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     "text": [
      "Inspection Time Test MSE: 4.0716280667897373e-13\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import pickle\n",
    "# Load the dataset from CSV\n",
    "selected_columns = ['Process type', 'Part Od', 'Part ID', 'Part Width', 'Finish Wt', 'Input Weight',\n",
    "                    'Raw material cost', 'Process cost', 'Machining Time', 'Machining cost ', 'Inspection Time']  # Add more columns as needed\n",
    "data = pd.read_csv('/Users/abhijay/Desktop/EY_INTERNSHIP/Data_4.csv', usecols=selected_columns)  # Replace 'your_dataset.csv' with the path to your CSV file\n",
    "\n",
    "# For predicting inspection time, assume 'Inspection Time' is the target variable\n",
    "X_inspection = data.drop(columns=['Inspection Time'])\n",
    "y_inspection = data['Inspection Time']\n",
    "\n",
    "# Split the data for inspection time into training and testing sets (80% train, 20% test)\n",
    "X_inspection_train, X_inspection_test, y_inspection_train, y_inspection_test = train_test_split(X_inspection, y_inspection, test_size=0.2, random_state=42)\n",
    "\n",
    "# Train the model for inspection time\n",
    "model_inspection = HistGradientBoostingRegressor()\n",
    "model_inspection.fit(X_inspection_train, y_inspection_train)\n",
    "\n",
    "# Evaluate the model\n",
    "\n",
    "test_predictions_inspection = model_inspection.predict(X_inspection_test)\n",
    "\n",
    "test_mse_inspection = mean_squared_error(y_inspection_test, test_predictions_inspection)\n",
    "\n",
    "print(\"Inspection Time Test MSE:\", test_mse_inspection)\n",
    "\n",
    "# Save the model to disk\n",
    "with open('inspection_model.pkl', 'wb') as model_file:\n",
    "    pickle.dump(model_inspection, model_file)\n"
   ]
  }
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