Ridge and Lasso Regression.ipynb

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    "# Import necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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     "ename": "NameError",
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      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[3], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m lin_regressor \u001b[38;5;241m=\u001b[39m LinearRegression()\n\u001b[1;32m----> 2\u001b[0m mse \u001b[38;5;241m=\u001b[39m cross_val_score(lin_regressor, X,y, scoring\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mneg_mean_squared_error\u001b[39m\u001b[38;5;124m'\u001b[39m, cv\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\n\u001b[0;32m      3\u001b[0m mean_mse \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmean(mse)\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(mean_mse)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'X' is not defined"
     ]
    }
   ],
   "source": [
    "lin_regressor = LinearRegression()\n",
    "mse = cross_val_score(lin_regressor, X,y, scoring='neg_mean_squared_error', cv=5)\n",
    "mean_mse = np.mean(mse)\n",
    "print(mean_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2207f406",
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   "outputs": [],
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