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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8417508417508418\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "# Load the dataset\n",
    "df = pd.read_csv('dataset.csv')\n",
    "\n",
    "# Split the dataset into features and target variable\n",
    "X = df.drop('PlacedOrNot', axis=1)  # Features\n",
    "y = df['PlacedOrNot']  # Target variable\n",
    "\n",
    "# Convert categorical features to numerical using one-hot encoding\n",
    "X = pd.get_dummies(X)\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create a Random Forest Classifier\n",
    "clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "\n",
    "# Train the model\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "accuracy = clf.score(X_test, y_test)\n",
    "print('Accuracy:', accuracy)\n",
    "\n",
    "# Export the trained model as a pickle file\n",
    "with open('random_forest_model.pkl', 'wb') as f:\n",
    "    pickle.dump(clf, f)"
   ]
  },
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