Project_4_median_playtime.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8d37a264",
   "metadata": {},
   "source": [
    "# Steam Data for supervised machine learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ddf094d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83b44e76",
   "metadata": {},
   "source": [
    "### importing csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2a07cfd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>appid</th>\n",
       "      <th>name</th>\n",
       "      <th>release_date</th>\n",
       "      <th>english</th>\n",
       "      <th>developer</th>\n",
       "      <th>publisher</th>\n",
       "      <th>platforms</th>\n",
       "      <th>required_age</th>\n",
       "      <th>categories</th>\n",
       "      <th>genres</th>\n",
       "      <th>steamspy_tags</th>\n",
       "      <th>achievements</th>\n",
       "      <th>positive_ratings</th>\n",
       "      <th>%_pos_rating</th>\n",
       "      <th>negative_ratings</th>\n",
       "      <th>total_number_of_ratings</th>\n",
       "      <th>median_playtime</th>\n",
       "      <th>owners</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>Counter-Strike</td>\n",
       "      <td>01/11/2000</td>\n",
       "      <td>1</td>\n",
       "      <td>Valve</td>\n",
       "      <td>Valve</td>\n",
       "      <td>windows;mac;linux</td>\n",
       "      <td>0</td>\n",
       "      <td>Multi-player;Online Multi-Player;Local Multi-P...</td>\n",
       "      <td>Action</td>\n",
       "      <td>Action;FPS;Multiplayer</td>\n",
       "      <td>0</td>\n",
       "      <td>124534</td>\n",
       "      <td>97</td>\n",
       "      <td>3339</td>\n",
       "      <td>127873</td>\n",
       "      <td>317</td>\n",
       "      <td>10000000-20000000</td>\n",
       "      <td>7.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>Team Fortress Classic</td>\n",
       "      <td>01/04/1999</td>\n",
       "      <td>1</td>\n",
       "      <td>Valve</td>\n",
       "      <td>Valve</td>\n",
       "      <td>windows;mac;linux</td>\n",
       "      <td>0</td>\n",
       "      <td>Multi-player;Online Multi-Player;Local Multi-P...</td>\n",
       "      <td>Action</td>\n",
       "      <td>Action;FPS;Multiplayer</td>\n",
       "      <td>0</td>\n",
       "      <td>3318</td>\n",
       "      <td>84</td>\n",
       "      <td>633</td>\n",
       "      <td>3951</td>\n",
       "      <td>62</td>\n",
       "      <td>5000000-10000000</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>Day of Defeat</td>\n",
       "      <td>01/05/2003</td>\n",
       "      <td>1</td>\n",
       "      <td>Valve</td>\n",
       "      <td>Valve</td>\n",
       "      <td>windows;mac;linux</td>\n",
       "      <td>0</td>\n",
       "      <td>Multi-player;Valve Anti-Cheat enabled</td>\n",
       "      <td>Action</td>\n",
       "      <td>FPS;World War II;Multiplayer</td>\n",
       "      <td>0</td>\n",
       "      <td>3416</td>\n",
       "      <td>90</td>\n",
       "      <td>398</td>\n",
       "      <td>3814</td>\n",
       "      <td>34</td>\n",
       "      <td>5000000-10000000</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>Deathmatch Classic</td>\n",
       "      <td>01/06/2001</td>\n",
       "      <td>1</td>\n",
       "      <td>Valve</td>\n",
       "      <td>Valve</td>\n",
       "      <td>windows;mac;linux</td>\n",
       "      <td>0</td>\n",
       "      <td>Multi-player;Online Multi-Player;Local Multi-P...</td>\n",
       "      <td>Action</td>\n",
       "      <td>Action;FPS;Multiplayer</td>\n",
       "      <td>0</td>\n",
       "      <td>1273</td>\n",
       "      <td>83</td>\n",
       "      <td>267</td>\n",
       "      <td>1540</td>\n",
       "      <td>184</td>\n",
       "      <td>5000000-10000000</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>Half-Life: Opposing Force</td>\n",
       "      <td>01/11/1999</td>\n",
       "      <td>1</td>\n",
       "      <td>Gearbox Software</td>\n",
       "      <td>Valve</td>\n",
       "      <td>windows;mac;linux</td>\n",
       "      <td>0</td>\n",
       "      <td>Single-player;Multi-player;Valve Anti-Cheat en...</td>\n",
       "      <td>Action</td>\n",
       "      <td>FPS;Action;Sci-fi</td>\n",
       "      <td>0</td>\n",
       "      <td>5250</td>\n",
       "      <td>95</td>\n",
       "      <td>288</td>\n",
       "      <td>5538</td>\n",
       "      <td>415</td>\n",
       "      <td>5000000-10000000</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   appid                       name release_date  english         developer  \\\n",
       "0     10             Counter-Strike   01/11/2000        1             Valve   \n",
       "1     20      Team Fortress Classic   01/04/1999        1             Valve   \n",
       "2     30              Day of Defeat   01/05/2003        1             Valve   \n",
       "3     40         Deathmatch Classic   01/06/2001        1             Valve   \n",
       "4     50  Half-Life: Opposing Force   01/11/1999        1  Gearbox Software   \n",
       "\n",
       "  publisher          platforms  required_age  \\\n",
       "0     Valve  windows;mac;linux             0   \n",
       "1     Valve  windows;mac;linux             0   \n",
       "2     Valve  windows;mac;linux             0   \n",
       "3     Valve  windows;mac;linux             0   \n",
       "4     Valve  windows;mac;linux             0   \n",
       "\n",
       "                                          categories  genres  \\\n",
       "0  Multi-player;Online Multi-Player;Local Multi-P...  Action   \n",
       "1  Multi-player;Online Multi-Player;Local Multi-P...  Action   \n",
       "2              Multi-player;Valve Anti-Cheat enabled  Action   \n",
       "3  Multi-player;Online Multi-Player;Local Multi-P...  Action   \n",
       "4  Single-player;Multi-player;Valve Anti-Cheat en...  Action   \n",
       "\n",
       "                  steamspy_tags  achievements  positive_ratings  %_pos_rating  \\\n",
       "0        Action;FPS;Multiplayer             0            124534            97   \n",
       "1        Action;FPS;Multiplayer             0              3318            84   \n",
       "2  FPS;World War II;Multiplayer             0              3416            90   \n",
       "3        Action;FPS;Multiplayer             0              1273            83   \n",
       "4             FPS;Action;Sci-fi             0              5250            95   \n",
       "\n",
       "   negative_ratings  total_number_of_ratings  median_playtime  \\\n",
       "0              3339                   127873              317   \n",
       "1               633                     3951               62   \n",
       "2               398                     3814               34   \n",
       "3               267                     1540              184   \n",
       "4               288                     5538              415   \n",
       "\n",
       "              owners  price  \n",
       "0  10000000-20000000   7.19  \n",
       "1   5000000-10000000   3.99  \n",
       "2   5000000-10000000   3.99  \n",
       "3   5000000-10000000   3.99  \n",
       "4   5000000-10000000   3.99  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# **************** i have added 2 columns in excel %_pos_rating and total_number_of_ratings *****************************\n",
    "\n",
    "# Import the data\n",
    "steam_data = pd.read_csv(Path('Data/steam.csv'))\n",
    "steam_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a2d1244",
   "metadata": {},
   "source": [
    "### counting number of steamspy_tags and converting to numerical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "988d5d86",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ebd66133",
   "metadata": {},
   "outputs": [],
   "source": [
    "replace_steamspy_tags_column = steam_data[\"steamspy_tags\"].str.count(\";\")+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "97b6c804",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3\n",
       "1    3\n",
       "2    3\n",
       "3    3\n",
       "4    3\n",
       "Name: steamspy_tags, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_steamspy_tag_data = steam_data['steamspy_tags'] = replace_steamspy_tags_column\n",
    "new_steamspy_tag_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1ffec6f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#steam_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "805eb8b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>appid</th>\n",
       "      <th>english</th>\n",
       "      <th>required_age</th>\n",
       "      <th>steamspy_tags</th>\n",
       "      <th>achievements</th>\n",
       "      <th>positive_ratings</th>\n",
       "      <th>%_pos_rating</th>\n",
       "      <th>negative_ratings</th>\n",
       "      <th>total_number_of_ratings</th>\n",
       "      <th>median_playtime</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.707500e+04</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>2.707500e+04</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>2.707500e+04</td>\n",
       "      <td>27075.00000</td>\n",
       "      <td>27075.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.962035e+05</td>\n",
       "      <td>0.981127</td>\n",
       "      <td>0.354903</td>\n",
       "      <td>2.880960</td>\n",
       "      <td>45.248864</td>\n",
       "      <td>1.000559e+03</td>\n",
       "      <td>71.460462</td>\n",
       "      <td>211.027147</td>\n",
       "      <td>1.211586e+03</td>\n",
       "      <td>146.05603</td>\n",
       "      <td>6.078193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.508942e+05</td>\n",
       "      <td>0.136081</td>\n",
       "      <td>2.406044</td>\n",
       "      <td>0.382222</td>\n",
       "      <td>352.670281</td>\n",
       "      <td>1.898872e+04</td>\n",
       "      <td>23.363417</td>\n",
       "      <td>4284.938531</td>\n",
       "      <td>2.242909e+04</td>\n",
       "      <td>2353.88008</td>\n",
       "      <td>7.874922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.012300e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.990700e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.600000e+01</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>3.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.987600e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.260000e+02</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>1.760000e+02</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>7.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.069460e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9821.000000</td>\n",
       "      <td>2.644404e+06</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>487076.000000</td>\n",
       "      <td>3.046717e+06</td>\n",
       "      <td>190625.00000</td>\n",
       "      <td>421.990000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              appid       english  required_age  steamspy_tags  achievements  \\\n",
       "count  2.707500e+04  27075.000000  27075.000000   27075.000000  27075.000000   \n",
       "mean   5.962035e+05      0.981127      0.354903       2.880960     45.248864   \n",
       "std    2.508942e+05      0.136081      2.406044       0.382222    352.670281   \n",
       "min    1.000000e+01      0.000000      0.000000       1.000000      0.000000   \n",
       "25%    4.012300e+05      1.000000      0.000000       3.000000      0.000000   \n",
       "50%    5.990700e+05      1.000000      0.000000       3.000000      7.000000   \n",
       "75%    7.987600e+05      1.000000      0.000000       3.000000     23.000000   \n",
       "max    1.069460e+06      1.000000     18.000000       3.000000   9821.000000   \n",
       "\n",
       "       positive_ratings  %_pos_rating  negative_ratings  \\\n",
       "count      2.707500e+04  27075.000000      27075.000000   \n",
       "mean       1.000559e+03     71.460462        211.027147   \n",
       "std        1.898872e+04     23.363417       4284.938531   \n",
       "min        0.000000e+00      0.000000          0.000000   \n",
       "25%        6.000000e+00     58.000000          2.000000   \n",
       "50%        2.400000e+01     76.000000          9.000000   \n",
       "75%        1.260000e+02     89.000000         42.000000   \n",
       "max        2.644404e+06    100.000000     487076.000000   \n",
       "\n",
       "       total_number_of_ratings  median_playtime         price  \n",
       "count             2.707500e+04      27075.00000  27075.000000  \n",
       "mean              1.211586e+03        146.05603      6.078193  \n",
       "std               2.242909e+04       2353.88008      7.874922  \n",
       "min               1.000000e+00          0.00000      0.000000  \n",
       "25%               1.000000e+01          0.00000      1.690000  \n",
       "50%               3.600000e+01          0.00000      3.990000  \n",
       "75%               1.760000e+02          0.00000      7.190000  \n",
       "max               3.046717e+06     190625.00000    421.990000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steam_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0784fdc",
   "metadata": {},
   "source": [
    "###  counting number of categories and converting to numerical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3d9a509",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0f36ae18",
   "metadata": {},
   "outputs": [],
   "source": [
    "replace_categories_column = steam_data[\"categories\"].str.count(\";\")+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "96c47725",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "1    4\n",
       "2    2\n",
       "3    4\n",
       "4    3\n",
       "Name: categories, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_categories_data = steam_data['categories'] = replace_categories_column\n",
    "new_categories_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3bd40d44",
   "metadata": {},
   "outputs": [
    {
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       "        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>appid</th>\n",
       "      <th>english</th>\n",
       "      <th>required_age</th>\n",
       "      <th>categories</th>\n",
       "      <th>steamspy_tags</th>\n",
       "      <th>achievements</th>\n",
       "      <th>positive_ratings</th>\n",
       "      <th>%_pos_rating</th>\n",
       "      <th>negative_ratings</th>\n",
       "      <th>total_number_of_ratings</th>\n",
       "      <th>median_playtime</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.707500e+04</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>2.707500e+04</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>27075.000000</td>\n",
       "      <td>2.707500e+04</td>\n",
       "      <td>27075.00000</td>\n",
       "      <td>27075.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.962035e+05</td>\n",
       "      <td>0.981127</td>\n",
       "      <td>0.354903</td>\n",
       "      <td>3.313980</td>\n",
       "      <td>2.880960</td>\n",
       "      <td>45.248864</td>\n",
       "      <td>1.000559e+03</td>\n",
       "      <td>71.460462</td>\n",
       "      <td>211.027147</td>\n",
       "      <td>1.211586e+03</td>\n",
       "      <td>146.05603</td>\n",
       "      <td>6.078193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.508942e+05</td>\n",
       "      <td>0.136081</td>\n",
       "      <td>2.406044</td>\n",
       "      <td>2.235836</td>\n",
       "      <td>0.382222</td>\n",
       "      <td>352.670281</td>\n",
       "      <td>1.898872e+04</td>\n",
       "      <td>23.363417</td>\n",
       "      <td>4284.938531</td>\n",
       "      <td>2.242909e+04</td>\n",
       "      <td>2353.88008</td>\n",
       "      <td>7.874922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.012300e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.990700e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.600000e+01</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>3.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.987600e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.260000e+02</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>1.760000e+02</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>7.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.069460e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9821.000000</td>\n",
       "      <td>2.644404e+06</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>487076.000000</td>\n",
       "      <td>3.046717e+06</td>\n",
       "      <td>190625.00000</td>\n",
       "      <td>421.990000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              appid       english  required_age    categories  steamspy_tags  \\\n",
       "count  2.707500e+04  27075.000000  27075.000000  27075.000000   27075.000000   \n",
       "mean   5.962035e+05      0.981127      0.354903      3.313980       2.880960   \n",
       "std    2.508942e+05      0.136081      2.406044      2.235836       0.382222   \n",
       "min    1.000000e+01      0.000000      0.000000      1.000000       1.000000   \n",
       "25%    4.012300e+05      1.000000      0.000000      2.000000       3.000000   \n",
       "50%    5.990700e+05      1.000000      0.000000      3.000000       3.000000   \n",
       "75%    7.987600e+05      1.000000      0.000000      4.000000       3.000000   \n",
       "max    1.069460e+06      1.000000     18.000000     18.000000       3.000000   \n",
       "\n",
       "       achievements  positive_ratings  %_pos_rating  negative_ratings  \\\n",
       "count  27075.000000      2.707500e+04  27075.000000      27075.000000   \n",
       "mean      45.248864      1.000559e+03     71.460462        211.027147   \n",
       "std      352.670281      1.898872e+04     23.363417       4284.938531   \n",
       "min        0.000000      0.000000e+00      0.000000          0.000000   \n",
       "25%        0.000000      6.000000e+00     58.000000          2.000000   \n",
       "50%        7.000000      2.400000e+01     76.000000          9.000000   \n",
       "75%       23.000000      1.260000e+02     89.000000         42.000000   \n",
       "max     9821.000000      2.644404e+06    100.000000     487076.000000   \n",
       "\n",
       "       total_number_of_ratings  median_playtime         price  \n",
       "count             2.707500e+04      27075.00000  27075.000000  \n",
       "mean              1.211586e+03        146.05603      6.078193  \n",
       "std               2.242909e+04       2353.88008      7.874922  \n",
       "min               1.000000e+00          0.00000      0.000000  \n",
       "25%               1.000000e+01          0.00000      1.690000  \n",
       "50%               3.600000e+01          0.00000      3.990000  \n",
       "75%               1.760000e+02          0.00000      7.190000  \n",
       "max               3.046717e+06     190625.00000    421.990000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steam_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81e5f74a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3f0311bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steam_data.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1e2ebe06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 27075 entries, 0 to 27074\n",
      "Data columns (total 19 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   appid                    27075 non-null  int64  \n",
      " 1   name                     27075 non-null  object \n",
      " 2   release_date             27075 non-null  object \n",
      " 3   english                  27075 non-null  int64  \n",
      " 4   developer                27075 non-null  object \n",
      " 5   publisher                27075 non-null  object \n",
      " 6   platforms                27075 non-null  object \n",
      " 7   required_age             27075 non-null  int64  \n",
      " 8   categories               27075 non-null  int64  \n",
      " 9   genres                   27075 non-null  object \n",
      " 10  steamspy_tags            27075 non-null  int64  \n",
      " 11  achievements             27075 non-null  int64  \n",
      " 12  positive_ratings         27075 non-null  int64  \n",
      " 13  %_pos_rating             27075 non-null  int64  \n",
      " 14  negative_ratings         27075 non-null  int64  \n",
      " 15  total_number_of_ratings  27075 non-null  int64  \n",
      " 16  median_playtime          27075 non-null  int64  \n",
      " 17  owners                   27075 non-null  object \n",
      " 18  price                    27075 non-null  float64\n",
      "dtypes: float64(1), int64(11), object(7)\n",
      "memory usage: 3.9+ MB\n"
     ]
    }
   ],
   "source": [
    "steam_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "17622894",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "309d6f53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'appid')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the positive_ratings & appid to find out if a linear trend exists\n",
    "\n",
    "### BEGIN SOLUTION\n",
    "plt.scatter(steam_data['positive_ratings'], steam_data['appid'])\n",
    "plt.xlabel(\"positive_ratings\")\n",
    "plt.ylabel(\"appid\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "089fd471",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'median_playtime')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.scatter(steam_data['positive_ratings'], steam_data['median_playtime'])\n",
    "plt.xlabel(\"positive_ratings\")\n",
    "plt.ylabel(\"median_playtime\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e8dcce9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'price')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the negative_ratings & average_playtime to find out if a linear trend exists\n",
    "\n",
    "### BEGIN SOLUTION\n",
    "plt.scatter(steam_data['positive_ratings'], steam_data['price'])\n",
    "plt.xlabel(\"positive_ratings\")\n",
    "plt.ylabel(\"price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5b8058d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'price')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the negative_ratings & appid to find out if a linear trend exists\n",
    "\n",
    "### BEGIN SOLUTION\n",
    "plt.scatter(steam_data['negative_ratings'], steam_data['price'])\n",
    "plt.xlabel(\"negative_ratings\")\n",
    "plt.ylabel(\"price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9b516efd",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_steam_data = steam_data.drop([\"appid\",\"name\",\"release_date\",\"developer\",\"publisher\",\"platforms\",\n",
    "    \"genres\", \"owners\",\"positive_ratings\",\"negative_ratings\"], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ee4e40a1",
   "metadata": {
    "scrolled": true
   },
   "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>english</th>\n",
       "      <th>required_age</th>\n",
       "      <th>categories</th>\n",
       "      <th>steamspy_tags</th>\n",
       "      <th>achievements</th>\n",
       "      <th>%_pos_rating</th>\n",
       "      <th>total_number_of_ratings</th>\n",
       "      <th>median_playtime</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>127873</td>\n",
       "      <td>317</td>\n",
       "      <td>7.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>84</td>\n",
       "      <td>3951</td>\n",
       "      <td>62</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>3814</td>\n",
       "      <td>34</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>83</td>\n",
       "      <td>1540</td>\n",
       "      <td>184</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>5538</td>\n",
       "      <td>415</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   english  required_age  categories  steamspy_tags  achievements  \\\n",
       "0        1             0           4              3             0   \n",
       "1        1             0           4              3             0   \n",
       "2        1             0           2              3             0   \n",
       "3        1             0           4              3             0   \n",
       "4        1             0           3              3             0   \n",
       "\n",
       "   %_pos_rating  total_number_of_ratings  median_playtime  price  \n",
       "0            97                   127873              317   7.19  \n",
       "1            84                     3951               62   3.99  \n",
       "2            90                     3814               34   3.99  \n",
       "3            83                     1540              184   3.99  \n",
       "4            95                     5538              415   3.99  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_steam_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4269a98b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# added coloun in excel for % ratings positive and total number of ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f1671c83",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>english</th>\n",
       "      <th>required_age</th>\n",
       "      <th>categories</th>\n",
       "      <th>steamspy_tags</th>\n",
       "      <th>achievements</th>\n",
       "      <th>%_pos_rating</th>\n",
       "      <th>total_number_of_ratings</th>\n",
       "      <th>median_playtime</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>3.99</td>\n",
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       "      <td>3814</td>\n",
       "      <td>34</td>\n",
       "      <td>3.99</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>83</td>\n",
       "      <td>1540</td>\n",
       "      <td>184</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>5538</td>\n",
       "      <td>415</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   english  required_age  categories  steamspy_tags  achievements  \\\n",
       "0        1             0           4              3             0   \n",
       "1        1             0           4              3             0   \n",
       "2        1             0           2              3             0   \n",
       "3        1             0           4              3             0   \n",
       "4        1             0           3              3             0   \n",
       "\n",
       "   %_pos_rating  total_number_of_ratings  median_playtime  price  \n",
       "0            97                   127873              317   7.19  \n",
       "1            84                     3951               62   3.99  \n",
       "2            90                     3814               34   3.99  \n",
       "3            83                     1540              184   3.99  \n",
       "4            95                     5538              415   3.99  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_steam_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6a6e6331",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 27075 entries, 0 to 27074\n",
      "Data columns (total 9 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   english                  27075 non-null  int64  \n",
      " 1   required_age             27075 non-null  int64  \n",
      " 2   categories               27075 non-null  int64  \n",
      " 3   steamspy_tags            27075 non-null  int64  \n",
      " 4   achievements             27075 non-null  int64  \n",
      " 5   %_pos_rating             27075 non-null  int64  \n",
      " 6   total_number_of_ratings  27075 non-null  int64  \n",
      " 7   median_playtime          27075 non-null  int64  \n",
      " 8   price                    27075 non-null  float64\n",
      "dtypes: float64(1), int64(8)\n",
      "memory usage: 1.9 MB\n"
     ]
    }
   ],
   "source": [
    "new_steam_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "279a017a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.unique(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d6ba70a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into X_train, X_test, y_train, y_test\n",
    "y = new_steam_data['median_playtime'].values\n",
    "X = new_steam_data.drop('median_playtime', axis=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f41753c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\VAIO\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression Training score:  0.7745001477395843\n",
      "LogisticRegression Test score:  0.7572758162210075\n"
     ]
    }
   ],
   "source": [
    "# Train a Logistic Regression model print the model score\n",
    "logistic_regression = LogisticRegression().fit(X_train, y_train)\n",
    "\n",
    "print('LogisticRegression Training score: ', logistic_regression.score(X_train,y_train))\n",
    "print('LogisticRegression Test score: ', logistic_regression.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "623171c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ************does not work with positive ratings or price but no error with 'achievements' ***************"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b4773420",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier Training score:  0.9994090416625628\n",
      "RandomForestClassifier Test score:  0.751071059240656\n"
     ]
    }
   ],
   "source": [
    "# Train a Random Forest Classifier model and print the model score\n",
    "RandomForest_classifier = RandomForestClassifier(random_state=1)\n",
    "RandomForest_classifier.fit(X_train, y_train)\n",
    "print('RandomForestClassifier Training score: ', RandomForest_classifier.score(X_train,y_train))\n",
    "print('RandomForestClassifier Test score: ', RandomForest_classifier.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f59c8743",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the data\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1da2c542",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\VAIO\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scaled LogisticRegression Training score:  0.7772579533142914\n",
      "Scaled LogisticRegression Test score:  0.7594918008568474\n"
     ]
    }
   ],
   "source": [
    "# Train the Logistic Regression model on the scaled data and print the model score\n",
    "scaled_logistic = LogisticRegression(\n",
    "    solver='lbfgs',\n",
    "    max_iter=100,\n",
    "    random_state=1\n",
    ")\n",
    "scaled_logistic.fit(X_train_scaled, y_train)\n",
    "\n",
    "print('Scaled LogisticRegression Training score: ', scaled_logistic.score(X_train_scaled, y_train))\n",
    "print('Scaled LogisticRegression Test score: ',scaled_logistic.score(X_test_scaled, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cbe63ab9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scaled RandomForestClassifier Training score:  0.9994582881906826\n",
      "Scaled RandomForestClassifier Test score:  0.7518097207859359\n"
     ]
    }
   ],
   "source": [
    "# Train a Random Forest Classifier model on the scaled data and print the model score\n",
    "scaled_random_forest = RandomForestClassifier(random_state=0)\n",
    "scaled_random_forest.fit(X_train_scaled, y_train)\n",
    "print('Scaled RandomForestClassifier Training score: ', scaled_random_forest.score(X_train_scaled,y_train))\n",
    "print('Scaled RandomForestClassifier Test score: ', scaled_random_forest.score(X_test_scaled, y_test))"
   ]
  }
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