",
       "4                      0                             1               1   \n",
       "\n",
       "   complaint_status  feedback    points_in_wallet age avg_time_spent  \\\n",
       "0                 1         2              733.83  50         386.26   \n",
       "1                 1         3               726.0  41           37.8   \n",
       "2                 3         0              713.78  31         215.36   \n",
       "3                 0         7              744.97  64          44.57   \n",
       "4                 3         3  299.04835123722256  16         349.88   \n",
       "\n",
       "   last_visit_time  joining_date days_since_last_login avg_transaction_value  \\\n",
       "0            26370         305.0                    12              40721.44   \n",
       "1            80476         425.0                    11                9644.4   \n",
       "2            60039         792.0                    18               3693.25   \n",
       "3            53777         960.0                  -999              36809.56   \n",
       "4            10673         124.0                     6              40675.86   \n",
       "\n",
       "  avg_frequency_login_days  \n",
       "0                      7.0  \n",
       "1                      9.0  \n",
       "2                     21.0  \n",
       "3                     11.0  \n",
       "4                      8.0  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_ip[['points_in_wallet', 'age', 'avg_time_spent', 'last_visit_time', 'joining_date', 'days_since_last_login', 'avg_transaction_value','avg_frequency_login_days']] = real_test_data2[['points_in_wallet', 'age', 'avg_time_spent', 'last_visit_time', 'joining_date', 'days_since_last_login', 'avg_transaction_value','avg_frequency_login_days']].astype(str)\n",
    "model_ip[['last_visit_time', 'joining_date']] = real_test_data2[['last_visit_time', 'joining_date']]\n",
    "model_ip.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "last_visit_time    0\n",
       "joining_date       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_ip[['last_visit_time', 'joining_date']].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                            int32\n",
       "region_category                   int32\n",
       "membership_category               int32\n",
       "joined_through_referral           int32\n",
       "preferred_offer_types             int32\n",
       "medium_of_operation               int32\n",
       "internet_option                   int32\n",
       "used_special_discount             int32\n",
       "offer_application_preference      int32\n",
       "past_complaint                    int32\n",
       "complaint_status                  int32\n",
       "feedback                          int32\n",
       "points_in_wallet                 object\n",
       "age                              object\n",
       "avg_time_spent                   object\n",
       "last_visit_time                   int64\n",
       "joining_date                    float64\n",
       "days_since_last_login            object\n",
       "avg_transaction_value            object\n",
       "avg_frequency_login_days         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_ip.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# str_needed = real_test_data.select_dtypes(exclude=[\"float64\",\"int64\"])\n",
    "real_test_pred = model.predict(model_ip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_result_df = pd.DataFrame({\n",
    "    \"customer_id\":real_test_data[\"customer_id\"],\n",
    "    \"churn_risk_score\":real_test_pred.astype(int)\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>churn_risk_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fffe43004900440031003700300030003400</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fffe43004900440031003900370037003300</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fffe43004900440034003800360037003000</td>\n",
       "      <td>4</td>\n",
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       "      <th>3</th>\n",
       "      <td>fffe43004900440036003200370033003400</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fffe43004900440035003000370031003900</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            customer_id  churn_risk_score\n",
       "0  fffe43004900440031003700300030003400                 3\n",
       "1  fffe43004900440031003900370037003300                 3\n",
       "2  fffe43004900440034003800360037003000                 4\n",
       "3  fffe43004900440036003200370033003400                 3\n",
       "4  fffe43004900440035003000370031003900                 5"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_result_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_result_df.to_csv(f\"try-score-{score}.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import seaborn as sns\n",
    "# sns.pairplot(model_ip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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