From cfc81148aea64a18ab1a46af9746d000740194e0 Mon Sep 17 00:00:00 2001 From: noah Date: Mon, 27 Feb 2023 13:36:02 -0600 Subject: [PATCH] Adding ipynb shell, lecture doc, and CSVs --- BostonHousing.csv | 507 ++++++ Lecture-Work.ipynb | 792 ++++++++++ Schrick-Noah_Learning-Practice-5.ipynb | 112 ++ Tayko.csv | 2001 ++++++++++++++++++++++++ 4 files changed, 3412 insertions(+) create mode 100644 BostonHousing.csv create mode 100644 Lecture-Work.ipynb create mode 100644 Schrick-Noah_Learning-Practice-5.ipynb create mode 100644 Tayko.csv diff --git a/BostonHousing.csv b/BostonHousing.csv new file mode 100644 index 0000000..d533180 --- /dev/null +++ b/BostonHousing.csv @@ -0,0 +1,507 @@ +CRIM,ZN,INDUS,CHAS,NOX,RM,AGE,DIS,RAD,TAX,PTRATIO,LSTAT,MEDV,CAT. 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2019 Galit Shmueli, Peter C. Bruce, Peter Gedeck \n", + ">\n", + "> Code included in\n", + ">\n", + "> _Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python_ (First Edition) \n", + "> Galit Shmueli, Peter C. Bruce, Peter Gedeck, and Nitin R. Patel. 2019.\n", + "\n", + "## Import required packages" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "from pathlib import Path\n", + "\n", + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LinearRegression, Lasso, Ridge, LassoCV, BayesianRidge\n", + "import statsmodels.formula.api as sm\n", + "import matplotlib.pylab as plt\n", + "\n", + "#from dmba import regressionSummary, exhaustive_search\n", + "#from dmba import backward_elimination, forward_selection, stepwise_selection\n", + "#from dmba import adjusted_r2_score, AIC_score, BIC_score\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Table 6.3" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "intercept -1319.3543800411808\n", + " Predictor coefficient\n", + "0 Age_08_04 -140.748761\n", + "1 KM -0.017840\n", + "2 HP 36.103419\n", + "3 Met_Color 84.281830\n", + "4 Automatic 416.781954\n", + "5 CC 0.017737\n", + "6 Doors -50.657863\n", + "7 Quarterly_Tax 13.625325\n", + "8 Weight 13.038711\n", + "9 Fuel_Type_Diesel 1066.464681\n", + "10 Fuel_Type_Petrol 2310.249543\n", + "\n", + "Regression statistics\n", + "\n", + " Mean Error (ME) : 0.0000\n", + " Root Mean Squared Error (RMSE) : 1400.5823\n", + " Mean Absolute Error (MAE) : 1046.9072\n", + " Mean Percentage Error (MPE) : -1.0223\n", + "Mean Absolute Percentage Error (MAPE) : 9.2994\n" + ] + } + ], + "source": [ + "# Reduce data frame to the top 1000 rows and select columns for regression analysis\n", + "car_df = pd.read_csv('ToyotaCorolla.csv')\n", + "car_df = car_df.iloc[0:1000]\n", + "\n", + "predictors = ['Age_08_04', 'KM', 'Fuel_Type', 'HP', 'Met_Color', 'Automatic', 'CC', \n", + " 'Doors', 'Quarterly_Tax', 'Weight']\n", + "outcome = 'Price'\n", + "\n", + "# partition data\n", + "X = pd.get_dummies(car_df[predictors], drop_first=True)\n", + "y = car_df[outcome]\n", + "train_X, valid_X, train_y, valid_y = train_test_split(X, y, test_size=0.4, random_state=1)\n", + "\n", + "car_lm = LinearRegression()\n", + "car_lm.fit(train_X, train_y)\n", + "\n", + "# print coefficients\n", + "print('intercept ', car_lm.intercept_)\n", + "print(pd.DataFrame({'Predictor': X.columns, 'coefficient': car_lm.coef_}))\n", + "\n", + "# print performance measures\n", + "regressionSummary(train_y, car_lm.predict(train_X))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "adjusted r2 : 0.8537958550253093\n", + "AIC : 10422.298278332171\n", + "BIC : 10479.45836384998\n" + ] + } + ], + "source": [ + "pred_y = car_lm.predict(train_X)\n", + "\n", + "print('adjusted r2 : ', adjusted_r2_score(train_y, pred_y, car_lm))\n", + "print('AIC : ', AIC_score(train_y, pred_y, car_lm))\n", + "print('BIC : ', BIC_score(train_y, pred_y, car_lm))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Table 6.4" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Predicted Actual Residual\n", + "507 10607.333940 11500 892.666060\n", + "818 9272.705792 8950 -322.705792\n", + "452 10617.947808 11450 832.052192\n", + "368 13600.396275 11450 -2150.396275\n", + "242 12396.694660 11950 -446.694660\n", + "929 9496.498212 9995 498.501788\n", + "262 12480.063217 13500 1019.936783\n", + "810 8834.146068 7950 -884.146068\n", + "318 12183.361282 9900 -2283.361282\n", + "49 19206.965683 21950 2743.034317\n", + "446 10987.498309 11950 962.501691\n", + "142 18501.527375 19950 1448.472625\n", + "968 9914.690947 9950 35.309053\n", + "345 13827.299932 14950 1122.700068\n", + "971 7966.732543 10495 2528.267457\n", + "133 17185.242041 15950 -1235.242041\n", + "104 19952.658062 19450 -502.658062\n", + "6 16570.609280 16900 329.390720\n", + "600 13739.409113 11250 -2489.409113\n", + "496 11267.513740 11750 482.486260\n", + "\n", + "Regression statistics\n", + "\n", + " Mean Error (ME) : 103.6803\n", + " Root Mean Squared Error (RMSE) : 1312.8523\n", + " Mean Absolute Error (MAE) : 1017.5972\n", + " Mean Percentage Error (MPE) : -0.2633\n", + "Mean Absolute Percentage Error (MAPE) : 9.0111\n" + ] + } + ], + "source": [ + "# Use predict() to make predictions on a new set\n", + "car_lm_pred = car_lm.predict(valid_X)\n", + "\n", + "result = pd.DataFrame({'Predicted': car_lm_pred, 'Actual': valid_y,\n", + " 'Residual': valid_y - car_lm_pred})\n", + "print(result.head(20))\n", + "\n", + "# Compute common accuracy measures\n", + "regressionSummary(valid_y, car_lm_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Figure 6.1\n", + "Determine the residuals and create a histogram" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7425\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "car_lm_pred = car_lm.predict(valid_X)\n", + "all_residuals = valid_y - car_lm_pred\n", + "\n", + "# Determine the percentage of datapoints with a residual in [-1406, 1406] = approx. 75\\%\n", + "print(len(all_residuals[(all_residuals > -1406) & (all_residuals < 1406)]) / len(all_residuals))\n", + "\n", + "ax = pd.DataFrame({'Residuals': all_residuals}).hist(bins=25)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Table 6.5\n", + "Run an exhaustive search. The Fuel type column is categorical and needs to be converted into dummy variables." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " n r2adj AIC Age_08_04 Automatic CC Doors Fuel_Type_Diesel \\\n", + "0 1 0.767901 10689.712094 True False False False False \n", + "1 2 0.801160 10597.910645 True False False False False \n", + "2 3 0.829659 10506.084235 True False False False False \n", + "3 4 0.846357 10445.174820 True False False False False \n", + "4 5 0.849044 10435.578836 True False False False False \n", + "5 6 0.853172 10419.932278 True False False False False \n", + "6 7 0.853860 10418.104025 True False False False True \n", + "7 8 0.854297 10417.290103 True True False False True \n", + "8 9 0.854172 10418.789079 True True False True True \n", + "9 10 0.854036 10420.330800 True True False True True \n", + "10 11 0.853796 10422.298278 True True True True True \n", + "\n", + " Fuel_Type_Petrol HP KM Met_Color Quarterly_Tax Weight \n", + "0 False False False False False False \n", + "1 False True False False False False \n", + "2 False True False False False True \n", + "3 False True True False False True \n", + "4 False True True False True True \n", + "5 True True True False True True \n", + "6 True True True False True True \n", + "7 True True True False True True \n", + "8 True True True False True True \n", + "9 True True True True True True \n", + "10 True True True True True True \n" + ] + } + ], + "source": [ + "def train_model(variables):\n", + " model = LinearRegression()\n", + " model.fit(train_X[variables], train_y)\n", + " return model\n", + "\n", + "def score_model(model, variables):\n", + " pred_y = model.predict(train_X[variables])\n", + " # we negate as score is optimized to be as low as possible\n", + " return -adjusted_r2_score(train_y, pred_y, model)\n", + "\n", + "allVariables = train_X.columns\n", + "results = exhaustive_search(allVariables, train_model, score_model)\n", + "\n", + "data = []\n", + "for result in results:\n", + " model = result['model']\n", + " variables = result['variables']\n", + " AIC = AIC_score(train_y, model.predict(train_X[variables]), model)\n", + " \n", + " d = {'n': result['n'], 'r2adj': -result['score'], 'AIC': AIC}\n", + " d.update({var: var in result['variables'] for var in allVariables})\n", + " data.append(d)\n", + "pd.set_option('display.width', 100)\n", + "print(pd.DataFrame(data, columns=('n', 'r2adj', 'AIC') + tuple(sorted(allVariables))))\n", + "pd.reset_option('display.width')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Table 6.6 backward elimination" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Variables: Age_08_04, KM, HP, Met_Color, Automatic, CC, Doors, Quarterly_Tax, Weight, Fuel_Type_Diesel, Fuel_Type_Petrol\n", + "Start: score=10422.30\n", + "Step: score=10420.33, remove CC\n", + "Step: score=10418.79, remove Met_Color\n", + "Step: score=10417.29, remove Doors\n", + "Step: score=10417.29, remove None\n", + "['Age_08_04', 'KM', 'HP', 'Automatic', 'Quarterly_Tax', 'Weight', 'Fuel_Type_Diesel', 'Fuel_Type_Petrol']\n" + ] + } + ], + "source": [ + "def train_model(variables):\n", + " model = LinearRegression()\n", + " model.fit(train_X[variables], train_y)\n", + " return model\n", + "\n", + "def score_model(model, variables):\n", + " return AIC_score(train_y, model.predict(train_X[variables]), model)\n", + "\n", + "best_model, best_variables = backward_elimination(train_X.columns, train_model, score_model, verbose=True)\n", + "\n", + "print(best_variables)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Regression statistics\n", + "\n", + " Mean Error (ME) : 103.3045\n", + " Root Mean Squared Error (RMSE) : 1314.4844\n", + " Mean Absolute Error (MAE) : 1016.8875\n", + " Mean Percentage Error (MPE) : -0.2700\n", + "Mean Absolute Percentage Error (MAPE) : 8.9984\n" + ] + } + ], + "source": [ + "regressionSummary(valid_y, best_model.predict(valid_X[best_variables]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Table 6.7 Forward selection" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Variables: Age_08_04, KM, HP, Met_Color, Automatic, CC, Doors, Quarterly_Tax, Weight, Fuel_Type_Diesel, Fuel_Type_Petrol\n", + "Start: score=11565.07, constant\n", + "Step: score=10689.71, add Age_08_04\n", + "Step: score=10597.91, add HP\n", + "Step: score=10506.08, add Weight\n", + "Step: score=10445.17, add KM\n", + "Step: score=10435.58, add Quarterly_Tax\n", + "Step: score=10419.93, add Fuel_Type_Petrol\n", + "Step: score=10418.10, add Fuel_Type_Diesel\n", + "Step: score=10417.29, add Automatic\n", + "Step: score=10417.29, add None\n", + "['Age_08_04', 'HP', 'Weight', 'KM', 'Quarterly_Tax', 'Fuel_Type_Petrol', 'Fuel_Type_Diesel', 'Automatic']\n" + ] + } + ], + "source": [ + "# The initial model is the constant model - this requires special handling\n", + "# in train_model and score_model\n", + "def train_model(variables):\n", + " if len(variables) == 0:\n", + " return None\n", + " model = LinearRegression()\n", + " model.fit(train_X[variables], train_y)\n", + " return model\n", + "\n", + "def score_model(model, variables):\n", + " if len(variables) == 0:\n", + " return AIC_score(train_y, [train_y.mean()] * len(train_y), model, df=1)\n", + " return AIC_score(train_y, model.predict(train_X[variables]), model)\n", + "\n", + "best_model, best_variables = forward_selection(train_X.columns, train_model, score_model, verbose=True)\n", + "\n", + "print(best_variables)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Variables: Age_08_04, KM, HP, Met_Color, Automatic, CC, Doors, Quarterly_Tax, Weight, Fuel_Type_Diesel, Fuel_Type_Petrol\n", + "Start: score=11565.07, constant\n", + "Step: score=10689.71, add Age_08_04\n", + "Step: score=10597.91, add HP\n", + "Step: score=10506.08, add Weight\n", + "Step: score=10445.17, add KM\n", + "Step: score=10435.58, add Quarterly_Tax\n", + "Step: score=10419.93, add Fuel_Type_Petrol\n", + "Step: score=10418.10, add Fuel_Type_Diesel\n", + "Step: score=10417.29, add Automatic\n", + "Step: score=10417.29, unchanged None\n", + "['Age_08_04', 'HP', 'Weight', 'KM', 'Quarterly_Tax', 'Fuel_Type_Petrol', 'Fuel_Type_Diesel', 'Automatic']\n" + ] + } + ], + "source": [ + "best_model, best_variables = stepwise_selection(train_X.columns, train_model, score_model, verbose=True)\n", + "\n", + "print(best_variables)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Table XX regularized methods" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Regression statistics\n", + "\n", + " Mean Error (ME) : 120.6311\n", + " Root Mean Squared Error (RMSE) : 1332.2752\n", + " Mean Absolute Error (MAE) : 1021.5286\n", + " Mean Percentage Error (MPE) : -0.2364\n", + "Mean Absolute Percentage Error (MAPE) : 9.0115\n", + "\n", + "Regression statistics\n", + "\n", + " Mean Error (ME) : 145.1571\n", + " Root Mean Squared Error (RMSE) : 1397.9428\n", + " Mean Absolute Error (MAE) : 1052.4649\n", + " Mean Percentage Error (MPE) : -0.2966\n", + "Mean Absolute Percentage Error (MAPE) : 9.2918\n", + "Lasso-CV chosen regularization: 3.5138446691310588\n", + "[-1.40370575e+02 -1.76669006e-02 3.38674037e+01 0.00000000e+00\n", + " 6.94393427e+01 0.00000000e+00 0.00000000e+00 2.70913468e+00\n", + " 1.24342596e+01 -0.00000000e+00 0.00000000e+00]\n", + "\n", + "Regression statistics\n", + "\n", + " Mean Error (ME) : 154.3286\n", + " Root Mean Squared Error (RMSE) : 1879.7426\n", + " Mean Absolute Error (MAE) : 1353.2735\n", + " Mean Percentage Error (MPE) : -2.3897\n", + "Mean Absolute Percentage Error (MAPE) : 11.1309\n", + "\n", + "Regression statistics\n", + "\n", + " Mean Error (ME) : 105.5382\n", + " Root Mean Squared Error (RMSE) : 1313.0217\n", + " Mean Absolute Error (MAE) : 1017.2356\n", + " Mean Percentage Error (MPE) : -0.2703\n", + "Mean Absolute Percentage Error (MAPE) : 9.0012\n", + "Bayesian ridge chosen regularization: 0.004622833440097622\n" + ] + } + ], + "source": [ + "lasso = Lasso(normalize=True, alpha=1)\n", + "lasso.fit(train_X, train_y)\n", + "regressionSummary(valid_y, lasso.predict(valid_X))\n", + "\n", + "lasso_cv = LassoCV(normalize=True, cv=5)\n", + "lasso_cv.fit(train_X, train_y)\n", + "regressionSummary(valid_y, lasso_cv.predict(valid_X))\n", + "print('Lasso-CV chosen regularization: ', lasso_cv.alpha_)\n", + "print(lasso_cv.coef_)\n", + "\n", + "ridge = Ridge(normalize=True, alpha=1)\n", + "ridge.fit(train_X, train_y)\n", + "regressionSummary(valid_y, ridge.predict(valid_X))\n", + "\n", + "bayesianRidge = BayesianRidge(normalize=True)\n", + "bayesianRidge.fit(train_X, train_y)\n", + "regressionSummary(valid_y, bayesianRidge.predict(valid_X))\n", + "print('Bayesian ridge chosen regularization: ', bayesianRidge.lambda_ / bayesianRidge.alpha_)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Regression statistics\n", + "\n", + " Mean Error (ME) : 103.6803\n", + " Root Mean Squared Error (RMSE) : 1312.8523\n", + " Mean Absolute Error (MAE) : 1017.5972\n", + " Mean Percentage Error (MPE) : -0.2633\n", + "Mean Absolute Percentage Error (MAPE) : 9.0111\n" + ] + } + ], + "source": [ + "linearRegression = LinearRegression(normalize=True).fit(train_X, train_y)\n", + "regressionSummary(valid_y, linearRegression.predict(valid_X))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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featureslinear regressionlassoCVbayesianRidge
0Age_08_04-140.748761-140.370575-139.754059
1KM-0.017840-0.017667-0.018131
2HP36.10341933.86740435.856074
3Met_Color84.2818300.00000085.088966
4Automatic416.78195469.439343408.599781
5CC0.0177370.0000000.020405
6Doors-50.6578630.000000-47.917629
7Quarterly_Tax13.6253252.70913513.269979
8Weight13.03871112.43426013.114412
9Fuel_Type_Diesel1066.464681-0.000000955.581484
10Fuel_Type_Petrol2310.2495430.0000002162.115762
\n", + "
" + ], + "text/plain": [ + " features linear regression lassoCV bayesianRidge\n", + "0 Age_08_04 -140.748761 -140.370575 -139.754059\n", + "1 KM -0.017840 -0.017667 -0.018131\n", + "2 HP 36.103419 33.867404 35.856074\n", + "3 Met_Color 84.281830 0.000000 85.088966\n", + "4 Automatic 416.781954 69.439343 408.599781\n", + "5 CC 0.017737 0.000000 0.020405\n", + "6 Doors -50.657863 0.000000 -47.917629\n", + "7 Quarterly_Tax 13.625325 2.709135 13.269979\n", + "8 Weight 13.038711 12.434260 13.114412\n", + "9 Fuel_Type_Diesel 1066.464681 -0.000000 955.581484\n", + "10 Fuel_Type_Petrol 2310.249543 0.000000 2162.115762" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame({'features': train_X.columns, 'linear regression': linearRegression.coef_, \n", + " 'lassoCV': lasso_cv.coef_, 'bayesianRidge': bayesianRidge.coef_})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Table 6.10" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Price R-squared: 0.856\n", + "Model: OLS Adj. R-squared: 0.854\n", + "Method: Least Squares F-statistic: 319.0\n", + "Date: Sun, 07 Apr 2019 Prob (F-statistic): 1.73e-239\n", + "Time: 13:53:06 Log-Likelihood: -5198.1\n", + "No. Observations: 600 AIC: 1.042e+04\n", + "Df Residuals: 588 BIC: 1.047e+04\n", + "Df Model: 11 \n", + "Covariance Type: nonrobust \n", + "====================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------------\n", + "Intercept -1319.3544 1728.427 -0.763 0.446 -4713.997 2075.288\n", + "Age_08_04 -140.7488 5.142 -27.374 0.000 -150.847 -130.650\n", + "KM -0.0178 0.002 -7.286 0.000 -0.023 -0.013\n", + "HP 36.1034 5.321 6.785 0.000 25.653 46.554\n", + "Met_Color 84.2818 127.005 0.664 0.507 -165.158 333.721\n", + "Automatic 416.7820 259.794 1.604 0.109 -93.454 927.018\n", + "CC 0.0177 0.099 0.179 0.858 -0.177 0.213\n", + "Doors -50.6579 65.187 -0.777 0.437 -178.686 77.371\n", + "Quarterly_Tax 13.6253 2.518 5.411 0.000 8.680 18.571\n", + "Weight 13.0387 1.602 8.140 0.000 9.893 16.185\n", + "Fuel_Type_Diesel 1066.4647 527.285 2.023 0.044 30.872 2102.057\n", + "Fuel_Type_Petrol 2310.2495 521.045 4.434 0.000 1286.914 3333.585\n", + "==============================================================================\n", + "Omnibus: 62.422 Durbin-Watson: 1.899\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 366.046\n", + "Skew: 0.186 Prob(JB): 3.27e-80\n", + "Kurtosis: 6.808 Cond. No. 2.20e+06\n", + "==============================================================================\n", + "\n", + "Warnings:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "[2] The condition number is large, 2.2e+06. This might indicate that there are\n", + "strong multicollinearity or other numerical problems.\n" + ] + } + ], + "source": [ + "# run a linear regression of Price on the remaining 11 predictors in the training set\n", + "train_df = train_X.join(train_y)\n", + "\n", + "predictors = train_X.columns\n", + "formula = 'Price ~ ' + ' + '.join(predictors)\n", + "\n", + "car_lm = sm.ols(formula=formula, data=train_df).fit()\n", + "print(car_lm.summary())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Schrick-Noah_Learning-Practice-5.ipynb b/Schrick-Noah_Learning-Practice-5.ipynb new file mode 100644 index 0000000..d2d6340 --- /dev/null +++ b/Schrick-Noah_Learning-Practice-5.ipynb @@ -0,0 +1,112 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Learning Practice 5 for the University of Tulsa's QM-7063 Data Mining Course\n", + "# Prediction and Classification Methods\n", + "# # Professor: Dr. Abdulrashid, Spring 2023\n", + "# Noah L. Schrick - 1492657" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Problem 6.1\n", + "The file BostonHousing.csv contains information collected by the US Bureau of the Census concerning housing in the area of Boston, Massachusetts. The dataset includes information on 506 census housing tracts in the Boston area. The goal is to predict the median house price in new tracts based on information such as crime rate, pollution, and number of rooms. The dataset contains 13 predictors, and the outcome variable is the median house price (MEDV). Table 6.11 describes each of the predictors and the outcome variable.\n", + "\n", + "TABLE 6.11 DESCRIPTION OF VARIABLES FOR BOSTON HOUSING EXAMPLE\n", + "CRIM Per capita crime rate by town\n", + "ZN Proportion of residential land zoned for lots over 25,000 ft2\n", + "INDUS Proportion of nonretail business acres per town\n", + "CHAS Charles River dummy variable (=1 if tract bounds river; =0 otherwise)\n", + "NOX Nitric oxide concentration (parts per 10 million)\n", + "RM Average number of rooms per dwelling\n", + "AGE Proportion of owner-occupied units built prior to 1940\n", + "DIS Weighted distances to five Boston employment centers\n", + "RAD Index of accessibility to radial highways\n", + "TAX Full-value property-tax rate per $10,000\n", + "PTRATIO Pupil/teacher ratio by town\n", + "LSTAT Percentage lower status of the population\n", + "MEDV Median value of owner-occupied homes in $100\n", + "\n", + "\n", + "a.\n", + " Why should the data be partitioned into training and validation sets? What will the training set be used for? What will the validation set be used for?\n", + "b.\n", + " Fit a multiple linear regression model to the median house price (MEDV) as a function of CRIM, CHAS, and RM. Write the equation for predicting the median house price from the predictors in the model.\n", + "c. \n", + " Using the estimated regression model, what median house price is predicted for a tract in the Boston area that does not bound the Charles River, has a crime rate of 0.1, and where the average number of rooms per house is 6?\n", + "d. \n", + " Reduce the number of predictors:\n", + " i. Which predictors are likely to be measuring the same thing among the 13 predictors? Discuss the relationships among INDUS, NOX, and TAX.\n", + " ii. Compute the correlation table for the 12 numerical predictors and search for highly correlated pairs. These have potential redundancy and can cause multi-collinearity. Choose which ones to remove based on this table.\n", + " iii. Use three subset selection algorithms: backward, forward, and stepwise) to reduce the remaining predictors. Compute the validation performance for each of the three selected models. Compare RMSE, MAPE, and mean error, as well as histograms of the errors. Finally, describe the best model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Problem 6.2\n", + "Tayko Software is a software catalog firm that sells games and educational software. It started out as a software manufacturer and then added third-party titles to its offerings. It recently revised its collection of\n", + "items in a new catalog, which it mailed out to its customers. This mailing yielded 2000 purchases. Based on these data, Tayko wants to devise a model for predicting the spending amount that a purchasing customer will yield. The file Tayko.csv contains information on 2000 purchases. Table 6.12 describes the variables to be used in the problem (the Excel file contains additional variables).\n", + "\n", + "TABLE 6.12 DESCRIPTION OF VARIABLES FOR TAYKO SOFTWARE EXAMPLE\n", + "FREQ Number of transactions in the preceding year\n", + "LAST_UPDATE Number of days since last update to customer record\n", + "WEB Whether customer purchased by Web order at least once\n", + "GENDER Male or female\n", + "ADDRESS_RES Whether it is a residential address\n", + "ADDRESS_US Whether it is a US address\n", + "SPENDING (outcome) Amount spent by customer in test mailing ($)\n", + "\n", + "a. \n", + " Explore the spending amount by creating a pivot table for the categorical variables and computing the average and standard deviation of spending in each category.\n", + "b. \n", + " Explore the relationship between spending and each of the two continuous predictors by creating two scatterplots (Spending vs. Freq, and Spending vs. last_update_days_ago). Does there seem to be a linear relationship?\n", + "c. \n", + " To fit a predictive model for Spending:\n", + " i. Partition the 2000 records into training and validation sets.\n", + " ii. Run a multiple linear regression model for Spending vs. all six predictors. Give the estimated predictive equation.\n", + " iii. Based on this model, what type of purchaser is most likely to spend a large amount of money?\n", + " iv. If we used backward elimination to reduce the number of predictors, which predictor would be dropped first from the model?\n", + " v. Show how the prediction and the prediction error are computed for the first purchase in the validation set.\n", + " vi. Evaluate the predictive accuracy of the model by examining its performance on the validation set.\n", + " vii. Create a histogram of the model residuals. Do they appear to follow a normal distribution? How does this affect the predictive performance of the model?" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.9" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "767d51c1340bd893661ea55ea3124f6de3c7a262a8b4abca0554b478b1e2ff90" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Tayko.csv b/Tayko.csv new file mode 100644 index 0000000..aefd231 --- /dev/null +++ b/Tayko.csv @@ -0,0 +1,2001 @@ +sequence_number,US,source_a,source_c,source_b,source_d,source_e,source_m,source_o,source_h,source_r,source_s,source_t,source_u,source_p,source_x,source_w,Freq,last_update_days_ago,1st_update_days_ago,Web order,Gender=male,Address_is_res,Purchase,Spending +1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,3662,3662,1,0,1,1,128 +2,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2900,2900,1,1,0,0,0 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