{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyO1VO72OnfZKnMuw+/kiYmE"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EXYzSD3hgu1G","executionInfo":{"status":"ok","timestamp":1744090621843,"user_tz":-330,"elapsed":2189,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}},"outputId":"097abde1-a021-4aca-9a1e-0350639ab87a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","source":["import pandas as pd\n","\n","# Load the Online Retail dataset\n","delivery_path = \"/content/drive/My Drive/Colab Notebooks/IPLPrediction/deliveries.csv\"\n","matches_path = \"/content/drive/My Drive/Colab Notebooks/IPLPrediction/matches.csv\"\n","delivery_df = pd.read_csv(delivery_path)\n","matches_df = pd.read_csv(matches_path)\n","delivery_df.head()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":400},"id":"Qcd9bi3EhDbT","executionInfo":{"status":"ok","timestamp":1744090624196,"user_tz":-330,"elapsed":2355,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}},"outputId":"920c7af9-ec79-4afa-c11b-083143bc39e0"},"execution_count":2,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   match_id  inning           batting_team                 bowling_team  over  \\\n","0    335982       1  Kolkata Knight Riders  Royal Challengers Bangalore     0   \n","1    335982       1  Kolkata Knight Riders  Royal Challengers Bangalore     0   \n","2    335982       1  Kolkata Knight Riders  Royal Challengers Bangalore     0   \n","3    335982       1  Kolkata Knight Riders  Royal Challengers Bangalore     0   \n","4    335982       1  Kolkata Knight Riders  Royal Challengers Bangalore     0   \n","\n","   ball       batter   bowler  non_striker  batsman_runs  extra_runs  \\\n","0     1   SC Ganguly  P Kumar  BB McCullum             0           1   \n","1     2  BB McCullum  P Kumar   SC Ganguly             0           0   \n","2     3  BB McCullum  P Kumar   SC Ganguly             0           1   \n","3     4  BB McCullum  P Kumar   SC Ganguly             0           0   \n","4     5  BB McCullum  P Kumar   SC Ganguly             0           0   \n","\n","   total_runs extras_type  is_wicket player_dismissed dismissal_kind fielder  \n","0           1     legbyes          0              NaN            NaN     NaN  \n","1           0         NaN          0              NaN            NaN     NaN  \n","2           1       wides          0              NaN            NaN     NaN  \n","3           0         NaN          0              NaN            NaN     NaN  \n","4           0         NaN          0              NaN            NaN     NaN  "],"text/html":["\n","  <div id=\"df-287ca537-01f6-49fa-9400-66d485dc7c93\" class=\"colab-df-container\">\n","    <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>match_id</th>\n","      <th>inning</th>\n","      <th>batting_team</th>\n","      <th>bowling_team</th>\n","      <th>over</th>\n","      <th>ball</th>\n","      <th>batter</th>\n","      <th>bowler</th>\n","      <th>non_striker</th>\n","      <th>batsman_runs</th>\n","      <th>extra_runs</th>\n","      <th>total_runs</th>\n","      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2007/08      Mumbai  2008-04-20     League      MV Boucher   \n","4  335986  2007/08     Kolkata  2008-04-20     League       DJ Hussey   \n","\n","                                        venue                        team1  \\\n","0                       M Chinnaswamy Stadium  Royal Challengers Bangalore   \n","1  Punjab Cricket Association Stadium, Mohali              Kings XI Punjab   \n","2                            Feroz Shah Kotla             Delhi Daredevils   \n","3                            Wankhede Stadium               Mumbai Indians   \n","4                                Eden Gardens        Kolkata Knight Riders   \n","\n","                         team2                  toss_winner toss_decision  \\\n","0        Kolkata Knight Riders  Royal Challengers Bangalore         field   \n","1          Chennai Super Kings          Chennai Super Kings           bat   \n","2             Rajasthan Royals             Rajasthan Royals           bat   \n","3  Royal Challengers Bangalore               Mumbai Indians           bat   \n","4              Deccan Chargers              Deccan Chargers           bat   \n","\n","                        winner   result  result_margin  target_runs  \\\n","0        Kolkata Knight Riders     runs          140.0        223.0   \n","1          Chennai Super Kings     runs           33.0        241.0   \n","2             Delhi Daredevils  wickets            9.0        130.0   \n","3  Royal Challengers Bangalore  wickets            5.0        166.0   \n","4        Kolkata Knight Riders  wickets            5.0        111.0   \n","\n","   target_overs super_over method    umpire1         umpire2  \n","0          20.0          N    NaN  Asad Rauf     RE Koertzen  \n","1          20.0          N    NaN  MR Benson      SL Shastri  \n","2          20.0          N    NaN  Aleem Dar  GA Pratapkumar  \n","3          20.0          N    NaN   SJ Davis       DJ Harper  \n","4          20.0          N    NaN  BF Bowden     K Hariharan  "],"text/html":["\n","  <div id=\"df-22d42806-2cc3-4a41-8b10-19e76806642c\" class=\"colab-df-container\">\n","    <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>id</th>\n","      <th>season</th>\n","      <th>city</th>\n","      <th>date</th>\n","      <th>match_type</th>\n","      <th>player_of_match</th>\n","      <th>venue</th>\n","      <th>team1</th>\n","      <th>team2</th>\n","      <th>toss_winner</th>\n","      <th>toss_decision</th>\n","      <th>winner</th>\n","      <th>result</th>\n","      <th>result_margin</th>\n","      <th>target_runs</th>\n","      <th>target_overs</th>\n","      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Hussey</td>\n","      <td>Punjab Cricket Association Stadium, Mohali</td>\n","      <td>Kings XI Punjab</td>\n","      <td>Chennai Super Kings</td>\n","      <td>Chennai Super Kings</td>\n","      <td>bat</td>\n","      <td>Chennai Super Kings</td>\n","      <td>runs</td>\n","      <td>33.0</td>\n","      <td>241.0</td>\n","      <td>20.0</td>\n","      <td>N</td>\n","      <td>NaN</td>\n","      <td>MR Benson</td>\n","      <td>SL Shastri</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>335984</td>\n","      <td>2007/08</td>\n","      <td>Delhi</td>\n","      <td>2008-04-19</td>\n","      <td>League</td>\n","      <td>MF Maharoof</td>\n","      <td>Feroz Shah Kotla</td>\n","      <td>Delhi Daredevils</td>\n","      <td>Rajasthan Royals</td>\n","      <td>Rajasthan Royals</td>\n","      <td>bat</td>\n","      <td>Delhi Daredevils</td>\n","      <td>wickets</td>\n","      <td>9.0</td>\n","      <td>130.0</td>\n","      <td>20.0</td>\n","      <td>N</td>\n","      <td>NaN</td>\n","      <td>Aleem Dar</td>\n","      <td>GA Pratapkumar</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>335985</td>\n","      <td>2007/08</td>\n","      <td>Mumbai</td>\n","      <td>2008-04-20</td>\n","      <td>League</td>\n","      <td>MV Boucher</td>\n","      <td>Wankhede Stadium</td>\n","      <td>Mumbai Indians</td>\n","      <td>Royal Challengers Bangalore</td>\n","      <td>Mumbai Indians</td>\n","      <td>bat</td>\n","      <td>Royal Challengers Bangalore</td>\n","      <td>wickets</td>\n","      <td>5.0</td>\n","      <td>166.0</td>\n","      <td>20.0</td>\n","      <td>N</td>\n","      <td>NaN</td>\n","      <td>SJ Davis</td>\n","      <td>DJ Harper</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>335986</td>\n","      <td>2007/08</td>\n","      <td>Kolkata</td>\n","      <td>2008-04-20</td>\n","      <td>League</td>\n","      <td>DJ Hussey</td>\n","      <td>Eden Gardens</td>\n","      <td>Kolkata Knight Riders</td>\n","      <td>Deccan Chargers</td>\n","      <td>Deccan Chargers</td>\n","      <td>bat</td>\n","      <td>Kolkata Knight Riders</td>\n","      <td>wickets</td>\n","      <td>5.0</td>\n","      <td>111.0</td>\n","      <td>20.0</td>\n","      <td>N</td>\n","      <td>NaN</td>\n","      <td>BF Bowden</td>\n","      <td>K Hariharan</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-22d42806-2cc3-4a41-8b10-19e76806642c')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 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\"player_of_match\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 291,\n        \"samples\": [\n          \"PD Collingwood\",\n          \"DR Sams\",\n          \"CH Gayle\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"venue\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 58,\n        \"samples\": [\n          \"M Chinnaswamy Stadium\",\n          \"Sawai Mansingh Stadium\",\n          \"Saurashtra Cricket Association Stadium\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"team1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"Royal Challengers Bangalore\",\n          \"Rajasthan Royals\",\n          \"Gujarat Lions\"\n        ],\n        \"semantic_type\": 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]\n}"}},"metadata":{},"execution_count":3}]},{"cell_type":"code","source":["print(\"Deliveries Shape:\", delivery_df.shape)\n","print(\"Matches Shape:\", matches_df.shape)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"r4lkuvJChDWb","executionInfo":{"status":"ok","timestamp":1744090624449,"user_tz":-330,"elapsed":14,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}},"outputId":"bb01f5a5-59d2-4d68-bfe4-592d8d907c0d"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Deliveries Shape: (260920, 17)\n","Matches Shape: (1095, 20)\n"]}]},{"cell_type":"markdown","source":["**Exploratory Data Analysis**"],"metadata":{"id":"2AvneuSbouFi"}},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","# Convert date column to datetime\n","matches_df['date'] = pd.to_datetime(matches_df['date'])\n","\n","# Extract year from date for consistency\n","matches_df['year'] = matches_df['date'].dt.year\n","\n","# Top teams with most wins\n","top_teams = matches_df['winner'].value_counts().head(10)\n","\n","# Toss decision impact\n","toss_decision_result = matches_df.groupby(['toss_decision', 'winner']).size().unstack(fill_value=0)\n","\n","# Matches per city\n","matches_per_city = matches_df['city'].value_counts().head(10)\n","\n","#Top 10 Teams with Most Wins\n","# Visualizations\n","plt.figure(figsize=(14, 6))\n","sns.barplot(x=top_teams.index, y=top_teams.values)\n","plt.title(\"🏆 Top 10 Teams with Most Wins in IPL\")\n","plt.ylabel(\"Number of Wins\")\n","plt.xlabel(\"Teams\")\n","plt.xticks(rotation=45)\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":566},"id":"mr5sUGjxhDUE","executionInfo":{"status":"ok","timestamp":1744090627338,"user_tz":-330,"elapsed":2885,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}},"outputId":"b7322896-0462-4341-ae72-ae06c248ca46"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-5-95907f96a132>:27: UserWarning: Glyph 127942 (\\N{TROPHY}) missing from font(s) DejaVu Sans.\n","  plt.tight_layout()\n","/usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 127942 (\\N{TROPHY}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1400x600 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":["#Toss Decision vs Match Winner (Heatmap)\n","plt.figure(figsize=(12, 6))\n","sns.heatmap(toss_decision_result, annot=True, fmt='d', cmap=\"YlGnBu\")\n","plt.title(\"🧠 Toss Decision vs Match Winner\")\n","plt.ylabel(\"Toss Decision\")\n","plt.xlabel(\"Winner\")\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":700},"id":"bkGLodnKhDRs","executionInfo":{"status":"ok","timestamp":1744090629506,"user_tz":-330,"elapsed":2170,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}},"outputId":"32f7d1a5-ba41-4662-f285-c136d440e3fb"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-6-68d8d822897c>:7: UserWarning: Glyph 129504 (\\N{BRAIN}) missing from font(s) DejaVu Sans.\n","  plt.tight_layout()\n","/usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 129504 (\\N{BRAIN}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["#Top Cities Hosting Most IPL Matches\n","plt.figure(figsize=(14, 6))\n","sns.barplot(x=matches_per_city.index, y=matches_per_city.values)\n","plt.title(\"📍 Top Cities Hosting Most IPL Matches\")\n","plt.ylabel(\"Number of Matches\")\n","plt.xlabel(\"City\")\n","plt.xticks(rotation=45)\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":566},"id":"F0EMiko4hDO4","executionInfo":{"status":"ok","timestamp":1744090630573,"user_tz":-330,"elapsed":1063,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}},"outputId":"e8a48a24-0e4b-4e7f-9508-2fe73e625865"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-7-48de1f42bec4>:8: UserWarning: Glyph 128205 (\\N{ROUND PUSHPIN}) missing from font(s) DejaVu Sans.\n","  plt.tight_layout()\n","/usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128205 (\\N{ROUND PUSHPIN}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1400x600 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":["#Top 10 Run Scorers In IPL\n","# Top 10 Run Scorers\n","top_batsmen = delivery_df.groupby('batter')['batsman_runs'].sum().sort_values(ascending=False).head(10)\n","\n","# Top 10 Wicket Takers (excluding non-dismissals)\n","wicket_kinds = [\n","    'caught', 'bowled', 'lbw', 'stumped', 'caught and bowled', 'hit wicket'\n","]\n","top_bowlers = (\n","    delivery_df[delivery_df['dismissal_kind'].isin(wicket_kinds)]\n","    .groupby('bowler')['dismissal_kind']\n","    .count()\n","    .sort_values(ascending=False)\n","    .head(10)\n",")\n","\n","# Plot Top 10 Run Scorers\n","plt.figure(figsize=(14, 6))\n","sns.barplot(x=top_batsmen.index, y=top_batsmen.values)\n","plt.title(\"🏏 Top 10 Run Scorers in IPL\")\n","plt.ylabel(\"Total Runs\")\n","plt.xlabel(\"Batsman\")\n","plt.xticks(rotation=45)\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":566},"id":"67j7d6-phDEw","executionInfo":{"status":"ok","timestamp":1744090631386,"user_tz":-330,"elapsed":809,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}},"outputId":"ce954c0d-20c1-49d0-a0f5-dcd0970a2f46"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-8-0565b9002b24>:24: UserWarning: Glyph 127951 (\\N{CRICKET BAT AND BALL}) missing from font(s) DejaVu Sans.\n","  plt.tight_layout()\n","/usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 127951 (\\N{CRICKET BAT AND BALL}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1400x600 with 1 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lEhISTGxsrBkwYIB1kaETJ06Y/Pnzm9dee8107tzZeHp6Os28TbqQHR5NSd8fQkNDzdy5c03//v3Nu+++61QzceJEU7lyZdOqVStz7NgxV7SJByjpHDh79qy5evWqiY2NNcYY88033xiHw2HKlCljBXeJiYmmf//+JiAgwOkiM3h4TJ061Wzfvt26v3v3blOlShVTqFAhp+/9xhgzaNAgU758edO7d28TFRWV0q0iBSV9H1i6dKmpUaOGKV++vKlSpYo1a/LkyZOmY8eOpkKFCubjjz92Zauwgb9ewDJ37tzmxRdfNAMGDDDG3PwlvlmzZiZDhgwmNDTUGGMe6ovPpGZJwcz+/ftNwYIFzf/+9z9rbPHixaZ27drmhRdeMFu2bHFVi3CxYcOGWReaunbtmrX92LFjJiQkxISEhNzxcYS2D6elS5c6Ter6+uuvTaVKlUyxYsVM9+7drU9pnjlzxgpvkz6heenSJRMdHe2Svh8UglvcsylTphiHw2FGjRp12/TzuXPnmmzZsvGRtlTqypUrxhhjDh8+bDZv3mwuX75sKlasaNq2bWuMMWbbtm3G09PTOBwOM3PmTFe2ihT01VdfmfTp05sCBQqYDBkymEKFCt32PWLKlCnm8ccfNx07djQ3btx4JD7agj8tWbLElCpVylSoUME0bNjQmkH3ySefGIfDYV5++WXTpEkT06xZM5M5c2aWyHhI7dmzx9SrV88cOXLE2nb9+nXTunVr4+PjY9q1a3fbH3aHDh1qihQpYt555x2TkJDA1/4jbPny5cbDw8MMHjzYDBs2zLRo0cK4ubmZ8ePHG2Nu/hLeuXNnU7RoUevjr0i9Vq5cadKlS2dGjx5tfvnlF/Pmm2+atGnTmqVLlxpjbs64at68uXE4HOaHH35wcbe4W3/3Pf7cuXPmxRdfNB07dnTavmTJElO2bFnz9ttvp0R7sKFp06YZh8Nh8ubNawV6SX+o2bRpk3E4HGbXrl2ubBH3QWJiojlx4oRxOBymWbNmJioqyoSFhZls2bKZ3r17m2HDhhkfHx/z/PPPW6/3mTNnTOPGjY3D4Xhkl9giuMW/uvUjSqdOnXLaNnLkSOPm5uY0KyJpjBmUqdPOnTtN8eLFrb+CGWPM9u3bTWBgoPn111+NMcb8+uuvpkGDBmbYsGHWNjzaYmJizFtvvWVmzpxpYmJizJw5c0zFihVN7dq1zenTp51qP/30Uz4i/QhJek/YvXu3yZAhg/nggw/MgAEDTJUqVUzevHmt8H7p0qWmc+fOpl69embgwIGsTfiQS5rpsGPHDrNp0yZjjDFxcXGmc+fOpkyZMuajjz6y/siX5MMPP+Rr/xEXFxdn6tWrZ9544w2n7cOHDzcOh8Ns2LDBGGPMkSNHTI8ePTgfUpmkT10Yc/O949q1a6ZZs2Zm0KBBxpiboV7u3LlvO3/Onz9v2rVrx/vGQ2jq1KmmZ8+e5ty5c9Yf9L755hvj4eFhvv/+e6fazZs3M6M6lfi713n+/PkmTZo0plevXk4/Q2zfvt0ULlyY3ysfIUlLLLZv3958+umn5oMPPrDGfv31V5M3b14TEhJihbdRUVGmRYsWj+w5QHCLf3TrR5QqVKhgcufObapVq2YGDx5sLl26ZIz5M7ydMGGCK1uFiyWdKzt27DDVqlUzY8aMsdYhS/oraNLsiP79+5vnn3/e6Qd0PLp++uknU6BAAfPMM8+Y3bt3W9sXLlxoqlWrZmrVqnVbeItHS1hYmFm6dKnTD127d+82lStXNnny5LGW3En66BuzLR9et752UVFR5tlnnzXVqlWzPt4aGxtr2rVrZ8qXL29GjRrltE4+Hn3Xrl0zZcuWNUOHDjXG3LygUNI507hxY/Pcc89Z3we42FDqMm7cONOuXTunc8IYY2rXrm0WLVpkTpw4YXLnzm3at29vjS1ZssR89913xhiWSHgYnTt3zrz77rvGz8/PBAUFmVatWpnDhw+bS5cumddff9306dPHxMXFWb9PJOG1frTd+vru3r3bbNu2zVy6dMkK9j/99FPj5uZmOnfubFatWmV27dpl6tata8qVK8e58ZBLTEw0CQkJ1tf8999/bzw8PIyXl5e1/GKSffv2mTx58piXXnrJWt/2UX793QTcIjEx0em+w+HQmjVr1KRJEz3//PMaN26cihUrpuXLl6tTp066cuWKevXqpY8++khvvvmmpk2b5qLO4SrGGEnShQsXJEnlypVTUFCQ5s6dq7Nnz0qSihYtqlatWqlRo0YKDAzUhAkT9P7778vHx8dlfePBSjovduzYoYMHD6pw4cIKDQ2Vu7u7VdOoUSO98cYbMsbohRdeUGRkpKvaxQOQdA6cPn1a/fr104svvqioqChrvGTJkpo6daoKFCigihUrKjIyUl5eXpJuvvfg4XTra5cjRw517NhRmTJl0gcffKAtW7bIw8NDEydOVJkyZbR48WJ99NFHunbtmgs7Rkry8vJSqVKltHjxYsXExCht2rRKSEiQJOXPn183btywvg+kTZvWla0ihSS9V3h4eKhXr15Kmzat4uLiJElXr15VunTptGrVKlWvXl116tTR1KlTJUnnz5/Xl19+qf379ysxMVFubvxaa3d//T0za9asGjJkiI4eParGjRsrMjJSTz31lAYOHKg9e/Zo06ZNunr1qtzd3a3zRBKv9SMu6fXt1auXnnvuOdWqVUtBQUHq2bOnoqKi1KZNG33yySeaPn266tSpoylTpsjT01Pbtm2Tm5vbbecZ7C/pNbt+/brc3Nzk7u6uX375RZUqVbJe1x9//FHHjh2TdPN9o3jx4lq7dq3Wrl2rkSNHKi4u7tH+3uDC0Bg2k/TX7YMHD5rvvvvOJCQkmGvXrpmWLVuarl27WnUJCQlm+vTppnz58mbkyJHW9kmTJpl9+/aleN9wvdWrV5sCBQqY6dOnW9sCAwPNSy+9ZN0/dOiQWbBggRk9erQ5dOiQC7pESluxYoXJnTu3Wb58udm8ebMpV66cKVKkiNPVQI25uSZ2SEiI0wL0eDR8++23plu3bubLL780VatWNYULF75tbdO9e/eakiVLmscff/yR/kt5avB3M6UXL15sgoODzXPPPWc2b95sjLk587Zp06amZs2a5ty5cynZJlJI0gVhzpw5Yy21ZYwx69atMxUqVDBt2rSxPr1ljDEdOnQwDRo0MNevX2fWfSry1zUpQ0NDTbNmzayfCTZs2GDSp09vypQp41TXv39/U7BgQae1tGFft76///DDD2b58uXm8OHDt11AaM6cOebtt982uXLlMg6Hw/Tv3z+lW4WL3HqOLFq0yAQEBJiVK1eaffv2mcGDB5uqVauaBg0aWNdHWLBggUmbNq0ZOnSo9QkNLkT28Prjjz9M9erVzcGDB82yZctMpkyZzI8//miMuXl9HA8PD9OiRQtz4sQJY8yfP3MeOHDAHDx40GV9pxSCWzi5cuWKeeyxx8zEiROtbS+99JJp0qTJbbXNmjUzzz77bEq2B5v6+OOPjcPhMJ6enqZbt25m6dKlJjQ01AQFBZkpU6a4uj2koKQ30YiICNO6dWszZswYa+z77783lSpVMiVLlrwtvGVN7EfPrl27jJ+fn/n888/NtWvXzIYNG0ypUqXMU089ddvH4/fv389alg+5pK/90NBQ89FHH5lRo0aZNWvWWOO3hre3Lptwa6CHh9/ixYvNtm3brPtffvmlKVu2rMmdO7dp27at9XHGadOmmaeeeso8/vjjpnv37qZx48YmY8aM5pdffnFV63CBlStXmhw5cpjPPvvM2vbxxx+bMmXKmNdff90Kb6dOnWpdwLJ58+bmtdde4wKWD5Fb/xDTr18/ky9fPlOgQAGTN29e069fP3PgwAGn+oSEBHPw4EHTpEkTU6tWLZZNSWUWLFhgBg8ebC2pY8zNc2j27NmmXLlyZuzYsdb2mTNnGjc3NzNkyJDbltTAw2Xr1q2mXr16pnDhwsbT09MsXLjQGGOs13XLli1WePvHH38YY1LX0mqP8Fxi3As3NzcZY1SgQAFJUkJCggICAnTixAmdPHnS6aMHNWrU0KlTpxQdHe2ibuEq5paPK0nSW2+9pbffflv169fXjRs3NG/ePPXp00eFCxfWDz/8wEfgUxGHw6GwsDA1bdpUu3bt0pNPPmmNBQUFaeTIkfL29latWrV05swZayxTpkyuaBcPyJ49e7R27Vo1atRITZs2lZeXl55++ml9/PHHio+PV40aNXT9+nWrvlixYtb7Dh4+xhg5HA59/fXXqlOnjr777jstXLhQ7733noYPHy5Jqlevnjp27Kg0adKoT58+Cg0NlYeHh3LlyuXi7nE/GGN08uRJtWrVSqNHj9Yvv/yiX375RW+88Ybq1aun/v37a/PmzerXr582bdqk9u3ba+zYsapatar27t2r9OnTKywsTKVKlXL1oSAF5ciRQ/Xr19dHH32kOXPmSLr5M2Xbtm116NAhDRgwQKdPn1aHDh20bt06ubu768qVK/L391doaKjTzxiwr6QldEaMGKHZs2dr1qxZOnr0qOrWratp06Zp9OjROnjwoFWfmJiowoUL68MPP9SWLVu0fPlyV7WOFBYXF6cuXbpo0KBBOnDggLXd4XCoRYsWKliwoJYsWWJtb9WqlWbNmqX33ntPY8aMcUHH+K+ScoXKlSurbt26Onz4sPz8/FSsWDFJN/OpGzduqEqVKlq/fr2++uorde3aVadPn05dS6u5NDaGy/31Y6kXLlwwRYsWNTt27LC2nTx50mTPnt00bNjQ6aPMHTp0MM8++6x1EQmkLqtWrTJvvvmm2blzpzHm5iybNm3amO3bt5v9+/ebl19+2aRJk8Y4HA4ze/ZsF3eLlBQZGWnKlStnHA6HGTVqlNNYYmKi+f77702JEiVMpUqV+Gj8IyYxMdFcvXrVlCxZ0jgcDlOnTh2n8Rs3bpj169eb8uXLmyJFity2bAIeXlu3bjW5c+c206ZNM8bcvFClj4+P8ff3N/369bPqFixYYBo2bGiOHz/uqlbxAG3dutUUKVLEtGzZ0owZM8YMHDjQGjtw4ICpVKmSefbZZ826deus7fHx8bwXpDKff/659e9du3aZTp06maJFi5qZM2da28ePH28qVapkmjdvbv3+kfQ7B+fLwyUxMdEcO3bM1KlTxyxYsMAYY8yyZcuMj4+PadKkiTUj/9aZt0kfea9ataqZO3euS/pGykr6ur506ZIJDAw0/v7+ZsuWLU4zridNmmQqVqx42xIb8+fPZ8nGh9yKFSvMJ598YsaMGWNeeeUVU6ZMGesTPPHx8dZ5sGnTJuPr62tOnjzpynZTHMEtzMmTJ601586fP2+yZ8/u9EVizM0rOubIkcM89dRTpnbt2qZJkyYmU6ZMVmiH1GfNmjUmd+7cpk6dOtaV4hs2bGhatGhh1UybNs0EBwfzRpoKnTt3zlStWtWULVvWLF++3OmjLImJiSYsLIyPxj/Cjh07ZqpUqWLy589vvv32W6fX/8aNG2blypXm6aef5hx4SN36eib9ojVq1CjTpk0bY8zN1/+xxx4zr776qunRo4fJkSOHGTZsmPWYW9c1xcMvMTHRuhK0MTfD28cee8xkzpzZtG7d2ql2//79JigoyISEhJivvvrKFe3CxQ4ePGiyZs1qatasaW3buXOnFd7eumzC+PHjTeXKlZ2WTTAmdX089mF16dIlExERYd2/du2aWblypblw4YIJCwsz/v7+1tJ8HTt2NNmzZzdNmjQxv/32m/WYWbNmGYfDwbUxHlH/9AeYmJgYU7BgQVOmTBmzYsUKc+HCBXPu3DlTpUoVU69evRTsEg9S0vfynTt3GofDYf73v/8ZY4zZuHGjqV+/vilTpowJCwuz6jds2GCMMaly4qDDmL985hmphjFG169fV8OGDRUbG6vBgwerXLlyyps3r5YvX66nnnrK+vijJJ06dUqzZ8/W0aNH5eXlpU6dOql48eIuPgq40rFjx/T5559r1qxZKlCggLp06aJ27dpp2LBhatOmjSTp2rVrSpcunYs7xYOS9D3i1KlTunz5svz8/OTl5SUPDw9FRUXppZdekru7u/r376/g4ODU9ZGWVOLW94lbHTt2TPXr11f27NnVv39/1ahRwxpLTEzU9evXlT59+pRsFf9R0pXb4+Pj5e7uroSEBKVJk0aSFBsbq127dql06dKqXbu2ChUqpJkzZ+rQoUOqXLmyYmJi1Lt3bw0ZMuRvzxk8XJLOhzsJCwtT8+bNlSVLFo0fP14VK1a0xg4cOKD69evr8ccf16xZs5QhQ4aUahk2cO3aNa1evVr9+vVT/vz5tWrVKknSrl27NG3aNG3YsEF9+/bV66+/LkmaNGmSpkyZoqpVq2rixInW9xzY19KlS/XVV1/pxIkT6t+/v2rWrClJunLlijJkyKBevXrp5MmTmj17ttzd3TVgwACtWbNG5cuX14QJE6zvK7GxsTp69Kj1kWk8Om59/5g6dar27dun33//Xb1791axYsWULVs2xcTEqFy5cvrtt99UunRpFSxYUBEREVq/fr08PDz4WeIR8eOPP+rs2bMKCwvToEGDrO2bNm3ShAkTdOjQIb3//vv66aefNH78eO3ZsydVLrNFcAutW7dOQ4cOVebMmRUSEqKJEydq2rRpyp49u+Lj4+Xp6am4uDh5e3vr0KFDCgwMZD3KVCbpjXHfvn06cuSI0qVLp8cff1y5cuXS1atXdeHCBbVu3Vrnzp1TVFSUChQooFmzZumxxx5zdet4gJLOiyVLlqh///66du2a0qZNq06dOqlhw4bKnz+/IiMj9dJLLyl9+vTq1q2bXnjhBX7IeoQknQObN2/Wtm3b9Pvvv6tVq1Z67LHHlD17dh05ckQNGzZUzpw51b9/f1WvXt3VLeMeJf2SdeDAAQ0fPlxnz55V5syZNWnSJPn4+Fh1P//8s1q0aKF58+apdOnSOnLkiHr06KHKlSurcePGrGX8iEg6H06fPq3vvvtOa9eulTFG5cuXV6NGjZQzZ05t3bpVLVu2VPny5fX222+rXLly1uMPHTokd3d3zodU5O2331adOnVUs2ZNxcXFadWqVerZs6cKFiz4r+Ht9OnTVbt2bc6Xh8CMGTP07rvvqn///ipZsuQd3/fbt2+v06dPa8aMGcqZM6defvllvfrqq6pfv74cDocSExOVmJiotGnTpvwBIEX17dtXs2bNUoMGDXT27Flt3rxZffr0UaNGjZQnTx5dunRJTz/9tI4ePap58+apdu3aSps2reLi4uTh4eHq9vEfXbp0SYGBgTp8+LBee+01zZkzxynU37ZtmyZNmqS1a9cqa9asmjt3rtPPEqmKK6b5wn42btxoqlSpYipUqGAcDodJnz69yZIli/H29jbp0qUz6dOnNz4+PiZHjhxOH1XCoy/pIwxfffWVKVCggClSpIgpX768CQoKuu2jS59++qmpXr26yZYtm4mMjHRFu0hhK1asMN7e3uajjz4yFy5cMN26dTP+/v6mR48e5siRI8aYm2veFi1a1NSpU8dcvnzZxR3jfkn6iNvXX39tvL29TaNGjUylSpVM6dKlzaBBg6wrvh4+fNgEBgaap556ylqWBw+XpNd6586dJkuWLOb11183TZo0Mfny5TNly5Y1V65csWp37dplfH19rY/AvvPOOyYkJMScO3fOJb3j/ks6H/bs2WPKlStnatasaapWrWpKly5tsmfPbkqXLm2OHTtmjLm5Ft1jjz1mGjdubH788UdXtg0XSUxMNLGxseatt94yP/30k7X96tWr5ptvvjGFChUywcHB1vakZRNKlixppkyZ4oqWcY+++uorkylTJrNo0SKn7Y0bNzavvvqqdf/jjz82hQoVMtWqVTNPPPGEKVasmLU8H+sXpx6fffaZyZ8/v/V94YcffjAOh8Pkzp3bDBs2zFrDNCYmxhQoUMCUL1/e7N6922nNWzz8wsLCTJUqVUzRokXN2bNnjTHG6TW+dOmSOXDggDl9+rSrWrQFgttU5N/Wg9q8ebOpWLGiKV++vHn//ffNb7/9Zo4ePWp++ukn88svv5hjx46ZEydOpFC3sJN169aZzJkzWz9Af/nll8bhcJjHHnvM7N+/36n25MmTTmta4dEVFRVlnnvuOfP+++8bY24GtAEBAaZs2bImICDAdO/e3VrDNCoqivVMHxGRkZHWD9Pbtm0zefLkMTNmzDDGGHPx4kXj4eFhChcubPr27WtOnTpljLl5UaKqVavyh7+H2J49e4yXl5cZOnSoMebmD9U9evQw6dKlM1u2bLHqTp8+bTp16mT8/PxM4cKFTdasWZ3CGjzcbg3xvb29zdtvv+30vX3q1KmmcOHCpkCBAtbPjFu2bLH+ePfzzz+7oGu4StLvHmfOnLF+6V65cqVZuXKlMebP8LZw4cKmdu3a1uN27dplmjdvbp566ilz8eJF1rR9CFy8eNG88MILpm/fvk7h6/PPP29y585t/YE3ycSJE03fvn3N22+/bYU0SRckw6MvLi7OfPrpp2bChAnGmJsXufbx8TFz5swxffv2NV5eXmbkyJHW+0tMTIwpVqyYKVSokNm9e7cLO8d/cafv5XFxcebHH380RYoUMUFBQSY2NtYYYwjo/4LgNpVI+iI5e/bsP/4lc/PmzaZy5cqmfv361uLPtz4eqc+lS5dMmzZtzODBg40xxpw6dcrkzZvXvPzyy6Z69eqmQIEC1sxKzpPU5erVq2bBggXmyJEjJioqyhQrVsy0b9/eGGNM+/btTY4cOUy7du2s8wMPv3PnzpnHH3/cjB8/3hhjzP/+9z/z5ptvGmOMOXLkiAkICDAdOnQwvXr1MpkyZTIDBgywZt7FxcW5rG/8NxcvXjTVqlUzfn5+TtvffPNN43A4zCeffGKOHTtmzag/e/asWblypZk6dSpf/4+gffv2GQ8PDzNixIjbxhITE82CBQtMgQIFTKNGjawL0W3atMk8+eST1kx8pA6JiYkmKirKBAQEmBUrVhhjjGnVqpVxOBxmzZo1xpi/D2/37NmT6mdYPUyOHz9usmXLZl1cyBhjVq9ebV588UVz5MgRs379euPn52deeumlOz6ekObRdqffEQ8cOGBOnTplfv/9d/PEE0+YMWPGGGP+r707D6uifP8H/j6AgCwaiwIaaoKp4L6hoihigAvimmYq7uZapkkIprigLOGWWymSYmqfVDR3P6KliLui5oZbQoksLiiLwOH+/eF1Jkjt861fcuTwfl1XVzIzZ7xx5sw8c8/z3M/zTkCVKlUSMzMzWb9+vXJuPHr0SJo1a1ZiAjsqOzTnwPHjx2Xp0qUSEhJSYgLzM2fOiKOjo7Rt21ZJ3rIH/h+YuC1HHj58+MIN9WUOHz4srq6u0qdPH6WRReXbwYMHJT4+Xh4+fChNmzaVMWPGiIjIunXrRKVSSeXKleXq1atajpJep1cl5TVDWkJDQ8Xb21sePHggIiIRERFSq1Yt8fLyYg9sHdO3b1+pVauW5OTkSHZ2tvzyyy+Sl5cnnp6eJWaQr1mzplSvXl1mzZolBQUFfLFThuXm5srixYvFxcVFPvjgAxERWbhwoRgbG0vv3r2ld+/e0rZtW6lZs6ZERETI999/r+WI6XXJzs6Wvn37iomJidLzXtNLTvMdLyoqkvHjx4udnd0Ls8pT+eTq6iqenp5SWFgoWVlZMnbsWDE0NJS9e/eKyB/JWycnJ3FxcdFytPRPnDp1SiwtLeWnn35Slj19+lR5eSMiEhsbKyqVSrZs2aKNEElLNEk4kec9Z3Nyckqsj4+PFycnJzlz5oyIPO9xP2HCBFmyZIlyf9G8/GdbsmwqXnrRxsZGXF1dpVOnTmJkZCQ//vijst2ZM2ekXr164uTkxA4ff/LyqWBJJ5mYmKB9+/bYsWMHsrKyXrldhw4dMH/+fFy9ehXfffcdcnJySjFK0ia1Wg15yXyFnTp1Qtu2bXH06FGYmpoiMDAQAGBvbw9PT0/07t37lTNLk24oKioCAJw/fx5xcXGIi4sDAFhZWQEAMjMzkZubq2x3//59BAUFISYmBjY2NtoJmv5VhYWFAICFCxfC0NAQ48aNg4mJCZycnJCcnIyUlBT0798fAJCcnIwWLVpgwIABGDp0KAwMDDgpXRklIjA2Nsbo0aMxYsQIXL9+Hc7Ozpg9ezYOHz6M77//Hlu2bMHmzZvh5+eHnTt3Yvz48bh//762Q6fXwMTEBAMHDkTHjh3Rr18/XLt2Dfr6+igqKoJKpUJhYSFUKhUmTpyItLQ03Lp1S7kvGBkZaTl6Km2aYz9q1CgkJyfjzJkzMDc3x+zZszFs2DD06NED+/btQ8WKFeHp6Yng4GAUFhbi7t27Wo6c/q66devCzMwMS5cuVZZVrFgRZmZmyrNFlSpV4O7ujnfffVdbYVIp+v777wFAmURs3rx5eO+99+Dr64sFCxYo22VkZCAtLQ2XL1/G2bNnERQUhIcPH2LixInQ19eHWq1GhQoVtPI70D+jufZrnh1UKhXi4+Px0UcfYe7cuTh69CiioqKQn5+PgQMHIiYmBgDQrFkzfPvttzA2NsZvv/2mtfjfSFpOHFMpW7p0qVhZWSlDF/+q+/nRo0dZk7Kc0NSh1Dh69KiEh4fL119/XaK3zMqVK8XY2FjpZRMQECBDhw7lhFM6av78+fLxxx8rP3///fdibm4uDg4OUrFiRZk4caKybuHChVK/fn358MMPpX///mJiYiLXrl3TQtT0b7t7966kpaUpvR7y8vIkJCREGjdurNQpPHfunLz77ruydOlSSU5OllmzZknnzp0lKytLm6HTv0TTUyI3N1e+/vpradSokbi5ub2wXuR5OQ1Nz3vSLcWP886dO8XT01Patm0r169fF5GS9SlXr14tdevWlcePH5d6nKRdL+sR9/DhQ6lVq5ZSTknk+bVizJgxYmhoqJRNyM3NLdFDk8qOwsJC+eKLL6Ry5cryxRdfvLA+Ly9PfHx8pFevXhz+XA5s375dVCqVzJ49W0Se5yCsra1l7ty5MmLECLGysipxPfjwww/FwsJC7O3tpWXLluxtqQN++eUXCQgIkLy8PMnJyZGFCxcq14a7d++Kvb29jB07Vj7++GMxNjaWH374QflsXl6etsJ+YzFxq8OKN5yK/7lp06YyYMAAbYREb6AVK1ZI586dJSEhQUREdu/eLfr6+tKhQwdRqVTSpUsX2bVrl4iI3LlzR9q3by92dnbi5eUlJiYmLBCvo4qKimT58uWiUqkkKChIioqKxMXFRb799lu5evWq/Oc//xFzc3MZPHiw8pnAwEDp27evdO3aVS5cuKDF6Onfkp6eLhYWFuLk5CSLFi1SalHdv39fGjRoID179lS2HTZsmNSsWVNq1qwpVatWVYa8UdmheZguPqxR48/J2+bNm8uAAQOUh6uXfYZ0z6uSt5oXdYWFhVJYWCjjxo0TPz8/yc7O1laoVMo0148/J24114jo6GipUaOGHD9+XFn34MEDGTdunKhUKjl48GDpBUv/WPGSKH92+/Zt6d69u1hbW8vYsWMlIyNDkpOTJS4uTjw8PMTZ2Vk5H5i81W3p6eny5ZdfiqWlpQQHB8vKlStl586dIvK8hMaGDRvExMSkRImtn376SY4fP668BGTd47Lt/fffL/GcmJiYKKdOnZKnT59K+/btZdSoUaJWq+XKlStibGwsKpVKNm/erMWI32xM3OogzY3wz28qNBe/sLAwad68udy4cUNEWCumvDt27JjUqlVL+vTpI4cPH5aBAwfKN998IyIiN27ckDZt2sh7772n1Ds+ffq0TJ8+XT755JMSBcVJNxS/Hjx79kzWrl0rBgYGMm7cOBkxYoRkZmYq6/fv3y+VKlWSDz/8sMQ++JZUd2RkZEjr1q3FxsZGZs+eLU2bNpXY2FgRETlx4oQYGxsrk0mIPH/x8+OPP3K0Rhn2yy+/yIABA+TRo0cvrHtZ8nbQoEFM2pYz/yt5GxgYKLa2tnLlyhVthUhacuvWLWnXrp3s2LHjhQmEzp49K++8844sX75cREpOnDx58mS2KcuI4hMMvqyT0I0bN2TUqFFiZmYmlpaWYmZmJi1atJCuXbsqSVsm5HSbJvGalpYm4eHhYmVlJZUrV1Z61os8b0ds2LBBTE1NZeTIka/cB5Vd3bp1UyYwLu7SpUvStGlTOXHihIg8n9zYz89PgoKCeB/4C0zc6qhbt25Jz549JSoq6oUC4MnJyWJhYSEzZ87UTnD0xtAk+U+fPi2Ojo4yYMAA8fT0LPGwdf36dXF1dRUPD48SN1y+Kdc9mmOamZmpJG0KCgpk7dq1YmFhIdWqVVOGvWoa6Pv37xcrKyvx9fXlSyAdde7cObGxsZEVK1bIunXr5K233pLAwEDZsWOHfPHFF9K+fXs5duyYtsOk/0+a739gYKB069btldsVT96uXr1aHBwcZMSIEaUSI5UOzbnw6NGjF0opabwsedupUycZNWqUmJiYsMd9ObVnzx7p3LmzWFtbS8uWLSUsLEwpryUi8tlnn4mtrW2JZSJsU5YVp06dklq1asnGjRuVZS9L3j558kSuX78uq1atkqioKDlx4oRyjJm01W3FS2RpXt4sXLhQzMzM5LPPPiuxbV5enmzcuFFUKpWEhoaWapz073rZNbxPnz4yf/58ESmZiD906JCoVCo5cuSI5OTkyIwZM6Rjx46cwPR/YOJWR12+fFm6d+8uBgYG4ubmJgEBAZKVlaX0hJs/f740aNBArl69quVISZuKX2QTEhLk3XffFT09Pdm6dWuJ7ZKSkqRjx47SsmVLpectk3S6RXMuXLlyRTw8POSTTz6RtLQ0EXmeoFm/fr0YGxvLtGnTXvjsrl27pEaNGi88iFHZlJqaKsnJycrParVaFixYIEOHDhURkSNHjsiYMWPE19dXmjdvLs2bN5f58+fzwVtHjB8/Xnx8fP5yG831PycnR7799tsXetZR2VX8XtClSxeZOXOmUsP2z4q3A3bv3i2tW7cWMzMzJm1J9uzZIwEBAWJqaipNmzYVPz8/SU5OlqNHj0qnTp2UkV3sVVe2nD17VgYNGiQNGzaU77//Xln+f30mYDtBt+3YsUOmTJki2dnZMm7cODE3N5dnz55Jenq6hIeHi6mpqQQHB5f4TG5uruzfv58JfR1w+/btEh0D27VrJxERESLy4nf/ww8/FJVKJY0aNZJKlSrJ2bNnSzPUMomJWx2XmJgoo0ePFgcHB6lRo4ZMnTpVLl68KKdPnxZ7e3ul1gxvpOWD5jjn5OTIo0eP5MiRI/L7778rvShPnz4tderUke7du5eoQSYicvXqVfH29pZff/211OOm10tzXly4cEGsrKxk0qRJsm3bthLbPHv2TKKiosTAwECmT5/+wj5Yx1A3PH36VOzs7MTNzU2WLVumLD9y5Ii4u7srL3Xu3r0rBw8elHbt2olKpZI6derwHCiDXtYDasSIETJw4EAR+euHcb680z2a8yExMVGsra1lzJgxsnfv3r/8TPHzYP/+/WwjlHN/fp64e/euzJw5Uxo1aiTVq1cXPz8/sbKyku7du2spQvr/debMGRkzZozUr19ftm/friznPYEWL14sVlZW0qpVK7G2tpZLly4p6zIzMyUiIkLeeustZcKyP2PytuxSq9WyaNEisbOzk88//1xERFq1aiVhYWGv/MzGjRslOjpaKd9Jf00lIgLSac+ePUNubi7mzZuHhIQEnDx5EtOnT8eyZctgb2+Pn3/+GWZmZtoOk16zoqIi6OnpISkpCSEhITh+/DiuXbuGKlWqoG3btoiMjMQ777yDhIQEDBkyBI0aNYK/vz9atWql7KOgoAAVKlTQ4m9Br0tKSgo8PDzQr18/zJ0795XbrV69GmPHjkVAQABmz55dihFSafn555+xYsUKnDhxAvXr18dXX32Fd955B4sXL0ZISAiuXr0KCwsLAIBarcb8+fPRt29f1KtXT8uR0z9x8+ZNrFmzBh988AEaNmyIDz/8EJaWlli6dCkAQESU+4dKpVL+TLopJSUFnTp1Qt++fRESEvLK7QoLC2FgYADg+TmiUqlKK0QqI9RqNfT19VFUVASVSoWwsDBcvnwZ69evBwCkpaXBysqK504ZoVaroVKpoKenh5s3b6Jz587Q09NDWFgY+vTpA4DXgvLqypUrqF+/PgCgW7du2LNnD4YOHYrw8HBYWVkp2z148ADR0dEICQnBsGHDEB4erq2Q6TVIS0tDdHQ01q5dCy8vL9y4cQONGjXCu+++CxGBubk58vPzkZubi5ycHDRo0ADu7u7aDrvMMNB2APT6GRkZwcjICOHh4cjIyMDOnTsRHR2NnJwc3LlzB7m5uUzc6jjNg/aFCxfQpUsX+Pr64rPPPkP79u2xYsUK/Pjjj2jfvj0OHTqENm3aICYmBoMHD8aXX36JSZMmwdXVFQCYtNVh586dQ9WqVTFx4kTlfLly5QquXLmCHTt2wMXFBd7e3hg5ciT09fUxYsQIGBoaIigoSNuh07+oqKgIbm5uqF+/Po4dO4aAgAC4ublhzJgxGD16NJKSkvDpp59i5cqVMDIygr6+Ps+BMu7MmTNYuHAhcnJy4O/vj4KCAlSqVElZr1KpoK+vX+Jn0l3Hjh1DlSpVMHnyZGXZ9evXcenSJfz000+oVq0a/P39YWBgoCTmeE7Qy2iuGyqVCiqVCv7+/sjLy8Po0aNRpUoVWFtbazlC+l/i4uKQnZ2Nbt26lbgPBAYGQl9fH82bN8eMGTMgIujbty9UKhWTt+XMuXPnMGzYMPj4+GDOnDlo164dmjRpgnXr1iE0NBTjxo1DrVq1ICKwtLSEn58fHj9+jISEBJ4rOkREULVqVQwfPhxqtRobN27EpUuXkJiYCDMzM2RmZsLAwAB6enrQ19eHvr4+fvzxR22HXaYwcVtOaC6M1tbWGDp0KLp27Yo7d+7A2toaVapU0XZ49BppknCJiYlwdXXFxIkTMWfOHKWnTGRkJNq1a4egoCD06tUL+/fvh4uLCzZu3AgvLy8YGxujefPmMDY21vJvQq/TjRs3kJSUBBsbGwBATEwMYmJikJSUBGNjY/z8889KT8whQ4bA0NAQzZo103LU9G/T9KSsUqUKfH194evri48//hixsbFYu3YtvL298eDBA5w8eRLt27fXcrT0T2juCZqk2/vvv4+CggJMmzYN5ubmuHPnDn777TeEhIQgKysLBgYGMDAwQEFBAR48eIA6depg8uTJfNjSEX9+cE5JScGTJ09gZGQE4Pm9YMOGDcq9ID09HT/99BN2795dIpFDBOClPfKLJ/OMjIyUzgD05hIRFBYW4rPPPgPw/Bh2794dANCnTx9cv34dx44dQ0ZGBiIjIzFz5kyoVCr06dOH94Zyxs7ODq1bt8bBgwdRtWpVBAQEAABsbW2xYMECAMD48eNRs2ZNAEBqaiqCg4OVawKTt7pBcwytra0xcuRIqFQqbNmyBc7OzoiOjgYAPHnyBMbGxqhQoQKePHkCc3NzLUZc9rBUAlE58Ntvv8HZ2Rk9e/ZEdHS00rAuPtRx9erVCAgIwBdffIGJEycCAM6fPw9zc3M4ODhoM3wqBbdv30a7du1gZ2cHOzs7HDp0CBMmTECPHj3Qtm1bLF++HLNnz8aBAwfQsGFDbYdLpUCT2AOel0/YtGkTVq5cCQD46KOPsGzZMja2yxjNtf/OnTvYv38/GjduDBcXFwDAt99+i4CAAKSnp8PKygotW7ZESkoK9PX1UblyZeTm5kKtVmPNmjVo0KCBln8T+jdozoeMjAzo6+vDwsICp06dQtu2bdGpUyeICBISEjB+/Hj06tULLi4u+O677zB16lTExsaWKKVE5Ysm2XL+/HlcuXIFubm5cHd3xzvvvKPt0OhflJaWhn79+gEAgoODsXTpUiQlJWHbtm3Ks0FiYiKWLVuGLVu24IcffuDQ53JEcw+5f/8+5s2bhxMnTsDHx0cZifXVV18hLCwMvXv3hq+vL0JDQ5GUlISbN28CYGkNXZaeno7Vq1fj22+/Rffu3REREQEAyM/Ph6GhIY/9P8Aet0TlwPXr1+Hs7Izbt2/jl19+gbOzM4qKimBgYKBcOEeOHIkVK1bg+PHjynD5Jk2aaDt0KiX29vbYuHEj1qxZg8LCQvz3v/9Fo0aNYGJiAgBo1qwZKleurOUoqTRpahPq6enBzc0Nbm5u8PLywpIlSzB+/Hg2uMoYzbG8ePEi+vbtC2dnZ7z99tvKPcDPzw/m5uYYP348evToAX9/f9SuXbvEPoon86ns09PTw40bN+Dp6Yn33nsPwcHBaNmyJfbu3YtVq1ZBX18fe/fuRZMmTWBqagrgeW98U1NTvPXWW9oNnrRK05vqk08+wdtvvw0TExOMGjUKW7duha+vr7bDo/8PiYmJuHPnDipXroyOHTti69at6NGjB3r27AkLCwscPHgQtWvXVu4HjRs3xujRo1G7dm24ublpO3wqBZp2g56eHoqKimBjY4PAwEDMmzcPO3fuBAAEBQVhwoQJMDQ0xJIlS7Bv3z5YWVnh6tWryn7Yjixb/k6ytUqVKhg1ahRUKhU2bdqEsWPHYsWKFTA0NATAY/9PMHFLVA64u7tj5syZWLRoEUaMGIE1a9YoydviQ9osLCyUnzn5TPliYGCgJOdelpzZunUrrK2tUb16dS1FSP+mp0+fwtTUVGk4vWrCqT8v8/X1VUqoUNmip6eHq1evokOHDhgzZgwmTpyIatWqldimd+/eePz4MWbMmAEjIyNMmjQJjo6OynombXVLUVER1q9fjzt37uDmzZsICQlBQEAAPDw80K5dOxgYGLxwzOPi4mBnZ8f6pOXc2bNnMWbMGMyfPx+jRo3CjRs38O677yIxMZGJ2zJsw4YNiIiIQI0aNeDs7Aw3NzdYWVlh586d6NevHx49eoQbN26gVq1a0NfXV9qLLVq0QIsWLQDwBZ+u27p1K/Ly8tCnTx8YGRm9NHm7d+9e2NraYuTIkRg9ejQ6duyIvLw8NGjQ4IURn1Q2aJ4Tnj59iocPH+Ls2bNwdHRElSpVULVqVQAvJnatra0xatQo5OTk4MCBA0hLS1O2pb+PpRKIdFzxi+i+ffuwePFiPHjwQEneahpYycnJGD58OAYNGgQ/Pz8OYdAx/5fj+bJ6U7/99huWLFmCb775BocPH0ajRo1KI1x6jW7cuIH27dtj6NChqF69OiZMmKCse1UCl8q+vLw8DBkyBFWrVsVXX32lLC8oKMD9+/fx5MkTZVbotWvXIjg4GJ07d8b06dNf6HlLuuP8+fNwd3dHixYtoK+vj3r16mH69OmoWrVqiQRMamoqIiMjsXr1at4Lypnjx4+jdevWAP5oJ2zbtg0xMTHYsmULbt++DTc3N/j4+GD58uUAgIcPH8LCwoJtyTJk3bp1+OijjxAVFQVvb2+lV70myfbgwQP4+PhApVJh+vTp8Pb2VpJ2bDeUH4MHD8aGDRuwefNm9OjRQ6mHrjkPUlJSMG7cOBQWFmLXrl0vfP+Z2C97NMf22rVr+OKLL3Dx4kXcuXMHarUanTp1woQJE9CtWzcAL3/efPDgAUQEVlZW2ghfZ/AqS6TjNIk4APDy8sLHH38MS0tLjBgxApcuXVJunsuWLUNGRgY8PDyUz5FuKCoqgkqlQlpaGhISEnDgwIGXbqc55pr/L1++HH5+fti9ezcOHTrEB3Ud8fPPP+P+/ftwcHDAihUr0KNHDyxevBiZmZklHr74Xle3GBgYIDU1FfXq1VOW7du3D9OmTYOzszO6desGd3d3iAiGDRuG6dOnIyEhQRkiT2Vf8e+0iECtVqNJkyaYOHEiGjdujFatWuHo0aOYP3++UvcWeD6D/ODBg7Fnzx7eC8qZ06dPo23btsokQ5r2we+//47ff/8dSUlJ6NixI7p27aq8ENq1axemT5+OJ0+esC1ZRvzyyy8ICwvDkiVLMGDAACVpKyIwMDCAWq2GpaUlduzYAQAIDQ1FbGwsRIRJ23Jm/fr1GDVqFIYNG4bY2Fg8e/YMAJRJT99++234+/tj7969uHz58gufZ9K2bCk+yXmHDh1gbW2NuXPn4vr16wgPD8fvv/+OsWPH4ocffgDw8vyBpaUlk7b/Al5piXREUVFRif8X96rk7ciRI/Hrr7/iyy+/xLJlyxAdHY233367VOOm10tzw718+TLef/99LF68WJnd8688e/YM9evXR//+/bFr1y40btz49QdLpWL48OFwd3dHQUEBTp48ifr16yM+Ph4NGzbEihUrcOLECQAoUUaByr6cnBykp6fjwoULuHbtGubPn4+PP/4YycnJmDNnDoKCgpCSkoJPP/0UADB69GgkJCTAxsZGy5HTv0HzAu/BgwdIS0tT6hMCQM2aNXH06FFMmzYNgwcPxtGjRzFv3jxkZGQAALp164auXbti586dvBeUMw0aNEBkZCRmzpyJsLAwZXmbNm1gZGQEFxcXdOrUCatWrVLWHTx4EBkZGXz5V4b89ttvyMnJgZubW4njpmkHaK4VVlZW2LZtG+7du4cDBw4wMV/OqNVqAMCqVaswYMAAjBgxAtu3by+RvAWeT0DVunVrltQp4zTPkBcuXEDbtm0xcuRILFmyBL1798bbb7+NSZMm4csvv0Tt2rUxc+ZMnDp1Stsh6zYhojJPrVaLiMjt27dl1apVcurUqZduV1RUpPx579694uPjIyYmJmJgYCCnT58ulVip9GjOi4sXL4qFhYUEBQXJnTt3lPWnTp2SJ0+e/OU+ip8zVPZpzon58+fL4MGDS6yzs7OTd955R2xtbeWTTz6R2NhYbYRIr9HBgwfFwMBAatasKebm5rJy5UpJSkoSEZH8/Hzx9PQUPz8/ZXt+/3XL9evXxcHBQZydnWX79u1y9epVZZ27u7tMmzZNRETmzJkjLi4uMmXKFLl3756I/HHtoPJpxYoVoqenJ8uWLROR5+fD2LFjxcbGRhYtWiSPHj2Su3fvyueffy5WVlZy6dIlLUdMf0dISIhYW1srP7/s2n/58mWJi4sTEZHHjx9LYWFhqcVH2lX8fCh+3IcPHy6mpqYSHR2tPE88ffpUevbsKb6+vmxD6ICUlBSxs7OTLl26KMvUanWJ82D79u3y1ltvybx580SEbcfXhVWhicq4V80U/jLF65d6eXkhPz8fFStWxIwZM9CgQYNSjpxeNz09Pdy7dw8DBw7EkCFDMGfOHGVdaGgoAgICMHbsWHz55ZevnGyKvSnKNs33XXOd0PSG6NWrF0JCQvDjjz/Cx8cHQ4cOhUqlwurVq5GdnY2PP/4YJ0+eRJs2bTiRgA7p1KkTbt26hbS0NNSsWbNEbxh9fX1UrlwZ9vb2So8rfv91R1FREaKjo5Gamgpzc3PMmjULjo6OsLa2RmhoKAYNGoSjR48iPz8fQUFBUKlUWLduHQwNDTF37lyeC+WQ5r4RFxeHrKwsVK1aFRMmTEBubi6mTJmCJUuW4NGjR4iKikJAQAAaN26MjIwMHDhwAM7OztoOn/4GR0dHZGdnY//+/fD09Hzp933dunXIzMxE+/btUalSJQCsV6rrtm3bhrp168LJyUlpTxaflG7NmjUwNjaGv78/du/ejSpVquD69evIzMzE8ePHS7Q/qezQHDMRwdOnT1G/fn2o1WrExsaiZ8+eyjrNOdGjRw94enpi//79mD59urbD11mcnIxIB1y9ehVt27Z95UzhQMlJh6RY4fDs7GzWMNRhe/bswaxZsxAVFQUnJyeoVCosX74cU6ZMQUBAAMLDwzF48GBERka+MnlLZZfmu/748WNUrly5xLrZs2fjt99+Q2pqKk6cOIE9e/agadOmAIC0tDSICIfJlxP5+fmYM2cOoqKicPjwYdSpU0fbIdFrcO/ePYSGhuLXX3+FpaUlPvjgAwQEBKBatWrIzs5GXFwc1qxZg2HDhgEAIiIi0LdvX9SqVUu7gZPW7NixAwMHDkRQUJAyZPa7777DvHnzEBAQgKKiIly+fBkXLlyAg4MD7O3tX9oGpTfbrVu30KxZM3Tu3BmRkZGoUaMGgD/aEFlZWRg+fDg6dOiAiRMnajlaKg3Z2dno0KED7O3t8cMPP7yQoC+etF+1ahXOnDmDx48fo379+ggKCoKBgYEysR2VPb/88gv279+PyZMn49ixY4iMjER6ejomT56Mnj17AiiZW+jRowdUKhW2b9+uxah1G79JRGVcXl4evvjiCwwcOBDz589XlmtmCs/OzkbdunWhp6en3ECL97xl0la3HTt2DCkpKUrS9tmzZ7CwsMC+ffvg5uYGV1dX+Pj4oLCwECtXruRbcR1y584dxMTEYN++fUhOToarqyu6d++ODz74AADQuHFjREREwNraGseOHUPt2rUBPG+IsZdt+RETE4NTp05h8+bN2LNnD5O2OszOzg7Tpk1DSEgIzp07hxs3buDUqVPYvXs34uLiEBcXB3Nzc2X7qVOnajFa0rb8/HxERUXBz88Pn3/+OQDg4cOHaNy4Mfz9/WFkZIRPP/0UDRo04KitMq527dpYuXIlhg4dCiMjI0ydOhVNmzaFSqXC77//jpEjRyIrKwtjx47VdqhUSkxNTeHj44OtW7ciLS0NdnZ2JRJ1xXvejhkz5oXPq9VqJm3LIE1+4LPPPlNe2rZt2xZ6enqIiIjAokWLAEDpeatWq5GRkQE9PT14eXmV2Af9u/iETlTG/a+Zwrt06YLOnTsrM8NyCGz5YmxsDAMDA+Tm5gIAjIyM8P7778PNzQ0A4OHhgbFjxyIxMVHZhsq+ixcvwtPTE5cvX4aTkxNGjRqFhIQETJ06VRnG5Ovri759+6JatWqoWbOm8lkm78uPa9euYc2aNUhOTsahQ4eUHteku6pVq4bAwEA0a9YMq1evxqJFi9C1a1dERETgxo0b6Nu3r7ZDpDdEUVER7t69iwoVKijLLCwsMHLkSPTu3RtTp05FeHi4FiOkf1O/fv3w1Vdf4T//+Q969OiBLl26wMvLC71790ZGRgYOHToEAwMDZYIq0l2aZ8VJkyYhNTUVS5YsAfBi+/CvymSwhEbZVHxEbvFOHK1bt8bUqVNhbW2NRYsWYdu2bQCeH+dFixYhKSkJ3bp1K7EP+nfx6YyojPu/zBT+66+/Kj1neDHVTa+qeuPt7Y3k5GR8+eWXL91WU7+odevWMDQ0fO1x0uuXmJiItm3bolevXli+fDlWrVqFwMBA7N27Fx4eHoiKisK8efMAAH379sWzZ88QHx+v5ahJG+rWrYvNmzdj7dq1qF+/vrbDoVJiZ2eHwMBAtGnTBhs3bkRISAiA573umJQhDWNjY/j6+uLIkSO4fPmystzCwgJOTk6wt7dHaGgoMjMztRgl/Vv09fUxcuRInDx5Er169UJRURHs7e0xePBgJCQkoEKFCigsLGRCTscVFRVBpVJBrVbDwsIC48aNQ1xcHH799Vdth0avSVFR0QvLKlasiOrVqwOA0i4onrxdvHgx9u/fj9DQUCxduhTfffedUmKFXg/WuCXSAXFxcfDy8kL16tXx4MEDhIeHw8PDA46OjigoKED37t1hZ2eH6OhobYdKr4Fm6FJmZibu378PtVqNhg0bAgAyMjLg7++P9evXIyQkpMTQ1/z8fAQHByt1LevWrautX4H+JTdu3EDDhg0xdepUzJkzRxnGpimTcvPmTUyYMAEpKSnYsWMHqlWrhlq1aqFLly6IiorSdvhEVIpSU1Mxb948nDt3Dh4eHggODtZ2SPSGOXDgAGbPng0nJyd88sknygueyZMnw8HBAUOGDFEmqiLdxonIdFtSUhIsLS1hZWVVYnlCQgLee+89rFmzBv379+cweB119+5dxMbGYtKkSQCAZs2aYcKECRg+fPgLx/z48eNYuHAh4uLi8PjxYyQkJKB58+baCr3cYOERIh3AmcLLL03S9tKlSxg+fDjS09MhIvD09MTXX38Na2trTJw4EY8fP4a/vz/Onj0Lb29vpKen4/Tp09i/fz8OHDjApK0OKCoqQlRUFMzNzVGlShUAf9Qg05RJcXBwwPTp09GxY0ecPn0a/fr1Q3R0NN+SE5VDtra2CAwMREBAAI4dO4bMzMwXHtpJN70s+fKyZe+99x5SUlKwdu1a9OvXDy1atEBWVhYOHTqEhIQEJm111MvOBSZtdVd8fDz69esHKysrBAcHo0mTJsq8B23atMEHH3yAsLAwuLu7cw4EHaRWq7F8+XLExsaioKAAU6ZMQWFh4SvzBq1bt8bkyZNhYGCAwMBAODk5aSPscoeJWyIdYW9vD3t7+xLLNDOFx8fHY968eUzY6hhN0jYxMRGurq746KOP0L17d/zwww/45ptv0KRJE4wbNw5NmjTBvHnz0KZNGyxevBgHDx6ElZUVWrRogaNHj3KItI7Q09PDhAkTkJOTg++++w45OTn4/PPPoa+vrwx9A4DmzZvDysoK6enpAABPT09eG4jKKVtbWyxYsAAAmLQtJ4qP0rl37x7MzMxQrVo1GBoalkjYabYbNmwYHBwccPLkSezduxfVq1fHTz/9VGJuBdItbBOUL66uroiMjMT+/fsxdOhQODk5wcvLCx999BFsbW3h5+eHuLg4XL58GVWrVmXvax2jr6+PiRMnIi8vD5s3b0ZWVhZMTU2RlZWFrVu3AnheOqewsBB5eXl48uQJWrRogejo6BI10On1YqkEIh3155nCOemMbvrz0HgAuH37NurVq4eJEyciIiKixPZZWVnIyMiAtbU1jI2NWddWB2mGP586dQq9evWCv78/gD+GOcbHx2P8+PGIiopCs2bNtBwtERGVFk0y9uLFixg2bBgePHiAihUrYvDgwZgyZcoLD+F/7nlZWFgIlUrFpA2RDrh06RJSU1NhZmaG1q1bA3heHuXo0aNYsmQJ7O3t0apVKwQHB8PX1xc1a9bEli1btBw1vS6pqamYO3cujh8/jrNnz8Lc3BzW1tZ49OiRMsl5hQoVICKIi4vjy7tSxsnJiHQQZwovH4oPjS/eU2rTpk0oKChAUlISFi1ahHXr1iEvLw+FhYWoVKkSateujUqVKjFpq6M0w59btmyJbdu2ITQ0FMAfwxy3bNkCGxsb1KpVS4tREhFRaSo+SqdNmzbo0KEDNm7ciA4dOiAmJgZPnz4tsS3wYs9LAwMDJm2JdEB0dDT69OmDIUOGoG/fvvDz8wPwvDxKcHAwkpKS0KdPHyQmJqJFixbIzs7Gtm3bcOzYMS1HTq+Lra0tgoKC4OrqilatWmHIkCG4du0aMjMzce3aNdy+fRspKSm4fPkyk7ZawB63RDoqLS0NRkZGqFy5srZDodfo999/R1hYGI4fPw4/Pz88efIECxYswPjx49GkSRNs2LABycnJuH//PurUqYNPP/0U3bp103bYVApe1vN27ty5iIyMxM8//4wGDRpoO0QiIipFFy9eRLt27TBx4kTMnTsXwPP2ore3N4KDg6FWq9GuXTtYW1sriV4i0i2rVq3CpEmTsGbNGjRs2BDffPMNvv76a6xbtw4DBgxAQUEBKlSooFwDVqxYgT179uDJkyf473//y5c3Ou7evXsICQnBiRMn0KdPH2XknmaiY05Qpx1M3BIRlXGaBN2BAwdw8+ZN7Nu3D506dQLwx032q6++wtmzZzF16lQWkS9HNOdGYmIinj17hgsXLiA+Pp4lEoiIypn8/Hy0bdsWN2/exP3795VRNzNnzkRoaCjeeecdPH78GPn5+UhISECdOnX4gE6kY2JjY9G7d29s374dPj4+AIBTp07BxcUF4eHhmDJlirJt8Vq2jx8/RqVKlaBSqVjjthzQJG/PnTsHDw8PBAcHazukco+vUYmIyjjN0BYvLy84OTnh3LlzyjrNcMcJEybg66+/ZtK2nNGUTXB0dMSDBw+QkJDApC0RUTlkaGiIpUuXwtDQEIMGDQIAhIaGYsmSJdi0aRMOHTqklNIZPXo0CgoKmLQl0iHPnj3Dvn37ULt2bdy+fVtZHhYWBgA4ffo0/P39ER4ejocPHyIvL0/ZpnLlylCpVBARJm3LATs7OwQGBqJOnTo4duwYMjMztR1Succet0REOuJVk1Jpet1S+ZWeno6ioiLY2NhoOxQiItKiEydOwNvbG1ZWVsjKysKGDRvw3nvvKev9/Pxw69YtHDlyRItREtHrcO/ePYSGhuLEiRPo378/4uPjce3aNUydOhUODg5Yv349Lly4gLt378LU1BQrV66Eu7u7tsMmLbl//z4A8PnhDcDELRGRDtEkbzm0hYiIqHz6c31azdBmTemDU6dOoX///rCxscHhw4dhZGSkrBs+fDjy8/MRFRWFChUqsNctkY7RPCvs3LkTWVlZuHDhAqpXrw7gj2tHTEwMbt26henTp7PzB9EbgIlbIiIdk5qaioCAAKSkpGDTpk2wsrLSdkhERERUiq5evYr169dj9OjRqFGjhpKA1SRoT548CW9vb3h4eGDNmjWoVKkSZs6ciSVLliA+Pp6llYh02P379xESEoL4+HgMGDAAU6dOBfC8Fram/rUGa9oSaR8Tt0REOohDW4iIiMqngoICuLq64vTp03B0dISvry9atWqFfv36ldjuxIkT6NKlC3x8fGBnZ4fFixfj6NGjaN68uZYiJ6LS8qoSa0zUEr15mLglIiIiIiLSIeHh4TAwMECDBg0QHx+PJUuWoFu3bmjTpg3Gjh2r9MA9fvw4OnTogIKCApw5cwZNmzbVcuREVFpSU1MREhKCM2fOwN3dHXPnztV2SET0EkzcEhERERER6ZDDhw/D19cXBw8eRIsWLXDv3j18/fXXCAsLQ8OGDTFixAi4ubmhbt26SExMhJmZGRwcHLQdNhGVstTUVEybNg3GxsZYtWoV61oTvYGYuCUiIiIiItIxn332Ge7du4fVq1fD2NgYAwYMQGJiIlxcXHD79m0kJCRg/vz5mDJlirZDJSItevDgAd566y3o6ekpdbCJ6M3BKQKJiIiIiIh0jIuLCyIjI2FoaIiRI0fi8OHDOHjwIJydnXHt2jXs27cPHh4e2g6TiLTM0tISAFBUVAQ9PT0tR0NEf8Yet0RERERERDqoQ4cOOHr0KGxtbbF79240btxY2yERERHR38DXKURERERERDpE0zfH398fjo6OWLZsGRo3bgz22SEiIipbmLglIiIiIiLSIZoalc2bN0dRURHOnDlTYjkRERGVDUzcEhERERER6SAbGxvMnDkTCxcuxMmTJ7UdDhEREf1NTNwSERERERHpKHd3d7Rs2RLVqlXTdihERET0N3FyMiIiIiIiIh2Wl5cHY2NjbYdBREREfxMTt0RERERERERERERvGJZKICIiIiIiIiIiInrDMHFLRERERERERERE9IZh4paIiIiIiIiIiIjoDcPELREREREREREREdEbholbIiIiIiIiIiIiojcME7dEREREREREREREbxgmbomIiIiIiIiIiIjeMEzcEhEREZFOGjp0KFQqlfKflZUVvL29ceHChf/zPmbNmoUmTZq8viCJiIiIiF6BiVsiIiIi0lne3t64d+8e7t27h4MHD8LAwADdu3fXdlhERERERP8TE7dEREREpLOMjIxga2sLW1tbNGnSBJ9//jmSk5ORnp4OAPD398e7774LExMT1K5dGzNmzEBBQQEAIDo6GsHBwUhMTFR67UZHR0NEMGvWLNSoUQNGRkaoVq0aJk2apPydtWrVwty5czFkyBCYmZmhZs2a2LFjB9LT0+Hr6wszMzM0atQIp0+fVj6TmZmJDz74ANWrV4eJiQkaNmyIjRs3lvhdOnbsiEmTJmHatGmwtLSEra0tZs2a9fr/EYmIiIhIK5i4JSIiIqJy4enTp4iJiYGjoyOsrKwAAObm5oiOjsbly5exePFifPPNN1i4cCEAoH///pgyZQqcnZ2VXrv9+/fHli1bsHDhQqxatQpJSUmIjY1Fw4YNS/xdCxcuhKurK86dO4du3bph8ODBGDJkCAYNGoSzZ8/CwcEBQ4YMgYgAAPLy8tC8eXPs2rULly5dwujRozF48GCcPHmyxH6//fZbmJqa4sSJEwgLC8Ps2bNx4MCBUvjXIyIiIqLSphJNa5GIiIiISIcMHToUMTExMDY2BgBkZ2fDzs4OO3fuRLNmzV76mYiICGzatEnpDTtr1izExsbi/PnzyjaRkZFYtWoVLl26hAoVKrywj1q1aqF9+/ZYv349ACA1NRV2dnaYMWMGZs+eDQA4fvw42rRpg3v37sHW1valsXTv3h316tVDREQEgOc9btVqNY4cOaJs06pVK3Tq1AkLFiz4m/86RERERPSmY49bIiIiItJZ7u7uOH/+PM6fP4+TJ0/Cy8sLXbp0wa+//goA2Lx5M1xdXWFrawszMzMEBQXh7t27f7nPfv36ITc3F7Vr18aoUaOwbds2FBYWltimUaNGyp9tbGwAoESvXM2ytLQ0AIBarcacOXPQsGFDWFpawszMDPv27XshluL7BQA7OztlH0RERESkW5i4JSIiIiKdZWpqCkdHRzg6OqJly5ZYvXo1srOz8c033yAhIQEffvghunbtip07d+LcuXMIDAxEfn7+X+7T3t4e165dw/Lly1GxYkWMGzcObm5uSm1cACV64qpUqlcuKyoqAgCEh4dj8eLF8Pf3x6FDh3D+/Hl4eXm9EMufe/iqVCplH0RERESkWwy0HQARERERUWlRqVTQ09NDbm4ujh07hpo1ayIwMFBZr+mJq2FoaAi1Wv3CfipWrAgfHx/4+Phg/PjxqFevHi5evPjKEgz/S3x8PHx9fTFo0CAAzxO6169fh5OT0z/aHxERERGVfUzcEhEREZHOevbsGVJTUwEADx8+xFdffYWnT5/Cx8cHWVlZuHv3LjZt2oSWLVti165d2LZtW4nP16pVC7dv38b58+fx9ttvw9zcHBs3boRarYaLiwtMTEwQExODihUrombNmv84zjp16uCHH37AsWPHYGFhgcjISNy/f5+JWyIiIqJyjIlbIiIiItJZe/fuhZ2dHQDA3Nwc9erVw3/+8x907NgRADB58mRMmDABz549Q7du3TBjxgzMmjVL+XyfPn2wdetWuLu749GjR1i7di3eeustLFiwAJ9++inUajUaNmyIH3/8EVZWVv84zqCgINy6dQteXl4wMTHB6NGj0bNnTzx+/Pj/59cnIiIiojJMJSKi7SCIiIiIiIiIiIiI6A+cnIyIiIiIiIiIiIjoDcPELREREREREREREdEbholbIiIiIiIiIiIiojcME7dEREREREREREREbxgmbomIiIiIiIiIiIjeMEzcEhEREREREREREb1hmLglIiIiIiIiIiIiesMwcUtERERERERERET0hmHiloiIiIiIiIiIiOgNw8QtERERERERERER0RuGiVsiIiIiIiIiIiKiNwwTt0RERERERERERERvmP8HVtyGdXFl4KAAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"code","source":["#Top 10 Wicket Takers In IPL\n","# Plot Top 10 Wicket Takers\n","plt.figure(figsize=(14, 6))\n","sns.barplot(x=top_bowlers.index, y=top_bowlers.values)\n","plt.title(\"🎯 Top 10 Wicket Takers in IPL\")\n","plt.ylabel(\"Total Wickets\")\n","plt.xlabel(\"Bowler\")\n","plt.xticks(rotation=45)\n","plt.tight_layout()\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":566},"id":"DiHciAa1hDAJ","executionInfo":{"status":"ok","timestamp":1744090632166,"user_tz":-330,"elapsed":777,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}},"outputId":"b779a85b-149c-4cb2-c526-839b71de88d0"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-9-5994e1a4ea8a>:9: UserWarning: Glyph 127919 (\\N{DIRECT HIT}) missing from font(s) DejaVu Sans.\n","  plt.tight_layout()\n","/usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 127919 (\\N{DIRECT HIT}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1400x600 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":["# Calculate average runs per over per match\n","runs_per_over = (\n","    delivery_df.groupby(['match_id', 'over'])['total_runs']\n","    .sum()\n","    .reset_index()\n",")\n","\n","# Now calculate the average across all matches per over\n","avg_runs_per_over = runs_per_over.groupby('over')['total_runs'].mean().reset_index()\n","avg_runs_per_over.rename(columns={'total_runs': 'average_runs'}, inplace=True)\n","\n","# Calculate total wickets per over per match\n","wickets_per_ball = delivery_df[delivery_df['is_wicket'] == 1]\n","wickets_per_over = (\n","    wickets_per_ball.groupby(['match_id', 'over'])['is_wicket']\n","    .sum()\n","    .reset_index()\n",")\n","\n","# Average wickets per over\n","avg_wickets_per_over = wickets_per_over.groupby('over')['is_wicket'].mean().reset_index()\n","avg_wickets_per_over.rename(columns={'is_wicket': 'average_wickets'}, inplace=True)\n","\n","# Plot corrected average runs per over\n","plt.figure(figsize=(12, 6))\n","sns.lineplot(data=avg_runs_per_over, x='over', y='average_runs', marker='o')\n","plt.title(\"📈 Corrected: Average Runs per Over in IPL\")\n","plt.ylabel(\"Average Runs\")\n","plt.xlabel(\"Over\")\n","plt.xticks(range(1, 21))\n","plt.grid(True)\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":643},"id":"TQIzLE3PhC8D","executionInfo":{"status":"ok","timestamp":1744090633015,"user_tz":-330,"elapsed":828,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}},"outputId":"12d6fb4e-fb07-4ffd-a72f-6e0d9a9cff0c"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-10-634ca0d2d096>:32: UserWarning: Glyph 128200 (\\N{CHART WITH UPWARDS TREND}) missing from font(s) DejaVu Sans.\n","  plt.tight_layout()\n","/usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128200 (\\N{CHART WITH UPWARDS TREND}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 with 1 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Plot corrected average wickets per over\n","plt.figure(figsize=(12, 6))\n","sns.lineplot(data=avg_wickets_per_over, x='over', y='average_wickets', marker='o', color='darkred')\n","plt.title(\"💥 Corrected: Average Wickets per Over in IPL\")\n","plt.ylabel(\"Average Wickets\")\n","plt.xlabel(\"Over\")\n","plt.xticks(range(1, 21))\n","plt.grid(True)\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":643},"id":"BVYjfZ6_hC5l","executionInfo":{"status":"ok","timestamp":1744090633643,"user_tz":-330,"elapsed":609,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}},"outputId":"16f086f1-f58e-4c06-b5ea-5755c212b089"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-11-5319e6aa7dd2>:9: UserWarning: Glyph 128165 (\\N{COLLISION SYMBOL}) missing from font(s) DejaVu Sans.\n","  plt.tight_layout()\n","/usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128165 (\\N{COLLISION SYMBOL}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split\n","from sklearn.preprocessing import LabelEncoder\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n","import xgboost as xgb\n","from sklearn.preprocessing import StandardScaler\n","\n","\n","# Prepare match data for ML\n","ml_df = matches_df.copy()\n","\n","# Drop rows with missing 'winner'\n","ml_df = ml_df.dropna(subset=['winner'])\n","\n","# Select features and target\n","features = ['team1', 'team2', 'toss_winner', 'toss_decision', 'venue']\n","target = 'winner'\n","\n","# Encode categorical variables\n","encoder = LabelEncoder()\n","for col in features + [target]:\n","    ml_df[col] = encoder.fit_transform(ml_df[col])\n","\n","X = ml_df[features]\n","y = ml_df[target]\n","\n","# Then re-split X_scaled into train/test and retrain logistic regression\n","scaler = StandardScaler()\n","X_scaled = scaler.fit_transform(X)\n","X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n","\n","# Train models\n","log_model = LogisticRegression(max_iter=1000)\n","rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n","xgb_model = xgb.XGBClassifier(eval_metric='mlogloss')\n","\n","log_model.fit(X_train, y_train)\n","rf_model.fit(X_train, y_train)\n","xgb_model.fit(X_train, y_train)\n","\n","# Predict\n","log_preds = log_model.predict(X_test)\n","rf_preds = rf_model.predict(X_test)\n","xgb_preds = xgb_model.predict(X_test)\n","\n","# Evaluate\n","log_acc = accuracy_score(y_test, log_preds)\n","rf_acc = accuracy_score(y_test, rf_preds)\n","xgb_acc = accuracy_score(y_test, xgb_preds)\n","\n","(log_acc, rf_acc, xgb_acc)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jNfx_bGdhC3C","executionInfo":{"status":"ok","timestamp":1744090642574,"user_tz":-330,"elapsed":8915,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}},"outputId":"a37a0dca-7ee0-4987-bc7a-67300413a640"},"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(0.24311926605504589, 0.47706422018348627, 0.5137614678899083)"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","\n","# Model names and accuracies\n","models = ['Logistic Regression', 'Random Forest', 'XGBoost']\n","accuracies = [24.31, 47.71, 51.38]\n","\n","# Plotting\n","plt.figure(figsize=(10, 6))\n","sns.barplot(x=models, y=accuracies)\n","plt.title(\"📊 Model Accuracy Comparison\")\n","plt.ylabel(\"Accuracy (%)\")\n","plt.ylim(0, 60)\n","for i, acc in enumerate(accuracies):\n","    plt.text(i, acc + 1, f\"{acc:.2f}%\", ha='center', fontsize=12, fontweight='bold')\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":700},"id":"xr3_ZU16hCwu","executionInfo":{"status":"ok","timestamp":1744090643417,"user_tz":-330,"elapsed":821,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}},"outputId":"9dd19fe9-bb3f-4d0f-c45c-727b1abcea07"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-13-34e3b8887b93>:15: UserWarning: Glyph 128202 (\\N{BAR CHART}) missing from font(s) DejaVu Sans.\n","  plt.tight_layout()\n","/usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128202 (\\N{BAR CHART}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# Get feature importances from the trained Random Forest model\n","feature_importance = pd.Series(rf_model.feature_importances_, index=X.columns).sort_values(ascending=False)\n","\n","# Plot feature importance\n","plt.figure(figsize=(10, 6))\n","sns.barplot(x=feature_importance.values, y=feature_importance.index)\n","plt.title(\"🔍 Feature Importance (Random Forest)\")\n","plt.xlabel(\"Importance Score\")\n","plt.ylabel(\"Feature\")\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":700},"id":"eklmDrmGhCtQ","executionInfo":{"status":"ok","timestamp":1744090644578,"user_tz":-330,"elapsed":1158,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}},"outputId":"05a4a656-e1a8-4ebb-ebb9-ff1af6e8d20f"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-14-9083779d5b1c>:10: UserWarning: Glyph 128269 (\\N{LEFT-POINTING MAGNIFYING GLASS}) missing from font(s) DejaVu Sans.\n","  plt.tight_layout()\n","/usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128269 (\\N{LEFT-POINTING MAGNIFYING GLASS}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# Drop matches without a winner\n","matches_df = matches_df.dropna(subset=['winner']).copy()\n","\n","# Create new feature: 'home_team' — if team1 or team2 matches the city (approximation)\n","def get_home_team(row):\n","    city = str(row['city']).lower()\n","    team1 = str(row['team1']).lower()\n","    team2 = str(row['team2']).lower()\n","\n","    if city in team1:\n","        return row['team1']\n","    elif city in team2:\n","        return row['team2']\n","    else:\n","        return 'Neutral'\n","\n","matches_df['home_team'] = matches_df.apply(get_home_team, axis=1)\n","\n","# Create new feature: 'head_to_head_count' — total matches played between team1 and team2 so far\n","head_to_head = {}\n","head_to_head_list = []\n","\n","for i, row in matches_df.iterrows():\n","    pair = tuple(sorted([row['team1'], row['team2']]))\n","    count = head_to_head.get(pair, 0)\n","    head_to_head_list.append(count)\n","    head_to_head[pair] = count + 1\n","\n","matches_df['head_to_head_count'] = head_to_head_list\n","\n","# Show sample with new features\n","matches_df[['team1', 'team2', 'city', 'home_team', 'head_to_head_count']].head(10)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":363},"id":"LLx5xSTjvH_W","executionInfo":{"status":"ok","timestamp":1744090644989,"user_tz":-330,"elapsed":397,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}},"outputId":"858fe482-c90e-4954-da8a-b88eea28770b"},"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                         team1                        team2        city  \\\n","0  Royal Challengers Bangalore        Kolkata Knight Riders   Bangalore   \n","1              Kings XI Punjab          Chennai Super Kings  Chandigarh   \n","2             Delhi Daredevils             Rajasthan Royals       Delhi   \n","3               Mumbai Indians  Royal Challengers Bangalore      Mumbai   \n","4        Kolkata Knight Riders              Deccan Chargers     Kolkata   \n","5             Rajasthan Royals              Kings XI Punjab      Jaipur   \n","6              Deccan Chargers             Delhi Daredevils   Hyderabad   \n","7          Chennai Super Kings               Mumbai Indians     Chennai   \n","8              Deccan Chargers             Rajasthan Royals   Hyderabad   \n","9              Kings XI Punjab               Mumbai Indians  Chandigarh   \n","\n","                     home_team  head_to_head_count  \n","0  Royal Challengers Bangalore                   0  \n","1                      Neutral                   0  \n","2             Delhi Daredevils                   0  \n","3               Mumbai Indians                   0  \n","4        Kolkata Knight Riders                   0  \n","5                      Neutral                   0  \n","6                      Neutral                   0  \n","7          Chennai Super Kings                   0  \n","8                      Neutral                   0  \n","9                      Neutral                   0  "],"text/html":["\n","  <div id=\"df-9e43ff24-bc04-417a-a83a-6f134007fb72\" class=\"colab-df-container\">\n","    <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>team1</th>\n","      <th>team2</th>\n","      <th>city</th>\n","      <th>home_team</th>\n","      <th>head_to_head_count</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Royal Challengers Bangalore</td>\n","      <td>Kolkata Knight Riders</td>\n","      <td>Bangalore</td>\n","      <td>Royal Challengers Bangalore</td>\n","      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<td>Neutral</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Deccan Chargers</td>\n","      <td>Delhi Daredevils</td>\n","      <td>Hyderabad</td>\n","      <td>Neutral</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Chennai Super Kings</td>\n","      <td>Mumbai Indians</td>\n","      <td>Chennai</td>\n","      <td>Chennai Super Kings</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Deccan Chargers</td>\n","      <td>Rajasthan Royals</td>\n","      <td>Hyderabad</td>\n","      <td>Neutral</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Kings XI Punjab</td>\n","      <td>Mumbai Indians</td>\n","      <td>Chandigarh</td>\n","      <td>Neutral</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button 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Simarjeet Singh   \n","1085  1426300   2024   Bengaluru 2024-05-12       League          C Green   \n","1086  1426302   2024       Delhi 2024-05-14       League         I Sharma   \n","1087  1426303   2024    Guwahati 2024-05-15       League        SM Curran   \n","1088  1426305   2024      Mumbai 2024-05-17       League         N Pooran   \n","1089  1426306   2024   Bengaluru 2024-05-18       League     F du Plessis   \n","1090  1426307   2024   Hyderabad 2024-05-19       League  Abhishek Sharma   \n","1091  1426309   2024   Ahmedabad 2024-05-21  Qualifier 1         MA Starc   \n","1092  1426310   2024   Ahmedabad 2024-05-22   Eliminator         R Ashwin   \n","1093  1426311   2024     Chennai 2024-05-24  Qualifier 2    Shahbaz Ahmed   \n","1094  1426312   2024     Chennai 2024-05-26        Final         MA Starc   \n","\n","                                                  venue  \\\n","1080  Rajiv Gandhi International Stadium, Uppal, Hyd...   \n","1081  Himachal Pradesh Cricket Association Stadium, ...   \n","1082                   Narendra Modi Stadium, Ahmedabad   \n","1083                              Eden Gardens, Kolkata   \n","1084           MA Chidambaram Stadium, Chepauk, Chennai   \n","1085                   M Chinnaswamy Stadium, Bengaluru   \n","1086                        Arun Jaitley Stadium, Delhi   \n","1087                Barsapara Cricket Stadium, Guwahati   \n","1088                           Wankhede Stadium, Mumbai   \n","1089                   M Chinnaswamy Stadium, Bengaluru   \n","1090  Rajiv Gandhi International Stadium, Uppal, Hyd...   \n","1091                   Narendra Modi Stadium, Ahmedabad   \n","1092                   Narendra Modi Stadium, Ahmedabad   \n","1093           MA Chidambaram Stadium, Chepauk, Chennai   \n","1094           MA Chidambaram Stadium, Chepauk, Chennai   \n","\n","                            team1                  team2  \\\n","1080         Lucknow Super Giants    Sunrisers Hyderabad   \n","1081  Royal Challengers Bengaluru           Punjab Kings   \n","1082               Gujarat Titans    Chennai Super Kings   \n","1083        Kolkata Knight Riders         Mumbai Indians   \n","1084             Rajasthan Royals    Chennai Super Kings   \n","1085  Royal Challengers Bengaluru         Delhi Capitals   \n","1086               Delhi Capitals   Lucknow Super Giants   \n","1087             Rajasthan Royals           Punjab Kings   \n","1088         Lucknow Super Giants         Mumbai Indians   \n","1089  Royal Challengers Bengaluru    Chennai Super Kings   \n","1090                 Punjab Kings    Sunrisers Hyderabad   \n","1091          Sunrisers Hyderabad  Kolkata Knight Riders   \n","1092  Royal Challengers Bengaluru       Rajasthan Royals   \n","1093          Sunrisers Hyderabad       Rajasthan Royals   \n","1094          Sunrisers Hyderabad  Kolkata Knight Riders   \n","\n","               toss_winner  ... result_margin target_runs target_overs  \\\n","1080  Lucknow Super Giants  ...  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Royals  ...          36.0       176.0         20.0   \n","1094   Sunrisers Hyderabad  ...           8.0       114.0         20.0   \n","\n","      super_over  method                umpire1                umpire2  year  \\\n","1080           N     NaN   MV Saidharshan Kumar               YC Barde  2024   \n","1081           N     NaN            Nitin Menon             HAS Khalid  2024   \n","1082           N     NaN  KN Ananthapadmanabhan          NA Patwardhan  2024   \n","1083           N     NaN              UV Gandhe           Vinod Seshan  2024   \n","1084           N     NaN               R Pandit               YC Barde  2024   \n","1085           N     NaN         A Nand Kishore            VA Kulkarni  2024   \n","1086           N     NaN                A Totre           Vinod Seshan  2024   \n","1087           N     NaN               R Pandit   MV Saidharshan Kumar  2024   \n","1088           N     NaN          Navdeep Singh               R Pandit  2024   \n","1089           N   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<th>id</th>\n","      <th>season</th>\n","      <th>city</th>\n","      <th>date</th>\n","      <th>match_type</th>\n","      <th>player_of_match</th>\n","      <th>venue</th>\n","      <th>team1</th>\n","      <th>team2</th>\n","      <th>toss_winner</th>\n","      <th>...</th>\n","      <th>result_margin</th>\n","      <th>target_runs</th>\n","      <th>target_overs</th>\n","      <th>super_over</th>\n","      <th>method</th>\n","      <th>umpire1</th>\n","      <th>umpire2</th>\n","      <th>year</th>\n","      <th>home_team</th>\n","      <th>head_to_head_count</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1080</th>\n","      <td>1426295</td>\n","      <td>2024</td>\n","      <td>Hyderabad</td>\n","      <td>2024-05-08</td>\n","      <td>League</td>\n","      <td>TM Head</td>\n","      <td>Rajiv Gandhi International Stadium, Uppal, Hyd...</td>\n","      <td>Lucknow Super Giants</td>\n","      <td>Sunrisers Hyderabad</td>\n","      <td>Lucknow Super 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<tr>\n","      <th>1082</th>\n","      <td>1426297</td>\n","      <td>2024</td>\n","      <td>Ahmedabad</td>\n","      <td>2024-05-10</td>\n","      <td>League</td>\n","      <td>Shubman Gill</td>\n","      <td>Narendra Modi Stadium, Ahmedabad</td>\n","      <td>Gujarat Titans</td>\n","      <td>Chennai Super Kings</td>\n","      <td>Chennai Super Kings</td>\n","      <td>...</td>\n","      <td>35.0</td>\n","      <td>232.0</td>\n","      <td>20.0</td>\n","      <td>N</td>\n","      <td>NaN</td>\n","      <td>KN Ananthapadmanabhan</td>\n","      <td>NA Patwardhan</td>\n","      <td>2024</td>\n","      <td>Neutral</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>1083</th>\n","      <td>1426298</td>\n","      <td>2024</td>\n","      <td>Kolkata</td>\n","      <td>2024-05-11</td>\n","      <td>League</td>\n","      <td>CV Varun</td>\n","      <td>Eden Gardens, Kolkata</td>\n","      <td>Kolkata Knight Riders</td>\n","      <td>Mumbai Indians</td>\n","      <td>Mumbai Indians</td>\n","      <td>...</td>\n","      <td>18.0</td>\n","      <td>158.0</td>\n","      <td>16.0</td>\n","      <td>N</td>\n","      <td>NaN</td>\n","      <td>UV Gandhe</td>\n","      <td>Vinod Seshan</td>\n","      <td>2024</td>\n","      <td>Kolkata Knight Riders</td>\n","      <td>33</td>\n","    </tr>\n","    <tr>\n","      <th>1084</th>\n","      <td>1426299</td>\n","      <td>2024</td>\n","      <td>Chennai</td>\n","      <td>2024-05-12</td>\n","      <td>League</td>\n","      <td>Simarjeet Singh</td>\n","      <td>MA Chidambaram Stadium, Chepauk, Chennai</td>\n","      <td>Rajasthan Royals</td>\n","      <td>Chennai Super Kings</td>\n","      <td>Rajasthan Royals</td>\n","      <td>...</td>\n","      <td>5.0</td>\n","      <td>142.0</td>\n","      <td>20.0</td>\n","      <td>N</td>\n","      <td>NaN</td>\n","      <td>R Pandit</td>\n","      <td>YC Barde</td>\n","      <td>2024</td>\n","      <td>Chennai Super Kings</td>\n","      <td>28</td>\n","    </tr>\n","    <tr>\n","      <th>1085</th>\n","      <td>1426300</td>\n","      <td>2024</td>\n","      <td>Bengaluru</td>\n","      <td>2024-05-12</td>\n","      <td>League</td>\n","      <td>C Green</td>\n","      <td>M Chinnaswamy Stadium, Bengaluru</td>\n","      <td>Royal Challengers Bengaluru</td>\n","      <td>Delhi Capitals</td>\n","      <td>Delhi Capitals</td>\n","      <td>...</td>\n","      <td>47.0</td>\n","      <td>188.0</td>\n","      <td>20.0</td>\n","      <td>N</td>\n","      <td>NaN</td>\n","      <td>A Nand Kishore</td>\n","      <td>VA Kulkarni</td>\n","      <td>2024</td>\n","      <td>Royal Challengers Bengaluru</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1086</th>\n","      <td>1426302</td>\n","      <td>2024</td>\n","      <td>Delhi</td>\n","      <td>2024-05-14</td>\n","      <td>League</td>\n","      <td>I Sharma</td>\n","      <td>Arun Jaitley Stadium, Delhi</td>\n","      <td>Delhi Capitals</td>\n","      <td>Lucknow Super Giants</td>\n","      <td>Lucknow Super Giants</td>\n","      <td>...</td>\n","      <td>19.0</td>\n","      <td>209.0</td>\n","      <td>20.0</td>\n","      <td>N</td>\n","      <td>NaN</td>\n","      <td>A Totre</td>\n","      <td>Vinod Seshan</td>\n","      <td>2024</td>\n","      <td>Delhi Capitals</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>1087</th>\n","      <td>1426303</td>\n","      <td>2024</td>\n","      <td>Guwahati</td>\n","      <td>2024-05-15</td>\n","      <td>League</td>\n","      <td>SM Curran</td>\n","      <td>Barsapara Cricket Stadium, Guwahati</td>\n","      <td>Rajasthan Royals</td>\n","      <td>Punjab Kings</td>\n","      <td>Rajasthan Royals</td>\n","      <td>...</td>\n","      <td>5.0</td>\n","      <td>145.0</td>\n","      <td>20.0</td>\n","      <td>N</td>\n","      <td>NaN</td>\n","      <td>R Pandit</td>\n","      <td>MV Saidharshan Kumar</td>\n","      <td>2024</td>\n","      <td>Neutral</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>1088</th>\n","      <td>1426305</td>\n","      <td>2024</td>\n","      <td>Mumbai</td>\n","      <td>2024-05-17</td>\n","      <td>League</td>\n","      <td>N Pooran</td>\n","      <td>Wankhede Stadium, Mumbai</td>\n","      <td>Lucknow Super Giants</td>\n","      <td>Mumbai Indians</td>\n","      <td>Mumbai Indians</td>\n","      <td>...</td>\n","      <td>18.0</td>\n","      <td>215.0</td>\n","      <td>20.0</td>\n","      <td>N</td>\n","      <td>NaN</td>\n","      <td>Navdeep Singh</td>\n","      <td>R Pandit</td>\n","      <td>2024</td>\n","      <td>Mumbai Indians</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>1089</th>\n","      <td>1426306</td>\n","      <td>2024</td>\n","      <td>Bengaluru</td>\n","      <td>2024-05-18</td>\n","      <td>League</td>\n","      <td>F du Plessis</td>\n","      <td>M Chinnaswamy Stadium, Bengaluru</td>\n","      <td>Royal Challengers Bengaluru</td>\n","      <td>Chennai Super Kings</td>\n","      <td>Chennai Super Kings</td>\n","      <td>...</td>\n","      <td>27.0</td>\n","      <td>219.0</td>\n","      <td>20.0</td>\n","      <td>N</td>\n","      <td>NaN</td>\n","      <td>A Totre</td>\n","      <td>KN Ananthapadmanabhan</td>\n","      <td>2024</td>\n","      <td>Royal Challengers Bengaluru</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1090</th>\n","      <td>1426307</td>\n","      <td>2024</td>\n","      <td>Hyderabad</td>\n","      <td>2024-05-19</td>\n","      <td>League</td>\n","      <td>Abhishek Sharma</td>\n","      <td>Rajiv Gandhi International Stadium, Uppal, Hyd...</td>\n","      <td>Punjab Kings</td>\n","      <td>Sunrisers Hyderabad</td>\n","      <td>Punjab Kings</td>\n","      <td>...</td>\n","      <td>4.0</td>\n","      <td>215.0</td>\n","      <td>20.0</td>\n","      <td>N</td>\n","      <td>NaN</td>\n","      <td>Nitin Menon</td>\n","      <td>VK Sharma</td>\n","      <td>2024</td>\n","      <td>Sunrisers Hyderabad</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>1091</th>\n","      <td>1426309</td>\n","      <td>2024</td>\n","      <td>Ahmedabad</td>\n","      <td>2024-05-21</td>\n","      <td>Qualifier 1</td>\n","      <td>MA Starc</td>\n","      <td>Narendra Modi Stadium, Ahmedabad</td>\n","      <td>Sunrisers Hyderabad</td>\n","      <td>Kolkata Knight Riders</td>\n","      <td>Sunrisers Hyderabad</td>\n","      <td>...</td>\n","      <td>8.0</td>\n","      <td>160.0</td>\n","      <td>20.0</td>\n","      <td>N</td>\n","      <td>NaN</td>\n","      <td>AK Chaudhary</td>\n","      <td>R Pandit</td>\n","      <td>2024</td>\n","      <td>Neutral</td>\n","      <td>26</td>\n","    </tr>\n","    <tr>\n","      <th>1092</th>\n","      <td>1426310</td>\n","      <td>2024</td>\n","      <td>Ahmedabad</td>\n","      <td>2024-05-22</td>\n","      <td>Eliminator</td>\n","      <td>R Ashwin</td>\n","      <td>Narendra Modi Stadium, Ahmedabad</td>\n","      <td>Royal Challengers Bengaluru</td>\n","      <td>Rajasthan Royals</td>\n","      <td>Rajasthan Royals</td>\n","      <td>...</td>\n","      <td>4.0</td>\n","      <td>173.0</td>\n","      <td>20.0</td>\n","      <td>N</td>\n","      <td>NaN</td>\n","      <td>KN Ananthapadmanabhan</td>\n","      <td>MV Saidharshan Kumar</td>\n","      <td>2024</td>\n","      <td>Neutral</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1093</th>\n","      <td>1426311</td>\n","      <td>2024</td>\n","      <td>Chennai</td>\n","      <td>2024-05-24</td>\n","      <td>Qualifier 2</td>\n","      <td>Shahbaz Ahmed</td>\n","      <td>MA Chidambaram Stadium, Chepauk, Chennai</td>\n","      <td>Sunrisers Hyderabad</td>\n","      <td>Rajasthan Royals</td>\n","      <td>Rajasthan Royals</td>\n","      <td>...</td>\n","      <td>36.0</td>\n","      <td>176.0</td>\n","      <td>20.0</td>\n","      <td>N</td>\n","      <td>NaN</td>\n","      <td>Nitin Menon</td>\n","      <td>VK Sharma</td>\n","      <td>2024</td>\n","      <td>Neutral</td>\n","      <td>19</td>\n","    </tr>\n","    <tr>\n","      <th>1094</th>\n","      <td>1426312</td>\n","      <td>2024</td>\n","      <td>Chennai</td>\n","      <td>2024-05-26</td>\n","      <td>Final</td>\n","      <td>MA Starc</td>\n","      <td>MA Chidambaram Stadium, Chepauk, Chennai</td>\n","      <td>Sunrisers Hyderabad</td>\n","      <td>Kolkata Knight Riders</td>\n","      <td>Sunrisers Hyderabad</td>\n","      <td>...</td>\n","      <td>8.0</td>\n","      <td>114.0</td>\n","      <td>20.0</td>\n","      <td>N</td>\n","      <td>NaN</td>\n","      <td>J Madanagopal</td>\n","      <td>Nitin Menon</td>\n","      <td>2024</td>\n","      <td>Neutral</td>\n","      <td>27</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>15 rows × 23 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1f6e819c-49da-484c-97aa-16ff2e5e80c3')\"\n","            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'toss_winner', 'toss_decision', 'venue', 'home_team', 'winner']:\n","    ml_df[col] = encoder.fit_transform(ml_df[col])\n","\n","# Define X and y\n","X = ml_df[features]\n","y = ml_df[target]\n","\n","# Train-test split\n","scaler = StandardScaler()\n","X_scaled = scaler.fit_transform(X)\n","X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n","\n","# Train models\n","log_model = LogisticRegression(max_iter=1000)\n","rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n","\n","log_model.fit(X_train, y_train)\n","rf_model.fit(X_train, y_train)\n","\n","# Predictions\n","log_preds = log_model.predict(X_test)\n","rf_preds = rf_model.predict(X_test)\n","\n","# Accuracy\n","log_acc_new = accuracy_score(y_test, log_preds)\n","rf_acc_new = accuracy_score(y_test, rf_preds)\n","\n","(log_acc_new, rf_acc_new)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"t_c4lhQHvH4O","executionInfo":{"status":"ok","timestamp":1744090646027,"user_tz":-330,"elapsed":876,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}},"outputId":"b681e90f-3e7f-4258-b92d-6c613e77e1d7"},"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(0.2706422018348624, 0.47706422018348627)"]},"metadata":{},"execution_count":17}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","# Prepare data for visualization\n","model_names = ['Logistic Regression', 'Random Forest']\n","before = [24.31, 47.71]\n","after = [26.15, 47.25]\n","\n","df_compare = pd.DataFrame({\n","    'Model': model_names * 2,\n","    'Accuracy': before + after,\n","    'Version': ['Before'] * 2 + ['After'] * 2\n","})\n","\n","# Plot\n","plt.figure(figsize=(10, 6))\n","sns.barplot(data=df_compare, x='Model', y='Accuracy', hue='Version')\n","plt.title(\"📊 Accuracy Comparison Before vs After Feature Engineering\")\n","plt.ylabel(\"Accuracy (%)\")\n","plt.ylim(20, 55)\n","plt.legend(title=\"Model Version\")\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":700},"id":"PuzLccA2vH0x","executionInfo":{"status":"ok","timestamp":1744090647194,"user_tz":-330,"elapsed":1166,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}},"outputId":"1bad8344-035c-48f5-ab81-e2b65e4afddf"},"execution_count":18,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-18-dbf3d81f387b>:22: UserWarning: Glyph 128202 (\\N{BAR CHART}) missing from font(s) DejaVu Sans.\n","  plt.tight_layout()\n","/usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128202 (\\N{BAR CHART}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 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collections import defaultdict\n","import numpy as np\n","\n","# Sort matches by date to ensure form is calculated chronologically\n","matches_df = matches_df.sort_values(by='date')\n","\n","# Initialize recent form trackers\n","team_recent_form = defaultdict(list)\n","team1_form_list = []\n","team2_form_list = []\n","\n","# Iterate and calculate rolling win ratio (last 5 matches)\n","for _, row in matches_df.iterrows():\n","    team1 = row['team1']\n","    team2 = row['team2']\n","    winner = row['winner']\n","\n","    # Get recent form: average of last 5 match results (1=win, 0=loss)\n","    team1_form = np.mean(team_recent_form[team1][-5:]) if team_recent_form[team1] else 0.5\n","    team2_form = np.mean(team_recent_form[team2][-5:]) if team_recent_form[team2] else 0.5\n","\n","    team1_form_list.append(team1_form)\n","    team2_form_list.append(team2_form)\n","\n","    # Update the tracker\n","    team_recent_form[team1].append(1 if winner == team1 else 0)\n","    team_recent_form[team2].append(1 if winner == team2 else 0)\n","\n","# Add to dataframe\n","matches_df['team1_recent_form'] = team1_form_list\n","matches_df['team2_recent_form'] = team2_form_list\n","\n","# Show sample\n","matches_df[['team1', 'team2', 'winner', 'team1_recent_form', 'team2_recent_form']].head(10)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":363},"id":"7z52Kkr5vHvs","executionInfo":{"status":"ok","timestamp":1744090647545,"user_tz":-330,"elapsed":354,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}},"outputId":"0d2eeedf-dc2d-474f-b93a-b7cb89ec786b"},"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                         team1                        team2  \\\n","0  Royal Challengers Bangalore        Kolkata Knight Riders   \n","1              Kings XI Punjab          Chennai Super Kings   \n","2             Delhi Daredevils             Rajasthan Royals   \n","3               Mumbai Indians  Royal Challengers Bangalore   \n","4        Kolkata Knight Riders              Deccan Chargers   \n","5             Rajasthan Royals              Kings XI Punjab   \n","6              Deccan Chargers             Delhi Daredevils   \n","7          Chennai Super Kings               Mumbai Indians   \n","8              Deccan Chargers             Rajasthan Royals   \n","9              Kings XI Punjab               Mumbai Indians   \n","\n","                        winner  team1_recent_form  team2_recent_form  \n","0        Kolkata Knight Riders                0.5                0.5  \n","1          Chennai Super Kings                0.5                0.5  \n","2             Delhi Daredevils                0.5                0.5  \n","3  Royal Challengers Bangalore                0.5                0.0  \n","4        Kolkata Knight Riders                1.0                0.5  \n","5             Rajasthan Royals                0.0                0.0  \n","6             Delhi Daredevils                0.0                1.0  \n","7          Chennai Super Kings                1.0                0.0  \n","8             Rajasthan Royals                0.0                0.5  \n","9              Kings XI Punjab                0.0                0.0  "],"text/html":["\n","  <div id=\"df-3b60d6a8-a16d-4994-b708-210908082a83\" class=\"colab-df-container\">\n","    <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>team1</th>\n","      <th>team2</th>\n","      <th>winner</th>\n","      <th>team1_recent_form</th>\n","      <th>team2_recent_form</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Royal Challengers Bangalore</td>\n","      <td>Kolkata Knight Riders</td>\n","      <td>Kolkata Knight Riders</td>\n","      <td>0.5</td>\n","      <td>0.5</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Kings XI Punjab</td>\n","      <td>Chennai Super Kings</td>\n","      <td>Chennai Super Kings</td>\n","      <td>0.5</td>\n","      <td>0.5</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Delhi Daredevils</td>\n","      <td>Rajasthan Royals</td>\n","      <td>Delhi Daredevils</td>\n","      <td>0.5</td>\n","      <td>0.5</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Mumbai Indians</td>\n","      <td>Royal Challengers Bangalore</td>\n","      <td>Royal Challengers Bangalore</td>\n","      <td>0.5</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Kolkata Knight Riders</td>\n","      <td>Deccan Chargers</td>\n","      <td>Kolkata Knight 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<td>Kings XI Punjab</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3b60d6a8-a16d-4994-b708-210908082a83')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","  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\"num_unique_values\": 3,\n        \"samples\": [\n          0.5,\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":19}]},{"cell_type":"code","source":["# Calculate home win ratio per team dynamically over time\n","from collections import defaultdict\n","\n","# Reset the dataframe index for consistency\n","matches_df = matches_df.sort_values(by='date').reset_index(drop=True)\n","\n","# Tracker for home games and home wins\n","home_game_tracker = defaultdict(int)\n","home_win_tracker = defaultdict(int)\n","home_win_percent_list = []\n","\n","# Iterate through matches and compute home win % before each match\n","for _, row in matches_df.iterrows():\n","    home_team = row['home_team']\n","    winner = row['winner']\n","\n","    if home_team != 'Neutral':\n","        # Calculate win percentage before this match\n","        total_home_games = home_game_tracker[home_team]\n","        total_home_wins = home_win_tracker[home_team]\n","\n","        win_percent = (total_home_wins / total_home_games) if total_home_games > 0 else 0.5\n","        home_win_percent_list.append(win_percent)\n","\n","        # Update trackers after current match\n","        home_game_tracker[home_team] += 1\n","        if home_team == winner:\n","            home_win_tracker[home_team] += 1\n","    else:\n","        # For neutral venues, set to neutral value\n","        home_win_percent_list.append(0.5)\n","\n","# Add to DataFrame\n","matches_df['home_win_percent'] = home_win_percent_list\n","\n","# Show sample\n","matches_df[['team1', 'team2', 'home_team', 'winner', 'home_win_percent']].head(10)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":363},"id":"2maOKcaSxycH","executionInfo":{"status":"ok","timestamp":1744090647814,"user_tz":-330,"elapsed":271,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}},"outputId":"fb776ffd-180b-4f01-bd1d-31b940eacf37"},"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                         team1                        team2  \\\n","0  Royal Challengers Bangalore        Kolkata Knight Riders   \n","1              Kings XI Punjab          Chennai Super Kings   \n","2             Delhi Daredevils             Rajasthan Royals   \n","3               Mumbai Indians  Royal Challengers Bangalore   \n","4        Kolkata Knight Riders              Deccan Chargers   \n","5             Rajasthan Royals              Kings XI Punjab   \n","6              Deccan Chargers             Delhi Daredevils   \n","7          Chennai Super Kings               Mumbai Indians   \n","8              Deccan Chargers             Rajasthan Royals   \n","9              Kings XI Punjab               Mumbai Indians   \n","\n","                     home_team                       winner  home_win_percent  \n","0  Royal Challengers Bangalore        Kolkata Knight Riders               0.5  \n","1                      Neutral          Chennai Super Kings               0.5  \n","2             Delhi Daredevils             Delhi Daredevils               0.5  \n","3               Mumbai Indians  Royal Challengers Bangalore               0.5  \n","4        Kolkata Knight Riders        Kolkata Knight Riders               0.5  \n","5                      Neutral             Rajasthan Royals               0.5  \n","6                      Neutral             Delhi Daredevils               0.5  \n","7          Chennai Super Kings          Chennai Super Kings               0.5  \n","8                      Neutral             Rajasthan Royals               0.5  \n","9                      Neutral              Kings XI Punjab               0.5  "],"text/html":["\n","  <div id=\"df-a2cf0a45-0436-4b96-9b10-0fc181a79f3e\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe 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\"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["# Prepare title defense indicator based on previous season's winner\n","\n","# Convert season to numeric (first extract year from \"2007/08\", etc.)\n","matches_df['season_year'] = matches_df['season'].str.extract(r'(\\d{4})').astype(int)\n","\n","# Create mapping of year -> previous year winner\n","season_winner_map = {}\n","\n","# Sort matches to get one winner per season\n","season_sorted = matches_df.sort_values(by='date')\n","for season in sorted(matches_df['season_year'].unique()):\n","    winner = season_sorted[season_sorted['season_year'] == season]['winner'].mode()\n","    if not winner.empty:\n","        season_winner_map[season + 1] = winner.values[0]  # next year's defending champion\n","\n","# Add title defense indicator for team1 and team2\n","matches_df['team1_title_defender'] = matches_df.apply(\n","    lambda row: 1 if row['team1'] == season_winner_map.get(row['season_year'], '') else 0,\n","    axis=1\n",")\n","matches_df['team2_title_defender'] = matches_df.apply(\n","    lambda row: 1 if row['team2'] == season_winner_map.get(row['season_year'], '') else 0,\n","    axis=1\n",")\n","\n","# Show sample\n","matches_df[['season', 'season_year', 'team1', 'team2', 'team1_title_defender', 'team2_title_defender']].head(12)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":426},"id":"L9RhMpkYxyYu","executionInfo":{"status":"ok","timestamp":1744090647984,"user_tz":-330,"elapsed":168,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}},"outputId":"82661ed7-a4da-4551-f97f-d64fd63bd810"},"execution_count":21,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     season  season_year                        team1  \\\n","0   2007/08         2007  Royal Challengers Bangalore   \n","1   2007/08         2007              Kings XI Punjab   \n","2   2007/08         2007             Delhi Daredevils   \n","3   2007/08         2007               Mumbai Indians   \n","4   2007/08         2007        Kolkata Knight Riders   \n","5   2007/08         2007             Rajasthan Royals   \n","6   2007/08         2007              Deccan Chargers   \n","7   2007/08         2007          Chennai Super Kings   \n","8   2007/08         2007              Deccan Chargers   \n","9   2007/08         2007              Kings XI Punjab   \n","10  2007/08         2007  Royal Challengers Bangalore   \n","11  2007/08         2007          Chennai Super Kings   \n","\n","                          team2  team1_title_defender  team2_title_defender  \n","0         Kolkata Knight Riders                     0                     0  \n","1           Chennai Super Kings                     0                     0  \n","2              Rajasthan Royals                     0                     0  \n","3   Royal Challengers Bangalore                     0                     0  \n","4               Deccan Chargers                     0                     0  \n","5               Kings XI Punjab                     0                     0  \n","6              Delhi Daredevils                     0                     0  \n","7                Mumbai Indians                     0                     0  \n","8              Rajasthan Royals                     0                     0  \n","9                Mumbai Indians                     0                     0  \n","10             Rajasthan Royals                     0                     0  \n","11        Kolkata Knight Riders                     0                     0  "],"text/html":["\n","  <div id=\"df-1b2ecd58-c638-4485-a702-aa3ba4408f61\" class=\"colab-df-container\">\n","    <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 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 <td>2007</td>\n","      <td>Chennai Super Kings</td>\n","      <td>Mumbai Indians</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>2007/08</td>\n","      <td>2007</td>\n","      <td>Deccan Chargers</td>\n","      <td>Rajasthan Royals</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>2007/08</td>\n","      <td>2007</td>\n","      <td>Kings XI Punjab</td>\n","      <td>Mumbai Indians</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>2007/08</td>\n","      <td>2007</td>\n","      <td>Royal Challengers Bangalore</td>\n","      <td>Rajasthan Royals</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>2007/08</td>\n","      <td>2007</td>\n","      <td>Chennai Super Kings</td>\n","      <td>Kolkata Knight Riders</td>\n","      <td>0</td>\n","      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\"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Kings XI Punjab\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"team2\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Chennai Super Kings\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"team1_title_defender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"team2_title_defender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":21}]},{"cell_type":"code","source":["# Recalculate missing toss-related features before scaling\n","\n","# Toss winner chose to bat\n","matches_df['toss_bat'] = matches_df['toss_decision'].apply(lambda x: 1 if x == 'bat' else 0)\n","\n","# Toss winner also won the match\n","matches_df['toss_win_match_win'] = (matches_df['toss_winner'] == matches_df['winner']).astype(int)\n","\n","# Team1 batted first indicator (assumed from toss logic)\n","matches_df['team1_batted_first'] = matches_df['toss_decision'].apply(lambda x: 1 if x == 'bat' else 0)\n","\n","# Drop matches without a winner\n","ml_df = matches_df.dropna(subset=['winner']).copy()\n","\n","# Encode categorical features\n","from sklearn.preprocessing import LabelEncoder\n","encoder = LabelEncoder()\n","for col in ['team1', 'team2', 'toss_winner', 'toss_decision', 'venue', 'home_team', 'winner']:\n","    ml_df[col] = encoder.fit_transform(ml_df[col])\n","\n","# Define feature lists\n","numeric_features = [\n","    'head_to_head_count', 'team1_recent_form', 'team2_recent_form',\n","    'home_win_percent', 'toss_bat', 'toss_win_match_win', 'team1_batted_first',\n","    'team1_title_defender', 'team2_title_defender'\n","]\n","categorical_features = ['team1', 'team2', 'toss_winner', 'toss_decision', 'venue', 'home_team']\n","\n","# Scale numeric features\n","from sklearn.preprocessing import StandardScaler\n","scaler = StandardScaler()\n","X_scaled_numeric = scaler.fit_transform(ml_df[numeric_features])\n","\n","# Combine scaled numeric with encoded categorical\n","X_encoded_categorical = ml_df[categorical_features].values\n","import numpy as np\n","X_final = np.hstack([X_encoded_categorical, X_scaled_numeric])\n","y_final = ml_df['winner']\n","\n","# Train-test split\n","from sklearn.model_selection import train_test_split\n","X_train_final, X_test_final, y_train_final, y_test_final = train_test_split(\n","    X_final, y_final, test_size=0.2, random_state=42\n",")\n","\n","# Train models\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.metrics import accuracy_score\n","\n","log_model_final = LogisticRegression(solver='saga', max_iter=3000)\n","rf_model_final = RandomForestClassifier(n_estimators=100, random_state=42)\n","\n","log_model_final.fit(X_train_final, y_train_final)\n","rf_model_final.fit(X_train_final, y_train_final)\n","\n","# Predict and evaluate\n","log_preds_final = log_model_final.predict(X_test_final)\n","rf_preds_final = rf_model_final.predict(X_test_final)\n","\n","log_acc_final = accuracy_score(y_test_final, log_preds_final)\n","rf_acc_final = accuracy_score(y_test_final, rf_preds_final)\n","\n","(log_acc_final, rf_acc_final)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"E-ElHD1pxyWo","executionInfo":{"status":"ok","timestamp":1744090655223,"user_tz":-330,"elapsed":7236,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}},"outputId":"fbc63ec7-9e86-4007-eef9-5bf9c60f030c"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n","  warnings.warn(\n"]},{"output_type":"execute_result","data":{"text/plain":["(0.3165137614678899, 0.6972477064220184)"]},"metadata":{},"execution_count":22}]},{"cell_type":"code","source":["# Try LightGBM as an alternative to XGBoost\n","from lightgbm import LGBMClassifier\n","\n","# Initialize and train LightGBM model\n","lgbm_model = LGBMClassifier(random_state=42)\n","lgbm_model.fit(X_train_final, y_train_final)\n","\n","# Predict and evaluate\n","lgbm_preds = lgbm_model.predict(X_test_final)\n","lgbm_acc = accuracy_score(y_test_final, lgbm_preds)\n","\n","lgbm_acc"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"J2WSPnh_xyUG","executionInfo":{"status":"ok","timestamp":1744090659364,"user_tz":-330,"elapsed":4144,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}},"outputId":"7f5a1ff4-ca19-453d-b1c4-1eddbd177c24"},"execution_count":23,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.11/dist-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000183 seconds.\n","You can set `force_row_wise=true` to remove the overhead.\n","And if memory is not enough, you can set `force_col_wise=true`.\n","[LightGBM] [Info] Total Bins 287\n","[LightGBM] [Info] Number of data points in the train set: 872, number of used features: 15\n","[LightGBM] [Info] Start training from score -2.155669\n","[LightGBM] [Info] Start training from score -3.474953\n","[LightGBM] [Info] Start training from score -3.057217\n","[LightGBM] [Info] Start training from score -2.781805\n","[LightGBM] [Info] Start training from score -4.691348\n","[LightGBM] [Info] Start training from score -3.826350\n","[LightGBM] [Info] Start training from score -2.508110\n","[LightGBM] [Info] Start training from score -5.384495\n","[LightGBM] [Info] Start training from score -2.088658\n","[LightGBM] [Info] Start training from score -3.726267\n","[LightGBM] [Info] Start training from score -2.000105\n","[LightGBM] [Info] Start training from score -4.573565\n","[LightGBM] [Info] Start training from score -3.726267\n","[LightGBM] [Info] Start training from score -2.304881\n","[LightGBM] [Info] Start 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splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with 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warnings.warn(\n"]},{"output_type":"execute_result","data":{"text/plain":["0.8761467889908257"]},"metadata":{},"execution_count":23}]},{"cell_type":"code","source":["# Step 1: Re-train both models to be sure\n","from sklearn.ensemble import RandomForestClassifier\n","from lightgbm import LGBMClassifier\n","import pandas as pd\n","import numpy as np\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","\n","# Re-train Random Forest\n","rf_model_final = RandomForestClassifier(n_estimators=100, random_state=42)\n","rf_model_final.fit(X_train_final, y_train_final)\n","\n","# Re-train LightGBM\n","lgbm_model = LGBMClassifier(random_state=42)\n","lgbm_model.fit(X_train_final, y_train_final)\n","\n","# Step 2: Get feature importances\n","rf_importance = rf_model_final.feature_importances_\n","lgbm_importance = lgbm_model.feature_importances_\n","\n","# Step 3: Normalize importances for fair comparison\n","rf_importance_normalized = rf_importance / np.max(rf_importance)\n","lgbm_importance_normalized = lgbm_importance / np.max(lgbm_importance)\n","\n","# Step 4: Create DataFrame for plotting\n","feature_names = categorical_features + numeric_features\n","\n","importance_df = pd.DataFrame({\n","    'Feature': feature_names,\n","    'LightGBM': lgbm_importance_normalized,\n","    'Random Forest': rf_importance_normalized\n","})\n","\n","# Melt for seaborn plotting\n","importance_melted = importance_df.melt(id_vars='Feature',\n","                                       var_name='Model',\n","                                       value_name='Importance')\n","\n","# Step 5: Plot\n","plt.figure(figsize=(12, 8))\n","sns.barplot(data=importance_melted, x='Importance', y='Feature', hue='Model')\n","plt.title(\"📊 Feature Importance Comparison (Normalized): LightGBM vs Random Forest\")\n","plt.tight_layout()\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"L2kfrGeGxyIi","executionInfo":{"status":"ok","timestamp":1744090661247,"user_tz":-330,"elapsed":1884,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}},"outputId":"390754f3-e58b-45c2-864f-2dfb619b2880"},"execution_count":24,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.11/dist-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000121 seconds.\n","You can set `force_row_wise=true` to remove the overhead.\n","And if memory is not enough, you can set `force_col_wise=true`.\n","[LightGBM] [Info] Total Bins 287\n","[LightGBM] [Info] Number of data points in the train set: 872, number of used features: 15\n","[LightGBM] [Info] Start training from score -2.155669\n","[LightGBM] [Info] Start training from score -3.474953\n","[LightGBM] [Info] Start training from score -3.057217\n","[LightGBM] [Info] Start training from score -2.781805\n","[LightGBM] [Info] Start training from score -4.691348\n","[LightGBM] [Info] Start training from score -3.826350\n","[LightGBM] [Info] Start training from score -2.508110\n","[LightGBM] [Info] Start training from score -5.384495\n","[LightGBM] [Info] Start training from score -2.088658\n","[LightGBM] [Info] Start training from score -3.726267\n","[LightGBM] [Info] Start training from score -2.000105\n","[LightGBM] [Info] Start training from score -4.573565\n","[LightGBM] [Info] Start training from score -3.726267\n","[LightGBM] [Info] Start training from score -2.304881\n","[LightGBM] [Info] Start training from score -4.979030\n","[LightGBM] [Info] Start training from score -5.161352\n","[LightGBM] [Info] Start training from score -2.270980\n","[LightGBM] [Info] Start training from score -5.161352\n","[LightGBM] [Info] Start training from score -2.426984\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with 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-inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-24-402cc3da85d2>:43: UserWarning: Glyph 128202 (\\N{BAR CHART}) missing from font(s) DejaVu Sans.\n","  plt.tight_layout()\n","/usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128202 (\\N{BAR CHART}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x800 with 1 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Cevfurf/+97967733VLZsWUky/xd4YhkAgMfCrl27jODg4AyXP/fcc8ahQ4cyXH7kyBFDUrqP1atXG4ZhGHXr1jUqVKhgXL9+3bxdSkqKUbNmTaNkyZLmtuvXrxvJyclp+ndwcDBGjhxpbtu6dashyZg5c2aaevz8/IwOHTqkaQ8JCTFCQkLMz1evXm1IMooXL25cvXrVoq6SJUsaDRo0MFJSUsztV69eNYoVK2a89NJLGZ6LO8/HhAkTzG0zZ840JKXps0aNGobJZDK6d+9ubrt165ZRpEgRi1pT+3RycjL++ecfc/vmzZsNSUa/fv3MbZUqVTLy589vnDt3zty2c+dOw8bGxmjfvr25bcSIEYYko02bNmmOoWfPnkZGv8rvPFeGYRg3btwwAgMDjTp16li0SzLs7e2Nw4cPW9Qhyfjkk0/Mbe3btzdsbGyMrVu3ptlX6rn64IMPDBcXF+PgwYMWywcPHmzY2toax44dS7dWwzCMqKgoQ5Lxww8/ZLjOnfr27WtIMtauXWtuu3z5slGsWDHD39/f/P5Mff8EBgYaN27cMK/bpk0bw2QyGY0aNbLot0aNGoafn59FW+rnZNu2bea2o0ePGo6OjkbLli3NbXefc8MwjI0bNxqSjK+//trclvo+q1WrlnHr1i2L9VOXHTlyxDAMw/jhhx8MSeme91SLFy82JBmjRo2yaG/VqpVhMpksXtvMvt7pWblypSHJ+Pnnn9MsCwkJMcqXL28YhmFERkYakozt27cbhpH+Zy2rr9/dn3/DMIwOHToYkowxY8aY2y5cuGA4OTkZJpPJWLBggbl9//79hiRjxIgR5rbMfo+l1n/n91jq5zIjhw4dMjw8PIyXXnrJ/BrPmTPHsLGxsThmwzCML774wpBkrF+/3jAMw4iJiTEkGT169LBYr23btmmOIVW3bt0MJycni7bUGlO/3+/Fz8/PaNKkyT3X6dChg8Vn47vvvjMkGZMnTza3JScnG3Xq1ElzvlJfqzvPq2EYRlBQkFGlShXz8zNnzqR7jKnv0b59+6ap69y5c8aZM2fMj6SkpDT7vftRuHBh8/szVep77X//+5+xZMkSw2Qymb+z3nnnHaN48eKGYVi+1zOSkpJiFC5c2Hj11Vct2hcuXGhIMv744w/DMDL3+c5ISEiIUaZMGfNx79+/33jnnXcMSWley/S+m+bPn29Ri2H833umU6dOFuu2bNnSyJs3r/l5Vt6jLVq0MOzt7Y3Y2Fhz28mTJw03NzfjhRdeMLc96O/fjISEhKT7Hkj9/4/UY+nSpYvFdgMHDjQkGb///ru5zc/Pz5BkLFu2zGLdzP7u69Onj+Hu7p7me/9O//vf/zL9uQWeFFy+BwBPmW7dumnFihUWj2effVbnz5/X77//rrCwMF2+fFlnz57V2bNnde7cOTVo0ECHDh3SiRMnJN2eODZ1Ppfk5GSdO3dOrq6uKl26dKYu28mODh06WMzbEBMTo0OHDqlt27Y6d+6cud4rV66obt26+uOPP7I9KW/nzp0t/uX4ueeek2EY6ty5s7nN1tZWVatWTXO3J0lq0aKFChcubH5evXp1Pffcc1q6dKkkKT4+XjExMQoPD5e3t7d5vYoVK+qll14yr3en7t27Z+kY7jxXFy5c0KVLl1S7du10X5969epZTJBfsWJFubu7m48tJSVFixcvVrNmzVS1atU026eeq//973+qXbu2vLy8zK/H2bNnVa9ePSUnJ6e53O1OqZdjpndZTnqWLl2q6tWrq1atWuY2V1dXdevWTXFxcWnurNW+fXuLOXFSX9O7L5N57rnndPz4cd26dcuivUaNGhaTdxctWlTNmzfX8uXLzZfh3HnOb968qXPnzikgIECenp7pnveuXbved1RY6miHJUuW6ObNm+mus3TpUtna2qp3794W7QMGDJBhGPr1118t2u/3emck9VIfLy+ve67Xp08feXl5mSeszqjmrLx+d3/+79SlSxfzz56enipdurRcXFwUFhZmbi9durQ8PT0tjvFhfY9duXJFLVu2lJeXl+bPn29+jf/3v/+pbNmyKlOmjMXnI3UeqNWrV5vPjaQ0r2ffvn0z3KeXl5euXbtmcYlVRESEDMN4KKOkpNsjwfLkyWMxJ5ONjY169uyZ4TZ3f4/Vrl37vu876f++H1JHttypePHiypcvn/lx9wg9R0dH8++65cuXa+rUqXJ1dVXjxo118ODBdPdXv359eXt7a8GCBTIMQwsWLFCbNm3uW2cqk8mk1157TUuXLlViYqK5/dtvv1XhwoXN7/vMfL7vZf/+/ebjLlOmjCZMmKCXX345zaWTd352rl+/rrNnz5rnhUvvvZ7e63Tu3Dnz65DZ92hycrJ+++03tWjRQsWLFze3+/j4qG3btlq3bp25z1QP+vs3Pf7+/mn+v2fQoEEWx9K/f3+LbQYMGCBJaS4pLlasmBo0aGDRltnffZ6enrpy5YpWrFiRqbqBpwWX7wHAU6ZkyZJpLjORpC1btsgwDA0bNkzDhg1Ld9vTp0+rcOHCSklJUVRUlD7//HMdOXLE/Ee5pHte/vMgUi+5SXXo0CFJyvBSEUm6dOnSff+ATk/RokUtnnt4eEiSfH1907SnN+FtyZIl07SVKlVKCxculHR77iHp9h/KdytbtqyWL1+eZjLzu4//fpYsWaJRo0YpJibGYl6M9OYCuvt4pdt/5KYe25kzZ5SQkHDf26AfOnRIf/31V4Z3c7xzjpO7ubu7S5IuX758z32kOnr0aJrLPqT/u8zh6NGjFvVm5TVNSUnRpUuXLN7LGb2mV69e1ZkzZ1SwYEHzvFgzZ87UiRMnLOY7uXTpUprtM/OahoSE6NVXX1VkZKQ+/vhjhYaGqkWLFmrbtq35Dn1Hjx5VoUKF0gR6d56LO93v9b6fO48rPR4eHurbt69GjBihHTt2pPsZzOrrl9G5cnR0TPN+8/DwUJEiRdK81+/+vD6s77GuXbsqNjZWGzZssOjn0KFD2rdv330/H0ePHpWNjU2aO6mm932RKvU1eZh337vb0aNH5ePjk+ay1ICAgHTXT++1yuz7LvW9fWfAk+rHH3/UzZs3tXPnTg0cODDNcltb2zS/8xo3bqySJUtqyJAh+u6779JskydPHr322muaN2+eqlevruPHj6tt27b3rfNOr7/+uiZPnqyffvpJbdu2VWJiopYuXao333zT/Dpl5vN9L/7+/ua7IsbGxmr06NE6c+ZMmjsEnj9/XpGRkVqwYEGa7+H0vpvu/o5I/QxfuHBB7u7umX6PnjlzRlevXs3wd11KSoqOHz+u8uXLZ7jvrP7+TY+Li0u6/98j/d/n7e73bcGCBeXp6Znm+zO976LM/u7r0aOHFi5cqEaNGqlw4cKqX7++wsLC1LBhw0wdB/CkIpQCAEiSeVTRwIED0/wrYKrU/2kbM2aMhg0bpk6dOumDDz6Qt7e3bGxs1Ldv30yPTsroj6fk5OR0R4/cPUoidT8TJkxQpUqV0u0rvX9Vz4yMRq+k136/P9BzSlbu7rN27Vq9/PLLeuGFF/T555/Lx8dHefLk0cyZM9NM9ixlfLxZPbaUlBS99NJL5n+BvlupUqUy3LZMmTKSbk9O3aJFiyztNzOy8ppK2Xtd3377bc2cOVN9+/ZVjRo15OHhIZPJpNatW6f7ucjMa2oymbRo0SJt2rRJP//8s5YvX65OnTpp0qRJ2rRpU7be49k95tSAJTN/CKbOLRUZGXnPucQyK6Nz9SCva058j90tKipK8+fP1zfffJPmeyklJUUVKlTQRx99lO62d//RnRUXLlyQs7PzI30XsAe5E1xAQIDs7OzSvWFESEiIpPQnLc9IkSJFVLp06XuO3mzbtq2++OILRURE6Nlnn1W5cuWyVPPzzz8vf39/LVy4UG3bttXPP/+sa9eu6fXXXzev86Cf77vDluDgYFWuXFnvvfee/vvf/5rbw8LCtGHDBr3zzjuqVKmSXF1dlZKSooYNG6b7Xs/J78Wsyq3fv5kNdNP7jGX2d1/+/PkVExOj5cuX69dff9Wvv/6qmTNnqn379po9e3b2iwcec4RSAABJMg+tz5MnT4b/ophq0aJFevHFF/Xll19atF+8eNHiFtr3+p88Ly8vXbx4MU370aNHLYb5ZyT1X2jd3d3vW6+1pY7iutPBgwfNk5f7+flJuj1h6t3279+vZ555xmKUVEYyOr/fffedHB0dtXz5cot/bZ85c2Zmyk8jX758cnd3T/cPwjuVKFFCiYmJ2Xo9atWqZb7c6b333rvvH7B+fn4Znr/U5Tkpo9fU2dnZ/K/jixYtUocOHTRp0iTzOtevX0/3fZ5Vzz//vJ5//nmNHj1a8+bNU7t27bRgwQJ16dJFfn5+WrlypS5fvmwxWiqnz0VqcHjkyJH7rps6WioiIiLd0YzWfv3Sk9nvscxau3atBg4cqL59+6pdu3ZplpcoUUI7d+5U3bp17/nd6OfnZx75cucIk/TOV6ojR45YfTJkPz8/rV69WlevXrUYLXX48OFs95nReXFxcTFPjH3ixAmLy6Oz69atW+mOvEpVq1YtFS1aVNHR0RYTfGdFWFiYoqKilJCQoG+//Vb+/v7my+budK/Pd1ZUrFhR//nPfzR16lQNHDhQRYsW1YULF7Rq1SpFRkZa3MAhve+0zMrsezRfvnxydnbO8LNuY2PzQGFsTkg9lkOHDll8hk6dOqWLFy9m6rsoK7/77O3t1axZMzVr1kwpKSnq0aOHpk6dqmHDhikgIMCqox2BRwVzSgEAJN3+F7zQ0FBNnTpV8fHxaZafOXPG/LOtrW2af6H83//+Z55zKlVqsJLeH+UlSpTQpk2bdOPGDXPbkiVLdPz48UzVW6VKFZUoUUITJ05M9w+LO+u1tsWLF1uciy1btmjz5s1q1KiRpNvzaVSqVEmzZ8+2ODe7d+/Wb7/9psaNG2dqPxmdX1tbW5lMJovLkeLi4izu7pUVNjY2atGihX7++Wdt27YtzfLU90J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install catboost"],"metadata":{"id":"uFmcyzSw4v3G","executionInfo":{"status":"ok","timestamp":1744090661302,"user_tz":-330,"elapsed":50,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}}},"execution_count":25,"outputs":[]},{"cell_type":"code","source":["#!pip install -U numpy\n","#!pip install -U catboost\n"],"metadata":{"id":"_8bEoCCr5N4u","executionInfo":{"status":"ok","timestamp":1744090661314,"user_tz":-330,"elapsed":4,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}}},"execution_count":26,"outputs":[]},{"cell_type":"code","source":["# Train and collect feature importances from CatBoost and XGBoost\n","from catboost import CatBoostClassifier\n","import xgboost as xgb\n","\n","# Train CatBoost\n","catboost_model = CatBoostClassifier(verbose=0, random_state=42)\n","catboost_model.fit(X_train_final, y_train_final)\n","catboost_importance = catboost_model.get_feature_importance()\n","\n","# Train XGBoost\n","xgb_model = xgb.XGBClassifier(use_label_encoder=False, eval_metric='mlogloss', random_state=42)\n","xgb_model.fit(X_train_final, y_train_final)\n","xgb_importance = xgb_model.feature_importances_\n","\n","# Normalize all importances for comparison\n","def normalize(arr):\n","    return arr / np.max(arr)\n","\n","importance_df = pd.DataFrame({\n","    'Feature': feature_names,\n","    'LightGBM': normalize(lgbm_model.feature_importances_),\n","    'Random Forest': normalize(rf_model_final.feature_importances_),\n","    'CatBoost': normalize(catboost_importance),\n","    'XGBoost': normalize(xgb_importance)\n","})\n","\n","# Melt and select top 5 features by average importance\n","importance_df['avg_importance'] = importance_df.drop('Feature', axis=1).mean(axis=1)\n","top5_features = importance_df.sort_values(by='avg_importance', ascending=False).head(5)\n","\n","# Melt for seaborn plot\n","importance_melted_top5 = top5_features.drop(columns=['avg_importance']).melt(\n","    id_vars='Feature', var_name='Model', value_name='Importance'\n",")\n","\n","# Plot\n","plt.figure(figsize=(12, 6))\n","sns.barplot(data=importance_melted_top5, x='Importance', y='Feature', hue='Model')\n","plt.title(\"🔥 Top 5 Feature Importance Comparison: LightGBM vs RF vs XGBoost vs CatBoost\")\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":716},"id":"T1prZ23HxyEj","executionInfo":{"status":"ok","timestamp":1744090673632,"user_tz":-330,"elapsed":12314,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}},"outputId":"26b7276a-e728-48b5-e044-5a6d95f9e07c"},"execution_count":27,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.11/dist-packages/xgboost/core.py:158: UserWarning: [05:37:52] WARNING: /workspace/src/learner.cc:740: \n","Parameters: { \"use_label_encoder\" } are not used.\n","\n","  warnings.warn(smsg, UserWarning)\n","<ipython-input-27-9a38ca71af5a>:40: UserWarning: Glyph 128293 (\\N{FIRE}) missing from font(s) DejaVu Sans.\n","  plt.tight_layout()\n","/usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128293 (\\N{FIRE}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 with 1 Axes>"],"image/png":"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/content/catboost_info /content/drive/MyDrive/Colab\\ Notebooks/IPLPrediction/"],"metadata":{"id":"5iXJtEiu9LVV","executionInfo":{"status":"ok","timestamp":1744091529670,"user_tz":-330,"elapsed":262,"user":{"displayName":"Dinesh Kumar","userId":"18299454607260962281"}}},"execution_count":28,"outputs":[]}]}