{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "0949d96f", "metadata": {}, "outputs": [], "source": [ "# for basic operations\n", "import numpy as np \n", "import pandas as pd \n", "import statsmodels.api as sm\n", "# for data visualizations\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "# reading the data\n", "data = pd.read_csv('data/house prices/house_price.csv')" ] }, { "cell_type": "code", "execution_count": 2, "id": "9d975a23", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1460, 81)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.shape" ] }, { "cell_type": "code", "execution_count": 3, "id": "54f26cd9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
0160RL65.08450PaveNaNRegLvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520032003GableCompShgVinylSdVinylSdBrkFace196.0GdTAPConcGdTANoGLQ706Unf0150856GasAExYSBrkr85685401710102131Gd8Typ0NaNAttchd2003.0RFn2548TATAY0610000NaNNaNNaN022008WDNormal208500
1220RL80.09600PaveNaNRegLvlAllPubFR2GtlVeenkerFeedrNorm1Fam1Story6819761976GableCompShgMetalSdMetalSdNone0.0TATACBlockGdTAGdALQ978Unf02841262GasAExYSBrkr1262001262012031TA6Typ1TAAttchd1976.0RFn2460TATAY29800000NaNNaNNaN052007WDNormal181500
2360RL68.011250PaveNaNIR1LvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520012002GableCompShgVinylSdVinylSdBrkFace162.0GdTAPConcGdTAMnGLQ486Unf0434920GasAExYSBrkr92086601786102131Gd6Typ1TAAttchd2001.0RFn2608TATAY0420000NaNNaNNaN092008WDNormal223500
3470RL60.09550PaveNaNIR1LvlAllPubCornerGtlCrawforNormNorm1Fam2Story7519151970GableCompShgWd SdngWd ShngNone0.0TATABrkTilTAGdNoALQ216Unf0540756GasAGdYSBrkr96175601717101031Gd7Typ1GdDetchd1998.0Unf3642TATAY035272000NaNNaNNaN022006WDAbnorml140000
4560RL84.014260PaveNaNIR1LvlAllPubFR2GtlNoRidgeNormNorm1Fam2Story8520002000GableCompShgVinylSdVinylSdBrkFace350.0GdTAPConcGdTAAvGLQ655Unf04901145GasAExYSBrkr1145105302198102141Gd9Typ1TAAttchd2000.0RFn3836TATAY192840000NaNNaNNaN0122008WDNormal250000
\n", "
" ], "text/plain": [ " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", "0 1 60 RL 65.0 8450 Pave NaN Reg \n", "1 2 20 RL 80.0 9600 Pave NaN Reg \n", "2 3 60 RL 68.0 11250 Pave NaN IR1 \n", "3 4 70 RL 60.0 9550 Pave NaN IR1 \n", "4 5 60 RL 84.0 14260 Pave NaN IR1 \n", "\n", " LandContour Utilities LotConfig LandSlope Neighborhood Condition1 \\\n", "0 Lvl AllPub Inside Gtl CollgCr Norm \n", "1 Lvl AllPub FR2 Gtl Veenker Feedr \n", "2 Lvl AllPub Inside Gtl CollgCr Norm \n", "3 Lvl AllPub Corner Gtl Crawfor Norm \n", "4 Lvl AllPub FR2 Gtl NoRidge Norm \n", "\n", " Condition2 BldgType HouseStyle OverallQual OverallCond YearBuilt \\\n", "0 Norm 1Fam 2Story 7 5 2003 \n", "1 Norm 1Fam 1Story 6 8 1976 \n", "2 Norm 1Fam 2Story 7 5 2001 \n", "3 Norm 1Fam 2Story 7 5 1915 \n", "4 Norm 1Fam 2Story 8 5 2000 \n", "\n", " YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType \\\n", "0 2003 Gable CompShg VinylSd VinylSd BrkFace \n", "1 1976 Gable CompShg MetalSd MetalSd None \n", "2 2002 Gable CompShg VinylSd VinylSd BrkFace \n", "3 1970 Gable CompShg Wd Sdng Wd Shng None \n", "4 2000 Gable CompShg VinylSd VinylSd BrkFace \n", "\n", " MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure \\\n", "0 196.0 Gd TA PConc Gd TA No \n", "1 0.0 TA TA CBlock Gd TA Gd \n", "2 162.0 Gd TA PConc Gd TA Mn \n", "3 0.0 TA TA BrkTil TA Gd No \n", "4 350.0 Gd TA PConc Gd TA Av \n", "\n", " BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 BsmtUnfSF TotalBsmtSF \\\n", "0 GLQ 706 Unf 0 150 856 \n", "1 ALQ 978 Unf 0 284 1262 \n", "2 GLQ 486 Unf 0 434 920 \n", "3 ALQ 216 Unf 0 540 756 \n", "4 GLQ 655 Unf 0 490 1145 \n", "\n", " Heating HeatingQC CentralAir Electrical 1stFlrSF 2ndFlrSF LowQualFinSF \\\n", "0 GasA Ex Y SBrkr 856 854 0 \n", "1 GasA Ex Y SBrkr 1262 0 0 \n", "2 GasA Ex Y SBrkr 920 866 0 \n", "3 GasA Gd Y SBrkr 961 756 0 \n", "4 GasA Ex Y SBrkr 1145 1053 0 \n", "\n", " GrLivArea BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr \\\n", "0 1710 1 0 2 1 3 \n", "1 1262 0 1 2 0 3 \n", "2 1786 1 0 2 1 3 \n", "3 1717 1 0 1 0 3 \n", "4 2198 1 0 2 1 4 \n", "\n", " KitchenAbvGr KitchenQual TotRmsAbvGrd Functional Fireplaces FireplaceQu \\\n", "0 1 Gd 8 Typ 0 NaN \n", "1 1 TA 6 Typ 1 TA \n", "2 1 Gd 6 Typ 1 TA \n", "3 1 Gd 7 Typ 1 Gd \n", "4 1 Gd 9 Typ 1 TA \n", "\n", " GarageType GarageYrBlt GarageFinish GarageCars GarageArea GarageQual \\\n", "0 Attchd 2003.0 RFn 2 548 TA \n", "1 Attchd 1976.0 RFn 2 460 TA \n", "2 Attchd 2001.0 RFn 2 608 TA \n", "3 Detchd 1998.0 Unf 3 642 TA \n", "4 Attchd 2000.0 RFn 3 836 TA \n", "\n", " GarageCond PavedDrive WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch \\\n", "0 TA Y 0 61 0 0 \n", "1 TA Y 298 0 0 0 \n", "2 TA Y 0 42 0 0 \n", "3 TA Y 0 35 272 0 \n", "4 TA Y 192 84 0 0 \n", "\n", " ScreenPorch PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold \\\n", "0 0 0 NaN NaN NaN 0 2 2008 \n", "1 0 0 NaN NaN NaN 0 5 2007 \n", "2 0 0 NaN NaN NaN 0 9 2008 \n", "3 0 0 NaN NaN NaN 0 2 2006 \n", "4 0 0 NaN NaN NaN 0 12 2008 \n", "\n", " SaleType SaleCondition SalePrice \n", "0 WD Normal 208500 \n", "1 WD Normal 181500 \n", "2 WD Normal 223500 \n", "3 WD Abnorml 140000 \n", "4 WD Normal 250000 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.set_option('max_columns',82)\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 4, "id": "b0e9b997", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n", " 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n", " 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n", " 'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n", " 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n", " 'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n", " 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n", " 'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n", " 'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n", " 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n", " 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n", " 'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n", " 'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n", " 'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n", " 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n", " 'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n", " 'SaleCondition', 'SalePrice'],\n", " dtype='object')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.columns" ] }, { "cell_type": "code", "execution_count": 5, "id": "ea7490bb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 1460 entries, 0 to 1459\n", "Data columns (total 81 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Id 1460 non-null int64 \n", " 1 MSSubClass 1460 non-null int64 \n", " 2 MSZoning 1460 non-null object \n", " 3 LotFrontage 1201 non-null float64\n", " 4 LotArea 1460 non-null int64 \n", " 5 Street 1460 non-null object \n", " 6 Alley 91 non-null object \n", " 7 LotShape 1460 non-null object \n", " 8 LandContour 1460 non-null object \n", " 9 Utilities 1460 non-null object \n", " 10 LotConfig 1460 non-null object \n", " 11 LandSlope 1460 non-null object \n", " 12 Neighborhood 1460 non-null object \n", " 13 Condition1 1460 non-null object \n", " 14 Condition2 1460 non-null object \n", " 15 BldgType 1460 non-null object \n", " 16 HouseStyle 1460 non-null object \n", " 17 OverallQual 1460 non-null int64 \n", " 18 OverallCond 1460 non-null int64 \n", " 19 YearBuilt 1460 non-null int64 \n", " 20 YearRemodAdd 1460 non-null int64 \n", " 21 RoofStyle 1460 non-null object \n", " 22 RoofMatl 1460 non-null object \n", " 23 Exterior1st 1460 non-null object \n", " 24 Exterior2nd 1460 non-null object \n", " 25 MasVnrType 1452 non-null object \n", " 26 MasVnrArea 1452 non-null float64\n", " 27 ExterQual 1460 non-null object \n", " 28 ExterCond 1460 non-null object \n", " 29 Foundation 1460 non-null object \n", " 30 BsmtQual 1423 non-null object \n", " 31 BsmtCond 1423 non-null object \n", " 32 BsmtExposure 1422 non-null object \n", " 33 BsmtFinType1 1423 non-null object \n", " 34 BsmtFinSF1 1460 non-null int64 \n", " 35 BsmtFinType2 1422 non-null object \n", " 36 BsmtFinSF2 1460 non-null int64 \n", " 37 BsmtUnfSF 1460 non-null int64 \n", " 38 TotalBsmtSF 1460 non-null int64 \n", " 39 Heating 1460 non-null object \n", " 40 HeatingQC 1460 non-null object \n", " 41 CentralAir 1460 non-null object \n", " 42 Electrical 1459 non-null object \n", " 43 1stFlrSF 1460 non-null int64 \n", " 44 2ndFlrSF 1460 non-null int64 \n", " 45 LowQualFinSF 1460 non-null int64 \n", " 46 GrLivArea 1460 non-null int64 \n", " 47 BsmtFullBath 1460 non-null int64 \n", " 48 BsmtHalfBath 1460 non-null int64 \n", " 49 FullBath 1460 non-null int64 \n", " 50 HalfBath 1460 non-null int64 \n", " 51 BedroomAbvGr 1460 non-null int64 \n", " 52 KitchenAbvGr 1460 non-null int64 \n", " 53 KitchenQual 1460 non-null object \n", " 54 TotRmsAbvGrd 1460 non-null int64 \n", " 55 Functional 1460 non-null object \n", " 56 Fireplaces 1460 non-null int64 \n", " 57 FireplaceQu 770 non-null object \n", " 58 GarageType 1379 non-null object \n", " 59 GarageYrBlt 1379 non-null float64\n", " 60 GarageFinish 1379 non-null object \n", " 61 GarageCars 1460 non-null int64 \n", " 62 GarageArea 1460 non-null int64 \n", " 63 GarageQual 1379 non-null object \n", " 64 GarageCond 1379 non-null object \n", " 65 PavedDrive 1460 non-null object \n", " 66 WoodDeckSF 1460 non-null int64 \n", " 67 OpenPorchSF 1460 non-null int64 \n", " 68 EnclosedPorch 1460 non-null int64 \n", " 69 3SsnPorch 1460 non-null int64 \n", " 70 ScreenPorch 1460 non-null int64 \n", " 71 PoolArea 1460 non-null int64 \n", " 72 PoolQC 7 non-null object \n", " 73 Fence 281 non-null object \n", " 74 MiscFeature 54 non-null object \n", " 75 MiscVal 1460 non-null int64 \n", " 76 MoSold 1460 non-null int64 \n", " 77 YrSold 1460 non-null int64 \n", " 78 SaleType 1460 non-null object \n", " 79 SaleCondition 1460 non-null object \n", " 80 SalePrice 1460 non-null int64 \n", "dtypes: float64(3), int64(35), object(43)\n", "memory usage: 924.0+ KB\n" ] } ], "source": [ "data.info()" ] }, { "cell_type": "code", "execution_count": 6, "id": "19f8076f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IdMSSubClassLotFrontageLotAreaOverallQualOverallCondYearBuiltYearRemodAddMasVnrAreaBsmtFinSF1BsmtFinSF2BsmtUnfSFTotalBsmtSF1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrTotRmsAbvGrdFireplacesGarageYrBltGarageCarsGarageAreaWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValMoSoldYrSoldSalePrice
count1460.0000001460.0000001201.0000001460.0000001460.0000001460.0000001460.0000001460.0000001452.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001379.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.000000
mean730.50000056.89726070.04995810516.8280826.0993155.5753421971.2678081984.865753103.685262443.63972646.549315567.2404111057.4294521162.626712346.9924665.8445211515.4636990.4253420.0575341.5650680.3828772.8664381.0465756.5178080.6130141978.5061641.767123472.98013794.24452146.66027421.9541103.40958915.0609592.75890443.4890416.3219182007.815753180921.195890
std421.61000942.30057124.2847529981.2649321.3829971.11279930.20290420.645407181.066207456.098091161.319273441.866955438.705324386.587738436.52843648.623081525.4803830.5189110.2387530.5509160.5028850.8157780.2203381.6253930.64466624.6897250.747315213.804841125.33879466.25602861.11914929.31733155.75741540.177307496.1230242.7036261.32809579442.502883
min1.00000020.00000021.0000001300.0000001.0000001.0000001872.0000001950.0000000.0000000.0000000.0000000.0000000.000000334.0000000.0000000.000000334.0000000.0000000.0000000.0000000.0000000.0000000.0000002.0000000.0000001900.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000001.0000002006.00000034900.000000
25%365.75000020.00000059.0000007553.5000005.0000005.0000001954.0000001967.0000000.0000000.0000000.000000223.000000795.750000882.0000000.0000000.0000001129.5000000.0000000.0000001.0000000.0000002.0000001.0000005.0000000.0000001961.0000001.000000334.5000000.0000000.0000000.0000000.0000000.0000000.0000000.0000005.0000002007.000000129975.000000
50%730.50000050.00000069.0000009478.5000006.0000005.0000001973.0000001994.0000000.000000383.5000000.000000477.500000991.5000001087.0000000.0000000.0000001464.0000000.0000000.0000002.0000000.0000003.0000001.0000006.0000001.0000001980.0000002.000000480.0000000.00000025.0000000.0000000.0000000.0000000.0000000.0000006.0000002008.000000163000.000000
75%1095.25000070.00000080.00000011601.5000007.0000006.0000002000.0000002004.000000166.000000712.2500000.000000808.0000001298.2500001391.250000728.0000000.0000001776.7500001.0000000.0000002.0000001.0000003.0000001.0000007.0000001.0000002002.0000002.000000576.000000168.00000068.0000000.0000000.0000000.0000000.0000000.0000008.0000002009.000000214000.000000
max1460.000000190.000000313.000000215245.00000010.0000009.0000002010.0000002010.0000001600.0000005644.0000001474.0000002336.0000006110.0000004692.0000002065.000000572.0000005642.0000003.0000002.0000003.0000002.0000008.0000003.00000014.0000003.0000002010.0000004.0000001418.000000857.000000547.000000552.000000508.000000480.000000738.00000015500.00000012.0000002010.000000755000.000000
\n", "
" ], "text/plain": [ " Id MSSubClass LotFrontage LotArea OverallQual \\\n", "count 1460.000000 1460.000000 1201.000000 1460.000000 1460.000000 \n", "mean 730.500000 56.897260 70.049958 10516.828082 6.099315 \n", "std 421.610009 42.300571 24.284752 9981.264932 1.382997 \n", "min 1.000000 20.000000 21.000000 1300.000000 1.000000 \n", "25% 365.750000 20.000000 59.000000 7553.500000 5.000000 \n", "50% 730.500000 50.000000 69.000000 9478.500000 6.000000 \n", "75% 1095.250000 70.000000 80.000000 11601.500000 7.000000 \n", "max 1460.000000 190.000000 313.000000 215245.000000 10.000000 \n", "\n", " OverallCond YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 \\\n", "count 1460.000000 1460.000000 1460.000000 1452.000000 1460.000000 \n", "mean 5.575342 1971.267808 1984.865753 103.685262 443.639726 \n", "std 1.112799 30.202904 20.645407 181.066207 456.098091 \n", "min 1.000000 1872.000000 1950.000000 0.000000 0.000000 \n", "25% 5.000000 1954.000000 1967.000000 0.000000 0.000000 \n", "50% 5.000000 1973.000000 1994.000000 0.000000 383.500000 \n", "75% 6.000000 2000.000000 2004.000000 166.000000 712.250000 \n", "max 9.000000 2010.000000 2010.000000 1600.000000 5644.000000 \n", "\n", " BsmtFinSF2 BsmtUnfSF TotalBsmtSF 1stFlrSF 2ndFlrSF \\\n", "count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 \n", "mean 46.549315 567.240411 1057.429452 1162.626712 346.992466 \n", "std 161.319273 441.866955 438.705324 386.587738 436.528436 \n", "min 0.000000 0.000000 0.000000 334.000000 0.000000 \n", "25% 0.000000 223.000000 795.750000 882.000000 0.000000 \n", "50% 0.000000 477.500000 991.500000 1087.000000 0.000000 \n", "75% 0.000000 808.000000 1298.250000 1391.250000 728.000000 \n", "max 1474.000000 2336.000000 6110.000000 4692.000000 2065.000000 \n", "\n", " LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath FullBath \\\n", "count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 \n", "mean 5.844521 1515.463699 0.425342 0.057534 1.565068 \n", "std 48.623081 525.480383 0.518911 0.238753 0.550916 \n", "min 0.000000 334.000000 0.000000 0.000000 0.000000 \n", "25% 0.000000 1129.500000 0.000000 0.000000 1.000000 \n", "50% 0.000000 1464.000000 0.000000 0.000000 2.000000 \n", "75% 0.000000 1776.750000 1.000000 0.000000 2.000000 \n", "max 572.000000 5642.000000 3.000000 2.000000 3.000000 \n", "\n", " HalfBath BedroomAbvGr KitchenAbvGr TotRmsAbvGrd Fireplaces \\\n", "count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 \n", "mean 0.382877 2.866438 1.046575 6.517808 0.613014 \n", "std 0.502885 0.815778 0.220338 1.625393 0.644666 \n", "min 0.000000 0.000000 0.000000 2.000000 0.000000 \n", "25% 0.000000 2.000000 1.000000 5.000000 0.000000 \n", "50% 0.000000 3.000000 1.000000 6.000000 1.000000 \n", "75% 1.000000 3.000000 1.000000 7.000000 1.000000 \n", "max 2.000000 8.000000 3.000000 14.000000 3.000000 \n", "\n", " GarageYrBlt GarageCars GarageArea WoodDeckSF OpenPorchSF \\\n", "count 1379.000000 1460.000000 1460.000000 1460.000000 1460.000000 \n", "mean 1978.506164 1.767123 472.980137 94.244521 46.660274 \n", "std 24.689725 0.747315 213.804841 125.338794 66.256028 \n", "min 1900.000000 0.000000 0.000000 0.000000 0.000000 \n", "25% 1961.000000 1.000000 334.500000 0.000000 0.000000 \n", "50% 1980.000000 2.000000 480.000000 0.000000 25.000000 \n", "75% 2002.000000 2.000000 576.000000 168.000000 68.000000 \n", "max 2010.000000 4.000000 1418.000000 857.000000 547.000000 \n", "\n", " EnclosedPorch 3SsnPorch ScreenPorch PoolArea MiscVal \\\n", "count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 \n", "mean 21.954110 3.409589 15.060959 2.758904 43.489041 \n", "std 61.119149 29.317331 55.757415 40.177307 496.123024 \n", "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "max 552.000000 508.000000 480.000000 738.000000 15500.000000 \n", "\n", " MoSold YrSold SalePrice \n", "count 1460.000000 1460.000000 1460.000000 \n", "mean 6.321918 2007.815753 180921.195890 \n", "std 2.703626 1.328095 79442.502883 \n", "min 1.000000 2006.000000 34900.000000 \n", "25% 5.000000 2007.000000 129975.000000 \n", "50% 6.000000 2008.000000 163000.000000 \n", "75% 8.000000 2009.000000 214000.000000 \n", "max 12.000000 2010.000000 755000.000000 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.describe()" ] }, { "cell_type": "code", "execution_count": 7, "id": "75625dd3", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", " warnings.warn(msg, FutureWarning)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.distplot(data['SalePrice'])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 8, "id": "29ae645c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "34900\n", "755000\n", "180921.19589041095\n", "163000.0\n", "0 140000\n", "dtype: int64\n", "79442.50288288663\n", "6311111264.297451\n", "6.536281860064529\n", "1.8828757597682129\n" ] } ], "source": [ "print(data['SalePrice'].min())\n", "print(data['SalePrice'].max())\n", "print(data['SalePrice'].mean())\n", "print(data['SalePrice'].median())\n", "print(data['SalePrice'].mode())\n", "print(data['SalePrice'].std())\n", "print(data['SalePrice'].var())\n", "print(data['SalePrice'].kurt())\n", "print(data['SalePrice'].skew())" ] }, { "cell_type": "code", "execution_count": 9, "id": "a1fa74d0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "181655.048" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.random.seed(23)\n", "sampleprice = np.random.choice(a=data['SalePrice'],size=500)\n", "sampleprice.mean()" ] }, { "cell_type": "code", "execution_count": 10, "id": "d0982353", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "NAmes 225\n", "CollgCr 150\n", "OldTown 113\n", "Edwards 100\n", "Somerst 86\n", "Gilbert 79\n", "NridgHt 77\n", "Sawyer 74\n", "NWAmes 73\n", "SawyerW 59\n", "BrkSide 58\n", "Crawfor 51\n", "Mitchel 49\n", "NoRidge 41\n", "Timber 38\n", "IDOTRR 37\n", "ClearCr 28\n", "StoneBr 25\n", "SWISU 25\n", "MeadowV 17\n", "Blmngtn 17\n", "BrDale 16\n", "Veenker 11\n", "NPkVill 9\n", "Blueste 2\n", "Name: Neighborhood, dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.Neighborhood.value_counts()" ] }, { "cell_type": "code", "execution_count": 11, "id": "4dc0d919", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-12.196987594087203 : 3.2253680352007412e-34\n" ] } ], "source": [ "from statsmodels.stats.weightstats import ztest\n", "zval , pval = ztest(x1 = data[data['Neighborhood']=='Edwards']['SalePrice'], value=data['SalePrice'].mean())\n", "print(zval ,' : ', pval)" ] }, { "cell_type": "code", "execution_count": 12, "id": "5857a2ba", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-10.639294263334575 : 1.9560526026260018e-26\n" ] } ], "source": [ "zval , pval = ztest(x1 = data[data['Neighborhood']=='OldTown']['SalePrice'], value=data['SalePrice'].mean())\n", "print(zval ,' : ', pval)" ] }, { "cell_type": "code", "execution_count": 23, "id": "11654cbe", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8846152543518949" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mean_ =data[data['Neighborhood']=='Edwards']['SalePrice'].mean()\n", "stdev =data[data['Neighborhood']=='Edwards']['SalePrice'].std()\n", "from scipy import stats\n", "z_sc=(180000-mean_)/stdev\n", "stats.norm.cdf(z_sc)\n" ] }, { "cell_type": "code", "execution_count": 24, "id": "61c1b9f1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(56483.2717735012, 199956.12822649878)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.t.interval(.90,len(data[data['Neighborhood']=='Edwards']['SalePrice']),mean_, scale=stdev)" ] }, { "cell_type": "code", "execution_count": 20, "id": "d0e127db", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9534318553545458" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mean_ =data[data['Neighborhood']=='SawyerW']['SalePrice'].mean()\n", "stdev =data[data['Neighborhood']=='SawyerW']['SalePrice'].std()\n", "from scipy import stats\n", "z_sc=(280000-mean_)/stdev\n", "stats.norm.cdf(z_sc)" ] }, { "cell_type": "code", "execution_count": 22, "id": "f20efae3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(93556.13084670802, 279555.4623736309)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.t.interval(.90,len(data[data['Neighborhood']=='SawyerW']['SalePrice']),mean_, scale=stdev)" ] }, { "cell_type": "code", "execution_count": 17, "id": "d525f89b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "320000" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[data['Neighborhood']=='SawyerW']['SalePrice'].max()" ] }, { "cell_type": "code", "execution_count": 19, "id": "c1070902", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Ttest_indResult(statistic=7.375410592264813, pvalue=8.880142338427138e-12)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a=data[data['Neighborhood']=='SawyerW']['SalePrice']\n", "b=data[data['Neighborhood']=='Edwards']['SalePrice']\n", "stats.ttest_ind(a,b,axis=0,equal_var=True)" ] }, { "cell_type": "code", "execution_count": 28, "id": "a45aa369", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.113184970351664 : 0.0030872258527891404\n" ] } ], "source": [ "# 1 sample t test.\n", "tscore, pvalue = stats.ttest_1samp(data[data['Neighborhood']=='CollgCr']['SalePrice'].sample(50), popmean=data['SalePrice'].mean())\n", "print(tscore, ' : ',pvalue)" ] }, { "cell_type": "code", "execution_count": 30, "id": "2bd45d42", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data['YearBuilt'].plot()" ] }, { "cell_type": "code", "execution_count": 33, "id": "31713fd5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Ttest_indResult(statistic=-25.56467512748203, pvalue=2.0817612468835544e-119)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a= data[data['YearBuilt']<=1990]['SalePrice']\n", "b=data[data['YearBuilt']>1990]['SalePrice']\n", "stats.ttest_ind(a,b,axis=0,equal_var=True)" ] }, { "cell_type": "code", "execution_count": 34, "id": "54531c0c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Frequency table\n", "============================\n", "SalePrice High Medium Low\n", "LandContour \n", "Bnk 32 20 11\n", "HLS 10 12 28\n", "Low 8 11 17\n", "Lvl 437 447 427\n", "============================\n", "ChiSquare test statistic: 26.252544346201447\n", "p-value: 0.00019976918050008285\n" ] } ], "source": [ "def compute_freq_chi2(x,y):\n", " freqtab = pd.crosstab(x,y)\n", " print(\"Frequency table\")\n", " print(\"============================\")\n", " print(freqtab)\n", " print(\"============================\")\n", " chi2, pval, dof, expected = stats.chi2_contingency(freqtab)\n", " print(\"ChiSquare test statistic: \",chi2)\n", " print(\"p-value: \",pval)\n", " return\n", "\n", "\n", "price = pd.qcut(data['SalePrice'], 3, labels = ['High', 'Medium', 'Low'])\n", "compute_freq_chi2(data.LandContour, price)" ] }, { "cell_type": "code", "execution_count": 37, "id": "2b6aa9b6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " -------------------- describe ---------------------\n", "\n", "LandContour Bnk HLS Low Lvl\n", "count 63.000000 50.000000 36.000000 1311.000000\n", "mean 143104.079365 231533.940000 203661.111111 180183.746758\n", "std 49361.244074 101790.139741 83935.353620 78463.567918\n", "min 52500.000000 82500.000000 39300.000000 34900.000000\n", "25% 113000.000000 151750.000000 143000.000000 130000.000000\n", "50% 139400.000000 222250.000000 190000.000000 162900.000000\n", "75% 171250.000000 281347.250000 263750.000000 212000.000000\n", "max 315000.000000 538000.000000 385000.000000 755000.000000\n", "\n", "\n", " -------------------- One way anova ---------------------\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print('\\n -------------------- describe ---------------------\\n')\n", "print(data.groupby('LandContour')['SalePrice'].describe().T)\n", " \n", " \n", "#box_plot(category_cols,independent_col,dependent_col)\n", "sns.boxplot(x='LandContour', y='SalePrice', data = data)\n", "print('\\n\\n -------------------- One way anova ---------------------\\n')" ] }, { "cell_type": "code", "execution_count": 38, "id": "c87204c9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "F_onewayResult(statistic=12.850188333283924, pvalue=2.7422167521379096e-08)" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.f_oneway(data['SalePrice'][data['LandContour'] == 'Lvl'],data['SalePrice'][data['LandContour'] == 'Bnk'],data['SalePrice'][data['LandContour'] == 'Low'],data['SalePrice'][data['LandContour'] == 'HLS'])\n" ] }, { "cell_type": "code", "execution_count": 39, "id": "e9bf5beb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "F_onewayResult(statistic=7.117438313079969, pvalue=0.00017761182066955203)" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.f_oneway(data['SalePrice'][data['LandContour'] == 'Lvl'].sample(35),data['SalePrice'][data['LandContour'] == 'Bnk'].sample(35),data['SalePrice'][data['LandContour'] == 'Low'].sample(35),data['SalePrice'][data['LandContour'] == 'HLS'].sample(35))\n" ] }, { "cell_type": "code", "execution_count": null, "id": "e66a69a9", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "0e861e35", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "a7d17737", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "791cbe08", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "0aa58bd9", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "c742405f", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "ab26fe96", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "806029a2", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "0b2e2765", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "2f603207", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "6d73e1e7", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "cbbf578a", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "f131082b", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "c09451f1", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "63a15e1a", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "f7bcc217", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "9634e0e7", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "f847b894", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "65138784", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "3c660384", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "3c230c86", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "5a272c3c", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "c9e2e4d1", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "5436dc8d", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "6712689b", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "d027c9dc", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "37c7b6b5", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "ac112ead", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "dbbf0f86", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "78fb264a", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 }