diff --git a/Proyecto.ipynb b/Proyecto.ipynb new file mode 100644 index 0000000..bd8cc4b --- /dev/null +++ b/Proyecto.ipynb @@ -0,0 +1,4210 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6c4997ce", + "metadata": {}, + "source": [ + "# Proyecto" + ] + }, + { + "cell_type": "markdown", + "id": "8e63bad1", + "metadata": {}, + "source": [ + "Housing Prices" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "61d95222", + "metadata": {}, + "outputs": [], + "source": [ + "## Importando todas las librerias que necesito\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import plotly\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from statistics import mean\n", + "\n", + "import statsmodels.api as sm\n", + "from statsmodels.formula.api import ols\n", + "from scipy.stats import linregress\n", + "from scipy import stats\n", + "\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LinearRegression" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "29b287db", + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv('train.csv')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3a027de4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilities...PoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
0160RL65.08450PaveNaNRegLvlAllPub...0NaNNaNNaN022008WDNormal208500
1220RL80.09600PaveNaNRegLvlAllPub...0NaNNaNNaN052007WDNormal181500
2360RL68.011250PaveNaNIR1LvlAllPub...0NaNNaNNaN092008WDNormal223500
3470RL60.09550PaveNaNIR1LvlAllPub...0NaNNaNNaN022006WDAbnorml140000
4560RL84.014260PaveNaNIR1LvlAllPub...0NaNNaNNaN0122008WDNormal250000
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5 rows × 81 columns

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" + ], + "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 ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold \\\n", + "0 Lvl AllPub ... 0 NaN NaN NaN 0 2 \n", + "1 Lvl AllPub ... 0 NaN NaN NaN 0 5 \n", + "2 Lvl AllPub ... 0 NaN NaN NaN 0 9 \n", + "3 Lvl AllPub ... 0 NaN NaN NaN 0 2 \n", + "4 Lvl AllPub ... 0 NaN NaN NaN 0 12 \n", + "\n", + " YrSold SaleType SaleCondition SalePrice \n", + "0 2008 WD Normal 208500 \n", + "1 2007 WD Normal 181500 \n", + "2 2008 WD Normal 223500 \n", + "3 2006 WD Abnorml 140000 \n", + "4 2008 WD Normal 250000 \n", + "\n", + "[5 rows x 81 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "2fb82694", + "metadata": {}, + "source": [ + "Vamos a empezar a ver algunos básicos de nuestra información" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c0c202ac", + "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": "8a14a87c", + "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": "29dbe16f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1460, 81)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "bab87ae9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IdMSSubClassLotFrontageLotAreaOverallQualOverallCondYearBuiltYearRemodAddMasVnrAreaBsmtFinSF1...WoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValMoSoldYrSoldSalePrice
count1460.0000001460.0000001201.0000001460.0000001460.0000001460.0000001460.0000001460.0000001452.0000001460.000000...1460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.000000
mean730.50000056.89726070.04995810516.8280826.0993155.5753421971.2678081984.865753103.685262443.639726...94.24452146.66027421.9541103.40958915.0609592.75890443.4890416.3219182007.815753180921.195890
std421.61000942.30057124.2847529981.2649321.3829971.11279930.20290420.645407181.066207456.098091...125.33879466.25602861.11914929.31733155.75741540.177307496.1230242.7036261.32809579442.502883
min1.00000020.00000021.0000001300.0000001.0000001.0000001872.0000001950.0000000.0000000.000000...0.0000000.0000000.0000000.0000000.0000000.0000000.0000001.0000002006.00000034900.000000
25%365.75000020.00000059.0000007553.5000005.0000005.0000001954.0000001967.0000000.0000000.000000...0.0000000.0000000.0000000.0000000.0000000.0000000.0000005.0000002007.000000129975.000000
50%730.50000050.00000069.0000009478.5000006.0000005.0000001973.0000001994.0000000.000000383.500000...0.00000025.0000000.0000000.0000000.0000000.0000000.0000006.0000002008.000000163000.000000
75%1095.25000070.00000080.00000011601.5000007.0000006.0000002000.0000002004.000000166.000000712.250000...168.00000068.0000000.0000000.0000000.0000000.0000000.0000008.0000002009.000000214000.000000
max1460.000000190.000000313.000000215245.00000010.0000009.0000002010.0000002010.0000001600.0000005644.000000...857.000000547.000000552.000000508.000000480.000000738.00000015500.00000012.0000002010.000000755000.000000
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8 rows × 38 columns

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" + ], + "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", + " WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch \\\n", + "count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 \n", + "mean 94.244521 46.660274 21.954110 3.409589 15.060959 \n", + "std 125.338794 66.256028 61.119149 29.317331 55.757415 \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 25.000000 0.000000 0.000000 0.000000 \n", + "75% 168.000000 68.000000 0.000000 0.000000 0.000000 \n", + "max 857.000000 547.000000 552.000000 508.000000 480.000000 \n", + "\n", + " PoolArea MiscVal MoSold YrSold SalePrice \n", + "count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 \n", + "mean 2.758904 43.489041 6.321918 2007.815753 180921.195890 \n", + "std 40.177307 496.123024 2.703626 1.328095 79442.502883 \n", + "min 0.000000 0.000000 1.000000 2006.000000 34900.000000 \n", + "25% 0.000000 0.000000 5.000000 2007.000000 129975.000000 \n", + "50% 0.000000 0.000000 6.000000 2008.000000 163000.000000 \n", + "75% 0.000000 0.000000 8.000000 2009.000000 214000.000000 \n", + "max 738.000000 15500.000000 12.000000 2010.000000 755000.000000 \n", + "\n", + "[8 rows x 38 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "25c40bdf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Id int64\n", + "MSSubClass int64\n", + "MSZoning object\n", + "LotFrontage float64\n", + "LotArea int64\n", + " ... \n", + "MoSold int64\n", + "YrSold int64\n", + "SaleType object\n", + "SaleCondition object\n", + "SalePrice int64\n", + "Length: 81, dtype: object" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "696178e2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countmeanstdmin25%50%75%max
Id1460.0730.500000421.6100091.0365.75730.51095.251460.0
MSSubClass1460.056.89726042.30057120.020.0050.070.00190.0
LotFrontage1201.070.04995824.28475221.059.0069.080.00313.0
LotArea1460.010516.8280829981.2649321300.07553.509478.511601.50215245.0
OverallQual1460.06.0993151.3829971.05.006.07.0010.0
OverallCond1460.05.5753421.1127991.05.005.06.009.0
YearBuilt1460.01971.26780830.2029041872.01954.001973.02000.002010.0
YearRemodAdd1460.01984.86575320.6454071950.01967.001994.02004.002010.0
MasVnrArea1452.0103.685262181.0662070.00.000.0166.001600.0
BsmtFinSF11460.0443.639726456.0980910.00.00383.5712.255644.0
BsmtFinSF21460.046.549315161.3192730.00.000.00.001474.0
BsmtUnfSF1460.0567.240411441.8669550.0223.00477.5808.002336.0
TotalBsmtSF1460.01057.429452438.7053240.0795.75991.51298.256110.0
1stFlrSF1460.01162.626712386.587738334.0882.001087.01391.254692.0
2ndFlrSF1460.0346.992466436.5284360.00.000.0728.002065.0
LowQualFinSF1460.05.84452148.6230810.00.000.00.00572.0
GrLivArea1460.01515.463699525.480383334.01129.501464.01776.755642.0
BsmtFullBath1460.00.4253420.5189110.00.000.01.003.0
BsmtHalfBath1460.00.0575340.2387530.00.000.00.002.0
FullBath1460.01.5650680.5509160.01.002.02.003.0
HalfBath1460.00.3828770.5028850.00.000.01.002.0
BedroomAbvGr1460.02.8664380.8157780.02.003.03.008.0
KitchenAbvGr1460.01.0465750.2203380.01.001.01.003.0
TotRmsAbvGrd1460.06.5178081.6253932.05.006.07.0014.0
Fireplaces1460.00.6130140.6446660.00.001.01.003.0
GarageYrBlt1379.01978.50616424.6897251900.01961.001980.02002.002010.0
GarageCars1460.01.7671230.7473150.01.002.02.004.0
GarageArea1460.0472.980137213.8048410.0334.50480.0576.001418.0
WoodDeckSF1460.094.244521125.3387940.00.000.0168.00857.0
OpenPorchSF1460.046.66027466.2560280.00.0025.068.00547.0
EnclosedPorch1460.021.95411061.1191490.00.000.00.00552.0
3SsnPorch1460.03.40958929.3173310.00.000.00.00508.0
ScreenPorch1460.015.06095955.7574150.00.000.00.00480.0
PoolArea1460.02.75890440.1773070.00.000.00.00738.0
MiscVal1460.043.489041496.1230240.00.000.00.0015500.0
MoSold1460.06.3219182.7036261.05.006.08.0012.0
YrSold1460.02007.8157531.3280952006.02007.002008.02009.002010.0
SalePrice1460.0180921.19589079442.50288334900.0129975.00163000.0214000.00755000.0
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" + ], + "text/plain": [ + " count mean std min 25% \\\n", + "Id 1460.0 730.500000 421.610009 1.0 365.75 \n", + "MSSubClass 1460.0 56.897260 42.300571 20.0 20.00 \n", + "LotFrontage 1201.0 70.049958 24.284752 21.0 59.00 \n", + "LotArea 1460.0 10516.828082 9981.264932 1300.0 7553.50 \n", + "OverallQual 1460.0 6.099315 1.382997 1.0 5.00 \n", + "OverallCond 1460.0 5.575342 1.112799 1.0 5.00 \n", + "YearBuilt 1460.0 1971.267808 30.202904 1872.0 1954.00 \n", + "YearRemodAdd 1460.0 1984.865753 20.645407 1950.0 1967.00 \n", + "MasVnrArea 1452.0 103.685262 181.066207 0.0 0.00 \n", + "BsmtFinSF1 1460.0 443.639726 456.098091 0.0 0.00 \n", + "BsmtFinSF2 1460.0 46.549315 161.319273 0.0 0.00 \n", + "BsmtUnfSF 1460.0 567.240411 441.866955 0.0 223.00 \n", + "TotalBsmtSF 1460.0 1057.429452 438.705324 0.0 795.75 \n", + "1stFlrSF 1460.0 1162.626712 386.587738 334.0 882.00 \n", + "2ndFlrSF 1460.0 346.992466 436.528436 0.0 0.00 \n", + "LowQualFinSF 1460.0 5.844521 48.623081 0.0 0.00 \n", + "GrLivArea 1460.0 1515.463699 525.480383 334.0 1129.50 \n", + "BsmtFullBath 1460.0 0.425342 0.518911 0.0 0.00 \n", + "BsmtHalfBath 1460.0 0.057534 0.238753 0.0 0.00 \n", + "FullBath 1460.0 1.565068 0.550916 0.0 1.00 \n", + "HalfBath 1460.0 0.382877 0.502885 0.0 0.00 \n", + "BedroomAbvGr 1460.0 2.866438 0.815778 0.0 2.00 \n", + "KitchenAbvGr 1460.0 1.046575 0.220338 0.0 1.00 \n", + "TotRmsAbvGrd 1460.0 6.517808 1.625393 2.0 5.00 \n", + "Fireplaces 1460.0 0.613014 0.644666 0.0 0.00 \n", + "GarageYrBlt 1379.0 1978.506164 24.689725 1900.0 1961.00 \n", + "GarageCars 1460.0 1.767123 0.747315 0.0 1.00 \n", + "GarageArea 1460.0 472.980137 213.804841 0.0 334.50 \n", + "WoodDeckSF 1460.0 94.244521 125.338794 0.0 0.00 \n", + "OpenPorchSF 1460.0 46.660274 66.256028 0.0 0.00 \n", + "EnclosedPorch 1460.0 21.954110 61.119149 0.0 0.00 \n", + "3SsnPorch 1460.0 3.409589 29.317331 0.0 0.00 \n", + "ScreenPorch 1460.0 15.060959 55.757415 0.0 0.00 \n", + "PoolArea 1460.0 2.758904 40.177307 0.0 0.00 \n", + "MiscVal 1460.0 43.489041 496.123024 0.0 0.00 \n", + "MoSold 1460.0 6.321918 2.703626 1.0 5.00 \n", + "YrSold 1460.0 2007.815753 1.328095 2006.0 2007.00 \n", + "SalePrice 1460.0 180921.195890 79442.502883 34900.0 129975.00 \n", + "\n", + " 50% 75% max \n", + "Id 730.5 1095.25 1460.0 \n", + "MSSubClass 50.0 70.00 190.0 \n", + "LotFrontage 69.0 80.00 313.0 \n", + "LotArea 9478.5 11601.50 215245.0 \n", + "OverallQual 6.0 7.00 10.0 \n", + "OverallCond 5.0 6.00 9.0 \n", + "YearBuilt 1973.0 2000.00 2010.0 \n", + "YearRemodAdd 1994.0 2004.00 2010.0 \n", + "MasVnrArea 0.0 166.00 1600.0 \n", + "BsmtFinSF1 383.5 712.25 5644.0 \n", + "BsmtFinSF2 0.0 0.00 1474.0 \n", + "BsmtUnfSF 477.5 808.00 2336.0 \n", + "TotalBsmtSF 991.5 1298.25 6110.0 \n", + "1stFlrSF 1087.0 1391.25 4692.0 \n", + "2ndFlrSF 0.0 728.00 2065.0 \n", + "LowQualFinSF 0.0 0.00 572.0 \n", + "GrLivArea 1464.0 1776.75 5642.0 \n", + "BsmtFullBath 0.0 1.00 3.0 \n", + "BsmtHalfBath 0.0 0.00 2.0 \n", + "FullBath 2.0 2.00 3.0 \n", + "HalfBath 0.0 1.00 2.0 \n", + "BedroomAbvGr 3.0 3.00 8.0 \n", + "KitchenAbvGr 1.0 1.00 3.0 \n", + "TotRmsAbvGrd 6.0 7.00 14.0 \n", + "Fireplaces 1.0 1.00 3.0 \n", + "GarageYrBlt 1980.0 2002.00 2010.0 \n", + "GarageCars 2.0 2.00 4.0 \n", + "GarageArea 480.0 576.00 1418.0 \n", + "WoodDeckSF 0.0 168.00 857.0 \n", + "OpenPorchSF 25.0 68.00 547.0 \n", + "EnclosedPorch 0.0 0.00 552.0 \n", + "3SsnPorch 0.0 0.00 508.0 \n", + "ScreenPorch 0.0 0.00 480.0 \n", + "PoolArea 0.0 0.00 738.0 \n", + "MiscVal 0.0 0.00 15500.0 \n", + "MoSold 6.0 8.00 12.0 \n", + "YrSold 2008.0 2009.00 2010.0 \n", + "SalePrice 163000.0 214000.00 755000.0 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.describe().T" + ] + }, + { + "cell_type": "markdown", + "id": "298dc103", + "metadata": {}, + "source": [ + "Ahora que ya tenemos nuestros básicos, empezaremos con la limpieza del dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "8bad1b1b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Id 0\n", + "MSSubClass 0\n", + "MSZoning 0\n", + "LotFrontage 259\n", + "LotArea 0\n", + " ... \n", + "MoSold 0\n", + "YrSold 0\n", + "SaleType 0\n", + "SaleCondition 0\n", + "SalePrice 0\n", + "Length: 81, dtype: int64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8fdc9b6e-4d16-4da8-9080-35b2de294342", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1460 rows × 81 columns

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" + ], + "text/plain": [ + " Id MSSubClass MSZoning LotFrontage LotArea Street Alley \\\n", + "0 False False False False False False True \n", + "1 False False False False False False True \n", + "2 False False False False False False True \n", + "3 False False False False False False True \n", + "4 False False False False False False True \n", + "... ... ... ... ... ... ... ... \n", + "1455 False False False False False False True \n", + "1456 False False False False False False True \n", + "1457 False False False False False False True \n", + "1458 False False False False False False True \n", + "1459 False False False False False False True \n", + "\n", + " LotShape LandContour Utilities ... PoolArea PoolQC Fence \\\n", + "0 False False False ... False True True \n", + "1 False False False ... False True True \n", + "2 False False False ... False True True \n", + "3 False False False ... False True True \n", + "4 False False False ... False True True \n", + "... ... ... ... ... ... ... ... \n", + "1455 False False False ... False True True \n", + "1456 False False False ... False True False \n", + "1457 False False False ... False True False \n", + "1458 False False False ... False True True \n", + "1459 False False False ... False True True \n", + "\n", + " MiscFeature MiscVal MoSold YrSold SaleType SaleCondition SalePrice \n", + "0 True False False False False False False \n", + "1 True False False False False False False \n", + "2 True False False False False False False \n", + "3 True False False False False False False \n", + "4 True False False False False False False \n", + "... ... ... ... ... ... ... ... \n", + "1455 True False False False False False False \n", + "1456 True False False False False False False \n", + "1457 False False False False False False False \n", + "1458 True False False False False False False \n", + "1459 True False False False False False False \n", + "\n", + "[1460 rows x 81 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.isnull()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "edf22f3c", + "metadata": {}, + "outputs": [], + "source": [ + "valores_nulos = data.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "fa1deef2", + "metadata": {}, + "outputs": [], + "source": [ + "valores_nulos_eliminar = valores_nulos.loc[valores_nulos.gt(0)]/len(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "fedc5b76", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LotFrontage 0.177397\n", + "Alley 0.937671\n", + "MasVnrType 0.005479\n", + "MasVnrArea 0.005479\n", + "BsmtQual 0.025342\n", + "BsmtCond 0.025342\n", + "BsmtExposure 0.026027\n", + "BsmtFinType1 0.025342\n", + "BsmtFinType2 0.026027\n", + "Electrical 0.000685\n", + "FireplaceQu 0.472603\n", + "GarageType 0.055479\n", + "GarageYrBlt 0.055479\n", + "GarageFinish 0.055479\n", + "GarageQual 0.055479\n", + "GarageCond 0.055479\n", + "PoolQC 0.995205\n", + "Fence 0.807534\n", + "MiscFeature 0.963014\n", + "dtype: float64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "valores_nulos_eliminar" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "8e019e98", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.05547945205479452" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "valores_nulos_eliminar.median()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "ecace328", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['LotFrontage', 'Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature']" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "columnas_fuera = list(valores_nulos_eliminar[valores_nulos_eliminar > valores_nulos_eliminar.median()].index)\n", + "columnas_fuera" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "a0e90641", + "metadata": {}, + "outputs": [], + "source": [ + "data_2 = data.drop(columnas_fuera,axis=1).copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "37b50026", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IdMSSubClassMSZoningLotAreaStreetLotShapeLandContourUtilitiesLotConfigLandSlope...EnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
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3470RL9550PaveIR1LvlAllPubCornerGtl...272000022006WDAbnorml140000
4560RL14260PaveIR1LvlAllPubFR2Gtl...00000122008WDNormal250000
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1455145660RL7917PaveRegLvlAllPubInsideGtl...0000082007WDNormal175000
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1459146020RL9937PaveRegLvlAllPubInsideGtl...0000062008WDNormal147500
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EnclosedPorch 3SsnPorch ScreenPorch \\\n", + "0 AllPub Inside Gtl ... 0 0 0 \n", + "1 AllPub FR2 Gtl ... 0 0 0 \n", + "2 AllPub Inside Gtl ... 0 0 0 \n", + "3 AllPub Corner Gtl ... 272 0 0 \n", + "4 AllPub FR2 Gtl ... 0 0 0 \n", + "... ... ... ... ... ... ... ... \n", + "1455 AllPub Inside Gtl ... 0 0 0 \n", + "1456 AllPub Inside Gtl ... 0 0 0 \n", + "1457 AllPub Inside Gtl ... 0 0 0 \n", + "1458 AllPub Inside Gtl ... 112 0 0 \n", + "1459 AllPub Inside Gtl ... 0 0 0 \n", + "\n", + " PoolArea MiscVal MoSold YrSold SaleType SaleCondition SalePrice \n", + "0 0 0 2 2008 WD Normal 208500 \n", + "1 0 0 5 2007 WD Normal 181500 \n", + "2 0 0 9 2008 WD Normal 223500 \n", + "3 0 0 2 2006 WD Abnorml 140000 \n", + "4 0 0 12 2008 WD Normal 250000 \n", + "... ... ... ... ... ... ... ... \n", + "1455 0 0 8 2007 WD Normal 175000 \n", + "1456 0 0 2 2010 WD Normal 210000 \n", + "1457 0 2500 5 2010 WD Normal 266500 \n", + "1458 0 0 4 2010 WD Normal 142125 \n", + "1459 0 0 6 2008 WD Normal 147500 \n", + "\n", + "[1460 rows x 75 columns]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_2" + ] + }, + { + "cell_type": "markdown", + "id": "c7ee8c30", + "metadata": {}, + "source": [ + "# Ahora se empezará a hacer el analisis de las ventas" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "de9e5b28", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "grafica_1 =sns.histplot(data['SalePrice'])\n", + "#grafica_1.set_xlabel('Precio de Venta')\n", + "#grafica_1.set_title('Distribución')" + ] + }, + { + "cell_type": "markdown", + "id": "8cb22703", + "metadata": {}, + "source": [ + "La concentración del precio de venta está entre los 100k y 200k" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "cfde4b07", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\MICH\\anaconda3\\lib\\site-packages\\statsmodels\\graphics\\gofplots.py:993: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"bo\" (-> marker='o'). The keyword argument will take precedence.\n", + " ax.plot(x, y, fmt, **plot_style)\n", + "C:\\Users\\MICH\\anaconda3\\lib\\site-packages\\statsmodels\\graphics\\gofplots.py:993: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"bo\" (-> marker='o'). The keyword argument will take precedence.\n", + " ax.plot(x, y, fmt, **plot_style)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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xoWW2Nm6E2bObQ23ZsnB8773DstD4Z//9u71pbQacu3/DzL4EPGxm/aPDm4DvuvtdefjsRuBKd19oZlXAAjNLu/szeTi3iEjBpNNw3XVhCinb8n/3EHJ77w233pqQYckdO+DJJ5vn0R5/PJRf6dcPTjoJLrwwBNqHPhSSvYiyXibg7ncDd0cBhLtvzNcHu/vrwOvxec1sGTAMUMCJSI+VTsOUKfDqq9nDzSyslBw0KIzMlWy4ucPzzzcH2oMPhl6bGRx9NFx9dRh2PP546Nu32K3dTU6luvIZbK2Jhj7HAI+38twUYArA8OGqECYixXXDDSHc2qtOEteZHDu2BIcm33ijeQ6tvh5WrAjHR44M24jX1cGkSWE5aA/WmVqUeWVmewD3AZe7+4aWz7v7NGAawNixY7P8/yURkcKYMQO++c3my7RyUVERdgD47ndLoPe2eTM88kjzPNpTT4Xje+4ZrkO7/voQaiNHFredHVTUgDOzSkK43ePuvytmW0REWpoxA664At55p2Pv23df+Na34PzzC9OuLovLYMWB9uij4cr03r1h/PjQ+NpaOOqokNQlKpdiy/2BK4Hh7v55M6sBDsnY6btTzMyA6cAyd7+9K+cSEcmHGTPgppuaR+Q6qlevHtxre/HF5uvRZs1qLoM1alS44ryuDk48EQYMKG478yiXHtwMQqmu46PHK4HfsvtGqJ0xHvg0sNTMFkfHrnP3v3bxvCIiHXbBBSHgOsMMjjkmFEquq8tvuzpt3bqwICReHPLii+F4nspglYJcAu5Ad/8XMzsHwN23RL2vLnH3Rwn1LUVEiqahAc47D554onPvr6yEH/2oBwxHbtsGc+c299Lmzw+rXKqqwkV4l1+e1zJYpSCXgNse7SrgAGZ2INBGIRoRkdKRTsNnPgOrV3fu/ZWVYXSvKOEWl8GK59Eyy2CNGxfGWmtrC1YGqxTkEnA3AX8D9jezewhDi58rZKNERApt6tRQCrFlaa1cVFSEBYXXXtvN4bZyZXMPrb4+LOeHUAbrwgtDD23ChG4pg1UKctnwNG1mC4FxhCHFy9x9bcFbJiJSIFdeCbd3Ymnb4MFhAUm3hdqGDfDQQ83zaM8+G47HZbDq6sJtdXU3Nai0ZNsP7qgWh16Pboeb2XB3X1i4ZomIFEY6HTYkzVUqFcoo3nRTNwTbjh1hMjDupbUsg/Wv/xpC7YMfLHoZrFKQrQf3vSzPOTApz20RESmYuOL/n/+c2+vHjYOf/azAy/3d4bnnmoccM8tgjR3bXAbrhBOgT58CNiSZshVbntidDRERKZSODkmefz789KcFakxcBisOtZUrw/GRI8MuqLW1JVEGqxTkcqF3X+CLwImEntsjwH+6+9YCt01EpEumTg21I9urGxlLpcJq+u9lG7/qqMwyWOk0LFkSjsdlsOJ5tBIrg1UKcllF+TNgIxCPWp9D2A/u44VqlIhIV6TT8PGPd6zEVq9eMG1aHubZdu6EhQube2lz5ry3DFZdHYwZU9JlsEpBLgF3iLsfmfH4QTN7qlANEhHpjIYG+MIXwjRWZ3Qp3F58sXnIMbMM1pFHNpfB+vCHoX//7OeRvMol4BaZ2Th3nwdgZscBcwrbLBGR3HS1EgmEYOtQuK1bF4Is7qXFZbCqq+GjHw1Djgkvg1UKcgm444DPmNmr0ePhwDIzWwq4u48qWOtERNqQj2Dr1y9c7H3tte28MC6DFffSWpbBuuKKEGplVAarFOQScKcWvBUiIjmaOhVuvjlMa3VWVRV8//tZem2ZZbDS6VAGa8uW3ctg1dWFCstlWgarFORSyeQVM9sT2D/z9brQW0S6UzoNZ50F777b+XP07h3CsdUeW7YyWPEF1iqDVVJyuUzgVkLtyeVEBZfRhd4i0o3OPht+18Utkc86C+67L+PAhg0we3ZzqKkMVuLkMkT5CcKWOV0YEBAR6Zh0OnScXn21/ddmc/DBcPfdUHfyDpjzRHMPbd68sKS/X7/QM4t7aR/6kObREiKXgHsaGAS8UdimiIgEXe2xDRgAd93pnH9CVAbrrjScPXv3MljXXBN6aCqDlVi5BNxUwqUCT5OxD5y7n16wVolI2ZkxIyxG7MjF2S0NrVjDD8+eyUf7p+GmVspg1dWFVY8qg1UWcgm4/wa+DSwFcix4IyKSm6lT4brrOvfefmzmJB7mlFQ9nxycZujaJfAbVAZLgNwCbq2731nwlohIWenMMGSKnRzFQupIU0s945lDH7ZDr94w6kSoVRksaZZLwC0ws6nAn9h9iFKXCYhIh1xwQRiK7IgDeJE60tSRZhKzGMzbAKzZ70j6fOrS0ENTGSxpRS4BNya6HZdxTJcJiEhOZsyASy/N/fq1PVnHJGbt6qUdSCiDtYJq/sAZvHxQHZ/72SRGHq8yWJJdLhd6a184EemwXOfWerON8cyhlnrqSHM0C0jhbKCKB5nIf3AFaeo47YqD+d7tWr4vuculB4eZ/RNwBNA3PubuXy9Uo0SktB12WPN10y0ZTXyIpbsC7SQepj9b2EEv5jGOW7iJNHU8wbHspBfjxsH9hd5ZWxIpl0om/wn0ByYCPwE+BnShvKmIJM2MGfBv/xbKNbZmGCt3DTnWUs8+0WW1z3AYP+bz1FPLQ0xgI6EMVu/ecOvNORRBFskilx7cCe4+ysyWuPstZvY9oItFc0QkKcaMgcWLdz9WxQZOZvauxSGH8hwAq9knOlLHTCazit3LYA0aBLffnodNR0XILeDi/0+22cyGAm8BBxSuSSLS07XssfViB8fyxK5AO47H6cVONtOPh5jANKaQpo6n+SDw3nm099SJFMmDXALufjMbBHwHWEhYQfnjQjZKRHqeGTPgoovibWqcQ3l21zzaycxmIBtpwpjPWL7NNaSp4zGOZzutl8EaPRp+8xvNrUnh5LKK8tbo7n1mdj/Q1927UExHREpJOg2nnQaDtq/hY9EcWi317E8og/UCB3Iv55KmjgeZyNu0XQYrlYLLL4fvfa+bGi9lrc2AM7NjgBXuvjp6/BngbOAVM7vZ3dd1UxtFpJs1NMBpkzZzwMqHqSPNE9RzJEsAeIvBzGQyt1JLmjpeznHG4stfVrBJ98rWg/sRUAtgZicBtwGXAKOBaYTVlCKSEP81fSfTLlrIyY1hHm0Jc+nDdrbRm0c5ka8ylXpqWcQYmsi9DJaGIqVYsgVcRUYv7V+Aae5+H2GocnHBWyYiBffFjyxn5wNhHu10ZvG5qAzWYo7kTi4lTR2PciJb6FgZLC0akZ4ga8CZWS93bwQmA1NyfF/OzOxU4PtABfATd78tH+cVkTasW8eDN8xk+Y/qmdSU5oe8BDSXwYqX77/J3p06/T77wM9/HuodixRbtqD6JfCQma0lXCrwCICZHQR0eZGJmVUAPwDqgJXAk2b2J3d/pqvnFpGg/i/b+I+PzWH81uYyWBNxjmIgDzKR2/kyaep4noNpbfl+LlIp+Oxn4ac/zW/bRbqqzYBz92+a2UxgP+ABd/foqRRhLq6rjgVecPcXAczsV8BHAQWcSCc0NMCEDzcxZM3SXVVDTuJhajPKYN3MzdRTu6sMVldo0Yj0dFn/C3f3ea0cez5Pnz0MWJHxeCVwXMsXmdkUouHR4cOH5+mjRZLj3JNW0ueRsDBkETPfUwYrTR0PMYF3qcrL52l+TUpFXubSOqm18RB/zwH3aYRVm4wdO/Y9z4uUm1/8cAP3XTKbSU0h1O5tpQxWPbW8xrC8fWZdHTzwQN5OJ9ItihlwK4H9Mx5XA68VqS0iPdeOHfD44/z68/VUP5vmkzzOeexkE/1zKoPVGWbwuc9pXk1KWzED7kmgxswOAFYBnwTOLWJ7RHoGd3j2Wf69Ls2hq+p3lcH6GCnmM5bb+Cr11GYtg9UZgwaF69W0AlKSomgB5+6NZvYl4P8Ilwn81N3/Xqz2iBTVmjXcVlvPvk+HxSHVrOIrhDJY9/CpXWWw1rNn3j9ac2qSVMXsweHufwX+Wsw2iBTFpk3cfuYjeDrMmo1iKV+luQxWPI+WaxmsXGkuTcpJUQNOpFw0PLuTS8cvYMy6cD3aCczlyy3KYKWpYzGjO1QGK5sTToA5c/JyKpGSpIATKYDx42H13OW7rkebxCz+NyqDtYjRXSqDlY3m0USaKeBEOunKK8Pu07HBvMUkZlFHmp9Tz8ioDNar7M/vOZN6artUBqstmkMTaZ0CTqQdU6fCDTdAU9Pux/uwlYnM3dVLO5oFpHDeicpgfY8rqae2S2Ww2tKvH/zgB3D++Xk9rUiiKOBECGWuzj4bli5t+zVGE6NYsmsX6w/zCP1blMFKU8eTHNPlMlgtjR4Nixbl9ZQiiaeAk7IydSrceCM0Nub2+mpW7Aq0WurZmzcB+DuHF6QM1q7PrYZZs7SHmkhXKOAk0dJp+MxnYPXq3F4/kHc4mdm7Qu3QqAzW6+zL//ER6qnNexks0JCjSCEo4KTkTZ0KN98M27d3/L292MFxPB5VcExzLE/QK6MM1o/4AvXU5rUMFuh6NJHuoICTktLQAOedB0880dkzOIfy7K5AO5nZVPEuOzPKYKWpYx7j8lYGS/NnIsWhgJMeJ52GL34RXnghP+fbh9VMZuauebRqVgGhDNYvOC8vZbAGDIC77tIQo0hPooCTokin4Yor4O8FqD7an03RRp/1u8pgAazl/cxk8q55tM6UwerdOwyHXnttnhstInmngJNu1ZX5srak2MnRLNjVQzuBufRhO1vps1sZrEWMwUnldE5dPC1S+hRwUhDpNFx3HSxc+N4LpPNhJMt3zaNNYhZ7sh4IZbC+z2XUU9tuGaxBg0IlEg0riiSTAk7yoqEBbrkF/vhHePfd/J8/swxWbYsyWL/jLNLUMYtJWctg9esHX/uahhdFyoUCTjqs0GEGoQzWeObsmkc7ioXvKYOVpo4Gashcvj9iBEybpmLDIqKAkyziILv/fnjnncJ+VlwGK+6hZZbBeozjWy2D1b8/fOsG9chEpHUKuDLV0AB33hl6YWvW5HfRR66qWbEr0FqWwZrGlFCoeN8JXPutKr5+Pny9+5soIiVMAZcw6TTccQcsWQIbN4YFHu7hObNQg3HHjtxrMeZTXAYrXhxyCM8D8Gavfdkx4SPwmTqYPJkjhg3jCODy7m+iiCSIAq7ENTTAPffAzJnhwui33mp+rrGxOdyKIVsZrDm9JrDilIuo/XYdQ444IqSviEgeKeBKVDzE+Pvfw4YNIR82b4adO8P9eGm+WXeGnHMYy3YtDMksg7Xx0GPodfZXoa6OAccfzym9e3dXo0SkTCngeriGBqivD0OOr74a5sveeAPefjv00HpF/4LbtjWHW2agFTrc9mH1btvJDOM1ADYPPYj+H/001NZSMXEig/bsfBksEZHOUMD1UHEP7S9/Cb2xpqawEGTTphBkO3aE8Irn0lKp5nArZK8tLoN1CmnqUvV8sCnaIfT974fJk8P6/Npa+o8YUZgGiIjkSAFXJC17Zhs3hlBKpUKIrV4NW7bAnnuGMFu3LrwvlQphV1ERgi7W1BSey+fikVQK+lbuZPKgBUw5IM2kpjT9F80NDerTB048Eepug9paGDMmvEFEpIdQwBVQZoitXx/Cau+9Q1jNnx+u41q3LjwX98o2bQrDjqlUCLi4Z1ZREXpw8TBkKhUCMV4l6R5eU1kZnovDr0+f5teaheONjVF49Q1buVx3XcaF0e6wfHloeDodtpVesx7WEELs8svDi088MZQGERHpoRRweRAH2apVIZzMQg/sxRehujocT6VgxYrm3tiIEbB2bXhur71CD27LFthjj3DfLITT9u3hnBUV4bPcQ0X7zACDEFqVlTB4cDj3pElw7rlQU5PDF3jrLfjNzOZQe/nlcHz//UPV4bqwfJ8hQ/L7ixMRKSAFXBc1NMD06SFYKivhwQfD8QEDQig99ljIicGD4fXXw3MVFaEySGPj7qsf45CK59D69AmlsOIeXTyvVlERXhMvMHnf++CMM+CSS3IMtK1bYc6cEGb19aEisjsMHBiS8aqrQqjV1Gj5voiULAVcF9XXh/AaNAieeircN4PnnoMjjwy9uLffDp2fHTvCe6qqwtL+gQNDb2zr1hBsvXqF1/TrF8Ju585wv0+fcH/vvUOYbd0aPmPo0Bx7ak1NYZw0nQ4/jzwSTtKrFxx/fKjHVVsLxxzTnJoiIiVOf81ayBxuHDYs/N2Hto/dcw8MHw6HHRbm0gYNaj7Xli3NYQYhxCDMxW3aFMJqzZoQXn36hDB7990QkvvtFz6vqQlGjuzgkCOElSvxkOPMmfBmKIPF4YfDF74QemgnnRQaKCKSQObFLHXRQWPHjvX58+fvdqy1QGovBNp6T+Zw48CBIZheeCH0lg48sPVjixaF4camphBg8RDj9u2hN7Z5c5hrGzEivDfuoY0aFebkXn45nBeaF4JAGOI87rgOhNo778Ds2c29tOdDGSz23Td8wWj5PkOHduA3LiLS85nZAncf2/J4SffgMgOpujoEyPTpcOGFbYdCtvdkDjdCuI07Pkcf3fqxww8P01nxHFm8nH/ixNAbW7IkLD5saoIDDghDlXFNyMMOyy2QW7VjB8yb19xLe+KJ0BUcMAAmTICLLgqhpjJYIlKmSjrgWguk+HhboZHtPatWhdDLtG3be8+ReWyffWD8eHjmGVi5Miw2jANs5EiYMqWTAdaSOyxb1txDe+ihkKCpVJg7u/bakJbHHx8m9kREylxRAs7MvgP8M7AdWA6c7+7rO3qe1gJp4MBwvDPvGTYs9Ogy59HiZfiZWh7bZ59w7KST4OKLO/QVslu9urmHVl8Pr4UyWBx0EHz606GHdvLJYVJPRER2U6weXBq41t0bzezbwLXANR09SWuBtGFD9mmmbO+prQ3DldA83xYPKa5fn/3YunVw5pkd/QYtbNoEDz/c3Et7+ulwvEUZLFQGS0SkXUUJOHd/IOPhPOBjnTlPa4HUXtBke09NTfNc3KpVIfSuvjq8tr1j8fs7ZOfOUNIk7qXNzSiD9eEPw3nnhVAbPVplsEREOqjoqyjN7M/Ar939F208PwWYAjB8+PCjX3nlld2ej1dEvvZacy8s11WUHXlPXsRlsOIhx1mzQjcQwkqUeLWjymCJiOSsrVWUBQs4M6sH9m3lqevd/Y/Ra64HxgJneQ4Nae0ygR7vrbfCdWhxqMVlsIYPbx5yVBksEZFO6/bLBNy9tp0GfRY4DZicS7iVjMwyWOl0uFAuswzW1Vc3dxm1fF9EpGCKtYryVMKikgnuvrkYbcibpqZQoyueR2utDFZdHYwdqzJYIiLdqFh/ce8G+gBpC72Yee5+UZHa0nGvvto85JhZBuuII5rLYE2YELYGEBGRoijWKsqDivG5nfbOO2GbgDjUMstgnXpqGHJUGSwRkR5FY2at2b4dHn+8eR7tiSfCUGRcBuvii0OgqQyWiEiPpYCD9stgxVtejxunMlgiIiWifAPu9dd3X74fl8GqqWkugzVx4u4lT0REpGSUT8Bt2hR6ZnGgZZbBiufQ6urgAx8objtFRCQvkhtwcRmsONBalsH69KdDqKkMlohIIiUn4DLLYKXTYdVjZhmsK64IgaYyWCIiZSEZATdzZqiSHNepHD4czj47DDlOmqQyWCIiZSgZATdsWOilfeUrIdQOOkjL90VEylwyAu7QQ+H3vy92K0REpAfR6goREUkkBZyIiCSSAk5ERBJJASciIomkgBMRkURSwImISCIp4EREJJEUcCIikkjm7sVuQ87M7E3glTydbi9gbZ7O1dPpuyZPuXxP0HdNonx/zw+4+3tqMpZUwOWTmc1397HFbkd30HdNnnL5nqDvmkTd9T01RCkiIomkgBMRkUQq54CbVuwGdCN91+Qpl+8J+q5J1C3fs2zn4EREJNnKuQcnIiIJpoATEZFEKuuAM7NbzWyJmS02swfMbGix21QoZvYdM3s2+r6/N7NBxW5TIZjZx83s72bWZGaJXG5tZqea2XNm9oKZfbXY7SkUM/upmb1hZk8Xuy2FZGb7m9mDZrYs+m/3smK3qVDMrK+ZPWFmT0Xf9ZaCfl45z8GZ2UB33xDdvxQ43N0vKnKzCsLMTgFmuXujmX0bwN2vKXKz8s7MDgOagB8BV7n7/CI3Ka/MrAJ4HqgDVgJPAue4+zNFbVgBmNlJwLvAz9z9g8VuT6GY2X7Afu6+0MyqgAXAGQn9NzVggLu/a2aVwKPAZe4+rxCfV9Y9uDjcIgOAxKa9uz/g7o3Rw3lAdTHbUyjuvszdnyt2OwroWOAFd3/R3bcDvwI+WuQ2FYS7PwysK3Y7Cs3dX3f3hdH9jcAyYFhxW1UYHrwbPayMfgr2d7esAw7AzL5pZiuATwE3Frs93eQC4H+L3QjplGHAiozHK0noH8NyZGYjgDHA40VuSsGYWYWZLQbeANLuXrDvmviAM7N6M3u6lZ+PArj79e6+P3AP8KXitrZr2vuu0WuuBxoJ37ck5fI9E8xaOZbYkYdyYmZ7APcBl7cYXUoUd9/p7qMJo0jHmlnBhp97FerEPYW71+b40nuBvwA3FbA5BdXedzWzzwKnAZO9hCdfO/BvmkQrgf0zHlcDrxWpLZIn0XzUfcA97v67YrenO7j7ejObDZwKFGQhUeJ7cNmYWU3Gw9OBZ4vVlkIzs1OBa4DT3X1zsdsjnfYkUGNmB5hZb+CTwJ+K3CbpgmjhxXRgmbvfXuz2FJKZDYlXcJtZP6CWAv7dLfdVlPcBhxBW3b0CXOTuq4rbqsIwsxeAPsBb0aF5SVwxamZnAncBQ4D1wGJ3/0hRG5VnZvaPwB1ABfBTd/9mcVtUGGb2S+BkwtYqa4Cb3H16URtVAGZ2IvAIsJTwtwjgOnf/a/FaVRhmNgr4b8J/uyngN+7+9YJ9XjkHnIiIJFdZD1GKiEhyKeBERCSRFHAiIpJICjgREUkkBZyIiCSSAk4Sz8zeH+0YsdjMVpvZquj+ejPr1oK2ZnaGmR2e8fjrZtbhC9fNbERbVfbN7Agzm2Vmz5vZcjO7xczy/r/1bN/FzGYndTcHKR0KOEk8d3/L3UdH5YH+E/iP6P5omq87yhszy1Yh6AxgVyi4+43uXp/Hz+5HuPD7Nnc/GPgQoUBzIbZgOYMCfheRrlLASbmrMLMfR3tTPRAFBGZ2oJn9zcwWmNkjZnZodPwDZjYz2ldvppkNj47/l5ndbmYPAt9u7f1mdgKhYs53oh7kgdH7Phad4xgzmxvtlfWEmVVFPbVHzGxh9HNCO9/nXGCOuz8AEFWt+RJwdfQZN5vZVfGLoxqeI6L7f4ja+3czm5LxmnejouRPmdk8M9unve+SycxOMbPHovb/Nqq5iJndZmbPRL/L73b8n04kOwWclLsa4AfufgSh8snZ0fFpwCXufjRwFfDD6PjdhP3JRhEKVt+Zca6DgVp3v7K197v7XELv6uqoR7k8fmNUduvXhL2xjiSUMNpCqLhe5+5HAf/S4vNacwRhP7Fdos/pZ+1vcntB1N6xwKVm9v7o+ABC5ZsjgYeBz2f7LpnMbC/ghuj3chQwH/iymQ0GzgSOiH6X32inbSIdlvhiyyLteMndF0f3FwAjoh7GCcBvQ5lAIJQ5AzgeOCu6/3Pg3zPO9Vt339nO+9tyCPC6uz8JzXsVmtkA4G4zGw3sJIRoNkbruwu0tgtBS5dGpc4gFHSuIZR22w7cHx1fQNhsNVfjCMOYc6LfRW/gMWADsBX4iZn9JeP8InmjgJNyty3j/k6gH2FkY300T9eezDDZFN125P2xtoLpCkIdxiOj825t5zx/B07a7cRmI4G1UfX2RnYfuekbveZkQq/xeHffbKHKe9/oNTsydp/YScf+bhhhz69z3vOE2bHAZELB6C8BkzpwXpF2aYhSpIWo9/SSmX0cQrV3Mzsyenou4Q8yhE1yH+3g+zcCVa187LPAUDM7JnpPVbRY5X2Enl0T8GlCkdps7gFOzFjN2I8wrBlvA/UycFT03FHAAdHx9wFvR+F2KKHn1Z62vkumecB4Mzso+sz+ZnZw1Mt9X1RQ+HLCgh+RvFLAibTuU8CFZvYUoVcUb6Z6KXC+mS0hBE5bqxPbev+vgKvNbJGZHRi/2N23E+bY7orekyb0oH4IfNbM5hGGJzeRhbtvISz+uN7MngfWEhadxBvc3gcMtrCj8sXA89HxvwG9ou91KyGY2tPqd2nRnjeBzwG/jM49DziUEIz3R8ceIvRURfJKuwmIJJiZnQHcDkx091eK3ByRbqWAExGRRNIQpYiIJJICTkREEkkBJyIiiaSAExGRRFLAiYhIIingREQkkf4/1LkeFye/fYMAAAAASUVORK5CYII=\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, grafica_2=plt.subplots(figsize=(7,4))\n", + "sm.qqplot(\n", + " data_2['SalePrice'], fit = True, line = 'q', alpha = 0.4, lw = 2, ax = grafica_2)\n", + "\n", + "fig, grafica_2=plt.subplots(figsize=(7,4))\n", + "sm.qqplot(\n", + " np.log(data_2['SalePrice']), fit = True, line = 'q', alpha = 0.4, lw = 2, ax = grafica_2)" + ] + }, + { + "cell_type": "markdown", + "id": "e76f3833", + "metadata": {}, + "source": [ + "Como podemos observar la serie no se comporta de manera normal." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "07b491a7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Kurtosis: 6.509812011089439\n", + "Skewness: 1.880940746034036\n" + ] + } + ], + "source": [ + "print('Kurtosis:', stats.kurtosis(data_2['SalePrice']))\n", + "print('Skewness:', stats.skew(data_2['SalePrice']))" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "ff82f7c7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Kurtosis: 0.8026555069117713\n", + "Skewness: 0.1212103673013655\n" + ] + } + ], + "source": [ + "print('Kurtosis:', stats.kurtosis(np.log(data_2['SalePrice'])))\n", + "print('Skewness:', stats.skew(np.log(data_2['SalePrice'])))" + ] + }, + { + "cell_type": "markdown", + "id": "3b3d958d", + "metadata": {}, + "source": [ + "Dado el coeficiente de Kurtosis los valores están más centralizados que una variable normal. " + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "3630cb3f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ShapiroResult(statistic=0.869671642780304, pvalue=3.206247534576162e-33)" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "shapiro_test =stats.shapiro(data_2['SalePrice'])\n", + "shapiro_test" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "2c4034df", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ShapiroResult(statistic=0.9912067651748657, pvalue=1.1490678986092462e-07)" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "shapiro_test =stats.shapiro(np.log(data_2['SalePrice']))\n", + "shapiro_test" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "dfe3e2ba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estadístico = 610.8359109472653, p-value = 2.283848353787728e-133\n" + ] + } + ], + "source": [ + "k2, p_value = stats.normaltest(data_2['SalePrice'])\n", + "print(f\"Estadístico = {k2}, p-value = {p_value}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "00487216", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estadístico = 25.507009834238303, p-value = 2.89216582205235e-06\n" + ] + } + ], + "source": [ + "k2, p_value = stats.normaltest(np.log(data_2['SalePrice']))\n", + "print(f\"Estadístico = {k2}, p-value = {p_value}\")" + ] + }, + { + "cell_type": "markdown", + "id": "8a08df72", + "metadata": {}, + "source": [ + "Nuestra hipótesis nula es falsa dado los valores de p-value, se intento buscar normalizar los datos aplicandole una transformación logaritmica, se obtuvieron mejores resultados del p-value y como podemos ver en la prueba de normalidad de los cuantiles mejoro." + ] + }, + { + "cell_type": "markdown", + "id": "91437a76", + "metadata": {}, + "source": [ + "# Análisis estadístico" + ] + }, + { + "cell_type": "markdown", + "id": "1a963e84", + "metadata": {}, + "source": [ + "Para este tipo de analisis vamos a hacer una transformación de datos de caracteres a numericos" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "1a5db20b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IdMSSubClassLotAreaOverallQualOverallCondYearBuiltYearRemodAddMasVnrAreaBsmtFinSF1BsmtFinSF2...WoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValMoSoldYrSoldSalePrice
016084507520032003196.07060...0610000022008208500
1220960068197619760.09780...29800000052007181500
2360112507520012002162.04860...0420000092008223500
3470955075191519700.02160...035272000022006140000
4560142608520002000350.06550...1928400000122008250000
..................................................................
1455145660791765199920000.000...0400000082007175000
1456145720131756619781988119.0790163...34900000022010210000
1457145870904279194120060.02750...0600000250052010266500
1458145920971756195019960.0491029...3660112000042010142125
1459146020993756196519650.0830290...736680000062008147500
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1460 rows × 37 columns

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" + ], + "text/plain": [ + " Id MSSubClass LotArea OverallQual OverallCond YearBuilt \\\n", + "0 1 60 8450 7 5 2003 \n", + "1 2 20 9600 6 8 1976 \n", + "2 3 60 11250 7 5 2001 \n", + "3 4 70 9550 7 5 1915 \n", + "4 5 60 14260 8 5 2000 \n", + "... ... ... ... ... ... ... \n", + "1455 1456 60 7917 6 5 1999 \n", + "1456 1457 20 13175 6 6 1978 \n", + "1457 1458 70 9042 7 9 1941 \n", + "1458 1459 20 9717 5 6 1950 \n", + "1459 1460 20 9937 5 6 1965 \n", + "\n", + " YearRemodAdd MasVnrArea BsmtFinSF1 BsmtFinSF2 ... WoodDeckSF \\\n", + "0 2003 196.0 706 0 ... 0 \n", + "1 1976 0.0 978 0 ... 298 \n", + "2 2002 162.0 486 0 ... 0 \n", + "3 1970 0.0 216 0 ... 0 \n", + "4 2000 350.0 655 0 ... 192 \n", + "... ... ... ... ... ... ... \n", + "1455 2000 0.0 0 0 ... 0 \n", + "1456 1988 119.0 790 163 ... 349 \n", + "1457 2006 0.0 275 0 ... 0 \n", + "1458 1996 0.0 49 1029 ... 366 \n", + "1459 1965 0.0 830 290 ... 736 \n", + "\n", + " OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea MiscVal \\\n", + "0 61 0 0 0 0 0 \n", + "1 0 0 0 0 0 0 \n", + "2 42 0 0 0 0 0 \n", + "3 35 272 0 0 0 0 \n", + "4 84 0 0 0 0 0 \n", + "... ... ... ... ... ... ... \n", + "1455 40 0 0 0 0 0 \n", + "1456 0 0 0 0 0 0 \n", + "1457 60 0 0 0 0 2500 \n", + "1458 0 112 0 0 0 0 \n", + "1459 68 0 0 0 0 0 \n", + "\n", + " MoSold YrSold SalePrice \n", + "0 2 2008 208500 \n", + "1 5 2007 181500 \n", + "2 9 2008 223500 \n", + "3 2 2006 140000 \n", + "4 12 2008 250000 \n", + "... ... ... ... \n", + "1455 8 2007 175000 \n", + "1456 2 2010 210000 \n", + "1457 5 2010 266500 \n", + "1458 4 2010 142125 \n", + "1459 6 2008 147500 \n", + "\n", + "[1460 rows x 37 columns]" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "columnas_nuevas = [x for x in data_2 if x in data_2.select_dtypes(include=['int64','float64'])]\n", + "data_3 = data_2[columnas_nuevas]\n", + "data_3" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "4edfbd8a", + "metadata": {}, + "outputs": [], + "source": [ + "scaler = StandardScaler()\n", + "data_4 = scaler.fit_transform(data_3)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "470916b0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IdMSSubClassLotAreaOverallQualOverallCondYearBuiltYearRemodAddMasVnrAreaBsmtFinSF1BsmtFinSF2...WoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValMoSoldYrSoldSalePrice
0-1.7308650.073375-0.2071420.651479-0.5172001.0509940.8786680.5100150.575425-0.288653...-0.7521760.216503-0.359325-0.116339-0.270208-0.068692-0.087688-1.5991110.1387770.347273
1-1.728492-0.872563-0.091886-0.0718362.1796280.156734-0.429577-0.5728351.171992-0.288653...1.626195-0.704483-0.359325-0.116339-0.270208-0.068692-0.087688-0.489110-0.6144390.007288
2-1.7261200.0733750.0734800.651479-0.5172000.9847520.8302150.3221740.092907-0.288653...-0.752176-0.070361-0.359325-0.116339-0.270208-0.068692-0.0876880.9908910.1387770.536154
3-1.7237470.309859-0.0968970.651479-0.517200-1.863632-0.720298-0.572835-0.499274-0.288653...-0.752176-0.1760484.092524-0.116339-0.270208-0.068692-0.087688-1.599111-1.367655-0.515281
4-1.7213740.0733750.3751481.374795-0.5172000.9516320.7333081.3608260.463568-0.288653...0.7801970.563760-0.359325-0.116339-0.270208-0.068692-0.0876882.1008920.1387770.869843
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5 rows × 37 columns

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" + ], + "text/plain": [ + " Id MSSubClass LotArea OverallQual OverallCond YearBuilt \\\n", + "0 -1.730865 0.073375 -0.207142 0.651479 -0.517200 1.050994 \n", + "1 -1.728492 -0.872563 -0.091886 -0.071836 2.179628 0.156734 \n", + "2 -1.726120 0.073375 0.073480 0.651479 -0.517200 0.984752 \n", + "3 -1.723747 0.309859 -0.096897 0.651479 -0.517200 -1.863632 \n", + "4 -1.721374 0.073375 0.375148 1.374795 -0.517200 0.951632 \n", + "\n", + " YearRemodAdd MasVnrArea BsmtFinSF1 BsmtFinSF2 ... WoodDeckSF \\\n", + "0 0.878668 0.510015 0.575425 -0.288653 ... -0.752176 \n", + "1 -0.429577 -0.572835 1.171992 -0.288653 ... 1.626195 \n", + "2 0.830215 0.322174 0.092907 -0.288653 ... -0.752176 \n", + "3 -0.720298 -0.572835 -0.499274 -0.288653 ... -0.752176 \n", + "4 0.733308 1.360826 0.463568 -0.288653 ... 0.780197 \n", + "\n", + " OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea MiscVal \\\n", + "0 0.216503 -0.359325 -0.116339 -0.270208 -0.068692 -0.087688 \n", + "1 -0.704483 -0.359325 -0.116339 -0.270208 -0.068692 -0.087688 \n", + "2 -0.070361 -0.359325 -0.116339 -0.270208 -0.068692 -0.087688 \n", + "3 -0.176048 4.092524 -0.116339 -0.270208 -0.068692 -0.087688 \n", + "4 0.563760 -0.359325 -0.116339 -0.270208 -0.068692 -0.087688 \n", + "\n", + " MoSold YrSold SalePrice \n", + "0 -1.599111 0.138777 0.347273 \n", + "1 -0.489110 -0.614439 0.007288 \n", + "2 0.990891 0.138777 0.536154 \n", + "3 -1.599111 -1.367655 -0.515281 \n", + "4 2.100892 0.138777 0.869843 \n", + "\n", + "[5 rows x 37 columns]" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame(data_4, index=data_3.index, columns=data_3.columns)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "6fd8dee5", + "metadata": {}, + "source": [ + "Se decidio reemplazar los NAN con cero debido a que no necesariamente nos crean un sesgo en los datos porque tomamos los vectores con menos NAN en sus columnas" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "5c43dfef", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "df=df.replace(np.nan,0)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "52e517e6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PCA(n_components=0.8)" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pca = PCA(0.8)\n", + "pca.fit(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "0ca821ca", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Componentes = 18 ;\n", + " Varianza explicada = 0.8141\n" + ] + } + ], + "source": [ + "print(\"Componentes = \", pca.n_components_ , \";\\n Varianza explicada = \", round(pca.explained_variance_ratio_.sum(),5))" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "3ada4280", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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IdMSSubClassLotAreaOverallQualOverallCondYearBuiltYearRemodAddMasVnrAreaBsmtFinSF1BsmtFinSF2...WoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValMoSoldYrSoldSalePrice
0-0.002872-0.0099770.0989600.295955-0.0750170.2300810.2049000.1893790.136770-0.012287...0.1306000.142586-0.0718080.0152920.0314030.044021-0.0106740.021124-0.0130930.321382
10.0175750.164815-0.030902-0.0164300.057036-0.192843-0.084434-0.019425-0.290857-0.071218...-0.0675970.0411770.115904-0.0302900.0156900.0149140.0263730.028909-0.036343-0.027752
20.004126-0.1660080.285557-0.0946140.151882-0.330122-0.2636130.0167430.2715200.192097...0.053187-0.0075620.185941-0.0102210.1468920.1626540.047477-0.0053780.0016580.049372
3-0.004451-0.3083120.0157570.004789-0.076216-0.078217-0.065707-0.035620-0.289752-0.058864...-0.121628-0.0284210.0887780.043928-0.032817-0.054874-0.0154180.048208-0.051643-0.026332
40.0555570.297026-0.036359-0.133964-0.4050950.047959-0.1445280.0183000.154821-0.077271...-0.042174-0.1339590.017122-0.071778-0.2197190.021476-0.002365-0.0717320.062129-0.100696
5-0.119537-0.0845710.0382870.0323110.277824-0.0460350.220461-0.074661-0.0649930.061892...0.179015-0.1344550.1301290.063037-0.232188-0.0611340.110641-0.5165320.5998060.043211
6-0.1724910.0555760.142220-0.0581270.2396470.0214120.135559-0.0050310.054289-0.005577...0.265172-0.167413-0.1751710.317888-0.300796-0.1436960.1238850.273346-0.085612-0.010916
70.1590430.069080-0.0691020.0643540.152433-0.0105650.236047-0.3209180.0063350.077215...0.1349480.2097460.175328-0.090072-0.2778190.4282560.0129750.149495-0.2963830.005006
80.169379-0.0247160.133068-0.106429-0.1122280.1405430.020266-0.197165-0.2097260.599013...0.1764610.055583-0.388927-0.3266120.2581400.0229880.095837-0.0260210.129683-0.073521
90.0317080.156821-0.1832490.0564430.287820-0.0409680.176808-0.0655890.084056-0.137443...-0.3316840.204701-0.2046090.2191330.4571590.0060070.4403840.1087190.1483340.017730
10-0.6900420.1162910.0401590.004851-0.2287400.038238-0.0544840.063680-0.0168300.108351...-0.0092690.086703-0.1520910.1690940.132337-0.039185-0.178406-0.103562-0.064344-0.010019
110.1240490.180459-0.1394450.012347-0.0452090.047318-0.0360060.2047710.062529-0.150099...-0.044538-0.2083520.009039-0.0959810.1353750.4811590.071345-0.458049-0.042554-0.017282
12-0.316882-0.0406600.212951-0.003758-0.1288790.050220-0.0569260.019755-0.049405-0.076019...0.147969-0.0761900.075024-0.223608-0.1004440.1015700.754309-0.031458-0.2367060.001371
130.234256-0.3105220.183046-0.147299-0.2363430.114701-0.117973-0.0916310.051859-0.151184...-0.0270070.036511-0.3360510.558276-0.0957730.2814830.091448-0.1192230.010651-0.061395
14-0.0377470.105384-0.0107590.035038-0.1309510.012293-0.013776-0.105185-0.1683490.534521...-0.256411-0.0491340.4097180.4640260.0067170.1050470.113061-0.029173-0.042127-0.003330
15-0.1229630.2481860.0038200.080769-0.0196570.0254930.057657-0.015301-0.0812280.038523...0.1573590.1618700.0407790.120926-0.0209490.364348-0.1941450.0955700.166559-0.003122
160.2055060.2023380.0231200.065008-0.016607-0.037096-0.0553300.023353-0.127549-0.037651...0.355122-0.577572-0.0582380.1634860.2015060.055616-0.0039610.2728400.1037750.036933
170.221874-0.026256-0.2961430.083635-0.3083930.077343-0.0725740.2037040.0818640.114196...-0.0139220.0577880.2319620.015116-0.179698-0.2289810.2871830.2425210.2840720.016763
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18 rows × 37 columns

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" + ], + "text/plain": [ + " Id MSSubClass LotArea OverallQual OverallCond YearBuilt \\\n", + "0 -0.002872 -0.009977 0.098960 0.295955 -0.075017 0.230081 \n", + "1 0.017575 0.164815 -0.030902 -0.016430 0.057036 -0.192843 \n", + "2 0.004126 -0.166008 0.285557 -0.094614 0.151882 -0.330122 \n", + "3 -0.004451 -0.308312 0.015757 0.004789 -0.076216 -0.078217 \n", + "4 0.055557 0.297026 -0.036359 -0.133964 -0.405095 0.047959 \n", + "5 -0.119537 -0.084571 0.038287 0.032311 0.277824 -0.046035 \n", + "6 -0.172491 0.055576 0.142220 -0.058127 0.239647 0.021412 \n", + "7 0.159043 0.069080 -0.069102 0.064354 0.152433 -0.010565 \n", + "8 0.169379 -0.024716 0.133068 -0.106429 -0.112228 0.140543 \n", + "9 0.031708 0.156821 -0.183249 0.056443 0.287820 -0.040968 \n", + "10 -0.690042 0.116291 0.040159 0.004851 -0.228740 0.038238 \n", + "11 0.124049 0.180459 -0.139445 0.012347 -0.045209 0.047318 \n", + "12 -0.316882 -0.040660 0.212951 -0.003758 -0.128879 0.050220 \n", + "13 0.234256 -0.310522 0.183046 -0.147299 -0.236343 0.114701 \n", + "14 -0.037747 0.105384 -0.010759 0.035038 -0.130951 0.012293 \n", + "15 -0.122963 0.248186 0.003820 0.080769 -0.019657 0.025493 \n", + "16 0.205506 0.202338 0.023120 0.065008 -0.016607 -0.037096 \n", + "17 0.221874 -0.026256 -0.296143 0.083635 -0.308393 0.077343 \n", + "\n", + " YearRemodAdd MasVnrArea BsmtFinSF1 BsmtFinSF2 ... WoodDeckSF \\\n", + "0 0.204900 0.189379 0.136770 -0.012287 ... 0.130600 \n", + "1 -0.084434 -0.019425 -0.290857 -0.071218 ... -0.067597 \n", + "2 -0.263613 0.016743 0.271520 0.192097 ... 0.053187 \n", + "3 -0.065707 -0.035620 -0.289752 -0.058864 ... -0.121628 \n", + "4 -0.144528 0.018300 0.154821 -0.077271 ... -0.042174 \n", + "5 0.220461 -0.074661 -0.064993 0.061892 ... 0.179015 \n", + "6 0.135559 -0.005031 0.054289 -0.005577 ... 0.265172 \n", + "7 0.236047 -0.320918 0.006335 0.077215 ... 0.134948 \n", + "8 0.020266 -0.197165 -0.209726 0.599013 ... 0.176461 \n", + "9 0.176808 -0.065589 0.084056 -0.137443 ... -0.331684 \n", + "10 -0.054484 0.063680 -0.016830 0.108351 ... -0.009269 \n", + "11 -0.036006 0.204771 0.062529 -0.150099 ... -0.044538 \n", + "12 -0.056926 0.019755 -0.049405 -0.076019 ... 0.147969 \n", + "13 -0.117973 -0.091631 0.051859 -0.151184 ... -0.027007 \n", + "14 -0.013776 -0.105185 -0.168349 0.534521 ... -0.256411 \n", + "15 0.057657 -0.015301 -0.081228 0.038523 ... 0.157359 \n", + "16 -0.055330 0.023353 -0.127549 -0.037651 ... 0.355122 \n", + "17 -0.072574 0.203704 0.081864 0.114196 ... -0.013922 \n", + "\n", + " OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea MiscVal \\\n", + "0 0.142586 -0.071808 0.015292 0.031403 0.044021 -0.010674 \n", + "1 0.041177 0.115904 -0.030290 0.015690 0.014914 0.026373 \n", + "2 -0.007562 0.185941 -0.010221 0.146892 0.162654 0.047477 \n", + "3 -0.028421 0.088778 0.043928 -0.032817 -0.054874 -0.015418 \n", + "4 -0.133959 0.017122 -0.071778 -0.219719 0.021476 -0.002365 \n", + "5 -0.134455 0.130129 0.063037 -0.232188 -0.061134 0.110641 \n", + "6 -0.167413 -0.175171 0.317888 -0.300796 -0.143696 0.123885 \n", + "7 0.209746 0.175328 -0.090072 -0.277819 0.428256 0.012975 \n", + "8 0.055583 -0.388927 -0.326612 0.258140 0.022988 0.095837 \n", + "9 0.204701 -0.204609 0.219133 0.457159 0.006007 0.440384 \n", + "10 0.086703 -0.152091 0.169094 0.132337 -0.039185 -0.178406 \n", + "11 -0.208352 0.009039 -0.095981 0.135375 0.481159 0.071345 \n", + "12 -0.076190 0.075024 -0.223608 -0.100444 0.101570 0.754309 \n", + "13 0.036511 -0.336051 0.558276 -0.095773 0.281483 0.091448 \n", + "14 -0.049134 0.409718 0.464026 0.006717 0.105047 0.113061 \n", + "15 0.161870 0.040779 0.120926 -0.020949 0.364348 -0.194145 \n", + "16 -0.577572 -0.058238 0.163486 0.201506 0.055616 -0.003961 \n", + "17 0.057788 0.231962 0.015116 -0.179698 -0.228981 0.287183 \n", + "\n", + " MoSold YrSold SalePrice \n", + "0 0.021124 -0.013093 0.321382 \n", + "1 0.028909 -0.036343 -0.027752 \n", + "2 -0.005378 0.001658 0.049372 \n", + "3 0.048208 -0.051643 -0.026332 \n", + "4 -0.071732 0.062129 -0.100696 \n", + "5 -0.516532 0.599806 0.043211 \n", + "6 0.273346 -0.085612 -0.010916 \n", + "7 0.149495 -0.296383 0.005006 \n", + "8 -0.026021 0.129683 -0.073521 \n", + "9 0.108719 0.148334 0.017730 \n", + "10 -0.103562 -0.064344 -0.010019 \n", + "11 -0.458049 -0.042554 -0.017282 \n", + "12 -0.031458 -0.236706 0.001371 \n", + "13 -0.119223 0.010651 -0.061395 \n", + "14 -0.029173 -0.042127 -0.003330 \n", + "15 0.095570 0.166559 -0.003122 \n", + "16 0.272840 0.103775 0.036933 \n", + "17 0.242521 0.284072 0.016763 \n", + "\n", + "[18 rows x 37 columns]" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_pca_c=pd.DataFrame(data=pca.components_, columns=df.columns.values)\n", + "df_pca_c" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "c6294d47", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.heatmap(df_pca_c.corr())" + ] + }, + { + "cell_type": "markdown", + "id": "23b14cb8", + "metadata": {}, + "source": [ + "# Ahora haremos en analisis de regresión" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "baf13eef", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize = (8,7))\n", + "sns.scatterplot(data=data_2, y=data_2.GrLivArea, x=data_2.SalePrice)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "1c765542", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize = (8,7))\n", + "sns.scatterplot(data=data_2, y=np.log(data_2.GrLivArea), x=np.log(data_2.SalePrice))" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "1d03dddf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: SalePrice R-squared: 0.502\n", + "Model: OLS Adj. R-squared: 0.502\n", + "Method: Least Squares F-statistic: 1471.\n", + "Date: Thu, 10 Feb 2022 Prob (F-statistic): 4.52e-223\n", + "Time: 21:11:19 Log-Likelihood: -18035.\n", + "No. Observations: 1460 AIC: 3.607e+04\n", + "Df Residuals: 1458 BIC: 3.608e+04\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "const 1.857e+04 4480.755 4.144 0.000 9779.612 2.74e+04\n", + "GrLivArea 107.1304 2.794 38.348 0.000 101.650 112.610\n", + "==============================================================================\n", + "Omnibus: 261.166 Durbin-Watson: 2.025\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 3432.287\n", + "Skew: 0.410 Prob(JB): 0.00\n", + "Kurtosis: 10.467 Cond. No. 4.90e+03\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "[2] The condition number is large, 4.9e+03. This might indicate that there are\n", + "strong multicollinearity or other numerical problems.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\MICH\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n", + " x = pd.concat(x[::order], 1)\n" + ] + } + ], + "source": [ + "x = sm.add_constant(data_2.GrLivArea)\n", + "y = data_2.SalePrice\n", + "\n", + "modelo = sm.OLS(y,x).fit()\n", + "predicciones = modelo.predict(x)\n", + "print(modelo.summary())" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "dd85b126", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: SalePrice R-squared: 0.533\n", + "Model: OLS Adj. R-squared: 0.533\n", + "Method: Least Squares F-statistic: 1666.\n", + "Date: Thu, 10 Feb 2022 Prob (F-statistic): 1.60e-243\n", + "Time: 21:12:44 Log-Likelihood: -175.10\n", + "No. Observations: 1460 AIC: 354.2\n", + "Df Residuals: 1458 BIC: 364.8\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "const 5.6681 0.156 36.360 0.000 5.362 5.974\n", + "GrLivArea 0.8745 0.021 40.815 0.000 0.833 0.917\n", + "==============================================================================\n", + "Omnibus: 111.954 Durbin-Watson: 2.022\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 184.755\n", + "Skew: -0.565 Prob(JB): 7.60e-41\n", + "Kurtosis: 4.327 Cond. No. 162.\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\MICH\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n", + " x = pd.concat(x[::order], 1)\n" + ] + } + ], + "source": [ + "x = sm.add_constant(np.log(data_2.GrLivArea))\n", + "y = np.log(data_2.SalePrice)\n", + "\n", + "modelo = sm.OLS(y,x).fit()\n", + "predicciones = modelo.predict(x)\n", + "print(modelo.summary())" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "4ae24d49", + "metadata": {}, + "outputs": [], + "source": [ + "prediccion = modelo.predict(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "9104bb0c", + "metadata": {}, + "outputs": [], + "source": [ + "x['prediccion'] = prediccion" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "64a47acc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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constGrLivAreaprediccion
01.07.44424912.178384
11.07.14045311.912704
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............
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14581.06.98286311.774886
14591.07.13568711.908536
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1460 rows × 3 columns

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" + ], + "text/plain": [ + " const GrLivArea prediccion\n", + "0 1.0 7.444249 12.178384\n", + "1 1.0 7.140453 11.912704\n", + "2 1.0 7.487734 12.216413\n", + "3 1.0 7.448334 12.181957\n", + "4 1.0 7.695303 12.397940\n", + "... ... ... ...\n", + "1455 1.0 7.406711 12.145556\n", + "1456 1.0 7.636752 12.346735\n", + "1457 1.0 7.757906 12.452689\n", + "1458 1.0 6.982863 11.774886\n", + "1459 1.0 7.135687 11.908536\n", + "\n", + "[1460 rows x 3 columns]" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x" + ] + }, + { + "cell_type": "markdown", + "id": "0c84e2c8", + "metadata": {}, + "source": [ + "Dados los resultados no podemos asegurar que el área sea un buen estimador del precio de venta." + ] + } + ], + "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": 5 +}