{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 919,
   "id": "c05c931b-4406-401d-b1fb-1a7c3e653955",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# To scale the data using z-score \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Algorithms to use\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "\n",
    "# Metrics to evaluate the model\n",
    "from sklearn import metrics\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, classification_report, precision_recall_curve,recall_score, accuracy_score, roc_auc_score, roc_curve, auc, f1_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "# To ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 920,
   "id": "9ff236cf-bba2-470a-9ac7-86835fc60efb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the dataset\n",
    "df = pd.read_csv('online_gaming_behavior_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 921,
   "id": "527b23a4-d91a-4090-83c7-18f832f301a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PlayerID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Location</th>\n",
       "      <th>GameGenre</th>\n",
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       "      <th>GameDifficulty</th>\n",
       "      <th>SessionsPerWeek</th>\n",
       "      <th>AvgSessionDurationMinutes</th>\n",
       "      <th>PlayerLevel</th>\n",
       "      <th>AchievementsUnlocked</th>\n",
       "      <th>EngagementLevel</th>\n",
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       "      <td>8.223755</td>\n",
       "      <td>0</td>\n",
       "      <td>Easy</td>\n",
       "      <td>16</td>\n",
       "      <td>142</td>\n",
       "      <td>35</td>\n",
       "      <td>41</td>\n",
       "      <td>High</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9003</td>\n",
       "      <td>35</td>\n",
       "      <td>Male</td>\n",
       "      <td>USA</td>\n",
       "      <td>Action</td>\n",
       "      <td>5.265351</td>\n",
       "      <td>1</td>\n",
       "      <td>Easy</td>\n",
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       "      <td>9004</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Action</td>\n",
       "      <td>15.531945</td>\n",
       "      <td>0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>131</td>\n",
       "      <td>95</td>\n",
       "      <td>37</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PlayerID  Age  Gender Location GameGenre  PlayTimeHours  InGamePurchases  \\\n",
       "0      9000   43    Male    Other  Strategy      16.271119                0   \n",
       "1      9001   29  Female      USA  Strategy       5.525961                0   \n",
       "2      9002   22  Female      USA    Sports       8.223755                0   \n",
       "3      9003   35    Male      USA    Action       5.265351                1   \n",
       "4      9004   33    Male   Europe    Action      15.531945                0   \n",
       "\n",
       "  GameDifficulty  SessionsPerWeek  AvgSessionDurationMinutes  PlayerLevel  \\\n",
       "0         Medium                6                        108           79   \n",
       "1         Medium                5                        144           11   \n",
       "2           Easy               16                        142           35   \n",
       "3           Easy                9                         85           57   \n",
       "4         Medium                2                        131           95   \n",
       "\n",
       "   AchievementsUnlocked EngagementLevel  \n",
       "0                    25          Medium  \n",
       "1                    10          Medium  \n",
       "2                    41            High  \n",
       "3                    47          Medium  \n",
       "4                    37          Medium  "
      ]
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     "execution_count": 921,
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 922,
   "id": "33552f6c-1adb-440e-bf46-6a2dabfd4cdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 40034 entries, 0 to 40033\n",
      "Data columns (total 13 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   PlayerID                   40034 non-null  int64  \n",
      " 1   Age                        40034 non-null  int64  \n",
      " 2   Gender                     40034 non-null  object \n",
      " 3   Location                   40034 non-null  object \n",
      " 4   GameGenre                  40034 non-null  object \n",
      " 5   PlayTimeHours              40034 non-null  float64\n",
      " 6   InGamePurchases            40034 non-null  int64  \n",
      " 7   GameDifficulty             40034 non-null  object \n",
      " 8   SessionsPerWeek            40034 non-null  int64  \n",
      " 9   AvgSessionDurationMinutes  40034 non-null  int64  \n",
      " 10  PlayerLevel                40034 non-null  int64  \n",
      " 11  AchievementsUnlocked       40034 non-null  int64  \n",
      " 12  EngagementLevel            40034 non-null  object \n",
      "dtypes: float64(1), int64(7), object(5)\n",
      "memory usage: 4.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
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   "execution_count": 923,
   "id": "81debc7d-aeaf-4e92-94a1-acc820698e6b",
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       "      <td>Simulation</td>\n",
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       "      <td>0</td>\n",
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       "      <td>49031</td>\n",
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       "      <td>Female</td>\n",
       "      <td>USA</td>\n",
       "      <td>RPG</td>\n",
       "      <td>0.240057</td>\n",
       "      <td>1</td>\n",
       "      <td>Easy</td>\n",
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       "      <td>176</td>\n",
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       "      <td>Sports</td>\n",
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       "      <td>128</td>\n",
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       "      <td>10.083804</td>\n",
       "      <td>0</td>\n",
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       "      <td>13</td>\n",
       "      <td>84</td>\n",
       "      <td>72</td>\n",
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       "<p>40034 rows × 13 columns</p>\n",
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      ],
      "text/plain": [
       "       PlayerID  Age  Gender Location   GameGenre  PlayTimeHours  \\\n",
       "0          9000   43    Male    Other    Strategy      16.271119   \n",
       "1          9001   29  Female      USA    Strategy       5.525961   \n",
       "2          9002   22  Female      USA      Sports       8.223755   \n",
       "3          9003   35    Male      USA      Action       5.265351   \n",
       "4          9004   33    Male   Europe      Action      15.531945   \n",
       "...         ...  ...     ...      ...         ...            ...   \n",
       "40029     49029   32    Male      USA    Strategy      20.619662   \n",
       "40030     49030   44  Female    Other  Simulation      13.539280   \n",
       "40031     49031   15  Female      USA         RPG       0.240057   \n",
       "40032     49032   34    Male      USA      Sports      14.017818   \n",
       "40033     49033   19    Male      USA      Sports      10.083804   \n",
       "\n",
       "       InGamePurchases GameDifficulty  SessionsPerWeek  \\\n",
       "0                    0         Medium                6   \n",
       "1                    0         Medium                5   \n",
       "2                    0           Easy               16   \n",
       "3                    1           Easy                9   \n",
       "4                    0         Medium                2   \n",
       "...                ...            ...              ...   \n",
       "40029                0           Easy                4   \n",
       "40030                0           Hard               19   \n",
       "40031                1           Easy               10   \n",
       "40032                1         Medium                3   \n",
       "40033                0           Easy               13   \n",
       "\n",
       "       AvgSessionDurationMinutes  PlayerLevel  AchievementsUnlocked  \\\n",
       "0                            108           79                    25   \n",
       "1                            144           11                    10   \n",
       "2                            142           35                    41   \n",
       "3                             85           57                    47   \n",
       "4                            131           95                    37   \n",
       "...                          ...          ...                   ...   \n",
       "40029                         75           85                    14   \n",
       "40030                        114           71                    27   \n",
       "40031                        176           29                     1   \n",
       "40032                        128           70                    10   \n",
       "40033                         84           72                    39   \n",
       "\n",
       "      EngagementLevel  \n",
       "0              Medium  \n",
       "1              Medium  \n",
       "2                High  \n",
       "3              Medium  \n",
       "4              Medium  \n",
       "...               ...  \n",
       "40029          Medium  \n",
       "40030            High  \n",
       "40031            High  \n",
       "40032          Medium  \n",
       "40033          Medium  \n",
       "\n",
       "[40034 rows x 13 columns]"
      ]
     },
     "execution_count": 923,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 924,
   "id": "96ec5dec-a064-454a-88f3-1aaaa16fd1e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PlayerID                     40034\n",
       "Age                             35\n",
       "Gender                           2\n",
       "Location                         4\n",
       "GameGenre                        5\n",
       "PlayTimeHours                40034\n",
       "InGamePurchases                  2\n",
       "GameDifficulty                   3\n",
       "SessionsPerWeek                 20\n",
       "AvgSessionDurationMinutes      170\n",
       "PlayerLevel                     99\n",
       "AchievementsUnlocked            50\n",
       "EngagementLevel                  3\n",
       "dtype: int64"
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     "execution_count": 924,
     "metadata": {},
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   ],
   "source": [
    "df.nunique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 925,
   "id": "32968bf3-9a9c-4a5d-8ea9-08c63180dcf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(columns=['PlayerID'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 926,
   "id": "7fc84e35-5be5-4a11-ac85-7dfc30634dd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cols=['Age','PlayTimeHours','PlayerLevel','SessionsPerWeek','AvgSessionDurationMinutes','AchievementsUnlocked']\n",
    "cat_cols=['Gender','GameGenre','GameDifficulty','EngagementLevel','InGamePurchases','Location']\n",
    "#Seperating data into numerical and categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 927,
   "id": "8474c268-3e78-4b51-857d-78cc26b5c354",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>40034.0</td>\n",
       "      <td>31.992531</td>\n",
       "      <td>10.043227</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>49.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PlayTimeHours</th>\n",
       "      <td>40034.0</td>\n",
       "      <td>12.024365</td>\n",
       "      <td>6.914638</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>6.067501</td>\n",
       "      <td>12.008002</td>\n",
       "      <td>17.963831</td>\n",
       "      <td>23.999592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PlayerLevel</th>\n",
       "      <td>40034.0</td>\n",
       "      <td>49.655568</td>\n",
       "      <td>28.588379</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SessionsPerWeek</th>\n",
       "      <td>40034.0</td>\n",
       "      <td>9.471774</td>\n",
       "      <td>5.763667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AvgSessionDurationMinutes</th>\n",
       "      <td>40034.0</td>\n",
       "      <td>94.792252</td>\n",
       "      <td>49.011375</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>179.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AchievementsUnlocked</th>\n",
       "      <td>40034.0</td>\n",
       "      <td>24.526477</td>\n",
       "      <td>14.430726</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>49.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             count       mean        std        min  \\\n",
       "Age                        40034.0  31.992531  10.043227  15.000000   \n",
       "PlayTimeHours              40034.0  12.024365   6.914638   0.000115   \n",
       "PlayerLevel                40034.0  49.655568  28.588379   1.000000   \n",
       "SessionsPerWeek            40034.0   9.471774   5.763667   0.000000   \n",
       "AvgSessionDurationMinutes  40034.0  94.792252  49.011375  10.000000   \n",
       "AchievementsUnlocked       40034.0  24.526477  14.430726   0.000000   \n",
       "\n",
       "                                 25%        50%         75%         max  \n",
       "Age                        23.000000  32.000000   41.000000   49.000000  \n",
       "PlayTimeHours               6.067501  12.008002   17.963831   23.999592  \n",
       "PlayerLevel                25.000000  49.000000   74.000000   99.000000  \n",
       "SessionsPerWeek             4.000000   9.000000   14.000000   19.000000  \n",
       "AvgSessionDurationMinutes  52.000000  95.000000  137.000000  179.000000  \n",
       "AchievementsUnlocked       12.000000  25.000000   37.000000   49.000000  "
      ]
     },
     "execution_count": 927,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This would check summary statistics for the data\n",
    "df[num_cols].describe().T\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 928,
   "id": "2bbfc81d-ec08-4401-b0db-3da38bbd3182",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x1400 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating histograms which is like a data chart although hisograms are different\n",
    "df[num_cols].hist(figsize=(14,14),edgecolor='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 929,
   "id": "cb97a886-05e1-465b-98c1-4407e6ac6c11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Below is the method of using clustering and elbow plots to read data\n",
    "df_sample = df.sample (200)\n",
    "\n",
    "plt.figure(figsize = (8,8))\n",
    "plt.scatter(df_sample[\"Age\"],df_sample[\"PlayTimeHours\"])\n",
    "plt.title(\"KMeans Clustering for age and playtimes\")\n",
    "plt.xlabel(\"age\")\n",
    "plt.ylabel(\"Playtime hours\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 930,
   "id": "0385e4fa-f38f-4185-a519-de54e3aea582",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#here is the session minutes and sessions per week \n",
    "\n",
    "df_sample = df.sample (200)\n",
    "plt.figure(figsize = (8,4))\n",
    "plt.scatter(df_sample[\"AvgSessionDurationMinutes\"],df_sample[\"SessionsPerWeek\"])\n",
    "plt.title(\"KMeans Clustering for age and playtimes\")\n",
    "plt.xlabel(\"Sessions per week\")\n",
    "plt.ylabel(\"Session minutes\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 931,
   "id": "f162869a-7911-4e81-ab53-3684d2956865",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Select relevant features from the dataset\n",
    "features = [\n",
    "    'SessionsPerWeek',\n",
    "    'AvgSessionDurationMinutes',\n",
    "    'PlayTimeHours',\n",
    "    'InGamePurchases',\n",
    "    'Age'\n",
    "]\n",
    "\n",
    "X = df[features]\n",
    "\n",
    "# Scale the data\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# Elbow method\n",
    "inertia = []\n",
    "k_range = range(1, 11)\n",
    "\n",
    "for k in k_range:\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)\n",
    "    kmeans.fit(X_scaled)\n",
    "    inertia.append(kmeans.inertia_)\n",
    "\n",
    "# Plot correctly\n",
    "plt.figure()\n",
    "plt.plot(k_range, inertia, marker='o')\n",
    "plt.xlabel('Sessions')\n",
    "plt.ylabel('Playtime')\n",
    "plt.title('Elbow Method for Optimal Clusters')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 932,
   "id": "516fd7df-d795-4b38-a7b2-dc023a29bc53",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Select relevant features from the dataset\n",
    "features = [\n",
    "    'SessionsPerWeek',\n",
    "    'AvgSessionDurationMinutes',\n",
    "    'PlayTimeHours',\n",
    "    'InGamePurchases',\n",
    "    'Age'\n",
    "]\n",
    "\n",
    "X = df[features]\n",
    "\n",
    "# Scale the data\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# Elbow method\n",
    "inertia = []\n",
    "k_range = range(1, 11)\n",
    "\n",
    "for k in k_range:\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)\n",
    "    kmeans.fit(X_scaled)\n",
    "    inertia.append(kmeans.inertia_)\n",
    "\n",
    "# Plot correctly\n",
    "plt.figure()\n",
    "plt.plot(k_range, inertia, marker='o')\n",
    "plt.xlabel('Playtime')\n",
    "plt.ylabel('Age')\n",
    "plt.title('Elbow Method for Optimal Clusters')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 933,
   "id": "ba70cdef-08ce-4f4b-8e7f-01e7746f85fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Data for the game genres and the demographies associated with those genres\n",
    "for i in cat_cols:\n",
    "    if i!='GameGenre':\n",
    "         (pd.crosstab(df[i],df['GameGenre'],normalize='index')*100).plot(kind='bar',figsize=(8,4),stacked=True)\n",
    "         plt.ylabel('Game Genres')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 934,
   "id": "ad8d8bad-8320-4d46-858b-7a4ffa6bdd07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>PlayTimeHours</th>\n",
       "      <th>PlayerLevel</th>\n",
       "      <th>SessionsPerWeek</th>\n",
       "      <th>AvgSessionDurationMinutes</th>\n",
       "      <th>AchievementsUnlocked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GameGenre</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Action</th>\n",
       "      <td>31.952482</td>\n",
       "      <td>12.164645</td>\n",
       "      <td>49.802836</td>\n",
       "      <td>9.472820</td>\n",
       "      <td>94.782933</td>\n",
       "      <td>24.582784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPG</th>\n",
       "      <td>31.813003</td>\n",
       "      <td>12.008113</td>\n",
       "      <td>49.433602</td>\n",
       "      <td>9.405810</td>\n",
       "      <td>93.741952</td>\n",
       "      <td>24.518234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Simulation</th>\n",
       "      <td>32.185895</td>\n",
       "      <td>11.898085</td>\n",
       "      <td>49.245396</td>\n",
       "      <td>9.440436</td>\n",
       "      <td>94.739572</td>\n",
       "      <td>24.554052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sports</th>\n",
       "      <td>31.994036</td>\n",
       "      <td>11.968329</td>\n",
       "      <td>49.890780</td>\n",
       "      <td>9.497391</td>\n",
       "      <td>95.046098</td>\n",
       "      <td>24.397242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strategy</th>\n",
       "      <td>32.016725</td>\n",
       "      <td>12.081855</td>\n",
       "      <td>49.900524</td>\n",
       "      <td>9.541687</td>\n",
       "      <td>95.641538</td>\n",
       "      <td>24.580504</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Age  PlayTimeHours  PlayerLevel  SessionsPerWeek  \\\n",
       "GameGenre                                                            \n",
       "Action      31.952482      12.164645    49.802836         9.472820   \n",
       "RPG         31.813003      12.008113    49.433602         9.405810   \n",
       "Simulation  32.185895      11.898085    49.245396         9.440436   \n",
       "Sports      31.994036      11.968329    49.890780         9.497391   \n",
       "Strategy    32.016725      12.081855    49.900524         9.541687   \n",
       "\n",
       "            AvgSessionDurationMinutes  AchievementsUnlocked  \n",
       "GameGenre                                                    \n",
       "Action                      94.782933             24.582784  \n",
       "RPG                         93.741952             24.518234  \n",
       "Simulation                  94.739572             24.554052  \n",
       "Sports                      95.046098             24.397242  \n",
       "Strategy                    95.641538             24.580504  "
      ]
     },
     "execution_count": 934,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean of numerical variables grouped by the game genres\n",
    "df.groupby(['GameGenre'])[num_cols].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 935,
   "id": "9e8f94fc-0016-4ebb-9eab-4e1ae2a38532",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 935,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here below is the correlation of the the game genres and its going to be useful for the model to analyise \n",
    "plt.figure(figsize=(15,8))\n",
    "sns.heatmap(df[num_cols].corr(), annot=True, fmt='0.2f', cmap='YlOrRd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 936,
   "id": "3adf20e2-d518-4e1a-87b0-770b2f307819",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating metric function \n",
    "def metrics_score(actual, predicted):\n",
    "    print(classification_report(actual, predicted))\n",
    "\n",
    "    cm = confusion_matrix(actual, predicted)\n",
    "    plt.figure(figsize=(8,5))\n",
    "    \n",
    "    sns.heatmap(cm, annot=True,  fmt='.2f', xticklabels=['Not Attrite', 'Attrite'], yticklabels=['Not Attrite', 'Attrite'])\n",
    "    plt.ylabel('Actual')\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 937,
   "id": "37992543-6d28-4ba2-a550-572e0c405627",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-7 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-7 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-7 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-7 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 937,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting logistic regression model. This is to analyise the binary data\n",
    "\n",
    "# Separate numerical and categorical features\n",
    "numerical_features = ['Age', 'PlayTimeHours', 'InGamePurchases', 'SessionsPerWeek', 'AvgSessionDurationMinutes', 'PlayerLevel', 'AchievementsUnlocked']\n",
    "categorical_features = ['GameDifficulty']\n",
    "\n",
    "# Create feature matrix with proper encoding\n",
    "X_numerical = df[numerical_features]\n",
    "X_categorical = pd.get_dummies(df[categorical_features], drop_first=True)  # Convert categorical to dummy variables\n",
    "\n",
    "# Combine numerical and encoded categorical features\n",
    "X = pd.concat([X_numerical, X_categorical], axis=1)\n",
    "y = df['EngagementLevel']  # Using EngagementLevel as target variable\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Scale the features (now all features are numerical)\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)  # Fit and transform training data\n",
    "X_test_scaled = scaler.transform(X_test)  # Transform test data using same scaler\n",
    "\n",
    "lg = LogisticRegression()\n",
    "lg.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 938,
   "id": "227b0696-9b31-4ac9-9df6-ae8b01294436",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        High       0.90      0.83      0.86      8301\n",
      "         Low       0.80      0.70      0.75      8231\n",
      "      Medium       0.80      0.89      0.84     15495\n",
      "\n",
      "    accuracy                           0.82     32027\n",
      "   macro avg       0.83      0.81      0.82     32027\n",
      "weighted avg       0.83      0.82      0.82     32027\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analysing the ML's performace to train from the data being used. With aspects like precision, recall, f1-score and support.\n",
    "y_pred_train = lg.predict(X_train_scaled)\n",
    "\n",
    "metrics_score(y_train, y_pred_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 939,
   "id": "3bc3360c-2dec-44bb-a908-2276a002b501",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        High       0.89      0.82      0.85      2035\n",
      "         Low       0.80      0.70      0.75      2093\n",
      "      Medium       0.80      0.89      0.84      3879\n",
      "\n",
      "    accuracy                           0.82      8007\n",
      "   macro avg       0.83      0.80      0.81      8007\n",
      "weighted avg       0.82      0.82      0.82      8007\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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2QNDJYDEmKysbIX9dQtPmDQEAZuamaNy0AZKTnmB/4FaE3gzC7oO+aObUqIirADo62qjXoDZO/6tfADh9MhhNmzcAAEnXpg+EU9WSqHVVNQB8+eWXWLFiBcqVK1cgJicn50MP66OnYy6HtpEBKnn2RsyCHYj+cStM2jdErU1TEN5vNtLOXS/wGquBHfA86gGeXYr8b9e2zK9eZCelKrVnJaVCv6IFAEDXsjzyFNnISctQislOSoOuRfn/dH0quS5cCMWw4eNx69ZdWFlaYMb0cTgT9BvqN+yAJ09S4DXhe6xftxj3Yy4jOzsbeXl5GPn1FPwVfLHIPq2tLZCQqHx/dEJiEqyt879r5uam0NbWRmLCY6WYxMTHsLLmHrGaqGatavjtzwDo6esiI+M5PNzH41bkXTT5J0F7nJSsFP84KRkV/rmtwc4+/17tidNGY94PPyMi/CY+/dwNO/b7oFPL3oi+e7/A9UzNTKCtrY2k1/pNSkyGhWX+PecWVuZvvTZRSaL2Z1X7+voWaLt+/Tp8fHywdevWQp+t+G8KhQIKhUKpLUvIha5Mq4hXlG6yMvmr05MDL+LRr4cAABkRMSjXrAash3QpkDiW0deFZZ/WuL9st8rGUODuB5msyHtZX8UAUMtNE/QhBP756pn013AT50IuIepmMIa4f4blK36F59jhcHJqjN59huHe/Vi0buWE1SvnIz4uEcdPnCmy39e/V7JCvmtSYkgz3Lkdja5t+6GcvBx69OyMZWt+wqc9h4nnC/uu4J822T/3YG/124X/bdsPAIgIv4lWbZwxYFBfcdq5MO/6PQW/px8WV1VLUmJWI6Snp2Pjxo1wcXFB/fr1cf78eXz77dvvD/D29oZcLlc6tmb8t8rZxyz7yTPkZefgeVSsUvvzWw/FVdX/Zu7qjDIGukjcFVTgXLGvnZgCANC1VL5vStdcjuzHaQCArMRUlNHTgbbcUClGx1yOrMep/3kM9HF4/jwT167dRNWqDtDX18eP877FlClzcOj3owgPv4E1a/3wv10HMHHCqCL7iI9PgrWVcuXQ0sIcCf9UGB8/foKcnBxY/VOBfMnCwgyJCQV3cqDSLzs7BzHRD3A1LAIL5i3H9WuRGDFqMJL++c68rAK+ZGZuiqTE/EpgYnz+d+ZW5B2lmFtRd1GhojUK8yQ5BTk5ObB8rV9zC1Oxwijl2vSBlKBnVZdkak8cz549i2HDhsHGxgYrV67ExYsXERQUhLNnz2LChAlvff306dORlpamdAw21Nx75YTsHKSH3YFBFeUpDgNHGyhiC/5laTWwI54cuYTs5Kf/+dov7iciKyEFJm3ri20yHW3IXWrj6cX8ZD796l3kZWWj/L9idCzLw7BmJTGGSj9dXV3UrFkN8fEJ0NHRhq6uboGVqbm5eSjzhpX2Iecvo1PH1kptnTu1wbmQSwCA7Oxs/P33VXTq2EYpptO/YkizyWQy6Orq4v69WCTEJ6FNu1fbkenoaMO5ZVNcuhAGAHhw/yHiHyXAsZq9Uh+OVewQ+yCu0P6zs3MQfuU6Wv+rXwBo3c4Fly7k3+ct5dpEJYnapqoXLVqETZs2IT09HV988QXOnj2LBg0aQEdHByYm0lf66enpQU9PeQ+t0j5NXaasPgwcXv0LV6+yFQzr2CMnNR2Kh48Ru+Y31Fw/AU9DbiD1r2sw6dAQZl2a4mrfWUr96NtbQ+5cCxGD5hd6Hb0K5tAubwT9CuaAVhkY1rEHAGRGxyPv+QsAQJMzKxAzPwDJf1wAADzc8DsqjeuLzLtxyIyOQ6VxfZGbqUDS3vzpxtxnz5Gw/QQcZw1FzpN0ZKemw3HWEGTcuI/U0+Gq/qiohFi04Hsc+v0o7j94CEsLc8yYMR7lyhlhi/8uPHuWjqCgYCxY8B0yM1/g3v1YtGntAvfB/TB5ylyxD99NK/DoUZy4GnrVKh+cPLEHUyaPxoGDf8KtZ1d07Ngabdv1EV+zbMUGbPZdgcuXryDk/GV4jBiMypUqYP2v/h/8MyD1mvbdeJw8dgaPHsbDyMgQbn27w6VVMwz+7GsAgM86f4yd6IHou/cRffcePCd4IPP5C+zf87vYx9rVvpj07RjcuBaZf4/jF71QtZoDRg179Wi5Hfs2IvD34/DbuB0A8OuaLVix1htXwyJw+eIVDBr6KSpUsIG/707xNVKuTR8Abw2QRG2J44wZMzBt2jTMnTtX6SHc9HbGDaug/t454u9V5g4DACTsPImo8b8g+Y8LuD1tAyp59oHjj18i884jXB/xM55euKnUj9UXHZAV90RpFfS/2U0dAKsB7cXfGx//GQBwte8spAVHAADKVqsArXKvtlKKXb0fZfR1UXWBB7TlhngWegvXPp+H3IwXYsydH/wg5OSi5q8TUUZfF6lnwxE5fnWpLu1rugoVbbDV/xeYm5siKSkZ5y/8jZate+L+/YcAgIGDR+OnH6djy+ZVMDUtj3v3H+L7HxZh/a9bxD4qV7JVqkqeC7mEgYNHY+6cqZgzewru3L2HLwZ9gwsXX+1FumvXAZiZmuC7mRNgY2OJaxGR6OnmLl6XNIeFpRlWrPOGpZUFnj19hhsRURj82dc4cyp/m6w1KzdB30AfPy3+DvLy5RB2+SoGfToSGenPxT581m2Fvp4eZv00DeXLl8P1iCh80dcD92IeiDF2DpVgavaq+HFwXyBMTOTwmvI1LK0sEHnjFoYM+AYPY19VKaVcmz4A/h0kidr2cZw/fz78/Pzw4sULfPHFF3B3d0fdunWho6ODK1euoHbt2u/ctybt40gfD03ex5FKJk3fx5FKHrXu47h91tuDisHgizlvD/oIqe0exxkzZiAqKgr+/v6Ij4+Hs7MzGjRoAEEQkJKSoq5hERERkSbi4hhJ1L44pm3btti8eTPi4uLwzTffoEmTJmjbti1atGiBpUuXqnt4REREpAm4Abgkak8cXzI2NsbXX3+N8+fPIzQ0FM2bN8eCBXwkGBEREVFJUWISx3+rV68eli9fjocPeQM7ERERfQCcqpZE7U+OeRMdHR11D4GIiIg0AbfjkaREVhyJiIiIqOQp0RVHIiIiog+iFE8vqxITRyIiIiImjpKofapaS0sLiYmJBdqTk5P5RBkiIiKiEkTtFceiHlyjUCigq6v7gUdDREREGqkU772oSmpLHFeuXAkAkMlk2LhxI4yMjMRzubm5OH36NGrWrKmu4REREZEGEfK4qloKtSWOy5YtA5BfcVy3bp3StLSuri7s7e2xbt06dQ2PiIiIiF6jtsQxOjoaANC+fXvs3bsXJiYm6hoKERERaToujpFE7fc4njx5Uvz55f2OMplMXcMhIiIiTcR7HCVR+6pqANiyZQvq1asHAwMDGBgYoH79+vD391f3sIiIiIjoX9RecVy6dCm+//57jB07Fi1btoQgCPjrr7/w9ddf4/Hjx5gwYYK6h0hERESlHRfHSKL2xHHVqlVYu3YthgwZIrb16tULderUwezZs5k4EhER0fvHexwlUftUdVxcHFq0aFGgvUWLFoiLi1PDiIiIiIioMGpPHKtWrYr//e9/Bdp37tyJatWqqWFEREREpHHy8lR7lFJqn6qeM2cOBgwYgNOnT6Nly5aQyWQ4e/Ysjh8/XmhCSURERKRyRTzJjpSpveLYr18/nD9/Hubm5ti/fz/27t0Lc3NzXLhwAX369FH38IiIiIjoH2qvOAJAkyZNsHXrVnUPg4iIiDRVKZ5eVqUSkTgSERERqRW345FEbYljmTJl3vqEGJlMhpycnA80IiIiIiJ6E7Uljvv27SvyXHBwMFatWiU+gpCIiIjoveIjByVRW+LYq1evAm03b97E9OnTcfDgQQwaNAjz5s1Tw8iIiIhI43CqWhK1r6oGgEePHsHDwwP169dHTk4OwsLCsHnzZlSuXFndQyMiIiKif6h1cUxaWhrmz5+PVatWoWHDhjh+/Dhat26tziERERGRBhK4qloStSWOixYtwsKFC2FtbY3t27cXOnVNRERE9EFwqloStSWO3377LQwMDFC1alVs3rwZmzdvLjRu7969H3hkRERERFQYtSWOQ4YMeet2PEREREQfBFdVS6K2xNHPz09dlyYiIiJSxqlqSUrEqmoiIiIiKvmYOBIRERHl5an2kMjb2xvNmjWDsbExLC0t0bt3b0RGRirFDBs2DDKZTOlwdnZWilEoFPD09IS5uTkMDQ3h5uaG2NhYpZiUlBS4u7tDLpdDLpfD3d0dqampxfqYmDgSERER5QmqPSQKCgrCmDFjEBISgqNHjyInJwddunRBRkaGUly3bt0QFxcnHocPH1Y67+XlhX379mHHjh04e/Ys0tPT4erqitzcXDFm4MCBCAsLQ2BgIAIDAxEWFgZ3d/difUxq3ceRiIiISJMFBgYq/e7r6wtLS0tcvnwZbdq0Edv19PRgbW1daB9paWnw8fGBv78/OnXqBADYunUrKlWqhGPHjqFr1664ceMGAgMDERISAicnJwDAhg0b4OLigsjISNSoUUPSeFlxJCIiIhLyVHooFAo8ffpU6VAoFG8dRlpaGgDA1NRUqf3UqVOwtLRE9erV4eHhgcTERPHc5cuXkZ2djS5duohttra2qFu3LoKDgwEA586dg1wuF5NGAHB2doZcLhdjpGDiSERERKTiqWpvb2/xXsKXh7e39xuHIAgCJk6ciFatWqFu3bpie/fu3REQEIATJ05gyZIluHjxIjp06CAmovHx8dDV1YWJiYlSf1ZWVoiPjxdjLC0tC1zT0tJSjJGCU9VEREREKjZ9+nRMnDhRqU1PT++Nrxk7diyuXr2Ks2fPKrUPGDBA/Llu3bpo2rQp7Ozs8Pvvv6Nv375F9icIgtKe2YXtn/16zNswcSQiIiKNp+pnVevp6b01Ufw3T09PHDhwAKdPn0bFihXfGGtjYwM7OzvcunULAGBtbY2srCykpKQoVR0TExPRokULMSYhIaFAX0lJSbCyspI8Tk5VExEREamJIAgYO3Ys9u7dixMnTsDBweGtr0lOTsaDBw9gY2MDAGjSpAl0dHRw9OhRMSYuLg7Xrl0TE0cXFxekpaXhwoULYsz58+eRlpYmxkjBiiMRERGRmp4cM2bMGGzbtg2//fYbjI2NxfsN5XI5DAwMkJ6ejtmzZ6Nfv36wsbFBTEwMZsyYAXNzc/Tp00eMHTFiBCZNmgQzMzOYmppi8uTJqFevnrjKulatWujWrRs8PDywfv16AMDIkSPh6uoqeUU1wMSRiIiISG2J49q1awEA7dq1U2r39fXFsGHDoKWlhfDwcGzZsgWpqamwsbFB+/btsXPnThgbG4vxy5Ytg7a2Nvr374/MzEx07NgRfn5+0NLSEmMCAgIwbtw4cfW1m5sbVq9eXazxygRBKHUPZzxj/am6h0BUQPsn59Q9BCIl1kYmbw8i+oBin1xT27XTp/RRaX9Gi/eptL+SghVHIiIiIkG1i2NKKyaORERERGqaqv7YcFU1EREREUnCiiMRERFpPIEVR0mYOBIRERExcZSEU9VEREREJAkrjkREREQqfuRgacXEkYiIiIhT1ZJwqpqIiIiIJGHFkYiIiIgVR0mYOBIREZHGK4VPYH4vOFVNRERERJKw4khERETEqWpJmDgSERERMXGUhFPVRERERCRJqaw4tn9yTt1DICogwKyduodApGR5mUfqHgJRicFnVUtTKhNHIiIiomJh4igJp6qJiIiISBJWHImIiIj4qGpJmDgSERGRxuM9jtJwqpqIiIiIJGHFkYiIiIgVR0mYOBIRERHxHkdJOFVNRERERJKw4khEREQaj4tjpGHiSERERMSpakk4VU1EREREkrDiSERERBqPU9XSMHEkIiIi4lS1JJyqJiIiIiJJWHEkIiIijSew4igJE0ciIiIiJo6ScKqaiIiIiCRhxZGIiIg0HqeqpWHiSERERMTEURJOVRMRERGRJKw4EhERkcbjVLU0TByJiIhI4zFxlIZT1UREREQkCSuOREREpPFYcZSGiSMRERGRIFP3CD4KnKomIiIiIklYcSQiIiKNx6lqaZg4EhERkcYT8jhVLQWnqomIiIhIElYciYiISONxqloaVhyJiIhI4wmCTKWHVN7e3mjWrBmMjY1haWmJ3r17IzIy8rWxCZg9ezZsbW1hYGCAdu3aISIiQilGoVDA09MT5ubmMDQ0hJubG2JjY5ViUlJS4O7uDrlcDrlcDnd3d6Smphbrc2LiSERERKQmQUFBGDNmDEJCQnD06FHk5OSgS5cuyMjIEGMWLVqEpUuXYvXq1bh48SKsra3RuXNnPHv2TIzx8vLCvn37sGPHDpw9exbp6elwdXVFbm6uGDNw4ECEhYUhMDAQgYGBCAsLg7u7e7HGKxMEQfjvb7tk0datoO4hEBUQYNZO3UMgUrK8zCN1D4FIybmHJ9V27VinDirtr+L5E+/0uqSkJFhaWiIoKAht2rSBIAiwtbWFl5cXpk2bBiC/umhlZYWFCxdi1KhRSEtLg4WFBfz9/TFgwAAAwKNHj1CpUiUcPnwYXbt2xY0bN1C7dm2EhITAyckJABASEgIXFxfcvHkTNWrUkDQ+VhyJiIhI4wl5MpUeCoUCT58+VToUCsVbx5GWlgYAMDU1BQBER0cjPj4eXbp0EWP09PTQtm1bBAcHAwAuX76M7OxspRhbW1vUrVtXjDl37hzkcrmYNAKAs7Mz5HK5GCMFE0ciIiIiFfP29hbvJXx5eHt7v/E1giBg4sSJaNWqFerWrQsAiI+PBwBYWVkpxVpZWYnn4uPjoaurCxMTkzfGWFpaFrimpaWlGCMFV1UTERGRxlP1jXvTp0/HxIkTldr09PTe+JqxY8fi6tWrOHv2bIFzMpnyghtBEAq0ve71mMLipfTzb0wciYiISOOpegNwPT29tyaK/+bp6YkDBw7g9OnTqFixothubW0NIL9iaGNjI7YnJiaKVUhra2tkZWUhJSVFqeqYmJiIFi1aiDEJCQkFrpuUlFSgmvkmnKomIiIiUhNBEDB27Fjs3bsXJ06cgIODg9J5BwcHWFtb4+jRo2JbVlYWgoKCxKSwSZMm0NHRUYqJi4vDtWvXxBgXFxekpaXhwoULYsz58+eRlpYmxkjBiiMRERFpPHU9cnDMmDHYtm0bfvvtNxgbG4v3G8rlchgYGEAmk8HLywvz589HtWrVUK1aNcyfPx9ly5bFwIEDxdgRI0Zg0qRJMDMzg6mpKSZPnox69eqhU6dOAIBatWqhW7du8PDwwPr16wEAI0eOhKurq+QV1QATRyIiIiKV3+Mo1dq1awEA7dq1U2r39fXFsGHDAABTp05FZmYmRo8ejZSUFDg5OeHIkSMwNjYW45ctWwZtbW30798fmZmZ6NixI/z8/KClpSXGBAQEYNy4ceLqazc3N6xevbpY4+U+jkQfCPdxpJKG+zhSSaPOfRyjG3RWaX8OV46+PegjxIojERERaTx1TVV/bJg4EhERkcYrzvOlNVmJWVWdlZWFyMhI5OTkqHsoRERERFQISRXHAwcOSO7Qzc2tWAN4/vw5PD09sXnzZgBAVFQUHB0dMW7cONja2uLbb78tVn9ERERExSXkqXsEHwdJiWPv3r0ldSaTyZCbm1usAUyfPh1XrlzBqVOn0K1bN7G9U6dOmDVrFhNHIiIieu/yOFUtiaTEMS/v/aXh+/fvx86dO+Hs7Kz0yJvatWvjzp077+26RERERFQ8al8ck5SUVOhDtzMyMor17EQiIiKid8XFMdK8U+KYkZGBoKAg3L9/H1lZWUrnxo0bV6y+mjVrht9//x2enp4AXj2Ae8OGDXBxcXmX4REREREVC7fjkabYiWNoaCh69OiB58+fIyMjA6ampnj8+DHKli0LS0vLYieO3t7e6NatG65fv46cnBysWLECEREROHfuHIKCgoo7PCIiIiJ6T4q9Hc+ECRPQs2dPPHnyBAYGBggJCcG9e/fQpEkT/Pzzz8UeQIsWLfDXX3/h+fPnqFKlCo4cOQIrKyucO3cOTZo0KXZ/RERERMUlCKo9SqtiVxzDwsKwfv16aGlpQUtLCwqFAo6Ojli0aBGGDh2Kvn37FnsQ9erVE7fjISIiIvrQOFUtTbErjjo6OuJ9iFZWVrh//z4AQC6Xiz8Xh5aWFhITEwu0JycnKz2Ym4iIiIjUq9gVx0aNGuHSpUuoXr062rdvjx9++AGPHz+Gv78/6tWrV+wBCEXUcxUKBXR1dYvdHxEREVFxcR9HaYqdOM6fPx/Pnj0DAMybNw9Dhw7FN998g6pVq8LX11dyPytXrgSQv4p648aNMDIyEs/l5ubi9OnTqFmzZnGHR0RERFRs3I5HmmInjk2bNhV/trCwwOHDh9/pwsuWLQOQX3Fct26d0rS0rq4u7O3tsW7dunfqm4iIiIhUT20bgEdHRwMA2rdvj71798LExERdQyEiIiINV5pXQqtSsRNHBweHNz7R5e7du8Xq7+TJk8UdAklga2sN7/kz0K1rBxgY6CPq1l2MHDkJf4eGAwB69+6OkV8NRuPG9WFuboomzbrgypWIt/bbp08PzJk9BVUc7XDn7j18/8NC/PZboFLM16OGYtLEr2FjY4mI61GYNGkWzv514b28TyoZLJxrosY3n8C0vgMMrE1w9suleBh4udDYpouGo4p7R4T+4I+oDcrfHbMmVVHv2/4wa1wFedm5SI24h9ODFiH3RTYsXGqhw97vCu3zaLfv8eRK0X/21JnUF1UGd4CO3BBPQm/j8nQ/PI16KJ4vo6uNhj8MROU+LaClr4OEMxG4PN0PmXFP3uHToJJoxMSh+GrSMKW25MQncG3UT/zdrmpljJk5Eo2cG0BWpgyio2Lw3ag5SHiUv4Dzl13L0LhFQ6U+jv52Aj+MnvfGa/cd2guDvh4AM0szREfFYPms1bhyIbzA+HoNckU5uTEiQm/g55krEB0V887vl4qP9zhKU+zE0cvLS+n37OxshIaGIjAwEFOmTJHUx8SJEzFv3jwYGhpi4sSJb4xdunRpcYeo8cqXl+P0qf04FRQM156DkZj0GFUc7ZGa9lSMMTQsi+BzF7F7zyH8ul7a/pvOTk2wPWAtZs1ejP2//YHevbpjx7Z1aNuuDy5cDAUAfPaZG5YumY2xnjMQfO4iPL5yx6GDW1GvQTs8ePDovbxfUj+tsnpIvX4f0TuD0MpnQpFxFbo1gWmjqnheSEJm1qQq2mybhhurDuDvmZuRl52D8rXtIOTllwGSL0Xht/qjlV5Td9pnsGpd541JY80xrqgxqgfOe61D+p141PbqjXY7p+Nwq8nIyXgBAGg01x22nRvj3NeroEhJR8NZg9B6y2Qc7TpTvD59/O7cjMa4zyeJv+fl5ok/V7Czxfr9K3Fw+x/Y+LMf0p9lwL6aHbIUyk9H27/1EDb8vEn8XfFC+fzrOrq1h9fsMVg8YzmuXryGPu49sXTrQgxsN0xMSAeP/hxfjPwM8yYsxIO7DzBsvDtWbF+Mz9sMwfOMTFW8dSKVKXbiOH78+ELbf/nlF1y6dElSH6GhocjOzgYA/P3330VWMPms6nczdcpoxMY+wlcer5Lye/dilWICAvYAAOzsKkrud9y4r3Ds2GksXLQaALBw0Wq0ae2MceO+wmD3MQCACeM9sMl3Bzb5bgcATJo8C126tMXXo4Zg5ncL/tP7opIr/sQVxJ+48sYYA2sTNP5pGIK+WIA2Wwv+I7PRHHfc8vkTN1cfFNvSoxPEn/Oyc/EiKU38XaathQpdGuOW75E3Xre6RzdcX7EfDw/n//l0fvw69Lq6BnZ9W+CO/wnoGBvA4Yt2OO+5Fgln8qvuIWPXoOflVbBqUxfxp8Lf1D19RHJzc/EkKaXQc6OmjUDwifP45af1Ytuj+3EF4hQvXhTZR2G+8PgMB3ccxsHt+esBls/6BU5tm6HvEDesXbARADDgq0/ht3Irgv44AwCY57UAv4ftRZc+nbB/68Ei+ybV4uIYaYq9j2NRunfvjj179kiKPXnyJMqXLw8AOHXqFE6ePFnoceLECVUNT6O4unbB5ctXsWP7ejyKvYKLF/7EiOED/3O/zk5NcPTYaaW2I0eD4OKcv2BKR0cHjRvXx9Fjyo+KPPqvGNJQMhmcVn2Dm2sPKU0Rv6RnVg5mTapC8fgpOh6YhV5X16D93u9g3rx6kV1W6NoYuqbGiN55usgYw8oWMLAyQXzQq+QvLysHSeduwqxpNQCASX0HaOlqIz7oqhjzIiEVT28+gFnToq9PH59KDhVw4PIu7Dm3DXPXfA/byjYA8osULTo648HdWCwLWITfr+zFxoNr0KZrywJ9dOnTCX+E70fACV94fv81yhoaFHk9bR1t1KhfHReClIsq54MuoV7TugAA28o2MLcyU4rJzspGaMgV1GtaRxVvmyTik2OkUVniuHv3bpiamhbrNTk5OdDW1sa1a9dUNQwC4OhQGaNGueP27Wj0cB2IX3/1x/JlczF48Kf/qV9rawskJCYptSUkJsHa2gIAYG5uCm1tbSQmPFaKSUx8DCtry/90bfq41RrbE0JuHm5t/LPQ80Z2+d+POpP64m7ASQQNXIiU8Bi0+98MGDlYFfoahy/aIf7UVWQ+Kvo+RH3L8gCgVKkEgBeP08Rz+pblkavIRnba89dinsLAUi7l7dFHICL0BuaOX4AJg6ZiwdSfYWZhil9/W41yJuVgYl4ehkZl4T7mC5w/dQFeA6cgKPAMvDfORSPnBmIff+47hllj5mHMp17wXb4F7Xq0gffGuUVes7ypHNraWnjyWLlCmfI4BaaW+QtCzSzz/958PeZJUgpMLYr3dyrRh/BOG4D/ewpZEATEx8cjKSkJa9asKd7FtbVhZ2eH3Nzc4g5DpFAooFAolNoEQdDoae4yZcrg8uWr+O77/KnhsLAI1K5dHV+PHIKtW3f/p75f37BdJpMVaJMSQ5rDpL49qn3VFUe6zCw6qEz+/1/vbD0hVhDDrt2DVas6cPiiHcLn71QKN7AxhXW7+jg3aqW0Qbz+9ZPJJJUE+LUtPUJOvlqgd+dmNMIvXcfu4AD0+Kwrjv2WP7t15s9g7NiQ/2fkrYg7qNe0Dnq790RoSP5tGAe2/S72cTcyBg+iH8IvcD2q162GqGu3irx2gT//ZCjwnSz45yb4BfzAuDhGmmInjr169VJKysqUKQMLCwu0a9funTbs/u677zB9+nRs3bq12BVLAPD29sacOXOU2mRljCDTKlfsvkqLuLhEXL8RpdR28+Zt9O3T4z/1Gx+fBGsr5cqhpYU5Ev6pMD5+/AQ5OTmw+qcC+ZKFhRkSE5QrlaQ5LJxqQt+8HHpeepXkldHWQoNZg1DdoxsONffCi4RUACgwjf301iMYVjAr0KfD522QlfIMD//8+43XfpGY36++pVz8GQD0zcqJVcgXianQ0tOBjrysUtVR37wcHl8qOhmgj9uLzBe4c/MuKjlUQOqTNORk5yD6VoxSTMyt+2jQvOgnokWGRyE7KxuVHCsWmjimPklDTk4uzF6rHJqYmYj3SSYn5lfMzSxMxZ8BwMTcpEAVkt4v3uMoTbETx9mzZ6t0ACtXrsTt27dha2sLOzs7GBoaKp3/++83/8Uwffr0AiuzTcw0+4kzwecuokb1Kkpt1as54v79gveWFUfI+cvo1LE1VqzcILZ17tQG50Ly783Jzs7G339fRaeObZS26OnUqQ0OHix8ipJKv5jdZ5FwWvl2lDbbp+He7rNidTHjQRKexz2BcRUbpThjR2vEnSy46MZhQFvE7DoLIefNsxUZ95OQmZAC6zb1kHrtHgCgjI4WLFxq4upPOwAAKVejkZuVA+s29fDg4HkA+dPX5WpWQvKP29/tTVOJp6OrA/tqdrhyPhw52Tm4ceUmKleppBRT2bEi4mMTiugBcKxhDx1dHSQnJBd6Pic7B5FXo9CsTVMEBZ4V25u3aYIzf/4FIH8BzuOEZDRr0xRREbcB5N8b2ci5AdbM//W/vk0ilSt24qilpYW4uDhYWipXnpKTk2FpaVnsaefXK5jFpaenBz09PaU2TZ6mBoAVKzbgzOnf8O00T+zafRDNmjXEV18Nwtejp4oxJiblUblyBdja5N8/Vv2fRDM+PhEJ/1QHfTetwKNHceJq6FWrfHDyxB5MmTwaBw7+CbeeXdGxY2u0bddH7HfZig3Y7LsCly9fQcj5y/AYMRiVK1XA+l/9P9TbJzXQLqsHIwdr8XfDyhYoX8cOWanpeP4wGVkp6UrxQk7+Culnd16tWo1c+zvqTO6H1Ij7SI24B/v+rWFc1RZ/eaxQeq1lqzowsrPE3e2nCh1L9zOLcXX+Tjz8I/8fNFEbAlFrnBueRccj/W48ao3rhdzMLNzbGwwAyH6Wiejtp9Bw1iAoUtKRlZqOhj8MRNqNBwUSXvp4eX7/Nc4ePYf4hwkwMTfBl+MHw9CoLA7vyv9HbcDanZi39geEhVzF38GhcG7XHC07t8CYT70A5G/X07VPJwSfCEHqkzQ4VLfHuB++QWR4FK5efPU9WbVzCYL+OIPdfvsBANs37MKsFdNx80okwi9HoPdgV1hVsMI+/1erpXdu3I2hnoMQGx2LB9GxGOo5GC8yX+DIvmMf7PMhTlVLVezEsah71RQKBXR1dYs9AFVXMAm4dPkKPv3sK/z447f4bqYXomMeYOKkWdi+fZ8Y09O1Czb5LBN/3x6wFgAwd94SzJ2Xv3dm5Uq2yMt7tc/ZuZBLGDh4NObOmYo5s6fgzt17+GLQN+IejgCwa9cBmJma4LuZE2BjY4lrEZHo6eb+n6udVLKZNHBU2py70Rx3AED0ztO44LW+qJcpidoQiDJ6Omg0ZzB0TQyRGnEfQZ97I+NeolKc4xft8PhCFJ7dKnxf0HJVbaFjXFb8/eYvh6Clr4sm3sOgKzdEcugdBH2+QNzDEQBCZ22FkJOLFus9oWWgi4QzETjv9TP3cCxFLGwsMOeX71DeVI7U5FRc+/sGvuo5BvEP8yuKQYFnsejbZRjiORAT53ri3t0HmOExS0wKs7Oz0bRVY/T/qi8Myhog8VES/joegk3LNiv9OVnBzhZy01eLqo4fOAm5STkMnzAEZpamuBsZg0nu34rXBYCta3ZAT18Pk+d7wVhujOuhN+A1cAr3cPzA+P92aWSCxFULK1fm3580YcIEzJs3D0ZGRuK53NxcnD59GjExMQgNDS2qi0I5Ojri4sWLMDNTvo8pNTUVjRs3LvaTaABAW7dCsV9D9L4FmLVT9xCIlCwvw035qWQ591B9T5MLse2r0v6cH+1VaX8lheSK47Jl+dUpQRCwbt06aGlpied0dXVhb2+PdevWFXsAMTExhU5vKxQKxMbGFvIKIiIiItXiVLU0khPH6OhoAED79u2xd+9emJiY/KcLHzhwQPz5zz//hFz+qrSfm5uL48ePw8HB4T9dg4iIiEgKrqqWptj3OJ48qZoycu/evcWfhw4dqnROR0cH9vb2WLJkiUquRURERET/XbGfHPPpp59iwYKCzxxevHgxPvvsM8n95OXlIS8vD3Z2dkhMTBR/z8vLg0KhQGRkJFxdXYs7PCIiIqJiy1PxUVoVO3EMCgrCJ598UqC9W7duOH266GfGFmXOnDkwNjYu0J6VlYUtW7YUuz8iIiKi4hIgU+lRWhU7cUxPTy902x0dHR08ffq02AP48ssvkZaWVqD92bNn+PLLL4vdHxERERG9H8VOHOvWrYudO3cWaN+xYwdq165d7AEU9Vzp2NhYpQUzRERERO9LnqDao7Qq9uKY77//Hv369cOdO3fQoUMHAMDx48exbds27N69W3I/jRo1gkwmg0wmQ8eOHaGt/Wooubm5iI6ORrdu3Yo7PCIiIqJiyyvF08uqVOzE0c3NDfv378f8+fOxe/duGBgYoEGDBjhx4gTKlSsnuZ+Xq6rDwsLQtWtXpQ3FX+4LWbVq1eIOj4iIiIjek2InjgDwySefiAtkUlNTERAQAC8vL1y5ckXys6pnzZoFALC3t8eAAQOgr68PAEhLS0NAQAAWL15crP6IiIiI3lVpXtCiSsW+x/GlEydOYPDgwbC1tcXq1avRo0cPXLp0qdj9DB06FPr6+mJ/NjY2WLVq1Tv3R0RERFRc3I5HmmJVHGNjY+Hn54dNmzYhIyMD/fv3R3Z2Nvbs2fNOC2NU3R8RERERvT+SK449evRA7dq1cf36daxatQqPHj3CqlWr3vnCL/uLiIhQSX9ERERE74r7OEojueJ45MgRjBs3Dt988w2qVav2ny+s6v6IiIiI3lVpnl5WJckVxzNnzuDZs2do2rQpnJycsHr1aiQlJb3zhVXdHxERERG9X5ITRxcXF2zYsAFxcXEYNWoUduzYgQoVKiAvLw9Hjx7Fs2fPinVhVfdHRERE9K64OEYamSAI77y/eWRkJHx8fODv74/U1FR07twZBw4ceOfBqKo/bd0K7zwGovclwKyduodApGR5mUfqHgKRknMPT6rt2r9bfaHS/j5J2K7S/kqKd96OBwBq1KiBRYsWITY2Ftu3//cPSNX9EREREZHq/KeKY0nFiiOVRKw4UknDiiOVNOqsOB60Vm3FsWd86SyA/aeKIxEREVFpkAeZSo/iOH36NHr27AlbW1vIZDLs379f6fywYcMgk8mUDmdnZ6UYhUIBT09PmJubw9DQEG5uboiNjVWKSUlJgbu7O+RyOeRyOdzd3ZGamlqssTJxJCIiIlKjjIwMNGjQAKtXry4yplu3boiLixOPw4cPK5338vLCvn37sGPHDpw9exbp6elwdXVVenTzwIEDERYWhsDAQAQGBiIsLAzu7u7FGus7PauaiIiIqDRR53173bt3R/fu3d8Yo6enB2tr60LPpaWliYuLO3XqBADYunUrKlWqhGPHjqFr1664ceMGAgMDERISAicnJwDAhg0b4OLigsjISNSoUUPSWFlxJCIiIo2n6u14FAoFnj59qnQoFIp3Ht+pU6dgaWmJ6tWrw8PDA4mJieK5y5cvIzs7G126dBHbbG1tUbduXQQHBwMAzp07B7lcLiaNAODs7Ay5XC7GSMHEkYiIiEjFvL29xXsJXx7e3t7v1Ff37t0REBCAEydOYMmSJbh48SI6dOggJqLx8fHQ1dWFiYmJ0uusrKwQHx8vxlhaWhbo29LSUoyRglPVREREpPHyZKp9vvT06dMxceJEpTY9Pb136mvAgAHiz3Xr1kXTpk1hZ2eH33//HX379i3ydYIgQPav9yUr5D2+HvM2TByJiIhI46n6Hkc9Pb13ThTfxsbGBnZ2drh16xYAwNraGllZWUhJSVGqOiYmJqJFixZiTEJCQoG+kpKSYGVlJfnanKomIiIi+ogkJyfjwYMHsLGxAQA0adIEOjo6OHr0qBgTFxeHa9euiYmji4sL0tLScOHCBTHm/PnzSEtLE2OkYMWRiIiINJ46ny+dnp6O27dvi79HR0cjLCwMpqamMDU1xezZs9GvXz/Y2NggJiYGM2bMgLm5Ofr06QMAkMvlGDFiBCZNmgQzMzOYmppi8uTJqFevnrjKulatWujWrRs8PDywfv16AMDIkSPh6uoqeUU1wMSRiIiICHmqvcWxWC5duoT27duLv7+8N3Lo0KFYu3YtwsPDsWXLFqSmpsLGxgbt27fHzp07YWxsLL5m2bJl0NbWRv/+/ZGZmYmOHTvCz88PWlpaYkxAQADGjRsnrr52c3N7496RheEjB4k+ED5ykEoaPnKQShp1PnJwu+0glfb3xaMAlfZXUrDiSERERBqvuI8J1FRMHImIiEjjlbrp1/eEq6qJiIiISBJWHImIiEjjqXNxzMeEiSMRERFpPHVux/Mx4VQ1EREREUnCiiMRERFpPC6OkYaJIxEREWk83uMoDaeqiYiIiEgSVhyJiIhI43FxjDRMHImIiEjjMXGUhlPVRERERCQJK45ERESk8QQujpGEiSMRERFpPE5VS8OpaiIiIiKShBVHIiIi0nisOErDxJGIiIg0Hp8cIw2nqomIiIhIElYciYiISOPxkYPSMHEkIiIijcd7HKXhVDURERERScKKIxEREWk8VhylYeJIREREGo+rqqXhVDURERERScKKIxEREWk8rqqWhokjERERaTze4ygNp6qJiIiISBJWHImIiEjjcXGMNEwciYiISOPlMXWUpFQmjnraOuoeAlEBkxRX1D0EIiXRUQfUPQQi+siUysSRiIiIqDi4OEYaJo5ERESk8ThRLQ1XVRMRERGRJKw4EhERkcbjVLU0TByJiIhI4/HJMdJwqpqIiIiIJGHFkYiIiDQe93GUhokjERERaTymjdJwqpqIiIiIJGHFkYiIiDQeV1VLw8SRiIiINB7vcZSGU9VEREREJAkrjkRERKTxWG+UhokjERERaTze4ygNp6qJiIiISBJWHImIiEjjcXGMNKw4EhERkcYTVHwUx+nTp9GzZ0/Y2tpCJpNh//79ymMTBMyePRu2trYwMDBAu3btEBERoRSjUCjg6ekJc3NzGBoaws3NDbGxsUoxKSkpcHd3h1wuh1wuh7u7O1JTU4s1ViaORERERGqUkZGBBg0aYPXq1YWeX7RoEZYuXYrVq1fj4sWLsLa2RufOnfHs2TMxxsvLC/v27cOOHTtw9uxZpKenw9XVFbm5uWLMwIEDERYWhsDAQAQGBiIsLAzu7u7FGqtMEIRSV5s1LGuv7iEQFWCib6TuIRApiY46oO4hECnRMXdU27XH23+u0v5WxOx4p9fJZDLs27cPvXv3BpBfbbS1tYWXlxemTZsGIL+6aGVlhYULF2LUqFFIS0uDhYUF/P39MWDAAADAo0ePUKlSJRw+fBhdu3bFjRs3ULt2bYSEhMDJyQkAEBISAhcXF9y8eRM1atSQND5WHImIiEjjCSr+n0KhwNOnT5UOhUJR7HFFR0cjPj4eXbp0Edv09PTQtm1bBAcHAwAuX76M7OxspRhbW1vUrVtXjDl37hzkcrmYNAKAs7Mz5HK5GCMFE0ciIiIiFfP29hbvJXx5eHt7F7uf+Ph4AICVlZVSu5WVlXguPj4eurq6MDExeWOMpaVlgf4tLS3FGCm4qpqIiIg0nqr3cZw+fTomTpyo1Kanp/fO/clkMqXfBUEo0Pa612MKi5fSz7+x4khEREQaLw+CSg89PT2UK1dO6XiXxNHa2hoAClQFExMTxSqktbU1srKykJKS8saYhISEAv0nJSUVqGa+CRNHIiIiohLKwcEB1tbWOHr0qNiWlZWFoKAgtGjRAgDQpEkT6OjoKMXExcXh2rVrYoyLiwvS0tJw4cIFMeb8+fNIS0sTY6TgVDURERFpPHVuMZOeno7bt2+Lv0dHRyMsLAympqaoXLkyvLy8MH/+fFSrVg3VqlXD/PnzUbZsWQwcOBAAIJfLMWLECEyaNAlmZmYwNTXF5MmTUa9ePXTq1AkAUKtWLXTr1g0eHh5Yv349AGDkyJFwdXWVvKIaYOJIREREpNYnx1y6dAnt27cXf395b+TQoUPh5+eHqVOnIjMzE6NHj0ZKSgqcnJxw5MgRGBsbi69ZtmwZtLW10b9/f2RmZqJjx47w8/ODlpaWGBMQEIBx48aJq6/d3NyK3DuyKNzHkegD4T6OVNJwH0cqadS5j+Mo+89U2t/6mF0q7a+kYMWRiIiINJ6qV1WXVkwciYiISOMJar3L8ePBVdVEREREJAkrjkRERKTxOFUtDRNHIiIi0nicqpaGU9VEREREJAkrjkRERKTxOFUtDRNHIiIi0nh5pW9b6/eCU9VEREREJAkrjkRERKTxWG+UhokjERERaTx1Pqv6Y8KpaiIiIiKShBVHIiIi0njcx1EaJo5ERESk8bgdjzScqiYiIiIiSVhxJCIiIo3HxTHSsOJIRERERJKw4khEREQaj4tjpGHiSERERBqPi2Ok4VQ1EREREUnCiiMRERFpPEHgVLUUTByJiIhI43FVtTScqiYiIiIiSVhxJCIiIo3HxTHSMHEkIiIijcfteKThVDURERERScKKIxEREWk8Lo6RhokjERERaTxuxyMNp6qJiIiISBJWHImIiEjjcVW1NEwciYiISONxVbU0nKouBVq2bI5duzfi9p3zyHgeA9eeXYqMXblqPjKex2DMmOFK7VZWFti4cSnuRl9EYtJ1/BV8CL17d3/rtT1GDkbE9TNIfhKJs38dRIsWzQrEzJjphdt3zuNx8k38EbgDtWpVK/6bpI/KGK+vcOjYDty8dx5hkUHY6L8CjlXtlWLMLcywdPWPuBRxArdiL2LrrnVwcKysFGNhaYYVa73x941TiHpwAX+c/B8+cev81usPGT4AwaGBuP3oMg6f2Inmzo0LxEycNhqXIk7g9sNL2HXAF9VrVvlP75lKlh37DqHPkG/g1LkvnDr3xaCRE3Dm3MVCY+csWom6LbvDf+e+Au3dPvsSTdr3QutPBsBz2hzcvfdAKWbs1Nno1HcIGrd3Qzu3gfh27mIkJiW/cWyCIOAXn61o7zYITdr3wrCxU3H77j2lmKysLMxfugategxAs469MXbqbMQnJr3DJ0GkWkwcSwFDw7IID7+BiRN/eGOca88uaNasIR49ii9wbuPGpahW3RGfffYVmjfrit9+C8QW/9Vo0KBOkf316+eKRYt+wKJFq9HCpQeC/7qIffv9ULGirRgzceLX8PQcgYkTf0Cb1m5ISEjCwUNbYWRk+O5vmEo8l5ZNsdlnO9y6DsQXfUdCW1sb2/b8CoOyBmKMz9YVqGxfESMGj0PXdp8h9sEjbN+3USlmxboFqFLVHsMHjUWnVn3xx6FjWOPzM+rUq1nktXv26YbZ87/FqqUb0K3dZ7gQ8jf8/7cOthWsxZjR44bDY/QQfD9tPj7p9DkSEx9j254NMDQq+34+EPrgrC3MMeHrL7HTZyV2+qxE8yYN4Pnt3AIJ2vHTwbgaEQlLc7MCfdSuURU/zpyIA9t+xfqlP0EQBIycMBO5ubliTPPGDbBk7nQc2r4By376Dg8exmHCdz+9cWybAnZhy469mDFxNHb4rIC5qQk8vGYgI+O5GLNgxXocPx2MxXO+xZa1P+N55guMmTJb6dqkWnkQVHqUVkwcS4EjR05h7pwlOPDbn0XG2NhaYenSORj+5XhkZ+cUON/cqTHWrd2My5euICbmARYtXI3U1Kdo0LDoxNFz3FfYvPl/2Oy3E5GRdzB16lzExsbBw2OwGDNm7HAsXvQLDvz2J65fj8JIj0kwMDBA/wG9/tubphJt8GdfY9f23xB18w5uRERi4tjvULGSLeo3qA0AcKhihybNGmLG5Hm4EnoNd2/HYMbkH2FoWBa9+/UQ+2nStAF8N2xD2N/XcP9eLFYu+RVP056h3j/9FGbk6CHYsXUvtvvvwe2ou5g9YyEePYrHkOGfizEjvnbHqiW/4o9DxxB54zYmjJ4Bg7L66N3vk/f3odAH1a6VM9q0aA77yhVhX7kixo8ahrIG+rgScVOMSUh6jPlL12DhrKnQ1tYq0MdnvXqgacN6qGBjhdo1qsJz5FDEJyThYVyCGDPk8z5oULcWbK2t0KhebXw1uD+uRtxEdk7BP2eB/Gqj///2Y+TQz9G5XUtUc7TH/O8m4YVCgd+PngIAPEvPwN5DRzB5rAdcmjVCrepVseCHKbh1NwYhl8JU+jnRK4IgqPQorZg4agCZTAafjcuwfNmvuHHjVqEx585dQr9PXWFiIodMJsOnn/aEnp4uzpwOKTReR0cHjRrVxfHjZ5TaTxw/AyfnJgAAe/tKsLa2VIrJysrC2bPn4ezUREXvjj4G5coZAQBSU9MAAHq6ugAAxYssMSYvLw9ZWdlo5tRIbLt4/m/07NMN5cuXg0wmg1vf7tDV1cW5s4VPOeroaKNeg9o4fTJYqf30yWA0bd4AAFDZriKsrC0Q9K+YrKxshPx1CU2bN/zvb5ZKnNzcXBw+dgqZL16gYd38anVeXh6mz/0ZwwZ+iqqOdm/t43nmC+z//Qgq2lrDxsqi0Ji0p89w6MhJNKxXCzrahS8hiH0Uj8fJKWjR/NXtE7q6umjasB7Cwq8DAK5H3kJOTo5SjKWFGao62iH0nxgideHiGA0wadI3yMnJwZo1vkXGDHEfiy3+qxH78Aqys7Px/Hkmvvh8FKKj7xcab2ZuAm1tbSQmKN9zk5CYhE5W5gDy75t82fZviYlJqFyp4n95S/SR+eHHqTh/7jIib9wGANy+FY0H9x/i2x/G49sJc/H8+XOMHD0UVtYWsLR+9ZfyN8MnY+2mn3HtbjCys7ORmfkCXw0Zj3sxDwq9jqlZ/vcy6bV7zJISk2Fhmf+9tPjn+/n4tZjHScmoUMkWVHpE3YnGoFETkZWVhbIGBlgx/3tUcchPEn227oKWVhkM/uzNsx879h7CkjU+yMx8AQe7Svh12U/Q0dFRilm6xgfb9xxE5gsFGtSpiV8Wzymyv8dPUgAAZiYmSu1mpuXxKD4xPyY5BTo62pCXM1aOMSmP5H9eT6pXmqeXVYkVx1KuYaO6GD3mS4wcNfmNcT/MmoTy5eX4pMdAtG7lhlWrfOC/dQ3q1Knxxte9Xo2XyWQF2l5vkMlkXL2mQX5cNBO16lTHWI+pYltOTg5GDp0Axyr2iIgOxq2Hl+DSqhlOHD2NvH/dwzV1pifk5cthQO8R6NHhc2xYswXrfJeg5lsWWL0+TZT/vRTeGlPwy0sfM4fKFbHH7xcErF+G/r0/wcyfluBO9D1E3LyFrbt+w08zJ+X/d3+DT7q0x27f1fD7ZRHsKtpi8g/eUCiylGK+HPgpdvmuxq/LfkIZrTKYPu/nt05Vvn5dQSjY9rr8Lt8cQ+9OUPH/SitWHEu5li2aw8LCDJGRr6bltLW14b1gJsaMHY7atVrBwaEyvvlmGJo26SxOZYeH30DLFs0wctQQjB83s0C/yY9TkJOTAytr5SkbSwtzJCY+BgAk/FONtLKyRHz8q6qjhYU5EhMeq/y9Uskzb8F0dOneHv0+GYq4RwlK58KvXEfXtp/C2NgIOro6eJKcgoNHt+FKaAQAwM6+Er4cOQgdWvRC1M07AIAbEZFo7twYQ7/6AtMnzS1wvSfJ+d9Ly3+qiy+ZW5iKFcakf757FpbK30Mzc1MkJb55NSx9XHR0dFD5n8V6dWtVR8TNKGzd9Rsc7SrhSUoqOvcbIsbm5uZh8eqN8P/ffhzZs1lsNzYyhLGRIewqVUCDOjXRottnOH46GD06txNjTMrLYVJeDvvKFeFoXwmd+gzBlYibaFi3VoExmZvmVxofP3kCC3NTsf1JSirMTMrnx5iZIDs7B2lPnylVHZ+kpqJhvYJ9En1IrDiWctu374VT825wce4hHo8exWP5sl/Ryy3/D82y/6xizctT3v40NzcPZcoU/q/b7OxshIZeQ4cOrZTa23dohfMhlwEAMTEPEB+fqBSjo6ODVq2cEHL+ssreI5VMPy6cge6unTCg13A8uP+wyLhnz9LxJDkFDo6VUb9hHRz54yQAwMBAHwCQl6f8L/fcvDd9L3MQfuU6WrdzUWpv3c4Fly5cAQDcvxeLhPgktPlXjI6ONpxbNsWlC2HFfp/08RAEAVlZ2ejZrSP2blmD3X6/iIeluRm+HNgP65e+eUW0IOTfE/um80DRMRVtrWFuZoJzF0PFtuzsbFwKC0fDevmLvmrXqAZtbW2lmKTHT3D77j00qlf0wjD6b/IEQaVHacWKYylgaFgWVarYi7/b21VC/fq18eRJKmJjH+HJk1Sl+OzsHCQkJOHWrbsAgMjIO7h9OxorV83HjBnz8SQ5BT17dkGHjq3wab9X+z3+/nsADhz8E+vXbQEArFq5ERt9liL076s4f/5vDB8+EJUq2WLjxgDxNb+s3oTJU8bg9p0Y3LkdjSlTxiAzMxP/2/nb+/tASO1+Wvwden/aAyMGjUN6egYsLPO3Onn2NB0vXigAAJ/06oInj1PwMDYONWtXwxzvb/Hn4RPiwpbbt6IRfeceFiz9AT/+8DNSnqSh6ycd0KadC4Z9Pka81o59GxH4+3H4bdwOAPh1zRasWOuNq2ERuHzxCgYN/RQVKtjA33en+Bqfdf4YO9ED0XfvI/ruPXhO8EDm8xfYv+f3D/UR0Xu2fJ0fWjs3hbWVBTKeP8cfx4JwMTQc65bMQ3l5OZSXl1OK19bWgrmpCRzs8u+/fvAwDoHHT6NF88YwLS9HwuNkbNq6C3p6umj9z3614dcjEX49Eo3r10G5ckaIfRiP1Rv9UamCjbgIBwB6fuGB8V8PQ6e2LSGTyeDevzc2bNmJyhVtYVepAjZs2Ql9PT188k8V09jIEH1du2Dx6g0oLzeGvJwxfl69EdUc7eHctOEH+fw0UelN9VSLiWMp0LhxfQT+uUP8feGi7wEAW/13Y9Rb7m0E8u8369vnS8ydNw27d22EoZEh7t65h5Eek/Dnn6fEOAdHO5iZvZpa2bPnEEzNyuPb6eNhbW2B69ej0LfPl3jw4FV1aenSddA30Mfy5fNQvrwcFy+Gwa2nO9LTM1TwzqmkGjoif+ub3Yf8lNonjJmJXdvz/9FgZWWBWT9OhbmFGRITkrB75wGsWLxOjM3JycGQAd9g+qwJ8N32CwwNDRAT/QATRs/EiWOvVurbOVSCqdmrhQYH9wXCxEQOrylfw9LKApE3bmHIgG/wMDZOjFmzchP0DfTx0+LvIC9fDmGXr2LQpyORkf5qHz36uCWnpGD6vMVISn4CY0NDVK/qgHVL5imtVH4TPV1d/H3lGvz/tx9Pn6XDzLQ8mjaoi63rlopTynp6ujgWFIxffLYi88ULWJiZoqVTEyye+y10/9k5AACi78ci/V/freGDPsMLRRZ+XPILnj5LR/3aNfDr8p9gaPhqH9Fp40ZBW0sLk77Pv6fSqWkDrJ45CVpaBbcNIvqQZEIp3GzIsKy9uodAVICJvpG6h0CkJDrqgLqHQKREx9xRbdduWaGDSvv76+EJlfZXUrDiSERERBqP2/FIw8UxRERERCQJK45ERESk8UrhnXvvBRNHIiIi0nicqpaGU9VEREREajJ79mzIZDKlw9raWjwvCAJmz54NW1tbGBgYoF27doiIiFDqQ6FQwNPTE+bm5jA0NISbmxtiY2Pfy3iZOBIREZHGU+cjB+vUqYO4uDjxCA8PF88tWrQIS5cuxerVq3Hx4kVYW1ujc+fOePbsmRjj5eWFffv2YceOHTh79izS09Ph6uqK3H89wlVVOFVNREREGk+d9zhqa2srVRlfEgQBy5cvx8yZM9G3b18AwObNm2FlZYVt27Zh1KhRSEtLg4+PD/z9/dGpUycAwNatW1GpUiUcO3YMXbt2VelYWXEkIiIiUjGFQoGnT58qHQqFotDYW7duwdbWFg4ODvj8889x927+k92io6MRHx+PLl26iLF6enpo27YtgoPzn7J1+fJlZGdnK8XY2tqibt26YowqMXEkIiIijZcHQaWHt7c35HK50uHt7V3guk5OTtiyZQv+/PNPbNiwAfHx8WjRogWSk5MRHx8PALCyslJ6jZWVlXguPj4eurq6MDExKTJGlThVTURERBpP1VPV06dPx8SJE5Xa9PT0CsR1795d/LlevXpwcXFBlSpVsHnzZjg7OwMAZDJZgbG+3vY6KTHvghVHIiIiIhXT09NDuXLllI7CEsfXGRoaol69erh165Z43+PrlcPExESxCmltbY2srCykpKQUGaNKTByJiIhI46l6qvpdKRQK3LhxAzY2NnBwcIC1tTWOHj0qns/KykJQUBBatGgBAGjSpAl0dHSUYuLi4nDt2jUxRpU4VU1EREQar7hb6KjK5MmT0bNnT1SuXBmJiYn48ccf8fTpUwwdOhQymQxeXl6YP38+qlWrhmrVqmH+/PkoW7YsBg4cCACQy+UYMWIEJk2aBDMzM5iammLy5MmoV6+euMpalZg4EhEREalJbGwsvvjiCzx+/BgWFhZwdnZGSEgI7OzsAABTp05FZmYmRo8ejZSUFDg5OeHIkSMwNjYW+1i2bBm0tbXRv39/ZGZmomPHjvDz84OWlpbKxysTSuHDGQ3L2qt7CEQFmOgbqXsIREqiow6oewhESnTMHdV27bpWzirt71pCiEr7KylYcSQiIiKNp66p6o8NF8cQERERkSSsOBIREZHGyyt9d+69F0wciYiISONxqloaTlUTERERkSSsOBIREZHG41S1NEwciYiISONxqloaTlUTERERkSSsOBIREZHG41S1NEwciYiISONxqloaTlUTERERkSSsOBIREZHGE4Q8dQ/ho8DEkYiIiDReHqeqJeFUNRERERFJwoojERERaTyBq6olYeJIREREGo9T1dJwqpqIiIiIJGHFkYiIiDQep6qlYeJIREREGo9PjpGGU9VEREREJAkrjkRERKTx+MhBaZg4EhERkcbjPY7ScKqaiIiIiCRhxZGIiIg0HvdxlIaJIxEREWk8TlVLw6lqIiIiIpKEFUciIiLSeNzHURomjkRERKTxOFUtDaeqiYiIiEgSVhyJiIhI43FVtTRMHImIiEjjcapaGk5VExEREZEkrDgSERGRxuOqammYOBIREZHGE3iPoyScqiYiIiIiSVhxJCIiIo3HqWppmDgSERGRxuOqamk4VU1EREREkrDiSERERBqPi2OkYeJIREREGo9T1dJwqpqIiIiIJGHFkYiIiDQeK47SMHEkIiIijce0URpOVRMRERGRJDKBtVkqgkKhgLe3N6ZPnw49PT11D4eI30kiIjVj4khFevr0KeRyOdLS0lCuXDl1D4eI30kiIjXjVDURERERScLEkYiIiIgkYeJIRERERJIwcaQi6enpYdasWVyEQCUGv5NEROrFxTFEREREJAkrjkREREQkCRNHIiIiIpKEiSMRERERScLEkT4omUyG/fv3q3sYVIq0a9cOXl5e6h4GEZFGYOJYAg0bNgwymQwLFixQat+/fz9kMlmx+rK3t8fy5cslx8+fPx9aWloFrg0U/hf0qVOnIJPJkJqaKqn/uLg4dO/eHQAQExMDmUyGsLAwyeOjj1NwcDC0tLTQrVs3pfbZs2ejYcOGBeKL8w+MvXv3Yt68eeLvxf3OExGRdEwcSyh9fX0sXLgQKSkpH/S6vr6+mDp1KjZt2qTSfrOysgAA1tbW3EpFA23atAmenp44e/Ys7t+/r5I+s7OzAQCmpqYwNjZWSZ9ERPQWApU4Q4cOFVxdXYWaNWsKU6ZMEdv37dsnvP6fbPfu3ULt2rUFXV1dwc7OTvj555/Fc23bthUAKB1vcurUKaFChQpCVlaWYGtrKwQFBSmN6fW+oqOjC7QNHTpUvPaYMWOECRMmCGZmZkKbNm0EQRAEAMK+ffvEn/99tG3bVrzepk2bhJo1awp6enpCjRo1hF9++eVdPkoqAdLT0wVjY2Ph5s2bwoABA4Q5c+YIgiAIvr6+Bb4Dvr6+gp2dnVKbnZ2dIAiCMGvWLKFBgwaCj4+P4ODgIMhkMiEvL09o27atMH78eEEQ3vyd/+uvv4TWrVsL+vr6QsWKFQVPT08hPT39Q38cREQfNSaOJdDQoUOFXr16CXv37hX09fWFBw8eCIJQMHG8dOmSUKZMGWHu3LlCZGSk4OvrKxgYGAi+vr6CIAhCcnKyULFiRWHu3LlCXFycEBcX98bruru7C5MnTxYEQRAmTZokDBkyRDyXmpoquLi4CB4eHmJfOTk5wp49ewQAQmRkpBAXFyekpqYKgpD/F7iRkZEwZcoU4ebNm8KNGzcEQVBOHC9cuCAAEI4dOybExcUJycnJgiAIwq+//irY2NgIe/bsEe7evSvs2bNHMDU1Ffz8/P77h0sfnI+Pj9C0aVNBEATh4MGDgr29vZCXlyc8f/5cmDRpklCnTh3xO/X8+XMhMTFRTCLj4uKExMREQRDyE0dDQ0Oha9euwt9//y1cuXKlQOJY1Hf+6tWrgpGRkbBs2TIhKipK+Ouvv4RGjRoJw4YNU8tnQkT0sdJ+3xVNend9+vRBw4YNMWvWLPj4+BQ4v3TpUnTs2BHff/89AKB69eq4fv06Fi9ejGHDhsHU1BRaWlowNjaGtbX1G6/19OlT7NmzB8HBwQCAwYMHo2XLlli1ahXKlSsHuVwOXV1dlC1bVqkvU1NTAIClpSXKly+v1GfVqlWxaNGiIq9pYWEBADAzM1Pqc968eViyZAn69u0LAHBwcMD169exfv16DB069I3vg0oeHx8fDB48GADQrVs3pKen4/jx4+jUqROMjIygra2t9N/fwMAAAFC+fPkC39usrCz4+/uL353XFfWdX7x4MQYOHCjeo1utWjWsXLkSbdu2xdq1a6Gvr6/Kt0xEVGrxHscSbuHChdi8eTOuX79e4NyNGzfQsmVLpbaWLVvi1q1byM3NLdZ1tm3bBkdHRzRo0AAA0LBhQzg6OmLHjh3vPPamTZsW+zVJSUl48OABRowYASMjI/H48ccfcefOnXceC6lHZGQkLly4gM8//xwAoK2tjQEDBrzzPbR2dnZFJo1vcvnyZfj5+Sl9p7p27Yq8vDxER0e/01iIiDQRK44lXJs2bdC1a1fMmDEDw4YNUzonCEKBVdbCOz5BctOmTYiIiIC29quvRF5eHnx8fDBy5Mh36tPQ0LDYr8nLywMAbNiwAU5OTkrntLS03mkcpD4+Pj7IyclBhQoVxDZBEKCjo/NOC7/e5TsF5H+vRo0ahXHjxhU4V7ly5Xfqk4hIEzFx/AgsWLAADRs2RPXq1ZXaa9eujbNnzyq1BQcHo3r16mKSpaur+9bqY3h4OC5duoRTp06JU88AkJqaijZt2uDatWuoW7duoX3p6uoCQLErnEW91srKChUqVMDdu3cxaNCgYvdJJUdOTg62bNmCJUuWoEuXLkrn+vXrh4CAgCK/nzo6Ou/0nQIK/843btwYERERqFq16jv1SURE+ThV/RGoV68eBg0ahFWrVim1T5o0CcePH8e8efMQFRWFzZs3Y/Xq1Zg8ebIYY29vj9OnT+Phw4d4/Phxof37+PigefPmaNOmDerWrSserVq1gouLi3h/pb29Pc6fP4+YmBg8fvwYeXl5sLOzg0wmw6FDh5CUlIT09HTJ78vS0hIGBgYIDAxEQkIC0tLSAOTv7eft7Y0VK1YgKioK4eHh8PX1xdKlS4v70ZEaHTp0CCkpKRgxYoTS96pu3br49NNP4ePjA3t7e0RHRyMsLAyPHz+GQqEAkP9dO378OOLj44tdmSzsOz9t2jScO3cOY8aMQVhYGG7duoUDBw7A09NT5e+biKhUU/PiHCrEy1XV/xYTEyPo6ekVuR2Pjo6OULlyZWHx4sVK58+dOyfUr1+/0NcKgiAoFArBzMxMWLRoUaFjWbJkiWBubi4oFAohMjJScHZ2FgwMDMTteARBEObOnStYW1sLMplMaTuelytd/w3/WlUtCIKwYcMGoVKlSkKZMmWUtuMJCAgQGjZsKOjq6gomJiZCmzZthL179xb+gVGJ5OrqKvTo0aPQc5cvXxYACCEhIUK/fv2E8uXLiyupBUEQDhw4IFStWlXQ1tYusB3P617/rhX1nb9w4YLQuXNnwcjISDA0NBTq168v/PTTT6p6u0REGkEmCO94UxwRERERaRROVRMRERGRJEwciYiIiEgSJo5EREREJAkTRyIiIiKShIkjEREREUnCxJGIiIiIJGHiSERERESSMHEkohJr9uzZaNiwofj7sGHD0Lt37w8+jpiYGMhkMoSFhX3waxMRlSRMHImo2IYNGwaZTAaZTAYdHR04Ojpi8uTJyMjIeK/XXbFiBfz8/CTFMtkjIlI9bXUPgIg+Tt26dYOvry+ys7Nx5swZfPXVV8jIyMDatWuV4rKzs6Gjo6OSa8rlcpX0Q0RE74YVRyJ6J3p6erC2tkalSpUwcOBADBo0CPv37xenlzdt2gRHR0fo6elBEASkpaVh5MiRsLS0RLly5dChQwdcuXJFqc8FCxbAysoKxsbGGDFiBF68eKF0/vWp6ry8PCxcuBBVq1aFnp4eKleujJ9++gkA4ODgAABo1KgRZDIZ2rVrJ77O19cXtWrVgr6+PmrWrIk1a9YoXefChQto1KgR9PX10bRpU4SGhqrwkyMi+nix4khEKmFgYIDs7GwAwO3bt/G///0Pe/bsgZaWFgDgk08+gampKQ4fPgy5XI7169ejY8eOiIqKgqmpKf73v/9h1qxZ+OWXX9C6dWv4+/tj5cqVcHR0LPKa06dPx4YNG7Bs2TK0atUKcXFxuHnzJoD85K958+Y4duwY6tSpA11dXQDAhg0bMGvWLKxevRqNGjVCaGgoPDw8YGhoiKFDhyIjIwOurq7o0KEDtm7diujoaIwfP/49f3pERB8JgYiomIYOHSr06tVL/P38+fOCmZmZ0L9/f2HWrFmCjo6OkJiYKJ4/fvy4UK5cOeHFixdK/VSpUkVYv369IAiC4OLiInz99ddK552cnIQGDRoUet2nT58Kenp6woYNGwodY3R0tABACA0NVWqvVKmSsG3bNqW2efPmCS4uLoIgCML69esFU1NTISMjQzy/du3aQvsiItI0nKomondy6NAhGBkZQV9fHy4uLmjTpg1WrVoFALCzs4OFhYUYe/nyZaSnp8PMzAxGRkbiER0djTt37gAAbty4ARcXF6VrvP77v924cQMKhQIdO3aUPOakpCQ8ePAAI0aMUBrHjz/+qDSOBg0aoGzZspLGQUSkSThVTUTvpH379li7di10dHRga2urtADG0NBQKTYvLw82NjY4depUgX7Kly//Ttc3MDAo9mvy8vIA5E9XOzk5KZ17OaUuCMI7jYeISBMwcSSid2JoaIiqVatKim3cuDHi4+Ohra0Ne3v7QmNq1aqFkJAQDBkyRGwLCQkpss9q1arBwMAAx48fx1dffVXg/Mt7GnNzc8U2KysrVKhQAXfv3sWgQYMK7bd27drw9/dHZmammJy+aRxERJqEU9VE9N516tQJLi4u6N27N/7880/ExMQgODgY3333HS5dugQAGD9+PDZt2oRNmzYhKioKs2bNQkRERJF96uvrY9q0aZg6dSq2bNmCO3fuICQkBD4+PgAAS0tLGBgYIDAwEAkJCUhLSwOQv6m4t7c3VqxYgaioKISHh8PX1xdLly4FAAwcOBBlypTBiBEjcP36dRw+fBg///zze/6EiIg+Dkwciei9k8lkOHz4MNq0aYPhw4ejevXq+PzzzxETEwMrKysAwIABA/DDDz9g2rRpaNKkCe7du4dvvvnmjf1+//33mDRpEn744QfUqlULAwYMQGJiIgBAW1sbK1euxPr162Fra4tevXoBAL766its3LgRfn5+qFevHtq2bQs/Pz9x+x4jIyMcPHgQ169fR6NGjTBz5kwsXLjwPX46REQfD5nAG3qIiIiISAJWHImIiIhIEiaORERERCQJE0ciIiIikoSJIxERERFJwsSRiIiIiCRh4khEREREkjBxJCIiIiJJmDgSERERkSRMHImIiIhIEiaORERERCQJE0ciIiIikoSJIxERERFJ8n88N0gbfEL1dAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking the performance while still using LR(Logistic Regression)\n",
    "y_pred_test = lg.predict(X_test_scaled)\n",
    "\n",
    "metrics_score(y_test, y_pred_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 940,
   "id": "f7aac9b9-e381-48fe-87c2-6ebe00fa7699",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SessionsPerWeek</th>\n",
       "      <td>2.137251</td>\n",
       "      <td>-2.026791</td>\n",
       "      <td>-0.110460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AvgSessionDurationMinutes</th>\n",
       "      <td>1.835559</td>\n",
       "      <td>-1.544741</td>\n",
       "      <td>-0.290818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AchievementsUnlocked</th>\n",
       "      <td>0.173374</td>\n",
       "      <td>-0.240278</td>\n",
       "      <td>0.066904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PlayerLevel</th>\n",
       "      <td>0.164641</td>\n",
       "      <td>-0.228362</td>\n",
       "      <td>0.063720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InGamePurchases</th>\n",
       "      <td>0.023005</td>\n",
       "      <td>-0.018166</td>\n",
       "      <td>-0.004840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PlayTimeHours</th>\n",
       "      <td>0.013136</td>\n",
       "      <td>0.003254</td>\n",
       "      <td>-0.016391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GameDifficulty_Hard</th>\n",
       "      <td>0.010025</td>\n",
       "      <td>-0.000865</td>\n",
       "      <td>-0.009160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GameDifficulty_Medium</th>\n",
       "      <td>0.001690</td>\n",
       "      <td>-0.006453</td>\n",
       "      <td>0.004763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-0.021715</td>\n",
       "      <td>0.007822</td>\n",
       "      <td>0.013893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  0         1         2\n",
       "SessionsPerWeek            2.137251 -2.026791 -0.110460\n",
       "AvgSessionDurationMinutes  1.835559 -1.544741 -0.290818\n",
       "AchievementsUnlocked       0.173374 -0.240278  0.066904\n",
       "PlayerLevel                0.164641 -0.228362  0.063720\n",
       "InGamePurchases            0.023005 -0.018166 -0.004840\n",
       "PlayTimeHours              0.013136  0.003254 -0.016391\n",
       "GameDifficulty_Hard        0.010025 -0.000865 -0.009160\n",
       "GameDifficulty_Medium      0.001690 -0.006453  0.004763\n",
       "Age                       -0.021715  0.007822  0.013893"
      ]
     },
     "execution_count": 940,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing the coefficients of logistic regression\n",
    "cols=X.columns\n",
    "\n",
    "coef_lg=lg.coef_\n",
    "\n",
    "pd.DataFrame(coef_lg,columns=cols).T.sort_values(by = 0,ascending = False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 942,
   "id": "37d63165-ff37-4665-a22b-ac643994ff7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC-ROC: 0.8715549316452232\n"
     ]
    }
   ],
   "source": [
    "Below is the AUC ROC score which evaulates the efficiency of the ML model.\n",
    "\n",
    "X = df.drop('EngagementLevel', axis=1)\n",
    "y = df['EngagementLevel']\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "y_prob = model.predict_proba(X_test)\n",
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "auc = roc_auc_score(y_test, y_prob, multi_class='ovr')\n",
    "\n",
    "print(\"AUC-ROC:\", auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c71428e3-d477-48d1-9803-5b9bcc75f5df",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0c48371-1928-429e-a14a-e332b1049aba",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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