Photovoltaic System Performance Prediction with Real-World Data and Linear Regression

import\u0020pandas\u0020as\u0020pd\nfrom\u0020sklearn.model_selection\u0020import\u0020train_test_split\nfrom\u0020sklearn.linear_model\u0020import\u0020LinearRegression\nfrom\u0020sklearn.metrics\u0020import\u0020mean_absolute_error,\u0020mean_squared_error\n\n#\u0020Load\u0020dataset\ndata\u0020=\u0020pd.read_csv('photovoltaic_data.csv')\n\n#\u0020Split\u0020data\u0020into\u0020features\u0020and\u0020target\u0020variable\nX\u0020=\u0020data[['Temperature',\u0020'Irradiance']]\ny\u0020=\u0020data['Performance']\n\n#\u0020Split\u0020data\u0020into\u0020training\u0020and\u0020testing\u0020sets\nX_train,\u0020X_test,\u0020y_train,\u0020y_test\u0020=\u0020train_test_split(X,\u0020y,\u0020test_size=0.2,\u0020random_state=42)\n\n#\u0020Train\u0020the\u0020model\nmodel\u0020=\u0020LinearRegression()\nmodel.fit(X_train,\u0020y_train)\n\n#\u0020Make\u0020predictions\u0020on\u0020the\u0020test\u0020set\ny_pred\u0020=\u0020model.predict(X_test)\n\n#\u0020Evaluate\u0020the\u0020model\nmae\u0020=\u0020mean_absolute_error(y_test,\u0020y_pred)\nmse\u0020=\u0020mean_squared_error(y_test,\u0020y_pred)\nrmse\u0020=\u0020mean_squared_error(y_test,\u0020y_pred,\u0020squared=False)\n\nprint(f'Mean\u0020Absolute\u0020Error:\u0020{mae}')\nprint(f'Mean\u0020Squared\u0020Error:\u0020{mse}')\nprint(f'Root\u0020Mean\u0020Squared\u0020Error:\u0020{rmse}')