Random-forest-classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('dataset/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36992, 25)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>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>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",
       "      <th>churn_risk_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [customer_id, Name, age, gender, security_no, region_category, membership_category, joining_date, joined_through_referral, referral_id, preferred_offer_types, medium_of_operation, internet_option, last_visit_time, days_since_last_login, avg_time_spent, avg_transaction_value, avg_frequency_login_days, points_in_wallet, used_special_discount, offer_application_preference, past_complaint, complaint_status, feedback, churn_risk_score]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 25 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleaning the Null and Negative values\n",
    "# df = df[df[\"churn_risk_score\"]>=0]\n",
    "\n",
    "# changinng negative values to zero\n",
    "# df[\"churn_risk_score\"] = np.where((df[\"churn_risk_score\"] < 0),0,df[\"churn_risk_score\"])\n",
    "df[df[\"churn_risk_score\"]<0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28373, 25)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                     28373\n",
       "Name                            28373\n",
       "age                             28373\n",
       "gender                          28373\n",
       "security_no                     28373\n",
       "region_category                 28373\n",
       "membership_category             28373\n",
       "joining_date                    28373\n",
       "joined_through_referral         28373\n",
       "referral_id                     28373\n",
       "preferred_offer_types           28373\n",
       "medium_of_operation             28373\n",
       "internet_option                 28373\n",
       "last_visit_time                 28373\n",
       "days_since_last_login           28373\n",
       "avg_time_spent                  28373\n",
       "avg_transaction_value           28373\n",
       "avg_frequency_login_days        28373\n",
       "points_in_wallet                28373\n",
       "used_special_discount           28373\n",
       "offer_application_preference    28373\n",
       "past_complaint                  28373\n",
       "complaint_status                28373\n",
       "feedback                        28373\n",
       "churn_risk_score                28373\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "label_encoder = preprocessing.LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.apply(label_encoder.fit_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "      <th>used_special_discount</th>\n",
       "      <th>offer_application_preference</th>\n",
       "      <th>past_complaint</th>\n",
       "      <th>complaint_status</th>\n",
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      "text/plain": [
       "   customer_id   Name  age  gender  security_no  region_category  \\\n",
       "0        24720  22051    8       0        26668                2   \n",
       "2         4529  19480   34       0         1144                1   \n",
       "3        24843   8084   27       1        24890                0   \n",
       "4         4653  19011   21       0        22828                0   \n",
       "5        26213  15752    3       1        20385                0   \n",
       "\n",
       "   membership_category  joining_date  joined_through_referral  referral_id  \\\n",
       "0                    3           959                        1         9603   \n",
       "2                    2           680                        2          405   \n",
       "3                    2           667                        2         4654   \n",
       "4                    2           985                        1         9603   \n",
       "5                    1           372                        1         9603   \n",
       "\n",
       "   ...  avg_time_spent  avg_transaction_value  avg_frequency_login_days  \\\n",
       "0  ...           12959                  26211                       570   \n",
       "2  ...           16721                  10720                       598   \n",
       "3  ...            2974                  12927                      1251   \n",
       "4  ...            6218                  12489                       567   \n",
       "5  ...           15623                   6937                       601   \n",
       "\n",
       "   points_in_wallet  used_special_discount  offer_application_preference  \\\n",
       "0             16395                      1                             1   \n",
       "2              2370                      0                             1   \n",
       "3              4376                      0                             1   \n",
       "4              8914                      0                             1   \n",
       "5             12704                      1                             0   \n",
       "\n",
       "   past_complaint  complaint_status  feedback  churn_risk_score  \n",
       "0               0                 1         4                 2  \n",
       "2               1                 3         3                 5  \n",
       "3               1                 4         3                 5  \n",
       "4               1                 2         3                 5  \n",
       "5               1                 4         0                 3  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = df[['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']]\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7014096539940197"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score,mean_squared_error\n",
    "score = mean_squared_error(y_test, y_pred)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_df = pd.DataFrame({\n",
    "    \"churn_risk_score\":y_pred.astype(int)\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "      churn_risk_score\n",
       "0                    5\n",
       "1                    4\n",
       "2                    3\n",
       "3                    4\n",
       "4                    5\n",
       "...                ...\n",
       "9359                 3\n",
       "9360                 3\n",
       "9361                 3\n",
       "9362                 4\n",
       "9363                 3\n",
       "\n",
       "[9364 rows x 1 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "  <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": 20,
     "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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                               int64\n",
       "gender                           object\n",
       "region_category                  object\n",
       "membership_category              object\n",
       "joining_date                     object\n",
       "joined_through_referral          object\n",
       "preferred_offer_types            object\n",
       "medium_of_operation              object\n",
       "internet_option                  object\n",
       "last_visit_time                  object\n",
       "days_since_last_login             int64\n",
       "avg_time_spent                  float64\n",
       "avg_transaction_value           float64\n",
       "avg_frequency_login_days         object\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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_test_data2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>region_category</th>\n",
       "      <th>membership_category</th>\n",
       "      <th>joining_date</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>last_visit_time</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>40</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>305</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5375</td>\n",
       "      <td>4</td>\n",
       "      <td>9699</td>\n",
       "      <td>12556</td>\n",
       "      <td>936</td>\n",
       "      <td>10445</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>425</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>16603</td>\n",
       "      <td>3</td>\n",
       "      <td>9400</td>\n",
       "      <td>19623</td>\n",
       "      <td>943</td>\n",
       "      <td>10110</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>792</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12359</td>\n",
       "      <td>10</td>\n",
       "      <td>5667</td>\n",
       "      <td>10979</td>\n",
       "      <td>458</td>\n",
       "      <td>9476</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>960</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11087</td>\n",
       "      <td>0</td>\n",
       "      <td>10635</td>\n",
       "      <td>10935</td>\n",
       "      <td>439</td>\n",
       "      <td>10903</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>124</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2190</td>\n",
       "      <td>23</td>\n",
       "      <td>8955</td>\n",
       "      <td>12529</td>\n",
       "      <td>942</td>\n",
       "      <td>1763</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender  region_category  membership_category  joining_date  \\\n",
       "0   40       0                2                    4           305   \n",
       "1   31       1                2                    1           425   \n",
       "2   21       0                1                    5           792   \n",
       "3   54       1                1                    5           960   \n",
       "4    6       0                1                    2           124   \n",
       "\n",
       "   joined_through_referral  preferred_offer_types  medium_of_operation  \\\n",
       "0                        1                      2                    3   \n",
       "1                        1                      2                    2   \n",
       "2                        1                      1                    1   \n",
       "3                        2                      0                    0   \n",
       "4                        2                      2                    3   \n",
       "\n",
       "   internet_option  last_visit_time  days_since_last_login  avg_time_spent  \\\n",
       "0                2             5375                      4            9699   \n",
       "1                0            16603                      3            9400   \n",
       "2                1            12359                     10            5667   \n",
       "3                0            11087                      0           10635   \n",
       "4                1             2190                     23            8955   \n",
       "\n",
       "   avg_transaction_value  avg_frequency_login_days  points_in_wallet  \\\n",
       "0                  12556                       936             10445   \n",
       "1                  19623                       943             10110   \n",
       "2                  10979                       458              9476   \n",
       "3                  10935                       439             10903   \n",
       "4                  12529                       942              1763   \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  \n",
       "0                 1         2  \n",
       "1                 1         3  \n",
       "2                 3         0  \n",
       "3                 0         7  \n",
       "4                 3         3  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# str_needed = real_test_data.select_dtypes(exclude=[\"float64\",\"int64\"])\n",
    "encoded = real_test_data2.astype(str).apply(label_encoder.fit_transform)\n",
    "real_test_pred = model.predict(encoded)\n",
    "encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fffe43004900440031003900370037003300</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fffe43004900440034003800360037003000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fffe43004900440036003200370033003400</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <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                 4\n",
       "2  fffe43004900440034003800360037003000                 4\n",
       "3  fffe43004900440036003200370033003400                 4\n",
       "4  fffe43004900440035003000370031003900                 5"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_result_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_result_df.to_csv(\"try-1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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