{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Descriptive Statistics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Descriptive Statistics, provides a summary of your dataset giving a measure of the centre, dispersion and shape of your data. Here the data is described as a sample of the whole population, and there are no inferences made from the sample to the whole population, unlike Inferential Statistics, in which we model the data on the basis of probability theory." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Key Elements of Descriptive Statistics\n", "\n", "### Measures Of Central Tendency\n", "\n", "* Mean\n", "* Median\n", "* Mode\n", "\n", "### Measures Of Spread\n", "\n", "* Range\n", "* Outliers\n", "* Interquantile Range\n", "* Variance\n", "\n", "### Dependence\n", "\n", "* Correlation v/s Causation\n", "* Covariance" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import ipywidgets as widgets\n", "from ipywidgets import interact\n", "from ipywidgets import interact_manual" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# house price prediction\n", "data = pd.read_csv('Datasets/train.csv')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1460, 81)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.shape" ] }, { "cell_type": "code", "execution_count": 5, "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": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.set_option('max_columns', 81)\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d9fc4ea95ff84b3bae672a13217a9354", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(Dropdown(description='column', options=('Id', 'MSSubClass', 'LotFrontage', 'LotArea', 'O…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "@interact\n", "def check(column = list(data.select_dtypes('number').columns),\n", " column2 = list(data.select_dtypes('number').columns)[1:]):\n", " print(\"Correlation : \",data[column].corr(data[column2]))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1a8d35cbe5fd4e45b2fea9583634c515", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(Dropdown(description='column1', options=('MSZoning', 'Street', 'Alley', 'LotShape', 'Lan…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams['figure.figsize'] = (15, 5)\n", "plt.style.use('fivethirtyeight')\n", "\n", "@interact_manual\n", "def check(column1 = list(data.select_dtypes('object').columns),\n", " column2 = list(data.select_dtypes('number').columns)):\n", " sns.boxplot(data[column1], data[column2])\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "21e49d0dd61f403a85075f80dc43f5be", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(Dropdown(description='column1', options=('MSSubClass', 'LotFrontage', 'LotArea', 'Overal…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams['figure.figsize'] = (15, 5)\n", "plt.style.use('fivethirtyeight')\n", "\n", "@interact_manual\n", "def check(column1 = list(data.select_dtypes('number').columns)[1:],\n", " column2 = list(data.select_dtypes('number').columns)[2:]):\n", " sns.scatterplot(data[column1], data[column2])\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1460, 81)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# lets check the shape of the data\n", "data.shape" ] }, { "cell_type": "code", "execution_count": 10, "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": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# lets check the column names\n", "data.columns" ] }, { "cell_type": "code", "execution_count": 11, "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": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# head of the dataset\n", "pd.set_option('max_columns', 81)\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2ecb2f518a6246b6930d0a0ac7b7ccac", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(Dropdown(description='column', options=('MSZoning', 'Street', 'Alley', 'LotShape', 'Land…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# stats at a glance\n", "@interact\n", "def check(column = list(data.select_dtypes('object').columns)):\n", " return data[[column,'SalePrice']].groupby(column).agg(['max','min','mean','median','std','sum','count'])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 208500\n", "1 181500\n", "2 223500\n", "3 140000\n", "4 250000\n", "5 143000\n", "6 307000\n", "7 200000\n", "8 129900\n", "9 118000\n", "10 129500\n", "11 345000\n", "12 144000\n", "13 279500\n", "14 157000\n", "15 132000\n", "16 149000\n", "17 90000\n", "18 159000\n", "19 139000\n", "Name: SalePrice, dtype: int64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# lets check the Target Column of the Data\n", "data['SalePrice'].head(20)" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "If we look at the values, they are spread all over.\n", "Some houses are ~120,000 dollars and some are over ~200,000\n", "AND THIS IS JUST IN 20 OBSERVATIONS OF THE DATA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Mean SalePrice" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "180921.19589041095\n" ] } ], "source": [ "# checking the average price of houses\n", "mean = np.mean(data['SalePrice'])\n", "print(mean)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Disadvantage of Mean\n", "\n", "* Finding mean is not a good approach as the 'Mean is often affected by Outliers' or in simple words if there are some observations larger or smaller than majority of the other observations then the mean tends to deviate towards these values.\n", "\n", "* To generalize it if the distribution of datasets is skewed(troubled by outliers), we do not choose mean. Here we will have to go for Median." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Median of SalePrice" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "163000.0\n" ] } ], "source": [ "# checking the average price of houses\n", "median = np.median(data['SalePrice'])\n", "print(median)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* We can see there is a Huge difference in the Mean and Median Values, which tells us that there are Outliers in this column" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Median and Inter Quantile Range\n", "\n", "* Taking the concept of median a step further, we can define the Inter - Quartile Range.\n", "* IQR is a measure of variability and is based on dividing a data set into quartiles.\n", "* Quartile is the division of a set of observations into four intervals based on the values of the data." ] }, { "attachments": { "image.png": { "image/png": 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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "![image.png](attachment:image.png)\n", "\n", "**The interquartile range is equal to Q3 minus Q1.**\n", "\n", "**For example,** \n", "* consider the following numbers: 1, 3, 4, 5, 5, 6, 7, 11.\n", "\n", " * Q1 is the middle value in the first half of the data set.\n", " * Since there are an even number of data points in the first half of the data set, the middle value is the average of the two middle values; that is, Q1 = (3 + 4)/2 or Q1 = 3.5. Q3 is the middle value in the second half of the data set. \n", " * Again, since the second half of the data set has an even number of observations, the middle value is the average of the two middle values; that is, Q3 = (6 + 7)/2 or Q3 = 6.5.\n", " * The interquartile range is Q3 minus Q1, so IQR = 6.5 - 3.5 = 3.\n", " \n", " \n", "### Box Plot View for IQR\n", "\n", "![image.png](attachment:image.png)\n", "\n", "\n", "### Outliers with Box Plot\n", "\n", "* The Boxplot above shows some additional observations below MINIMUM and above MAXIMUM. These are Outliers.\n", "* There are many ways to mathematically represent or define outliers. One such method is using IQR." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "326099.9999999999" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x= data['SalePrice'].quantile(0.95)\n", "x" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median : 163000.0\n", "Q1: 129975.0\n", "Q3: 214000.0\n", "IQR: 84025.0\n" ] } ], "source": [ "### IQR \n", "\n", "# Median\n", "median = np.median(data['SalePrice'])\n", "print(\"Median :\",median)\n", "\n", "# lower quartile \n", "q1 = data['SalePrice'].quantile(0.25)\n", "\n", "# upper quartile\n", "q3 = data['SalePrice'].quantile(0.75)\n", "\n", "# printing Results\n", "print(\"Q1:\", q1)\n", "print(\"Q3:\", q3)\n", "print(\"IQR:\", q3 - q1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Here, IQR is Representing the Middle 50% of the values in the sales price column, Whereas the Mean and Median Values are having a hug gap in their values that means there are so many outliers in the data, let's try checking these outliers using a box plot" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Paola\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", " warnings.warn(\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(data['SalePrice'])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Outlier Upper Limit : 3937.5\n", "Outlier Lower Limit : 340037.5\n" ] } ], "source": [ "## lets find no. of outliers\n", "\n", "# for that we have to find the upper and ower outlier limit\n", "outlier_lower_limit = q1 - 1.5*(q3 - q1)\n", "outlier_upper_limit = q3 + 1.5*(q3 - q1)\n", "print(\"Outlier Upper Limit :\", outlier_lower_limit)\n", "print(\"Outlier Lower Limit :\", outlier_upper_limit)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "lower_limit_outliers: 0\n", "upper_limit_outliers: 61\n", "total outliers: 61\n" ] } ], "source": [ "Sales_price = data['SalePrice']\n", "\n", "lower_limit_outliers = Sales_price[Sales_price < outlier_lower_limit].count()\n", "\n", "upper_limit_outliers = Sales_price[Sales_price > outlier_upper_limit].count()\n", "\n", "print(\"lower_limit_outliers:\", lower_limit_outliers)\n", "print(\"upper_limit_outliers:\", upper_limit_outliers)\n", "print(\"total outliers:\", upper_limit_outliers + lower_limit_outliers)" ] }, { "attachments": { "image.png": { "image/png": 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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "## Skewness\n", "\n", "In probability theory and statistics, skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. The skewness value can be positive or negative, or undefined\n", "\n", "![image.png](attachment:image.png)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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6vSOXWF/H5Uwum1nCqw0n7GRKY9KUJZc00dYzSGF2BhnBifmRXzazlKNdAzSe6J2Q1zPGJBZf3zQiskJE6kSkXkTWh9kvIrLB7X9DRJZEqysiZSLytIjsdfelIfvuceXrROT6kO1LRWSn27fBXe4YEblVRFpF5DV3+/S5fiCpqq1nfJbaP5MlM0sArGvMmDQVNbmISBC4D1gJLARuEZGFY4qtxLvWfTWwDnjAR931wDZVrQa2uee4/auBRcAK4H7XDq7ddSGvtSIkhsdU9VJ3+7bvTyBNeBcJG//B/FHzqwrJCgbYdbhjwl7TGJM4/By5LAPqVXWfqg4Am4FVY8qsAjapZwdQIiJTotRdBWx0jzcCN4Vs36yq/aq6H6gHlrn2ilR1u3od+ZtC6pgIRkbUOzt/gsZbwLt42IVTCnnzkE1HNiYd+Uku04CDIc8b3TY/ZSLVrVLVJgB3X+mjrcYIcfyp65L7kYjMCPdGRGSdiNSKSG1ra2u4IinpWPcAQyM6od1iAIumFvPmoXYb1DcmDflJLuFWORz7bXGmMn7q+n29SG39HJitqhcDv+bUEdHphVUfUtUaVa2pqKiIEkbqONTmDapPZLcYwOJpRXT0DdmgvjFpyE9yaQRCjwSmA4d9lolUt9l1deHuW3y0NT1cW6p6TFX73fb/BJb6eF9p49CJ0eQysUcui6cWA1jXmDFpyE9yeQmoFpE5IpKFN9i+dUyZrcAaN2tsOdDuuroi1d0KrHWP1wKPh2xfLSLZIjIHb+D+Rddep4gsd7PE1ozWGU1Szo3AW34/gHRwePTIZZwXrRxrweRCggHhzcOWXIxJN1GvGqWqQyJyN/AUEAQeVtVdInKH2/8g8ARwA97gew9wW6S6rul7gS0icjvQANzs6uwSkS3AbmAIuEtVh12dO4FHgFzgSXcD+KyI3OjKHwduPadPI0UdauslOyNAblYweuEYyskMUl1ZwJuHbMaYMelG0nWwtaamRmtra+MdxoT49MZadh5q46+vmT/hr/2jlxupO9LB/3PDRfzfy2dN+OsbY2JLRF5W1Zpo5ewM/TRwuK13wrvERk0tyaF7YJiOPlt+35h0YsklDRxq653wwfxR00q8yx6PjvsYY9KDJZcU19U/5C5vHJ8jl8nFOQiWXIxJN5ZcUtzJmWJxOnLJzghSXpBtycWYNGPJJcWdPMdlApd+GWtqSQ6H2/vi9vrGmIlnySXFxevs/FBTS3Jp7x3kaFd/9MLGmJRgySXFHWrrJTMoFOZEPaVp3Ex1g/q2QrIx6cOSS4o7dKKXycU5BCTc0mwTY2qxl1xsGRhj0ocllxR3uK335HTgeMnNClKWn8UuWwbGmLRhySXFHWrrZVpJXrzDYGpxji0DY0waseSSwgaHR2ju6GNaSU68Q2FqSS4Nx3to7xmMdyjGmAlgySWFHWnvY0RhWml8u8UgZFC/ybrGjEkHllxS2Og05IToFhtNLtY1ZkxasOSSwkavAJkIRy4F2RlMKc6xa7sYkyYsuaSwhuM9iBD32WKjFk0ttunIxqQJSy4p7ODxHqYW55KVkRg/5sXTith3tJvuflt+35hU5+tbR0RWiEidiNSLyPow+0VENrj9b4jIkmh1RaRMRJ4Wkb3uvjRk3z2ufJ2IXB+yfamI7HT7NrjLHYfG8TERURGJeiGbdNBwvIcZZYlx1AJw8fRiVOGNRjt6MSbVRU0uIhIE7gNWAguBW0Rk4ZhiK/GudV8NrAMe8FF3PbBNVauBbe45bv9qYBGwArjftYNrd13Ia60IibMQ+Czwgv+3n9oajvcwsyz+g/mjLpvh/f/wSsOJOEdijBlvfo5clgH1qrpPVQeAzcCqMWVWAZvUswMoEZEpUequAja6xxuBm0K2b1bVflXdD9QDy1x7Raq6Xb1rM28KqQPwT8DXAFt+F+gdGKa1sz+hkktpfhZzK/J51ZKLMSnPT3KZBhwMed7otvkpE6lulao2Abj7Sh9tNYZrS0QuA2ao6n9FeiMisk5EakWktrW1NVLRpNd4ogeAGQmUXACWzizllYY2vP8PjDGpyk9yCbfi4dhvhjOV8VPX7+uF3S4iAeDfgL+N0i6q+pCq1qhqTUVFRbTiSa3huJdcEunIBWDJrFKOdw9w4FhPvEMxxowjP8mlEZgR8nw6cNhnmUh1m11XF+6+xUdb08NsLwQWA8+KyAFgObA13Qf1EzW5LJ3ljbu8/K51jRmTyvwkl5eAahGZIyJZeIPtW8eU2QqscbPGlgPtrqsrUt2twFr3eC3weMj21SKSLSJz8AbuX3TtdYrIcjdLbA3wuKq2q2q5qs5W1dnADuBGVa09608jhTQc7yHfrUacSC6oKKAwJ8MG9Y1JcVGvIKWqQyJyN/AUEAQeVtVdInKH2/8g8ARwA97gew9wW6S6rul7gS0icjvQANzs6uwSkS3AbmAIuEtVh12dO4FHgFzgSXczYRw83sOMsjwkjtdxCScQEC6dUcIrduRiTErzdXlCVX0CL4GEbnsw5LECd/mt67YfA645Q52vAl8Ns70WrwssUqxXR9qfLhqO9zB7Un68wwhr6axS/n3bXjr7BinMyYx3OMaYcZAYp26bmFLVhDvHJdSSmaWowusH7WRKY1KVJZcU1NrVT9/gCDMnJWZyuXRmCSLw0oHj8Q7FGDNOLLmkoIPHE/Mcl1FFOZlcPK2Y5/am9rlGxqQzSy4pKFGnIYf64IJKXjvYRlvPQLxDMcaMA0suKajhWG9CLbUfztULKhhR+N3eo/EOxRgzDiy5pKCG4z1MLsohJzMYvXCcXDK9hNK8TJ6ta4le2BiTdCy5pKB3j3Un7HjLqGBA+EB1Bb/b08rIiK0zZkyqseSSgvYf7WZeRWKe4xLq6gUVHO0aYNfhjniHYoyJMUsuKaa9Z5Bj3QPMKU/85HLVfG/xUOsaMyb1WHJJMfuOdgEwt7wgzpFEV16QzcXTi3nGkosxKceSS4rZ19oNwJwk6BYDuPaiKl492Hby+jPGmNRgySXF7D/aTTAgCX2OS6g/uWwaqvDjlw/FOxRjTAxZckkx+452MbMsj8xgcvxoZ5TlceW8SfzolYM2a8yYFOJrVWSTPPa1djM3QQfzH32hIez2m2um87nHXueF/ce5Yt6kCY7KGDMekuPfW+PLyIiy/2h3UswUC7Vi0RQKszP44csH4x2KMSZG7MglhRxu76V/aIS5FYk/UyxUblaQj1wyhZ+9eph/XDVEQXbGGY9yPnH5zAmOzhhzLnwduYjIChGpE5F6EVkfZr+IyAa3/w0RWRKtroiUicjTIrLX3ZeG7LvHla8TketDti8VkZ1u3wZ3uWNE5A63/TUR+W8RWXiuH0gyefSFhtNu3/nv/QDMTZKZYqFurplB7+AwP3mlMd6hGGNiIGpyEZEgcB+wElgI3BLmy3sl3rXuq4F1wAM+6q4HtqlqNbDNPcftXw0sAlYA97t2cO2uC3mtFW77o6r6HlW9FPga8K9n8RmkjKOd/QAJO+YSyWUzSlgys4SHfrePoeGReIdjjDlPfrrFlgH1qroPQEQ2A6vwrnE/ahWwyV3ueIeIlIjIFGB2hLqrgKtd/Y3As8AX3PbNqtoP7BeRemCZiBwAilR1u2trE3AT8KSqhq4fkg+k5bSjo10DZGcEeHp3M+6gLmmICHdefQGf2VTLL3Y2xTscY8x58pNcpgGhI62NwOU+ykyLUrdKVZsAVLVJRCpD2toRpq1B93jsdgBE5C7g80AW8OFwb0RE1uEd+TBzZur13R/t6qe8IDvpEsvo+MqIKpWF2fyvJ97mrz58QdK9D2PMKX7GXML9hY89MjhTGT91/b5exLZU9T5VnYd39POlcA2r6kOqWqOqNRUVFVHCSD5ecsmKdxjnLCDCVfMrONLRx57mzniHY4w5D36SSyMwI+T5dOCwzzKR6ja7rjPc/egCU5Hamh4lDoDNeN1laWVweIS2nkHKC7LjHcp5uWR6CcW5mTxT14rXy2qMSUZ+kstLQLWIzBGRLLzB9q1jymwF1rhZY8uBdtflFanuVmCte7wWeDxk+2oRyRaROXgD9y+69jpFZLmbJbZmtI6IVIfE8sfAXr8fQKo42tWPAhWFyZ1cggHhqupyGo73sO9od7zDMcaco6hjLqo6JCJ3A08BQeBhVd0lIne4/Q8CTwA3APVAD3BbpLqu6XuBLSJyO9AA3Ozq7BKRLXiD/kPAXao67OrcCTwC5AJPuhvA3SJyLd64zAlOJa200dzhzRSrLMqJcyTnr2Z2Gc/WtfJMXQvzkuycHWOMx9dJlKr6BF4CCd32YMhjBe7yW9dtPwZcc4Y6XwW+GmZ7LbA4zPa/jvwOUl9zRx8BIanHXEZlBgN8oLqcJ948wrvHupk1KfmmVhuT7mz5lxTR0tFHeUE2GYHU+JEumzOJvKygXevFmCSVGt9EhubOfqpSoEtsVFZGgPdfUM6e5i671osxSciSSwoYGBrhRPcAlUXJPZg/1vK5k8jNDPJMXWu8QzHGnCVLLimgtdObKVZVmDpHLgA5mUGunDeJt5o6aGrvjXc4xpizYMklBTR39AGkVLfYqCvnlZOdEeBZO3oxJqlYckkBzZ19BANCWX7yzxQbKzcryPK5k3jzUDstLokaYxKfJZcU0NzRR2VhNsFAaq7F9b4LyskICr/dY0cvxiQLSy4poKWjn8okPzM/koLsDJbNLuP1xjYOHreZY8YkA0suSa5vcJi23sGUHG8J9f7qCgThP5/bF+9QjDE+WHJJci3uAmGpnlyKczO5dGYJj710kFb3no0xicuSS5JL5ZliY11VXcHA8AjffX5/vEMxxkRhySXJHenoIzMolORlxjuUcVdRmM0Ni6fwve3v0tE3GO9wjDERWHJJck1tfUwpziWQJldtvPPqeXT2D/G97e/GOxRjTASWXJKYqtLU3suU4tTvEhu1eFoxV82v4LvP76dvcDh6BWNMXFhySWInegbpHxphSnFuvEOZUH959TyOdg2wpfZgvEMxxpyBJZckdrjNW28rnY5cAC6fU8aSmSV867f7GBweiXc4xpgwfCUXEVkhInUiUi8i68PsFxHZ4Pa/ISJLotUVkTIReVpE9rr70pB997jydSJyfcj2pSKy0+3b4C53jIh8XkR2u9feJiKzzvUDSSZN7X0IMDnNkouIcNeHLuBQWy8/frkx3uEYY8KImlxEJAjcB6wEFgK3iMjCMcVW4l3rvhpYBzzgo+56YJuqVgPb3HPc/tXAImAFcL9rB9fuupDXWuG2vwrUqOrFwI+Ar/n/CJJXU3svFYXZZAbT7wD0wxdWctnMEv7t13voHbCxF2MSjZ9vpWVAvaruU9UBYDOwakyZVcAm9ewASkRkSpS6q4CN7vFG4KaQ7ZtVtV9V9wP1wDLXXpGqbneXVd40WkdVn1HV0XVBdgDTz+ZDSFZN7X1p1yU2SkS4Z+VFNHf087Cd92JMwvGTXKYBoSOnjW6bnzKR6lapahOAu6/00VZjmO1j3Q48GfEdpYAT3QO09w6m3WB+qGVzyrj2okoefPYdjncPxDscY0wIP8kl3AkU6rOMn7p+Xy9qWyLySaAG+HrYhkXWiUitiNS2tib3Cru7mzoAmFqSvskF4AsrLqR7YIgN2/bGOxRjTAg/yaURmBHyfDpw2GeZSHWbXVcX7r7FR1vTw2zHtXEt8EXgRlUNu/iUqj6kqjWqWlNRURH2zSaL3Ye95JJug/ljVVcVsnrZTL63413ecgnXGBN/fpLLS0C1iMwRkSy8wfatY8psBda4WWPLgXbX1RWp7lZgrXu8Fng8ZPtqEckWkTl4A/cvuvY6RWS5myW2ZrSOiFwGfAsvsYwmqZS2u6mDopwMCrIz4h1K3P399Qsozs3kSz97k5GRaAfGxpiJEPWbSVWHRORu4CkgCDysqrtE5A63/0HgCeAGvMH3HuC2SHVd0/cCW0TkdqABuNnV2SUiW4DdwBBwl6qOTge6E3gEyMUbVxkdW/k6UAD80M1OblDVG8/pE0kSuw63p/V4S6gndh7hQwsq+fErjfyPH75OzewyAD5x+cw4R2ZM+vL1b6+qPoGXQEK3PRjyWIG7/NZ1248B15yhzleBr4bZXgssDrP92sjvILV09zBId88AABZySURBVA9R39LFB+dXRi+cJi6bWULtu8f55a4jLJxSRJ4d0RkTV/YXmITePNTOiMKMsvQ7cnn0hYaw2wMirLpkGv/xzF6e2n2EP7ksLWajG5Ow0u/suxTw2sE2AKaX5sU5ksQyuTiHK+eV89KBEzTY5ZCNiStLLkno9cY2ZpTl2mB+GNdcVElRTgaPv3aIIVt3zJi4seSShF5raOOS6SXxDiMhZWcE+cjFU2lq7+N7O+yaL8bEiyWXJNPS0cfh9j4unWHJ5UwWTS1iflUB//KrPScvA22MmViWXJLM643tAJZcIhARPnrxVAaGR/h/f/FWvMMxJi1Zp32See3gCYIBYfG0YvY0d8U7nIQ1qSCbD1xQzs9fP0xFQTYXVBYAdu6LMRPFjlySzOsH27lwciE5mcHohdPcVfMrmJSfxdbXbXDfmIlmySWJjIworx9ssy4xnzKDAW68ZCpHuwZ4rv5ovMMxJq1Yckki+45209k/xCWWXHyrripk8bRinnm7xZblN2YCWXJJIi+/exyAJTMtuZyNP37PFAIB4eevH8ZbqcgYM94suSSR7e8co7wgm3kVBfEOJakU52Zy7YWV1DV38qvdzfEOx5i0YMklSagq2/cdY/ncMtzKz+YsXDGvnMlFOXxl6y56BobiHY4xKc+mIieJ/Ue7ae7o54p5k+IdSlIKBoRVl07lW7/bx4Zt9axfeWHUOmdaJNOmMxsTnR25JInt+44BcMVcSy7natakfD5eM51vP7ePvc2d8Q7HmJRmySVJbH/nGFVF2cwpz493KElt/cqLKMjJ4G9/+Dp9g8PRKxhjzokllySgquzYd5wr5k6y8ZbzVJafxdf+9GLeaGzniz9902aPGTNOfCUXEVkhInUiUi8i68PsFxHZ4Pa/ISJLotUVkTIReVpE9rr70pB997jydSJyfcj2pSKy0+3bIO6bVkSuEpFXRGRIRD52rh9Goqpv6eJoVz/LrUssJq5bNJm/ubaaH7/SyCO/PxDvcIxJSVGTi4gEgfuAlcBC4BYRWTim2Eqg2t3WAQ/4qLse2Kaq1cA29xy3fzWwCFgB3O/awbW7LuS1VrjtDcCtwKP+33ryODneYoP5MfPZD1dz3cIq/vG/dvONX+9heMSOYIyJJT9HLsuAelXdp6oDwGZg1Zgyq4BN6tkBlIjIlCh1VwEb3eONwE0h2zerar+q7gfqgWWuvSJV3a5eX8am0TqqekBV3wBScgGp5+uPMrU4h5llduXJWAkEhH9ffRl/ctk0vvHrvXzqOy9Q32ILgRoTK36SyzTgYMjzRrfNT5lIdatUtQnA3Vf6aKsxShwRicg6EakVkdrW1tazqRo3vQPD/G7PUT58UaWNt8RYblaQf7n5Er72sYt5peEE1/7rb7npvud55Pn9vHusO97hGZPU/JznEu4bbWwfwpnK+Knr9/XOpa3TC6s+BDwEUFNTkxT9IL/d00Lv4DA3LJ4S71BSkojw8ZoZXL2ggp+9eogfv3yIL/98N1/++W4m5WexfO4k3ju7jKwMm/tizNnwk1wagRkhz6cDh32WyYpQt1lEpqhqk+vyaonSVqN7HCmOlPPEziOU5WexbE5ZvENJaZWFOay7ah7rrprHgaPd/HZPKw8/v59f7GzimboWrrmwkuU2W88Y3/z8O/YSUC0ic0QkC2+wfeuYMluBNW7W2HKg3XV1Raq7FVjrHq8FHg/ZvlpEskVkDt7A/YuuvU4RWe5mia0JqZOS+gaH2fZWM9cvqiIjaP85T5TZ5fmsvXI2f3HVPP7iqrlMLcnl5280sfmlg/QP2bkxxvgR9chFVYdE5G7gKSAIPKyqu0TkDrf/QeAJ4Aa8wfce4LZIdV3T9wJbROR2vNleN7s6u0RkC7AbGALuUtXRv+g7gUeAXOBJd0NE3gv8FCgFPioiX1HVRef8qSSI5/YepXtgmBXWJRYzZ7uky6xJ+dx65Wye29PKr3Y309zRxx+/ZwqVRTnjGaYxSU/S9SSympoara2tjXcYEX1+y2tse6uF2i9dS2aYI5czfVGa8bG3pZPv72hg5qQ8fvCZ5VQUZsc7JGMmnIi8rKo10cpZX0uC6h8a5undzfzRwqqwicVMvOrKQtZcOYvGEz188tsv2MXHjInAvrUS1NbXDtPZN8SqS6fGOxQTYm55Ad9Z+14OHOvmk99+gbYeSzDGhGPJJQGpKt/57/0sqCrk/ReUxzscM8b7LijnoTU11Ld08anvvEh772C8QzIm4VhySUDP7T3K20c6+fQH5tjU1wT1wfkVPPipJbx9pIO1D79IZ58lGGNC2cXCEtB/PrePysJsbgzpErPB+8QR+rP4s5qZPPriu9z63ZfY9OfLyM+2PyljwI5cEs5bTR08t/coa6+cTXZGMHoFE1cLpxax+r0zee1gG7c98hLtPXYEYwzYkUtCUVU++4NXycoIkJ0RsKOVJLF4WjFXzJvE5x57jRX//ju+/rFLeH/1mcfK7PLJJh3YkUsC+ckrh9jb0sX1C6vIy7K8n0w+eslUfvKXV5KXFeST33mBu77/Cr95u5nB4fALdauqLfNvUpp9gyWIo139/NMvdjOzLI/L7aJgSeni6SX84rMf4Bu/3stjLzXwi51N5GQGKC/IpjQvi8HhETr7hjjePUD/0DAjCjmZAYpyMplSnENuVoCrqiuYVGAnZ5rkZ8klAagq//D4m/T0D3PrFbMJ2AyxpJWTGWT9ygv5/B/N53d7Wvnu8/vpHhimZ2CIjECAysJsZpblkZ0ZICMgdPUP0947SH1LF5977HUyAsL1iybzyeWzWD63zGYLmqRlySUBfO2pOp7YeYQvrLiQ4tzMeIdjYiArI8C1C6to6ez3VX5ElUuml7D19UP88OVGfrGzierKAj51xSz+5LJpFObY74VJLjbmEmfffm4fDzz7Dp+4fCZ3fHBuvMMxcRIQYeehduaUF/C5a+fzp0um0zs4zD88vovL/79tfPGnO3mrqSPeYRrjmx25xImq8sBv3+Frv6xj5eLJ/NOqxdYFksRiObMvMxhg6axSls4qZdHUIr63411+9HIj33+hgTnl+VxzYSVXzJvE4mnFVBZm2++NSUi2KnIcdPcP8fc/eoNf7GziPdOK+djS6bY4pYmoZ2CI1xvbebupg31Hu0/ONCsvyGbR1CIWTytiweQi5lcVMKc8386RMuPG76rIduQywZ6ta+F/bt3FweM9rFg0mQ9Ul9t/niaqvKwMrpg7iSvmTmJgaISm9l4OtfVyuK2PuiOdPLe3ldGZzQGBSfnZVBZlU1WUw/+1ZBrzqwqZPSnfLtdsJowllwny5qF2vvmbvTy1q5m5Ffl8/9PL2X+0O95hmSSUlRFg1qR8Zk3KP7ltaHiE1q5+Wjr6ae7so6WjnyPtfew+3MFv3vauIJ4REOaU5zO/qpC+wWFK87Ioys2k2N2yMgJ2IqeJGV/JRURWAP+OdzXJb6vqvWP2i9t/A96VKG9V1Vci1RWRMuAxYDZwAPi4qp5w++4BbgeGgc+q6lNu+1JOXYnyCeCvVVVFJBvYBCwFjgF/pqoHzvrTiLH2nkGeqWthS+1Bfv/OMfKzgvyP6+bzmavmkp0RtORiYiYjGGBKcS5TinNP2z44PELN7FL2Nnexp7mTPc1d7DzUzsHjPYztEM/NDLJp+wEmF+e4tnLc49FbbkzWThsZUboGhvje9ncZGlaCASHD3YJBYc0VswkG7Gg+3DheMiX/qL8pIhIE7gP+CGgEXhKRraq6O6TYSrxr3VcDlwMPAJdHqbse2Kaq94rIevf8CyKyEFgNLAKmAr8WkfnuUscPAOuAHXjJZQXepY5vB06o6gUishr4Z+DPzueDiWZ4RBkYGqF/aJiBoRHaewdp6eznUFsvuw93sOtwO682tDE0okwpzuGelReyetlMm2psJlRmMMCiqcUsmlp82vaNvz9AR+8g7e7W0TtIe98gBdmZHOno5c1D7Rzt+sNr1RTmZFBRmE1xbiYluZmU5GVRnJtJRkBQQNWbVq2qdPUP09Hntd3RN+TuB+nqHyLSUO9Xfr6b3Mwg+dlB8rMzyMvKID/Le5yTGSAgQkAEEW+WneIlrOERZVjVe+xWQBhx96pe7KNHakU5p47YinMzKcjJoCA7g/zsDPKzg+RlZXjJLiAERQjEKNkNDo/QMzBM3+AwvQPD9AwM09XvfTad/YN09J76nF5taKN30Cs7MDRCRjDAr99qJi8rSH6WF2txbiYled6tKORnUpSTQWZG4OR7yAgECAgT2gXv59+QZUC9qu4DEJHNwCq8a9yPWgVsUm92wA4RKRGRKXhHJWequwq42tXfCDwLfMFt36yq/cB+EakHlonIAaBIVbe7tjYBN+Ell1XAl11bPwL+Q0REx2G2wref28c///JtBofP3HRuZpCFU4v4zFVzuW5hFZdML4nZL6cxZyvcf8CZwQCTCrIjrgYwNDxCR9/QaQmorXeQ7v4hOvuGGBpW6lu7aO8ZZGhEEbwvr9Ev/YLsjJNf6Kh6J5BOyiM3M0hORoCczCAZwQDDI8rQyIh3P6xcOKXw5JduT/8QXf3D7DvaxZGOPgaHR1D1kpii5GdnIEDAJYFgQGjvHXQJyIvH+9MTGo730Ou+1PuHwi/LE8mpRHNqmxcH4OIZ/cbxEq2eTLhnKzczSGZQyMkMnrwNDistnX309J9KSl39Q2fV7miy+fKNi7hl2fgeBflJLtOAgyHPG/GOTqKVmRalbpWqNgGoapOIVIa0tSNMW4Pu8djtp72+qg6JSDswCTgaGqSIrMM78gHoEpG68G/5rJWPfa23gZ/gHY7F0R/ElQASMSZIzLgSMSZIzLgSMSZIzLjKP/FVjn7i3OvP8lPIT3IJ9y/32Fx8pjJ+6vp9vUht+XodVX0IeCjK6581Ean1MzVvoiViXIkYEyRmXIkYEyRmXIkYEyRmXBMVk595iY3AjJDn04HDPstEqtvsus5w9y0+2pp+hrZO1hGRDKAYOO7jvRljjBkHfpLLS0C1iMwRkSy8wfatY8psBdaIZznQ7rq8ItXdCqx1j9cCj4dsXy0i2SIyB2+SwIuuvU4RWe5mp60ZU2e0rY8BvxmP8RZjjDH+RO0Wc2MYdwNP4U0nflhVd4nIHW7/g3gzt24A6vGmIt8Wqa5r+l5gi4jcDjQAN7s6u0RkC96g/xBwl5spBnAnp6YiP+luAN8BvucG/4/jJbGJFPOuthhJxLgSMSZIzLgSMSZIzLgSMSZIzLgmJKa0Xf7FGGPM+LG1IIwxxsScJRdjjDGxp+5sWrud2w1vlYA6vPGm9TFq82G82XNvhmwrA54G9rr70pB997jXrwOuD9m+FNjp9m3gVDdoNt7SO/XAC8DskDpr3WvsBdaGbJ8BPAO8BezCW3onEeLKAV4EXndxfSUR4nL7gsCrwH8lUEwHXHuvAbWJEBdQgnfy89t4v19XxDMmYIH7fEZvHcDfxPtzcvs+h/d7/ibwA7zf/7jHFfZ7LBZfhul6w/vyeAeYC2ThfcEtjEG7VwFLOD25fA2XvPDOzfxn93ihe91sYI6LJ+j2vej+UAVv8sNKt/0vgQfd49XAY+5xGbDP3Ze6x6Vu3xRgiXtcCOxxrx3vuAQocI8z3R/E8njH5fZ/HniUU8klEWI6AJSP+X2L989wI/Bp9zgLL9nE/bMK+Rs/gnfiYLw/p2nAfiDXPd8C3BrvuM74PRbvL+hkvrkfzlMhz+8B7olR27M5PbnUAVPc4ylAXbjXxJuZd4Ur83bI9luAb4WWcY8z8M4gltAybt+3gFvOEN/jeGvGJUxcQB7wCt4qEHGNC+88rG3AhzmVXOL+WRE+ucQtLqAI7wtTEiWmMXFcBzyfCDFxaiWSMlfnv1x8CfFZjb3ZmMv5OdOyN+PhtOVygNDlcs609I6v5XKA0eVyfL0fEZkNXIZ3lBD3uEQkKCKv4XUlPq2qiRDXN4C/B0IXsYp3TOCtXPErEXnZLYcU77jmAq3Ad0XkVRH5tojkJ8hnBd5/7z9wj+Mak6oeAv433qkbTXjnE/4q3nGdiSWX83Muy9tMVAznslxO1PcjIgXAj4G/UdVIF3WfsLhUdVhVL8U7WlgmIovjGZeIfARoUdWXI8QxoTGFPH6fqi7BW8n8LhG5Ks5xZeB1AT+gqpcB3URekm/CPit34veNwA8jxDNhMYlIKd4ivXPwVozPF5FPxjuuM7Hkcn78LI0TKxOxXE7E9yMimXiJ5fuq+pNEiWuUqrbhra69Is5xvQ+40a3kvRn4sIj8nzjHNPoZHXb3LcBP8VY9j2dcjUCjO9oEb2B/SSJ8VngJ+BVVbXbP4x3TtcB+VW1V1UG8tXGvTIC4wovUZ2a3qOMiGXgDW3M4NaC/KEZtz+b0MZevc/qg3dfc40WcPmi3j1ODdi/hDW6PDtrd4LbfxemDdlvc4zK8/u9Sd9sPlLl9gndBtm+MiTPecVUAJe5xLvAc8JF4xxXy+VzNqTGXeH9W+UBhyOPf4yXieMf1HLDAPf6yiyfuPz+8fwxuS6Df9cvxZorlufY2An8V77jO+B0W7y/oZL/hLXuzB28mxhdj1OYP8PpURy8zcDtev+c2vGmA28b8EXzRvX4dbtaH216DN2XxHeA/ODXdMAfvUL8eb9bI3JA6f+6214/5w3o/3mHwG5yaonlDAsR1Md503zdcm//gtsc1rpD9V3MqucT7s5qL92UzOm37iwkS16VArfsZ/gzvyyveMeXhXdW2OGRb3H+ngK/gTdl+E/geXuKIe1zhbrb8izHGmJizMRdjjDExZ8nFGGNMzFlyMcYYE3OWXIwxxsScJRdjjDExZ8nFmPMgIl8UkV0i8oaIvCYil0co+4iIfCxKe4+IyH7X1isicsUZyt0hImvON35jxkvUyxwbY8JzX/wfwVstul9EyvFOpj1ff6eqPxKR6/AWCLx4zOtmqHd5cWMSliUXY87dFOCoqvYDqOpRABH5B+CjeCsG/B74Cx1zQpmILAX+FSjAW3n2VnWLD4b4HXCBK/+sa+t9wFYRKQS6VPV/i8gFwIN4qxUMAzer6jsi8nfAx/FOtPupqv7PGL9/Y87IusWMOXe/AmaIyB4RuV9EPui2/4eqvldVF+MlmI+EVnJrtH0T+JiqLsW7ONxXw7T/UbwLOo0qUdUPquq/jCn3feA+Vb0Eb62pJnfUU423dtilwNIoi1QaE1N25GLMOVLVLncE8gHgQ8BjIrIe6BSRv8dbQqQMb6mVn4dUXQAsBp4WEfAuSBV61PJ1EfkS3lL0t4dsf2xsDO4IZpqq/tTF1Oe2X4d3rY9XXdECvGTzu/N5z8b4ZcnFmPOgqsN4KzE/KyI7gb/AGyOpUdWDIvJlvPWaQgmwS1XDDtbjxlzCbO8Osy3cUuij2/+Xqn4rylswZlxYt5gx50hEFohIdcimS/EWCAQ46q59E252WB1QMToTTEQyRWTRucSg3jV1GkXkJtdWtojk4V1R8M9dDIjINBGpjNCUMTFlRy7GnLsC4JsiUgIM4a0Wuw5owxsrOYC3tPlpVHXATUneICLFeH+H38DrPjsXnwK+JSL/iLeS9s2q+isRuQjY7rreuoBPcupaH8aMK1sV2RhjTMxZt5gxxpiYs+RijDEm5iy5GGOMiTlLLsYYY2LOkosxxpiYs+RijDEm5iy5GGOMibn/H8/OeN6YSYsyAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# lets check the skewness of the data\n", "sns.distplot(data['SalePrice'])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Thus, we see that our Histogram is \"Positively Skewed\"\n", "\n", "We can see different examples of Skewness from the image on the previous slide and see how Mean, and the Median are affected in each distribution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mode" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 140000\n", "dtype: int64\n" ] } ], "source": [ "mode = Sales_price.mode()\n", "print(mode)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "## plot the hist with mean median and mode - This needs to be checked! \n", "\n", "plt.figure(figsize=(10, 6)) \n", "plt.hist(Sales_price, bins=40, color = 'yellow')\n", "plt.plot([mode]*300, range(300), color = 'black', label='mode') \n", "plt.plot([median]*300, range(300), label='median')\n", "plt.plot([mean]*300, range(300), label='mean')\n", "plt.ylim(0, 250)\n", "plt.legend()\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Spread of the Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Let's choose the value 250,000 from the SalePrice column and check how far this value is from the mean when compared to other points in the data set\n", "* We measure this as follows:\n", " (250,000 - mean)/Random Variation\n", "* We know the mean, we found that before\n", "\n", "* What is Random Variation?\n", " * It's nothing but the Average variation of the data from the mean\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Range of the Data\n", "\n", "* Range of data is simply:\n", " * Max Value of Data - Min Value of data\n" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "720100" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Range = np.max(Sales_price)-np.min(Sales_price)\n", "Range\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Variance of the Data" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6311111264.297448\n" ] } ], "source": [ "\n", "variance = Sales_price.var()\n", "print(variance)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Standard Deviation" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "79442.50288288662\n" ] } ], "source": [ "from math import sqrt\n", "\n", "std = sqrt(variance)\n", "print(std)" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "If we know that our data is Normally Distributed, we can confidently say that:\n", "~68% of the data is within one Std. Dev. from the mean\n", "~95% of the data is within 2 Std. Dev. from the mean\n", "~99.7% of the data is within 3 Std Dev from the mean" ] }, { "attachments": { "image.png": { "image/png": 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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "![image.png](attachment:image.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Correlation" ] }, { "attachments": { "image.png": { "image/png": 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} }, "cell_type": "markdown", "metadata": {}, "source": [ "![image.png](attachment:image.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* A correlation coefficient of 1 means that for every positive increase of 1 in one variable, there is a positive increase of 1 in the other.\n", "* A correlation coefficient of -1 means that for every positive increase of 1 in one variable, there is a negative decrease of 1 in the other.\n", "* Zero means that for every increase, there isn’t a positive or negative increase. The two just aren’t related." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What is the Correlation between the Sales price and the Living Room Area?\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "864 22\n", "1040 14\n", "894 11\n", "848 10\n", "1456 10\n", " ..\n", "3447 1\n", "1396 1\n", "1395 1\n", "1393 1\n", "2054 1\n", "Name: GrLivArea, Length: 861, dtype: int64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['GrLivArea'].value_counts()" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Correlation Between Sales Price and the Living Room Area is 70.86\n" ] } ], "source": [ "# lets find out the correlation\n", "\n", "living_room_area = data.GrLivArea\n", "\n", "# Returns Pearson product-moment correlation coefficients.\n", "corr = np.corrcoef(Sales_price, living_room_area)[0,1] \n", "print(\"Correlation Between Sales Price and the Living Room Area is {0:.2f}\".format(corr*100))" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " LotArea GrLivArea GarageArea SalePrice\n", "LotArea 1.000000 0.263116 0.180403 0.263843\n", "GrLivArea 0.263116 1.000000 0.468997 0.708624\n", "GarageArea 0.180403 0.468997 1.000000 0.623431\n", "SalePrice 0.263843 0.708624 0.623431 1.000000\n" ] } ], "source": [ "#considering 4 continous variable and finding the correlation\n", "\n", "x = data[['LotArea','GrLivArea','GarageArea','SalePrice']]\n", "corr = x.corr() \n", "print(corr)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Correlation doesn't imply Causation\n", "\n", "* However, correlation does not imply causation. There may be, for example, an unknown factor that influences both variables similarly.\n", "\n", "* Causation indicates that one event is the result of the occurrence of the other event; i.e. there is a causal relationship between the two events. This is also referred to as cause and effect.\n", "\n", "* A statistically significant correlation has been reported, for example, between yellow cars and a lower incidence of accidents. That does not indicate that yellow cars are safer, but just that fewer yellow cars are involved in accidents. A third factor, such as the personality type of the purchaser of yellow cars, is more likely to be responsible than the color of the paint itself." ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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LotAreaGrLivAreaGarageAreaSalePrice
LotArea9.962565e+071.380033e+063.849872e+052.092111e+08
GrLivArea1.380033e+062.761296e+055.269198e+042.958187e+07
GarageArea3.849872e+055.269198e+044.571251e+041.058910e+07
SalePrice2.092111e+082.958187e+071.058910e+076.311111e+09
\n", "
" ], "text/plain": [ " LotArea GrLivArea GarageArea SalePrice\n", "LotArea 9.962565e+07 1.380033e+06 3.849872e+05 2.092111e+08\n", "GrLivArea 1.380033e+06 2.761296e+05 5.269198e+04 2.958187e+07\n", "GarageArea 3.849872e+05 5.269198e+04 4.571251e+04 1.058910e+07\n", "SalePrice 2.092111e+08 2.958187e+07 1.058910e+07 6.311111e+09" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Covariance\n", "\n", "data[['LotArea','GrLivArea','GarageArea','SalePrice']].cov()" ] } ], "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": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 4 }