{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Game_Type</th>\n",
       "      <th>Daily_Gaming_Hours</th>\n",
       "      <th>Weekly_Gaming_Hours</th>\n",
       "      <th>Primary_Gaming_Time</th>\n",
       "      <th>Sleep_Hours</th>\n",
       "      <th>Stress_Level</th>\n",
       "      <th>Focus_Level</th>\n",
       "      <th>Academic_or_Work_Score</th>\n",
       "      <th>Productivity_Level</th>\n",
       "      <th>Performance_Impact</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U0001</td>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>Working Professional</td>\n",
       "      <td>Action</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28.0</td>\n",
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       "    <tr>\n",
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       "      <td>U0002</td>\n",
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       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>Sports</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Night</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>67</td>\n",
       "      <td>72</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U0003</td>\n",
       "      <td>26</td>\n",
       "      <td>Male</td>\n",
       "      <td>Student</td>\n",
       "      <td>Puzzle</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Morning</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U0004</td>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>Working Professional</td>\n",
       "      <td>Action</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Night</td>\n",
       "      <td>4.9</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>71</td>\n",
       "      <td>66</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U0005</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>Working Professional</td>\n",
       "      <td>Action</td>\n",
       "      <td>2.1</td>\n",
       "      <td>14.7</td>\n",
       "      <td>Morning</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>67</td>\n",
       "      <td>63</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  User_ID  Age  Gender            Occupation Game_Type  Daily_Gaming_Hours  \\\n",
       "0   U0001   21    Male  Working Professional    Action                 4.0   \n",
       "1   U0002   35  Female               Student    Sports                 1.0   \n",
       "2   U0003   26    Male               Student    Puzzle                 2.0   \n",
       "3   U0004   32    Male  Working Professional    Action                 1.0   \n",
       "4   U0005   19    Male  Working Professional    Action                 2.1   \n",
       "\n",
       "   Weekly_Gaming_Hours Primary_Gaming_Time  Sleep_Hours  Stress_Level  \\\n",
       "0                 28.0             Morning          4.6             6   \n",
       "1                  7.0               Night          5.4             2   \n",
       "2                 14.0             Morning          8.0             4   \n",
       "3                  7.0               Night          4.9             7   \n",
       "4                 14.7             Morning          7.0             7   \n",
       "\n",
       "   Focus_Level  Academic_or_Work_Score  Productivity_Level Performance_Impact  \n",
       "0            4                      69                  66           Negative  \n",
       "1            7                      67                  72            Neutral  \n",
       "2            8                      82                  82           Positive  \n",
       "3            7                      71                  66            Neutral  \n",
       "4            7                      67                  63            Neutral  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Gaming_Hours_vs_Performance_1000_Rows.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['User_ID', 'Age', 'Gender', 'Occupation', 'Game_Type',\n",
       "       'Daily_Gaming_Hours', 'Weekly_Gaming_Hours', 'Primary_Gaming_Time',\n",
       "       'Sleep_Hours', 'Stress_Level', 'Focus_Level', 'Academic_or_Work_Score',\n",
       "       'Productivity_Level', 'Performance_Impact'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "student_df = df[df['Occupation']=='Student']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Positive</td>\n",
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       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>Working Professional</td>\n",
       "      <td>Action</td>\n",
       "      <td>2.1</td>\n",
       "      <td>14.7</td>\n",
       "      <td>Morning</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>67</td>\n",
       "      <td>63</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>23</td>\n",
       "      <td>Female</td>\n",
       "      <td>Working Professional</td>\n",
       "      <td>Simulation</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Morning</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>95</td>\n",
       "      <td>96</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>30</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>Puzzle</td>\n",
       "      <td>2.9</td>\n",
       "      <td>20.3</td>\n",
       "      <td>Morning</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>62</td>\n",
       "      <td>67</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>Working Professional</td>\n",
       "      <td>Action</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Night</td>\n",
       "      <td>4.6</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>92</td>\n",
       "      <td>89</td>\n",
       "      <td>Neutral</td>\n",
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       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>25</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>Simulation</td>\n",
       "      <td>4.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>Morning</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>75</td>\n",
       "      <td>Neutral</td>\n",
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       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>19</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>Strategy</td>\n",
       "      <td>4.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>Night</td>\n",
       "      <td>7.7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>71</td>\n",
       "      <td>76</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Gender            Occupation   Game_Type  Daily_Gaming_Hours  \\\n",
       "0     21    Male  Working Professional      Action                 4.0   \n",
       "1     35  Female               Student      Sports                 1.0   \n",
       "2     26    Male               Student      Puzzle                 2.0   \n",
       "3     32    Male  Working Professional      Action                 1.0   \n",
       "4     19    Male  Working Professional      Action                 2.1   \n",
       "..   ...     ...                   ...         ...                 ...   \n",
       "995   23  Female  Working Professional  Simulation                 4.0   \n",
       "996   30  Female               Student      Puzzle                 2.9   \n",
       "997   18    Male  Working Professional      Action                 1.0   \n",
       "998   25  Female               Student  Simulation                 4.5   \n",
       "999   19  Female               Student    Strategy                 4.5   \n",
       "\n",
       "     Weekly_Gaming_Hours Primary_Gaming_Time  Sleep_Hours  Stress_Level  \\\n",
       "0                   28.0             Morning          4.6             6   \n",
       "1                    7.0               Night          5.4             2   \n",
       "2                   14.0             Morning          8.0             4   \n",
       "3                    7.0               Night          4.9             7   \n",
       "4                   14.7             Morning          7.0             7   \n",
       "..                   ...                 ...          ...           ...   \n",
       "995                 28.0             Morning          7.8             8   \n",
       "996                 20.3             Morning          4.9             4   \n",
       "997                  7.0               Night          4.6             2   \n",
       "998                 31.5             Morning          8.4             7   \n",
       "999                 31.5               Night          7.7             8   \n",
       "\n",
       "     Focus_Level  Academic_or_Work_Score  Productivity_Level  \\\n",
       "0              4                      69                  66   \n",
       "1              7                      67                  72   \n",
       "2              8                      82                  82   \n",
       "3              7                      71                  66   \n",
       "4              7                      67                  63   \n",
       "..           ...                     ...                 ...   \n",
       "995            4                      95                  96   \n",
       "996            7                      62                  67   \n",
       "997            8                      92                  89   \n",
       "998            4                      70                  75   \n",
       "999            9                      71                  76   \n",
       "\n",
       "    Performance_Impact  \n",
       "0             Negative  \n",
       "1              Neutral  \n",
       "2             Positive  \n",
       "3              Neutral  \n",
       "4              Neutral  \n",
       "..                 ...  \n",
       "995            Neutral  \n",
       "996            Neutral  \n",
       "997            Neutral  \n",
       "998            Neutral  \n",
       "999            Neutral  \n",
       "\n",
       "[1000 rows x 13 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_cols = [\n",
    "    'Age', 'Daily_Gaming_Hours', 'Weekly_Gaming_Hours', 'Sleep_Hours',\n",
    "    'Stress_Level', 'Focus_Level', 'Academic_or_Work_Score', 'Productivity_Level'\n",
    "]\n",
    "\n",
    "df[numeric_cols] = df[numeric_cols].apply(pd.to_numeric, errors='coerce')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ilias\\AppData\\Local\\Temp\\ipykernel_10148\\2864634474.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  stress_trend = df.groupby('Gaming_Hours_Group')['Stress_Level'].mean()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stress_trend = df.groupby('Gaming_Hours_Group')['Stress_Level'].mean()\n",
    "\n",
    "plt.figure(figsize=(7,5))\n",
    "plt.plot(stress_trend.index, stress_trend.values, marker='o')\n",
    "plt.title('Stress Level Trend by Gaming Intensity')\n",
    "plt.xlabel('Daily Gaming Hours')\n",
    "plt.ylabel('Average Stress Level')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(7,5))\n",
    "sns.violinplot(\n",
    "    data=df,\n",
    "    x='Primary_Gaming_Time',\n",
    "    y='Academic_or_Work_Score'\n",
    ")\n",
    "plt.title('Distribution of Academic Score by Gaming Time of Day')\n",
    "plt.xlabel('Primary Gaming Time')\n",
    "plt.ylabel('Academic Score')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ilias\\AppData\\Local\\Temp\\ipykernel_10148\\2741827796.py:4: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n",
      "  heatmap_data = df.pivot_table(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "heatmap_data = df.pivot_table(\n",
    "    values='Academic_or_Work_Score',\n",
    "    index='Primary_Gaming_Time',\n",
    "    columns='Gaming_Hours_Group',\n",
    "    aggfunc='mean'\n",
    ")\n",
    "\n",
    "plt.figure(figsize=(8,5))\n",
    "sns.heatmap(heatmap_data, annot=True, fmt=\".1f\", cmap='coolwarm')\n",
    "plt.title('Average Academic Score by Gaming Time & Intensity')\n",
    "plt.xlabel('Daily Gaming Hours Group')\n",
    "plt.ylabel('Primary Gaming Time')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 700x500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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",
    "\n",
    "plt.figure(figsize=(7,5))\n",
    "df.boxplot(\n",
    "    column='Productivity_Level',\n",
    "    by='Performance_Impact'\n",
    ")\n",
    "plt.title('Productivity Level by Performance Impact')\n",
    "plt.suptitle('')\n",
    "plt.xlabel('Performance Impact')\n",
    "plt.ylabel('Productivity Level')\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}