{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# lab 11\n", "\n", "***Regression Analysis***\n", "\n", "Suppose you have dataset with cars (CO2_passenger_cars2018b.csv). You what to know what features contribute to the increase of CO2 emission\n", "\n", "1 Import needed libraries\n", "\n", "2 read data from file\n", "\n", "3 list dataset showing first 5 lines. View data types\n", "\n", "4 verify possible values of categoric variable Ft\n", "\n", "5 convert Petrol to PETROL and Diesel to DIESEL\n", "\n", "6 verify again values of categoric variable Ft\n", "\n", "7 convert variables 'm (kg)','ec (cm3)','ep (KW) and 'Enedc (g/km)' to numeric\n", "\n", " If ‘raise’, then invalid parsing will raise an exception\n", " If ‘coerce’, then invalid parsing will be set as NaN\n", " If ‘ignore’, then invalid parsing will return the input\n", "\n", "8 remove all lines with NaN from df dataset and set to XY\n", "\n", "9 create a Y vector and X matrix\n", "\n", "10 create a regression model\n", "\n", "11 analyse correlation between variables. Use seaborn. Like for example in:\n", "\n", " import seaborn as sns\n", " import matplotlib.pyplot as plt\n", " fig = plt.figure(figsize=[12, 12])\n", " corr_mtx = XY.corr()\n", " sns.heatmap(corr_mtx, xticklabels=corr_mtx.columns, yticklabels=corr_mtx.columns, annot=True, cmap='Blues')\n", " plt.title('Correlation analysis')\n", " plt.show()\n", "\n", "12 convert Ft into dummy variables\n", "\n", "13 add dummy variable to a new data set XY2\n", "\n", "14 create a Y vector and X matrix\n", "\n", "15 create a regression model\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from sklearn.linear_model import LinearRegression\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3049: DtypeWarning: Columns (2,5) have mixed types. Specify dtype option on import or set low_memory=False.\n", " interactivity=interactivity, compiler=compiler, result=result)\n" ] } ], "source": [ "df=pd.read_csv(\"CO2_passenger_cars2018b.csv\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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FabricanteCommercialNamem (kg)Enedc (g/km)Ftec (cm3)ep (KW)
0VOLKSWAGENGOLF1375104.0DIESEL159885.0
1VOLKSWAGENGOLF1340119.0PETROL1498110.0
2VOLKSWAGENGOLF1320120.0PETROL1498110.0
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" ], "text/plain": [ " Fabricante CommercialName m (kg) Enedc (g/km) Ft ec (cm3) ep (KW)\n", "0 VOLKSWAGEN GOLF 1375 104.0 DIESEL 1598 85.0\n", "1 VOLKSWAGEN GOLF 1340 119.0 PETROL 1498 110.0\n", "2 VOLKSWAGEN GOLF 1320 120.0 PETROL 1498 110.0\n", "3 VOLKSWAGEN POLO 1145 104.0 PETROL 999 70.0\n", "4 VOLKSWAGEN POLO 1145 104.0 PETROL 999 70.0" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Fabricante object\n", "CommercialName object\n", "m (kg) object\n", "Enedc (g/km) float64\n", "Ft object\n", "ec (cm3) object\n", "ep (KW) float64\n", "dtype: object" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['DIESEL', 'PETROL', nan, 'Petrol', 'Diesel'], dtype=object)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Ft'].unique()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['DIESEL', 'PETROL', nan], dtype=object)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"Ft\"]=df[\"Ft\"].replace(\"Petrol\",\"PETROL\")\n", "df[\"Ft\"]=df[\"Ft\"].replace(\"Diesel\",\"DIESEL\")\n", "df['Ft'].unique()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Fabricante object\n", "CommercialName object\n", "m (kg) float64\n", "Enedc (g/km) float64\n", "Ft object\n", "ec (cm3) float64\n", "ep (KW) float64\n", "dtype: object" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[:,2]=pd.to_numeric(df.iloc[:,2],errors='coerce')\n", "df.iloc[:,5]=pd.to_numeric(df.iloc[:,5],errors='coerce')\n", "df.dtypes" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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FabricanteCommercialNamem (kg)Enedc (g/km)Ftec (cm3)ep (KW)
0VOLKSWAGENGOLF1375.0104.0DIESEL1598.085.0
1VOLKSWAGENGOLF1340.0119.0PETROL1498.0110.0
2VOLKSWAGENGOLF1320.0120.0PETROL1498.0110.0
3VOLKSWAGENPOLO1145.0104.0PETROL999.070.0
4VOLKSWAGENPOLO1145.0104.0PETROL999.070.0
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" ], "text/plain": [ " Fabricante CommercialName m (kg) Enedc (g/km) Ft ec (cm3) ep (KW)\n", "0 VOLKSWAGEN GOLF 1375.0 104.0 DIESEL 1598.0 85.0\n", "1 VOLKSWAGEN GOLF 1340.0 119.0 PETROL 1498.0 110.0\n", "2 VOLKSWAGEN GOLF 1320.0 120.0 PETROL 1498.0 110.0\n", "3 VOLKSWAGEN POLO 1145.0 104.0 PETROL 999.0 70.0\n", "4 VOLKSWAGEN POLO 1145.0 104.0 PETROL 999.0 70.0" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "XY=df.dropna()\n", "XY.head()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "Y=XY['Enedc (g/km)']\n", "X=XY[['m (kg)','ec (cm3)','ep (KW)']]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n", " normalize=False)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm=LinearRegression()\n", "lm.fit(X,Y)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0.02414271, -0.00170046, 0.31175694])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm.coef_" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.494459098000691" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm.score(X,Y)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "fig = plt.figure(figsize=[12, 12])\n", "corr_mtx = XY.corr()\n", "sns.heatmap(corr_mtx, xticklabels=corr_mtx.columns, yticklabels=corr_mtx.columns, annot=True, cmap='Blues')\n", "plt.title('Correlation analysis')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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FabricanteCommercialNamem (kg)Enedc (g/km)Ftec (cm3)ep (KW)
0VOLKSWAGENGOLF1375.0104.01.01598.085.0
1VOLKSWAGENGOLF1340.0119.00.01498.0110.0
2VOLKSWAGENGOLF1320.0120.00.01498.0110.0
3VOLKSWAGENPOLO1145.0104.00.0999.070.0
4VOLKSWAGENPOLO1145.0104.00.0999.070.0
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" ], "text/plain": [ " Fabricante CommercialName m (kg) Enedc (g/km) Ft ec (cm3) ep (KW)\n", "0 VOLKSWAGEN GOLF 1375.0 104.0 1.0 1598.0 85.0\n", "1 VOLKSWAGEN GOLF 1340.0 119.0 0.0 1498.0 110.0\n", "2 VOLKSWAGEN GOLF 1320.0 120.0 0.0 1498.0 110.0\n", "3 VOLKSWAGEN POLO 1145.0 104.0 0.0 999.0 70.0\n", "4 VOLKSWAGEN POLO 1145.0 104.0 0.0 999.0 70.0" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2=df\n", "df2[\"Ft\"]=df2[\"Ft\"].replace(\"PETROL\",0)\n", "df2[\"Ft\"]=df2[\"Ft\"].replace(\"DIESEL\",1)\n", "XY2=df2.dropna()\n", "XY2.head()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "Y2=XY2['Enedc (g/km)']\n", "X2=XY2[['m (kg)','ec (cm3)','ep (KW)','Ft']]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n", " normalize=False)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm2=LinearRegression()\n", "lm2.fit(X2,Y2)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 5.40879228e-02, 1.72812603e-02, 4.57921958e-02, -2.66822078e+01])" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm2.coef_" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6740223371787819" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm2.score(X2,Y2)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "29.705456863284923" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm2.intercept_" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }