model.py

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     "text": [
      "Accuracy: 0.9989244007375537\n"
     ]
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   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import pickle\n",
    "\n",
    "# Load the dataset\n",
    "df = pd.read_csv('dataset.csv')\n",
    "\n",
    "# Split the data into features (X) and target (y)\n",
    "X = df.iloc[:, :-1]\n",
    "y = df.iloc[:, -1]\n",
    "\n",
    "# Split the data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=100)\n",
    "\n",
    "# Define the ColumnTransformer\n",
    "trf = ColumnTransformer([\n",
    "    ('trf', OneHotEncoder(sparse_output=False, drop='first'), ['batting_team', 'bowling_team', 'city'])\n",
    "], remainder='passthrough')\n",
    "\n",
    "# Define the Pipeline\n",
    "ra_pipe = Pipeline([\n",
    "    ('step1', trf),\n",
    "    ('step2', RandomForestClassifier(random_state=100))  # Setting random_state for reproducibility\n",
    "])\n",
    "\n",
    "# Train the pipeline on the training data\n",
    "ra_pipe.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test data\n",
    "y_pred = ra_pipe.predict(X_test)\n",
    "\n",
    "# Evaluate the model's accuracy\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
    "\n",
    "# Save the trained pipeline to a file\n",
    "with open('ra_pipe.pkl', 'wb') as file:\n",
    "    pickle.dump(ra_pipe, file)\n"
   ]
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