model_building.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Neccessary Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sb\n",
    "\n",
    "# For data splitting\n",
    "from sklearn.model_selection import train_test_split\n",
    "# Import the encoder from sklearn\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "# For resampling\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.over_sampling import SMOTEN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>APP_NAME</th>\n",
       "      <th>RATING</th>\n",
       "      <th>CATEGORY</th>\n",
       "      <th>RATING_COUNT</th>\n",
       "      <th>REVIEW_COUNT</th>\n",
       "      <th>INSTALLS</th>\n",
       "      <th>MAX_INSTALLS</th>\n",
       "      <th>FREE</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>SIZE</th>\n",
       "      <th>...</th>\n",
       "      <th>DAYS_SINCE_RELEASED</th>\n",
       "      <th>SIZEBAND</th>\n",
       "      <th>PRICEBAND</th>\n",
       "      <th>DAYS_SINCE_RELEASED_RANGE</th>\n",
       "      <th>DAYS_SINCE_UPDATE_RANGE</th>\n",
       "      <th>REVIEW_PER_DAY</th>\n",
       "      <th>REVIEW_RATE</th>\n",
       "      <th>RATING_PER_DAY</th>\n",
       "      <th>RATING_RATE</th>\n",
       "      <th>INSTALLS_GROUP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>\"Ghost Voice Catcher\" AUTO EVP</td>\n",
       "      <td>3.933333</td>\n",
       "      <td>Lifestyle</td>\n",
       "      <td>68</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1,000+</td>\n",
       "      <td>3608</td>\n",
       "      <td>False</td>\n",
       "      <td>5.99</td>\n",
       "      <td>1100000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3482.0</td>\n",
       "      <td>very small</td>\n",
       "      <td>expensive</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>0.006893</td>\n",
       "      <td>Low</td>\n",
       "      <td>0.019529</td>\n",
       "      <td>Low</td>\n",
       "      <td>Less than 100K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>\"OXXO\"</td>\n",
       "      <td>4.740000</td>\n",
       "      <td>Puzzle</td>\n",
       "      <td>975</td>\n",
       "      <td>54.0</td>\n",
       "      <td>10,000+</td>\n",
       "      <td>21680</td>\n",
       "      <td>False</td>\n",
       "      <td>0.99</td>\n",
       "      <td>81000000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>945.0</td>\n",
       "      <td>large</td>\n",
       "      <td>cheap</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>Few days ago</td>\n",
       "      <td>0.057143</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1.031746</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Less than 100K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#DRIVE</td>\n",
       "      <td>4.434152</td>\n",
       "      <td>Racing</td>\n",
       "      <td>229679</td>\n",
       "      <td>6473.0</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>9119244</td>\n",
       "      <td>True</td>\n",
       "      <td>0.00</td>\n",
       "      <td>132000000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1084.0</td>\n",
       "      <td>very large</td>\n",
       "      <td>Free</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>Few days ago</td>\n",
       "      <td>5.971402</td>\n",
       "      <td>More than once per day</td>\n",
       "      <td>211.880996</td>\n",
       "      <td>More than 50 per day</td>\n",
       "      <td>Between 100K and 10M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#SelfCare</td>\n",
       "      <td>4.463476</td>\n",
       "      <td>Role Playing</td>\n",
       "      <td>14932</td>\n",
       "      <td>1501.0</td>\n",
       "      <td>1,000,000+</td>\n",
       "      <td>1209692</td>\n",
       "      <td>True</td>\n",
       "      <td>0.00</td>\n",
       "      <td>83000000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1275.0</td>\n",
       "      <td>large</td>\n",
       "      <td>Free</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>1.177255</td>\n",
       "      <td>More than once per day</td>\n",
       "      <td>11.711373</td>\n",
       "      <td>High</td>\n",
       "      <td>Between 100K and 10M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>#open Polyamorous + ENM Dating</td>\n",
       "      <td>2.550000</td>\n",
       "      <td>Dating</td>\n",
       "      <td>708</td>\n",
       "      <td>362.0</td>\n",
       "      <td>50,000+</td>\n",
       "      <td>93463</td>\n",
       "      <td>True</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19000000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1026.0</td>\n",
       "      <td>small</td>\n",
       "      <td>Free</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>Within 1 month</td>\n",
       "      <td>0.352827</td>\n",
       "      <td>High</td>\n",
       "      <td>0.690058</td>\n",
       "      <td>Low</td>\n",
       "      <td>Less than 100K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         APP_NAME    RATING      CATEGORY  RATING_COUNT  \\\n",
       "0  \"Ghost Voice Catcher\" AUTO EVP  3.933333     Lifestyle            68   \n",
       "1                          \"OXXO\"  4.740000        Puzzle           975   \n",
       "2                          #DRIVE  4.434152        Racing        229679   \n",
       "3                       #SelfCare  4.463476  Role Playing         14932   \n",
       "4  #open Polyamorous + ENM Dating  2.550000        Dating           708   \n",
       "\n",
       "   REVIEW_COUNT    INSTALLS  MAX_INSTALLS   FREE  PRICE         SIZE  ...  \\\n",
       "0          24.0      1,000+          3608  False   5.99    1100000.0  ...   \n",
       "1          54.0     10,000+         21680  False   0.99   81000000.0  ...   \n",
       "2        6473.0  5,000,000+       9119244   True   0.00  132000000.0  ...   \n",
       "3        1501.0  1,000,000+       1209692   True   0.00   83000000.0  ...   \n",
       "4         362.0     50,000+         93463   True   0.00   19000000.0  ...   \n",
       "\n",
       "  DAYS_SINCE_RELEASED    SIZEBAND  PRICEBAND  DAYS_SINCE_RELEASED_RANGE  \\\n",
       "0              3482.0  very small  expensive         More than 6 months   \n",
       "1               945.0       large      cheap         More than 6 months   \n",
       "2              1084.0  very large       Free         More than 6 months   \n",
       "3              1275.0       large       Free         More than 6 months   \n",
       "4              1026.0       small       Free         More than 6 months   \n",
       "\n",
       "   DAYS_SINCE_UPDATE_RANGE  REVIEW_PER_DAY             REVIEW_RATE  \\\n",
       "0       More than 6 months        0.006893                     Low   \n",
       "1             Few days ago        0.057143                  Medium   \n",
       "2             Few days ago        5.971402  More than once per day   \n",
       "3       More than 6 months        1.177255  More than once per day   \n",
       "4           Within 1 month        0.352827                    High   \n",
       "\n",
       "  RATING_PER_DAY           RATING_RATE        INSTALLS_GROUP  \n",
       "0       0.019529                   Low        Less than 100K  \n",
       "1       1.031746                Medium        Less than 100K  \n",
       "2     211.880996  More than 50 per day  Between 100K and 10M  \n",
       "3      11.711373                  High  Between 100K and 10M  \n",
       "4       0.690058                   Low        Less than 100K  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./datasets/google_app_scrap_cleaned2.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f953dce6",
   "metadata": {},
   "source": [
    "<a id='model building'></a>\n",
    "## Model Building\n",
    "\n",
    "First we identify the which features are usable in the dataset:\n",
    "\n",
    "1. `AD_SUPPORTED` (cat)\n",
    "2. `CATEGORY` (cat)\n",
    "3. `CONTENT_RATING` (cat)\n",
    "4. `DAYS_SINCE_UPDATE_RANGE` (cat)\n",
    "5. `DAYS_SINCE_RELEASED` (num)\n",
    "6. `DAYS_SINCE_RELEASED_RANGE` (cat)\n",
    "7. `FREE` (cat)\n",
    "8. `IN_APP_PURCHASES` (cat)\n",
    "9. `INSTALLS` (cat)\n",
    "10. `PRICEBAND` (cat)\n",
    "11. `RATING_RATE` (cat)\n",
    "12. `REVIEW_RATE` (cat)\n",
    "13. `SIZEBAND` (cat)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8ceb3341",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RATING_RATE</th>\n",
       "      <th>REVIEW_RATE</th>\n",
       "      <th>CATEGORY</th>\n",
       "      <th>INSTALLS</th>\n",
       "      <th>SIZEBAND</th>\n",
       "      <th>FREE</th>\n",
       "      <th>PRICEBAND</th>\n",
       "      <th>AD_SUPPORTED</th>\n",
       "      <th>CONTENT_RATING</th>\n",
       "      <th>IN_APP_PURCHASES</th>\n",
       "      <th>DAYS_SINCE_UPDATE_RANGE</th>\n",
       "      <th>DAYS_SINCE_RELEASED_RANGE</th>\n",
       "      <th>DAYS_SINCE_RELEASED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Lifestyle</td>\n",
       "      <td>1,000+</td>\n",
       "      <td>very small</td>\n",
       "      <td>False</td>\n",
       "      <td>expensive</td>\n",
       "      <td>False</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>False</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>3482.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Puzzle</td>\n",
       "      <td>10,000+</td>\n",
       "      <td>large</td>\n",
       "      <td>False</td>\n",
       "      <td>cheap</td>\n",
       "      <td>False</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>False</td>\n",
       "      <td>Few days ago</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>945.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>More than 50 per day</td>\n",
       "      <td>More than once per day</td>\n",
       "      <td>Racing</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>very large</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>True</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>True</td>\n",
       "      <td>Few days ago</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>1084.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>High</td>\n",
       "      <td>More than once per day</td>\n",
       "      <td>Role Playing</td>\n",
       "      <td>1,000,000+</td>\n",
       "      <td>large</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>False</td>\n",
       "      <td>Teen</td>\n",
       "      <td>True</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>1275.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "      <td>Dating</td>\n",
       "      <td>50,000+</td>\n",
       "      <td>small</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>False</td>\n",
       "      <td>Adults</td>\n",
       "      <td>True</td>\n",
       "      <td>Within 1 month</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>1026.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            RATING_RATE             REVIEW_RATE      CATEGORY    INSTALLS  \\\n",
       "0                   Low                     Low     Lifestyle      1,000+   \n",
       "1                Medium                  Medium        Puzzle     10,000+   \n",
       "2  More than 50 per day  More than once per day        Racing  5,000,000+   \n",
       "3                  High  More than once per day  Role Playing  1,000,000+   \n",
       "4                   Low                    High        Dating     50,000+   \n",
       "\n",
       "     SIZEBAND   FREE  PRICEBAND  AD_SUPPORTED CONTENT_RATING  \\\n",
       "0  very small  False  expensive         False       Everyone   \n",
       "1       large  False      cheap         False       Everyone   \n",
       "2  very large   True       Free          True       Everyone   \n",
       "3       large   True       Free         False           Teen   \n",
       "4       small   True       Free         False         Adults   \n",
       "\n",
       "   IN_APP_PURCHASES DAYS_SINCE_UPDATE_RANGE DAYS_SINCE_RELEASED_RANGE  \\\n",
       "0             False      More than 6 months        More than 6 months   \n",
       "1             False            Few days ago        More than 6 months   \n",
       "2              True            Few days ago        More than 6 months   \n",
       "3              True      More than 6 months        More than 6 months   \n",
       "4              True          Within 1 month        More than 6 months   \n",
       "\n",
       "   DAYS_SINCE_RELEASED  \n",
       "0               3482.0  \n",
       "1                945.0  \n",
       "2               1084.0  \n",
       "3               1275.0  \n",
       "4               1026.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_list = [\"RATING_RATE\", \"REVIEW_RATE\", \"CATEGORY\", \"INSTALLS\", \"SIZEBAND\", \"FREE\", \"PRICEBAND\", \"AD_SUPPORTED\", \n",
    "                \"CONTENT_RATING\", \"IN_APP_PURCHASES\", \"DAYS_SINCE_UPDATE_RANGE\", \"DAYS_SINCE_RELEASED_RANGE\", \"DAYS_SINCE_RELEASED\"]\n",
    "model_df = df[feature_list].copy()\n",
    "model_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c37e885b",
   "metadata": {},
   "source": [
    "Handling null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "da8e5cb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RATING_RATE                  0\n",
       "REVIEW_RATE                  0\n",
       "CATEGORY                     0\n",
       "INSTALLS                     0\n",
       "SIZEBAND                     0\n",
       "FREE                         0\n",
       "PRICEBAND                    0\n",
       "AD_SUPPORTED                 0\n",
       "CONTENT_RATING               0\n",
       "IN_APP_PURCHASES             0\n",
       "DAYS_SINCE_UPDATE_RANGE      0\n",
       "DAYS_SINCE_RELEASED_RANGE    0\n",
       "DAYS_SINCE_RELEASED          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdf1b07a",
   "metadata": {},
   "source": [
    "Seems like there are no null values in our dataset :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>RATING_RATE</th>\n",
       "      <th>REVIEW_RATE</th>\n",
       "      <th>CATEGORY</th>\n",
       "      <th>INSTALLS</th>\n",
       "      <th>SIZEBAND</th>\n",
       "      <th>FREE</th>\n",
       "      <th>PRICEBAND</th>\n",
       "      <th>AD_SUPPORTED</th>\n",
       "      <th>CONTENT_RATING</th>\n",
       "      <th>IN_APP_PURCHASES</th>\n",
       "      <th>DAYS_SINCE_UPDATE_RANGE</th>\n",
       "      <th>DAYS_SINCE_RELEASED_RANGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>More than 50 per day</td>\n",
       "      <td>More than once per day</td>\n",
       "      <td>Simulation</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>very large</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>True</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>False</td>\n",
       "      <td>3 to 6 months</td>\n",
       "      <td>More than 6 months</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>More than 50 per day</td>\n",
       "      <td>More than once per day</td>\n",
       "      <td>Simulation</td>\n",
       "      <td>10,000,000+</td>\n",
       "      <td>very large</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>True</td>\n",
       "      <td>Adults</td>\n",
       "      <td>True</td>\n",
       "      <td>Within 1 month</td>\n",
       "      <td>More than 6 months</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Lifestyle</td>\n",
       "      <td>5,000+</td>\n",
       "      <td>medium</td>\n",
       "      <td>False</td>\n",
       "      <td>cheap</td>\n",
       "      <td>False</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>False</td>\n",
       "      <td>Within 1 month</td>\n",
       "      <td>3 to 6 months</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Trivia</td>\n",
       "      <td>100,000+</td>\n",
       "      <td>medium</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>True</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>True</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>More than 6 months</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tools</td>\n",
       "      <td>50,000+</td>\n",
       "      <td>small</td>\n",
       "      <td>False</td>\n",
       "      <td>cheap</td>\n",
       "      <td>False</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>False</td>\n",
       "      <td>Few days ago</td>\n",
       "      <td>More than 6 months</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2976</th>\n",
       "      <td>Low</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Simulation</td>\n",
       "      <td>1,000+</td>\n",
       "      <td>very small</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>False</td>\n",
       "      <td>Teen</td>\n",
       "      <td>True</td>\n",
       "      <td>1 to 3 months</td>\n",
       "      <td>1 to 3 months</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2977</th>\n",
       "      <td>More than 50 per day</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Role Playing</td>\n",
       "      <td>100,000+</td>\n",
       "      <td>medium</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>False</td>\n",
       "      <td>Teen</td>\n",
       "      <td>True</td>\n",
       "      <td>3 to 6 months</td>\n",
       "      <td>More than 6 months</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2978</th>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Role Playing</td>\n",
       "      <td>50,000+</td>\n",
       "      <td>very large</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>False</td>\n",
       "      <td>Teen</td>\n",
       "      <td>True</td>\n",
       "      <td>Few days ago</td>\n",
       "      <td>3 to 6 months</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2979</th>\n",
       "      <td>More than 50 per day</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Card</td>\n",
       "      <td>100,000+</td>\n",
       "      <td>very large</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>False</td>\n",
       "      <td>Teen</td>\n",
       "      <td>True</td>\n",
       "      <td>1 to 3 months</td>\n",
       "      <td>3 to 6 months</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2980</th>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>Music</td>\n",
       "      <td>100,000+</td>\n",
       "      <td>medium</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>True</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>False</td>\n",
       "      <td>3 to 6 months</td>\n",
       "      <td>3 to 6 months</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2981 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               RATING_RATE             REVIEW_RATE      CATEGORY     INSTALLS  \\\n",
       "0     More than 50 per day  More than once per day    Simulation   5,000,000+   \n",
       "1     More than 50 per day  More than once per day    Simulation  10,000,000+   \n",
       "2                   Medium                    High     Lifestyle       5,000+   \n",
       "3                   Medium                    High        Trivia     100,000+   \n",
       "4                   Medium                  Medium         Tools      50,000+   \n",
       "...                    ...                     ...           ...          ...   \n",
       "2976                   Low                  Medium    Simulation       1,000+   \n",
       "2977  More than 50 per day                  Medium  Role Playing     100,000+   \n",
       "2978                  High                  Medium  Role Playing      50,000+   \n",
       "2979  More than 50 per day                  Medium          Card     100,000+   \n",
       "2980                Medium                     Low         Music     100,000+   \n",
       "\n",
       "        SIZEBAND   FREE PRICEBAND  AD_SUPPORTED CONTENT_RATING  \\\n",
       "0     very large   True      Free          True       Everyone   \n",
       "1     very large   True      Free          True         Adults   \n",
       "2         medium  False     cheap         False       Everyone   \n",
       "3         medium   True      Free          True       Everyone   \n",
       "4          small  False     cheap         False       Everyone   \n",
       "...          ...    ...       ...           ...            ...   \n",
       "2976  very small   True      Free         False           Teen   \n",
       "2977      medium   True      Free         False           Teen   \n",
       "2978  very large   True      Free         False           Teen   \n",
       "2979  very large   True      Free         False           Teen   \n",
       "2980      medium   True      Free          True       Everyone   \n",
       "\n",
       "      IN_APP_PURCHASES DAYS_SINCE_UPDATE_RANGE DAYS_SINCE_RELEASED_RANGE  \n",
       "0                False           3 to 6 months        More than 6 months  \n",
       "1                 True          Within 1 month        More than 6 months  \n",
       "2                False          Within 1 month             3 to 6 months  \n",
       "3                 True      More than 6 months        More than 6 months  \n",
       "4                False            Few days ago        More than 6 months  \n",
       "...                ...                     ...                       ...  \n",
       "2976              True           1 to 3 months             1 to 3 months  \n",
       "2977              True           3 to 6 months        More than 6 months  \n",
       "2978              True            Few days ago             3 to 6 months  \n",
       "2979              True           1 to 3 months             3 to 6 months  \n",
       "2980             False           3 to 6 months             3 to 6 months  \n",
       "\n",
       "[2981 rows x 12 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_df = model_df.loc[model_df[\"DAYS_SINCE_RELEASED\"] <= 365, :]\n",
    "model_df.reset_index(inplace= True, drop=True)\n",
    "model_df.drop([\"DAYS_SINCE_RELEASED\"], inplace=True, axis=1)\n",
    "model_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Less than 1M    2091\n",
      "More than 1M     890\n",
      "Name: INSTALLS_GROUP, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_df['INSTALLS_GROUP'] = 'More than 1M'\n",
    "model_df.loc[(model_df['INSTALLS'] == '1+') | (model_df['INSTALLS'] == '10+') | (model_df['INSTALLS'] == '50+') | \n",
    "       (model_df['INSTALLS'] == '100+') | (model_df['INSTALLS'] == '500+' ) | (model_df['INSTALLS'] == '1,000+' ) | \n",
    "       (model_df['INSTALLS'] == '5,000+' ) | (model_df['INSTALLS'] == '10,000+') | (model_df['INSTALLS'] == '50,000+') |\n",
    "       (model_df['INSTALLS'] == '100,000+') | (model_df['INSTALLS'] == '500,000+'), 'INSTALLS_GROUP'] = 'Less than 1M'\n",
    "print(model_df['INSTALLS_GROUP'] .value_counts())\n",
    "sub_plot1 = sb.countplot(x=\"INSTALLS_GROUP\", data=model_df)\n",
    "for p in sub_plot1.patches:\n",
    "    if p.get_height() == 0:\n",
    "        sub_plot1.annotate(format(p.get_height(), '.0f'), \n",
    "        (p.get_x() + p.get_width() / 2., p.get_height()), \n",
    "        ha = 'center', va = 'center', \n",
    "        xytext = (0, 9), \n",
    "        textcoords = 'offset points')\n",
    "    else:\n",
    "        sub_plot1.annotate(format(p.get_height(), '.0f'), \n",
    "        (p.get_x() + p.get_width() / 2., p.get_height()), \n",
    "        ha = 'center', va = 'center', \n",
    "        xytext = (0, -12), \n",
    "        textcoords = 'offset points')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2baed1f7",
   "metadata": {},
   "source": [
    "### Data Preprocessing for model\n",
    "\n",
    "From eda, we noticed that it is unlikely to accurately predict the rating of the app, due to its nature that rating is not only affected by the features of the app, but the user experience and user interface also play an important role too. In this case, our group set the indicator for the performance of the app to how much it has been installed. \n",
    "\n",
    "For data preprosessing, we picked 10 predictors for predicting `INSTALLS_GROUP` that the app is going to fall into, namely\n",
    "1. `AD_SUPPORTED` (cat)\n",
    "2. `CATEGORY` (cat)\n",
    "3. `CONTENT_RATING` (cat)\n",
    "4. `DAYS_SINCE_UPDATE_RANGE` (cat)\n",
    "5. `FREE` (cat)\n",
    "6. `IN_APP_PURCHASES` (cat)\n",
    "7.  `PRICEBAND` (cat)\n",
    "8.  `RATING_RATE` (cat)\n",
    "9.  `REVIEW_RATE` (cat)\n",
    "10. `SIZEBAND` (cat)\n",
    "\n",
    "Before we continue, we need to do some changes on `CATEGORY`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a79a8c38",
   "metadata": {},
   "source": [
    "For `CATEGORY`, we noticed from eda that most apps falls under \"Game\" category, with that we change the category values to either \"Game\" or \"Non Game\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "games_categories = [\"Puzzle\", \"Racing\", \"Role Playing\", \"Simulation\", \"Trivia\", \"Card\", \"Strategy\", \n",
    "                    \"Arcade\", \"Action\", \"Word\", \"Casino\", \"Adventure\", \"Educational\", \"Sports\", \"Board\", \n",
    "                    \"Casual\", \"Music\"]\n",
    "for i in range(len(model_df)):\n",
    "    if (model_df.loc[i, \"CATEGORY\"] in games_categories):\n",
    "        model_df.loc[i, \"CATEGORY\"] = \"Game\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "794a07a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Game        1529\n",
       "Non Game    1452\n",
       "Name: CATEGORY, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_df.loc[model_df[\"CATEGORY\"] != \"Game\", \"CATEGORY\"] = \"Non Game\"\n",
    "model_df[\"CATEGORY\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Split the dataset into train and test data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "INSTALLS_GROUP\n",
       "Less than 1M      1571\n",
       "More than 1M       664\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract Response and Predictors\n",
    "response = \"INSTALLS_GROUP\"\n",
    "y = pd.DataFrame(model_df[response])\n",
    "X = pd.DataFrame(model_df.drop(response, axis = 1))\n",
    "\n",
    "# Split the dataset into 75% train and 25% test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.75, random_state=20)\n",
    "\n",
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>RATING_RATE</th>\n",
       "      <th>REVIEW_RATE</th>\n",
       "      <th>CATEGORY</th>\n",
       "      <th>INSTALLS</th>\n",
       "      <th>SIZEBAND</th>\n",
       "      <th>FREE</th>\n",
       "      <th>PRICEBAND</th>\n",
       "      <th>AD_SUPPORTED</th>\n",
       "      <th>CONTENT_RATING</th>\n",
       "      <th>IN_APP_PURCHASES</th>\n",
       "      <th>DAYS_SINCE_UPDATE_RANGE</th>\n",
       "      <th>DAYS_SINCE_RELEASED_RANGE</th>\n",
       "      <th>INSTALLS_GROUP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>More than 50 per day</td>\n",
       "      <td>More than once per day</td>\n",
       "      <td>Game</td>\n",
       "      <td>1,000,000+</td>\n",
       "      <td>very large</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>True</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>True</td>\n",
       "      <td>3 to 6 months</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>More than 1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>More than 50 per day</td>\n",
       "      <td>More than once per day</td>\n",
       "      <td>Game</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>medium</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>True</td>\n",
       "      <td>Adults</td>\n",
       "      <td>False</td>\n",
       "      <td>Almost a week</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>More than 1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>More than 50 per day</td>\n",
       "      <td>More than once per day</td>\n",
       "      <td>Game</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>very large</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>True</td>\n",
       "      <td>Teen</td>\n",
       "      <td>False</td>\n",
       "      <td>Within 1 month</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>More than 1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>Non Game</td>\n",
       "      <td>1,000,000+</td>\n",
       "      <td>small</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>True</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>False</td>\n",
       "      <td>1 to 3 months</td>\n",
       "      <td>1 to 3 months</td>\n",
       "      <td>More than 1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Game</td>\n",
       "      <td>1,000+</td>\n",
       "      <td>very large</td>\n",
       "      <td>False</td>\n",
       "      <td>cheap</td>\n",
       "      <td>False</td>\n",
       "      <td>Teen</td>\n",
       "      <td>False</td>\n",
       "      <td>3 to 6 months</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>Less than 1M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            RATING_RATE             REVIEW_RATE  CATEGORY    INSTALLS  \\\n",
       "0  More than 50 per day  More than once per day      Game  1,000,000+   \n",
       "1  More than 50 per day  More than once per day      Game  5,000,000+   \n",
       "2  More than 50 per day  More than once per day      Game  5,000,000+   \n",
       "3                  High                    High  Non Game  1,000,000+   \n",
       "4                   Low                     Low      Game      1,000+   \n",
       "\n",
       "     SIZEBAND   FREE PRICEBAND  AD_SUPPORTED CONTENT_RATING  IN_APP_PURCHASES  \\\n",
       "0  very large   True      Free          True       Everyone              True   \n",
       "1      medium   True      Free          True         Adults             False   \n",
       "2  very large   True      Free          True           Teen             False   \n",
       "3       small   True      Free          True       Everyone             False   \n",
       "4  very large  False     cheap         False           Teen             False   \n",
       "\n",
       "  DAYS_SINCE_UPDATE_RANGE DAYS_SINCE_RELEASED_RANGE INSTALLS_GROUP  \n",
       "0           3 to 6 months        More than 6 months   More than 1M  \n",
       "1           Almost a week        More than 6 months   More than 1M  \n",
       "2          Within 1 month        More than 6 months   More than 1M  \n",
       "3           1 to 3 months             1 to 3 months   More than 1M  \n",
       "4           3 to 6 months        More than 6 months   Less than 1M  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concat training data\n",
    "train_df = pd.concat([X_train, y_train], axis=1).reset_index(drop=True)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resampling INSTALLS_GROUP\n",
    "\n",
    "We noticed that there is an imbalanced distribution of `INSTALLS_GROUP`, we handle them by oversampling techniques below.\n",
    "1. Random Oversampling\n",
    "2. SMOTEN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "INSTALLS_GROUP\n",
       "Less than 1M      1571\n",
       "More than 1M       664\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Random Oversampling**\n",
    "\n",
    "We will resample the data to equal portions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\imblearn\\utils\\_validation.py:299: UserWarning: After over-sampling, the number of samples (2000) in class Less than 1M will be larger than the number of samples in the majority class (class #Less than 1M -> 1571)\n",
      "  warnings.warn(\n",
      "C:\\Users\\Lenovo\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\imblearn\\utils\\_validation.py:299: UserWarning: After over-sampling, the number of samples (2000) in class More than 1M will be larger than the number of samples in the majority class (class #Less than 1M -> 1571)\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "INSTALLS_GROUP\n",
       "Less than 1M      2000\n",
       "More than 1M      2000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strategy = {\"Less than 1M\": 2000, \n",
    "            \"More than 1M\": 2000,}\n",
    "over_sampler = RandomOverSampler(sampling_strategy=strategy, random_state=20)\n",
    "X_over, y_over = over_sampler.fit_resample(X_train, y_train)\n",
    "\n",
    "y_over.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>RATING_RATE</th>\n",
       "      <th>REVIEW_RATE</th>\n",
       "      <th>CATEGORY</th>\n",
       "      <th>INSTALLS</th>\n",
       "      <th>SIZEBAND</th>\n",
       "      <th>FREE</th>\n",
       "      <th>PRICEBAND</th>\n",
       "      <th>AD_SUPPORTED</th>\n",
       "      <th>CONTENT_RATING</th>\n",
       "      <th>IN_APP_PURCHASES</th>\n",
       "      <th>DAYS_SINCE_UPDATE_RANGE</th>\n",
       "      <th>DAYS_SINCE_RELEASED_RANGE</th>\n",
       "      <th>INSTALLS_GROUP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>More than 50 per day</td>\n",
       "      <td>More than once per day</td>\n",
       "      <td>Game</td>\n",
       "      <td>1,000,000+</td>\n",
       "      <td>very large</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>True</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>True</td>\n",
       "      <td>3 to 6 months</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>More than 1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>More than 50 per day</td>\n",
       "      <td>More than once per day</td>\n",
       "      <td>Game</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>medium</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>True</td>\n",
       "      <td>Adults</td>\n",
       "      <td>False</td>\n",
       "      <td>Almost a week</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>More than 1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>More than 50 per day</td>\n",
       "      <td>More than once per day</td>\n",
       "      <td>Game</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>very large</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>True</td>\n",
       "      <td>Teen</td>\n",
       "      <td>False</td>\n",
       "      <td>Within 1 month</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>More than 1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>Non Game</td>\n",
       "      <td>1,000,000+</td>\n",
       "      <td>small</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>True</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>False</td>\n",
       "      <td>1 to 3 months</td>\n",
       "      <td>1 to 3 months</td>\n",
       "      <td>More than 1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Game</td>\n",
       "      <td>1,000+</td>\n",
       "      <td>very large</td>\n",
       "      <td>False</td>\n",
       "      <td>cheap</td>\n",
       "      <td>False</td>\n",
       "      <td>Teen</td>\n",
       "      <td>False</td>\n",
       "      <td>3 to 6 months</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>Less than 1M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            RATING_RATE             REVIEW_RATE  CATEGORY    INSTALLS  \\\n",
       "0  More than 50 per day  More than once per day      Game  1,000,000+   \n",
       "1  More than 50 per day  More than once per day      Game  5,000,000+   \n",
       "2  More than 50 per day  More than once per day      Game  5,000,000+   \n",
       "3                  High                    High  Non Game  1,000,000+   \n",
       "4                   Low                     Low      Game      1,000+   \n",
       "\n",
       "     SIZEBAND   FREE PRICEBAND  AD_SUPPORTED CONTENT_RATING  IN_APP_PURCHASES  \\\n",
       "0  very large   True      Free          True       Everyone              True   \n",
       "1      medium   True      Free          True         Adults             False   \n",
       "2  very large   True      Free          True           Teen             False   \n",
       "3       small   True      Free          True       Everyone             False   \n",
       "4  very large  False     cheap         False           Teen             False   \n",
       "\n",
       "  DAYS_SINCE_UPDATE_RANGE DAYS_SINCE_RELEASED_RANGE INSTALLS_GROUP  \n",
       "0           3 to 6 months        More than 6 months   More than 1M  \n",
       "1           Almost a week        More than 6 months   More than 1M  \n",
       "2          Within 1 month        More than 6 months   More than 1M  \n",
       "3           1 to 3 months             1 to 3 months   More than 1M  \n",
       "4           3 to 6 months        More than 6 months   Less than 1M  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_oversampled_df = pd.concat([X_over, y_over], axis=1)\n",
    "train_oversampled_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**SMOTEN Oversampling** \n",
    "\n",
    "Since our data are purely categorical, we apply the SMOTEN oversampling technique to resample our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\imblearn\\utils\\_validation.py:299: UserWarning: After over-sampling, the number of samples (2000) in class Less than 1M will be larger than the number of samples in the majority class (class #Less than 1M -> 1571)\n",
      "  warnings.warn(\n",
      "C:\\Users\\Lenovo\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\imblearn\\utils\\_validation.py:299: UserWarning: After over-sampling, the number of samples (2000) in class More than 1M will be larger than the number of samples in the majority class (class #Less than 1M -> 1571)\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "INSTALLS_GROUP\n",
       "Less than 1M      2000\n",
       "More than 1M      2000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strategy = {\"Less than 1M\": 2000, \n",
    "            \"More than 1M\": 2000,}\n",
    "smoten_sampler = SMOTEN(sampling_strategy=strategy, random_state=20)\n",
    "X_smoten, y_smoten = smoten_sampler.fit_resample(X_train, y_train)\n",
    "\n",
    "y_smoten.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>RATING_RATE</th>\n",
       "      <th>REVIEW_RATE</th>\n",
       "      <th>CATEGORY</th>\n",
       "      <th>INSTALLS</th>\n",
       "      <th>SIZEBAND</th>\n",
       "      <th>FREE</th>\n",
       "      <th>PRICEBAND</th>\n",
       "      <th>AD_SUPPORTED</th>\n",
       "      <th>CONTENT_RATING</th>\n",
       "      <th>IN_APP_PURCHASES</th>\n",
       "      <th>DAYS_SINCE_UPDATE_RANGE</th>\n",
       "      <th>DAYS_SINCE_RELEASED_RANGE</th>\n",
       "      <th>INSTALLS_GROUP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>More than 50 per day</td>\n",
       "      <td>More than once per day</td>\n",
       "      <td>Game</td>\n",
       "      <td>1,000,000+</td>\n",
       "      <td>very large</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>True</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>True</td>\n",
       "      <td>3 to 6 months</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>More than 1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>More than 50 per day</td>\n",
       "      <td>More than once per day</td>\n",
       "      <td>Game</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>medium</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>True</td>\n",
       "      <td>Adults</td>\n",
       "      <td>False</td>\n",
       "      <td>Almost a week</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>More than 1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>More than 50 per day</td>\n",
       "      <td>More than once per day</td>\n",
       "      <td>Game</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>very large</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>True</td>\n",
       "      <td>Teen</td>\n",
       "      <td>False</td>\n",
       "      <td>Within 1 month</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>More than 1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>Non Game</td>\n",
       "      <td>1,000,000+</td>\n",
       "      <td>small</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>True</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>False</td>\n",
       "      <td>1 to 3 months</td>\n",
       "      <td>1 to 3 months</td>\n",
       "      <td>More than 1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Game</td>\n",
       "      <td>1,000+</td>\n",
       "      <td>very large</td>\n",
       "      <td>False</td>\n",
       "      <td>cheap</td>\n",
       "      <td>False</td>\n",
       "      <td>Teen</td>\n",
       "      <td>False</td>\n",
       "      <td>3 to 6 months</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>Less than 1M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            RATING_RATE             REVIEW_RATE  CATEGORY    INSTALLS  \\\n",
       "0  More than 50 per day  More than once per day      Game  1,000,000+   \n",
       "1  More than 50 per day  More than once per day      Game  5,000,000+   \n",
       "2  More than 50 per day  More than once per day      Game  5,000,000+   \n",
       "3                  High                    High  Non Game  1,000,000+   \n",
       "4                   Low                     Low      Game      1,000+   \n",
       "\n",
       "     SIZEBAND   FREE PRICEBAND  AD_SUPPORTED CONTENT_RATING  IN_APP_PURCHASES  \\\n",
       "0  very large   True      Free          True       Everyone              True   \n",
       "1      medium   True      Free          True         Adults             False   \n",
       "2  very large   True      Free          True           Teen             False   \n",
       "3       small   True      Free          True       Everyone             False   \n",
       "4  very large  False     cheap         False           Teen             False   \n",
       "\n",
       "  DAYS_SINCE_UPDATE_RANGE DAYS_SINCE_RELEASED_RANGE INSTALLS_GROUP  \n",
       "0           3 to 6 months        More than 6 months   More than 1M  \n",
       "1           Almost a week        More than 6 months   More than 1M  \n",
       "2          Within 1 month        More than 6 months   More than 1M  \n",
       "3           1 to 3 months             1 to 3 months   More than 1M  \n",
       "4           3 to 6 months        More than 6 months   Less than 1M  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_smoten_df = pd.concat([X_smoten, y_smoten], axis=1)\n",
    "train_smoten_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Combining test_df**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>RATING_RATE</th>\n",
       "      <th>REVIEW_RATE</th>\n",
       "      <th>CATEGORY</th>\n",
       "      <th>INSTALLS</th>\n",
       "      <th>SIZEBAND</th>\n",
       "      <th>FREE</th>\n",
       "      <th>PRICEBAND</th>\n",
       "      <th>AD_SUPPORTED</th>\n",
       "      <th>CONTENT_RATING</th>\n",
       "      <th>IN_APP_PURCHASES</th>\n",
       "      <th>DAYS_SINCE_UPDATE_RANGE</th>\n",
       "      <th>DAYS_SINCE_RELEASED_RANGE</th>\n",
       "      <th>INSTALLS_GROUP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>More than 50 per day</td>\n",
       "      <td>More than once per day</td>\n",
       "      <td>Game</td>\n",
       "      <td>500,000+</td>\n",
       "      <td>very large</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>False</td>\n",
       "      <td>Teen</td>\n",
       "      <td>True</td>\n",
       "      <td>Few days ago</td>\n",
       "      <td>Within 1 month</td>\n",
       "      <td>Less than 1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>More than 50 per day</td>\n",
       "      <td>More than once per day</td>\n",
       "      <td>Game</td>\n",
       "      <td>1,000,000+</td>\n",
       "      <td>medium</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>True</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>True</td>\n",
       "      <td>Within 1 month</td>\n",
       "      <td>1 to 3 months</td>\n",
       "      <td>More than 1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Non Game</td>\n",
       "      <td>50,000+</td>\n",
       "      <td>small</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>True</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>False</td>\n",
       "      <td>3 to 6 months</td>\n",
       "      <td>3 to 6 months</td>\n",
       "      <td>Less than 1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Game</td>\n",
       "      <td>100,000+</td>\n",
       "      <td>medium</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>True</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>True</td>\n",
       "      <td>Almost a week</td>\n",
       "      <td>Within 1 month</td>\n",
       "      <td>Less than 1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Game</td>\n",
       "      <td>1,000,000+</td>\n",
       "      <td>medium</td>\n",
       "      <td>True</td>\n",
       "      <td>Free</td>\n",
       "      <td>True</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>True</td>\n",
       "      <td>Within 1 month</td>\n",
       "      <td>More than 6 months</td>\n",
       "      <td>More than 1M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            RATING_RATE             REVIEW_RATE  CATEGORY    INSTALLS  \\\n",
       "0  More than 50 per day  More than once per day      Game    500,000+   \n",
       "1  More than 50 per day  More than once per day      Game  1,000,000+   \n",
       "2                Medium                  Medium  Non Game     50,000+   \n",
       "3                  High                  Medium      Game    100,000+   \n",
       "4                  High                  Medium      Game  1,000,000+   \n",
       "\n",
       "     SIZEBAND  FREE PRICEBAND  AD_SUPPORTED CONTENT_RATING  IN_APP_PURCHASES  \\\n",
       "0  very large  True      Free         False           Teen              True   \n",
       "1      medium  True      Free          True       Everyone              True   \n",
       "2       small  True      Free          True       Everyone             False   \n",
       "3      medium  True      Free          True       Everyone              True   \n",
       "4      medium  True      Free          True       Everyone              True   \n",
       "\n",
       "  DAYS_SINCE_UPDATE_RANGE DAYS_SINCE_RELEASED_RANGE INSTALLS_GROUP  \n",
       "0            Few days ago            Within 1 month   Less than 1M  \n",
       "1          Within 1 month             1 to 3 months   More than 1M  \n",
       "2           3 to 6 months             3 to 6 months   Less than 1M  \n",
       "3           Almost a week            Within 1 month   Less than 1M  \n",
       "4          Within 1 month        More than 6 months   More than 1M  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.concat([X_test, y_test], axis=1).reset_index(drop=True)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "061bbbb3",
   "metadata": {},
   "source": [
    "#### Encoding categorical values\n",
    "\n",
    "Before we continue, our categorical variables are further divided into 2 types —\n",
    "- Ordinal categorical variables - `INSTALLS_GROUP`, `SIZEBAND`, `PRICEBAND`, `RATING_GROUP`,`FREE`, `AD_SUPPORTED`, `IN_APP_PURCHASES`, `EDITORS_CHOICE`\n",
    "- Nominal categorical variable - `CATEGORY`,  `CONTENT_RATING`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "aa36d2b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "nominal_cat = [\"CATEGORY\", \"CONTENT_RATING\"]\n",
    "ordinal_cat = [\"FREE\",\"AD_SUPPORTED\", \"INSTALLS_GROUP\", \"PRICEBAND\", \"SIZEBAND\", \"IN_APP_PURCHASES\", \"DAYS_SINCE_UPDATE_RANGE\", \"REVIEW_RATE\", \"RATING_RATE\"]\n",
    "\n",
    "numerical = []"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01cb06d2",
   "metadata": {},
   "source": [
    "**Function for handling Ordinal Categorical Variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8102bb30",
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_ord_cat(df):\n",
    "    # label encoding\n",
    "    ig_mapping = {\"Less than 1M\": 0, \"More than 1M\": 1}\n",
    "    pb_mapping = {\"Free\": 0, \"cheap\": 1, \"normal\": 2, \"expensive\": 3, \"very expensive\": 4}\n",
    "    sb_mapping = {\"very small\": 0, \"small\": 1, \"medium\": 2, \"large\": 3, \"very large\": 4}\n",
    "    dsu_mapping = {\"Few days ago\": 0, \"Almost a week\": 1, \"Within 1 month\": 2, \"1 to 3 months\": 3, \"3 to 6 months\": 4, \"More than 6 months\": 5}\n",
    "    revr_mapping = {\"Low\": 0, \"Medium\": 1, \"High\": 2, \"More than once per day\": 3}\n",
    "    ratr_mapping = {\"Low\": 0, \"Medium\": 1, \"High\": 2, \"More than 50 per day\": 3}\n",
    "    free_mapping = {False: 0, True: 1}\n",
    "    ads_mapping = {False: 0, True: 1}\n",
    "    iap_mapping = {False: 0, True: 1}\n",
    "    \n",
    "    model_df_ord = df.copy()\n",
    "    \n",
    "    model_df_ord[\"INSTALLS_GROUP\"] = model_df_ord[\"INSTALLS_GROUP\"].map(ig_mapping)\n",
    "    model_df_ord[\"PRICEBAND\"] = model_df_ord[\"PRICEBAND\"].map(pb_mapping)\n",
    "    model_df_ord[\"SIZEBAND\"] = model_df_ord[\"SIZEBAND\"].map(sb_mapping)\n",
    "    model_df_ord[\"FREE\"] = model_df_ord[\"FREE\"].map(free_mapping)\n",
    "    model_df_ord[\"AD_SUPPORTED\"] = model_df_ord[\"AD_SUPPORTED\"].map(ads_mapping)\n",
    "    model_df_ord[\"IN_APP_PURCHASES\"] = model_df_ord[\"IN_APP_PURCHASES\"].map(iap_mapping)\n",
    "    model_df_ord[\"DAYS_SINCE_UPDATE_RANGE\"] = model_df_ord[\"DAYS_SINCE_UPDATE_RANGE\"].map(dsu_mapping)\n",
    "    model_df_ord[\"REVIEW_RATE\"] = model_df_ord[\"REVIEW_RATE\"].map(revr_mapping)\n",
    "    model_df_ord[\"RATING_RATE\"] = model_df_ord[\"RATING_RATE\"].map(ratr_mapping)\n",
    "    \n",
    "    return model_df_ord"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92a6adbf",
   "metadata": {},
   "source": [
    "**Function for handling Nominal Categorical Variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "881168bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_norm_cat(df):\n",
    "    ohe = OneHotEncoder()\n",
    "    model_df_cat = df[nominal_cat]\n",
    "    ohe.fit(model_df_cat)\n",
    "    model_df_cat_ohe = pd.DataFrame(ohe.transform(model_df_cat).toarray(), \n",
    "                                    columns=ohe.get_feature_names_out(model_df_cat.columns))\n",
    "    return model_df_cat_ohe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Encode all datasets**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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",
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       "\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>CATEGORY_Game</th>\n",
       "      <th>CATEGORY_Non Game</th>\n",
       "      <th>CONTENT_RATING_Adults</th>\n",
       "      <th>CONTENT_RATING_Everyone</th>\n",
       "      <th>CONTENT_RATING_Teen</th>\n",
       "      <th>FREE</th>\n",
       "      <th>AD_SUPPORTED</th>\n",
       "      <th>INSTALLS_GROUP</th>\n",
       "      <th>PRICEBAND</th>\n",
       "      <th>SIZEBAND</th>\n",
       "      <th>IN_APP_PURCHASES</th>\n",
       "      <th>DAYS_SINCE_UPDATE_RANGE</th>\n",
       "      <th>REVIEW_RATE</th>\n",
       "      <th>RATING_RATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CATEGORY_Game  CATEGORY_Non Game  CONTENT_RATING_Adults  \\\n",
       "0            1.0                0.0                    0.0   \n",
       "1            1.0                0.0                    1.0   \n",
       "2            1.0                0.0                    0.0   \n",
       "3            0.0                1.0                    0.0   \n",
       "4            1.0                0.0                    0.0   \n",
       "\n",
       "   CONTENT_RATING_Everyone  CONTENT_RATING_Teen  FREE  AD_SUPPORTED  \\\n",
       "0                      1.0                  0.0     1             1   \n",
       "1                      0.0                  0.0     1             1   \n",
       "2                      0.0                  1.0     1             1   \n",
       "3                      1.0                  0.0     1             1   \n",
       "4                      0.0                  1.0     0             0   \n",
       "\n",
       "   INSTALLS_GROUP  PRICEBAND  SIZEBAND  IN_APP_PURCHASES  \\\n",
       "0               1          0         4                 1   \n",
       "1               1          0         2                 0   \n",
       "2               1          0         4                 0   \n",
       "3               1          0         1                 0   \n",
       "4               0          1         4                 0   \n",
       "\n",
       "   DAYS_SINCE_UPDATE_RANGE  REVIEW_RATE  RATING_RATE  \n",
       "0                        4            3            3  \n",
       "1                        1            3            3  \n",
       "2                        2            3            3  \n",
       "3                        3            2            2  \n",
       "4                        4            0            0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_df_ord = encode_ord_cat(train_df)[ordinal_cat]\n",
    "model_df_norm = encode_norm_cat(train_df)\n",
    "model_df_num = train_df[numerical]\n",
    "# Combine all features\n",
    "train_df_encoded = pd.concat([ model_df_norm, model_df_ord, model_df_num], \n",
    "                        sort = False, axis = 1).reindex(index=model_df_ord.index)\n",
    "train_df_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CATEGORY_Game</th>\n",
       "      <th>CATEGORY_Non Game</th>\n",
       "      <th>CONTENT_RATING_Adults</th>\n",
       "      <th>CONTENT_RATING_Everyone</th>\n",
       "      <th>CONTENT_RATING_Teen</th>\n",
       "      <th>FREE</th>\n",
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       "      <th>INSTALLS_GROUP</th>\n",
       "      <th>PRICEBAND</th>\n",
       "      <th>SIZEBAND</th>\n",
       "      <th>IN_APP_PURCHASES</th>\n",
       "      <th>DAYS_SINCE_UPDATE_RANGE</th>\n",
       "      <th>REVIEW_RATE</th>\n",
       "      <th>RATING_RATE</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\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</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CATEGORY_Game  CATEGORY_Non Game  CONTENT_RATING_Adults  \\\n",
       "0            1.0                0.0                    0.0   \n",
       "1            1.0                0.0                    1.0   \n",
       "2            1.0                0.0                    0.0   \n",
       "3            0.0                1.0                    0.0   \n",
       "4            1.0                0.0                    0.0   \n",
       "\n",
       "   CONTENT_RATING_Everyone  CONTENT_RATING_Teen  FREE  AD_SUPPORTED  \\\n",
       "0                      1.0                  0.0     1             1   \n",
       "1                      0.0                  0.0     1             1   \n",
       "2                      0.0                  1.0     1             1   \n",
       "3                      1.0                  0.0     1             1   \n",
       "4                      0.0                  1.0     0             0   \n",
       "\n",
       "   INSTALLS_GROUP  PRICEBAND  SIZEBAND  IN_APP_PURCHASES  \\\n",
       "0               1          0         4                 1   \n",
       "1               1          0         2                 0   \n",
       "2               1          0         4                 0   \n",
       "3               1          0         1                 0   \n",
       "4               0          1         4                 0   \n",
       "\n",
       "   DAYS_SINCE_UPDATE_RANGE  REVIEW_RATE  RATING_RATE  \n",
       "0                        4            3            3  \n",
       "1                        1            3            3  \n",
       "2                        2            3            3  \n",
       "3                        3            2            2  \n",
       "4                        4            0            0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_df_ord = encode_ord_cat(train_oversampled_df)[ordinal_cat]\n",
    "model_df_norm = encode_norm_cat(train_oversampled_df)\n",
    "model_df_num = train_oversampled_df[numerical]\n",
    "train_df_oversampled_encoded = pd.concat([ model_df_norm, model_df_ord, model_df_num], \n",
    "                        sort = False, axis = 1).reindex(index=model_df_ord.index)\n",
    "train_df_oversampled_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0879a5a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>CATEGORY_Game</th>\n",
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       "      <th>CONTENT_RATING_Adults</th>\n",
       "      <th>CONTENT_RATING_Everyone</th>\n",
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       "      <th>FREE</th>\n",
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       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>3</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CATEGORY_Game  CATEGORY_Non Game  CONTENT_RATING_Adults  \\\n",
       "0            1.0                0.0                    0.0   \n",
       "1            1.0                0.0                    1.0   \n",
       "2            1.0                0.0                    0.0   \n",
       "3            0.0                1.0                    0.0   \n",
       "4            1.0                0.0                    0.0   \n",
       "\n",
       "   CONTENT_RATING_Everyone  CONTENT_RATING_Teen  FREE  AD_SUPPORTED  \\\n",
       "0                      1.0                  0.0     1             1   \n",
       "1                      0.0                  0.0     1             1   \n",
       "2                      0.0                  1.0     1             1   \n",
       "3                      1.0                  0.0     1             1   \n",
       "4                      0.0                  1.0     0             0   \n",
       "\n",
       "   INSTALLS_GROUP  PRICEBAND  SIZEBAND  IN_APP_PURCHASES  \\\n",
       "0               1          0         4                 1   \n",
       "1               1          0         2                 0   \n",
       "2               1          0         4                 0   \n",
       "3               1          0         1                 0   \n",
       "4               0          1         4                 0   \n",
       "\n",
       "   DAYS_SINCE_UPDATE_RANGE  REVIEW_RATE  RATING_RATE  \n",
       "0                        4            3            3  \n",
       "1                        1            3            3  \n",
       "2                        2            3            3  \n",
       "3                        3            2            2  \n",
       "4                        4            0            0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_df_ord = encode_ord_cat(train_smoten_df)[ordinal_cat]\n",
    "model_df_norm = encode_norm_cat(train_smoten_df)\n",
    "model_df_num = train_smoten_df[numerical]\n",
    "train_df_smoten_encoded = pd.concat([ model_df_norm, model_df_ord, model_df_num], \n",
    "                        sort = False, axis = 1).reindex(index=model_df_ord.index)\n",
    "train_df_smoten_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "      <th></th>\n",
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       "      <th>CATEGORY_Non Game</th>\n",
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       "      <th>CONTENT_RATING_Everyone</th>\n",
       "      <th>CONTENT_RATING_Teen</th>\n",
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       "      <th>DAYS_SINCE_UPDATE_RANGE</th>\n",
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       "      <th>RATING_RATE</th>\n",
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       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CATEGORY_Game  CATEGORY_Non Game  CONTENT_RATING_Adults  \\\n",
       "0            1.0                0.0                    0.0   \n",
       "1            1.0                0.0                    0.0   \n",
       "2            0.0                1.0                    0.0   \n",
       "3            1.0                0.0                    0.0   \n",
       "4            1.0                0.0                    0.0   \n",
       "\n",
       "   CONTENT_RATING_Everyone  CONTENT_RATING_Teen  FREE  AD_SUPPORTED  \\\n",
       "0                      0.0                  1.0     1             0   \n",
       "1                      1.0                  0.0     1             1   \n",
       "2                      1.0                  0.0     1             1   \n",
       "3                      1.0                  0.0     1             1   \n",
       "4                      1.0                  0.0     1             1   \n",
       "\n",
       "   INSTALLS_GROUP  PRICEBAND  SIZEBAND  IN_APP_PURCHASES  \\\n",
       "0               0          0         4                 1   \n",
       "1               1          0         2                 1   \n",
       "2               0          0         1                 0   \n",
       "3               0          0         2                 1   \n",
       "4               1          0         2                 1   \n",
       "\n",
       "   DAYS_SINCE_UPDATE_RANGE  REVIEW_RATE  RATING_RATE  \n",
       "0                        0            3            3  \n",
       "1                        2            3            3  \n",
       "2                        4            1            1  \n",
       "3                        1            1            2  \n",
       "4                        2            1            2  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_df_ord = encode_ord_cat(test_df)[ordinal_cat]\n",
    "model_df_norm = encode_norm_cat(test_df)\n",
    "model_df_num = test_df[numerical]\n",
    "test_df_encoded = pd.concat([ model_df_norm, model_df_ord, model_df_num], \n",
    "                        sort = False, axis = 1).reindex(index=model_df_ord.index)\n",
    "test_df_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2235 entries, 0 to 2234\n",
      "Data columns (total 14 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   CATEGORY_Game            2235 non-null   float64\n",
      " 1   CATEGORY_Non Game        2235 non-null   float64\n",
      " 2   CONTENT_RATING_Adults    2235 non-null   float64\n",
      " 3   CONTENT_RATING_Everyone  2235 non-null   float64\n",
      " 4   CONTENT_RATING_Teen      2235 non-null   float64\n",
      " 5   FREE                     2235 non-null   int64  \n",
      " 6   AD_SUPPORTED             2235 non-null   int64  \n",
      " 7   INSTALLS_GROUP           2235 non-null   int64  \n",
      " 8   PRICEBAND                2235 non-null   int64  \n",
      " 9   SIZEBAND                 2235 non-null   int64  \n",
      " 10  IN_APP_PURCHASES         2235 non-null   int64  \n",
      " 11  DAYS_SINCE_UPDATE_RANGE  2235 non-null   int64  \n",
      " 12  REVIEW_RATE              2235 non-null   int64  \n",
      " 13  RATING_RATE              2235 non-null   int64  \n",
      "dtypes: float64(5), int64(9)\n",
      "memory usage: 244.6 KB\n"
     ]
    }
   ],
   "source": [
    "train_df_encoded.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The categorical variables are now encoded, and is ready for machine learning :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export the files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = \"./datasets/train.csv\"\n",
    "train_df_encoded.to_csv(path_or_buf=file_path, index=False);\n",
    "\n",
    "file_path = \"./datasets/train_oversampled.csv\"\n",
    "train_df_oversampled_encoded.to_csv(path_or_buf=file_path, index=False);\n",
    "\n",
    "file_path = \"./datasets/train_smoten.csv\"\n",
    "train_df_smoten_encoded.to_csv(path_or_buf=file_path, index=False);\n",
    "\n",
    "file_path = \"./datasets/test.csv\"\n",
    "test_df_encoded.to_csv(path_or_buf=file_path, index=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "- https://machinelearningmastery.com/smote-oversampling-for-imbalanced-classification/"
   ]
  }
 ],
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