From 6b0b714eea0f47a6981fbfb575a393dab1ff87e1 Mon Sep 17 00:00:00 2001 From: marcosend9 <102812656+marcosend9@users.noreply.github.com> Date: Sun, 31 Jul 2022 15:53:29 -0500 Subject: [PATCH] Add files via upload --- houses_analysis.ipynb | 2792 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 2792 insertions(+) create mode 100644 houses_analysis.ipynb diff --git a/houses_analysis.ipynb b/houses_analysis.ipynb new file mode 100644 index 0000000..fb14a1c --- /dev/null +++ b/houses_analysis.ipynb @@ -0,0 +1,2792 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ab74312f-0ac3-4d4e-9102-c65db7b128e6", + "metadata": {}, + "source": [ + "### Lo que se debe hacer primero es importar las librerías y analizar cómo es la base de datos" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3c5f56e2-4b25-49a2-b153-89401ec0d349", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.preprocessing import StandardScaler\n", + "import statsmodels.api as sm\n", + "from statsmodels.formula.api import ols\n", + "from scipy.stats import linregress\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "07362a5c-e65f-43aa-8d24-dbb05fee5bfe", + "metadata": {}, + "outputs": [], + "source": [ + "houses = pd.read_csv('../../Data/house_prices/train.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4fc01fbf-3768-4012-ac50-915c71262ce0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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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", + "\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", + "\n", + " YrSold SaleType SaleCondition SalePrice \n", + "0 2008 WD Normal 208500 \n", + "1 2007 WD Normal 181500 \n", + "2 2008 WD Normal 223500 \n", + "\n", + "[3 rows x 81 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "houses.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3f06b2da-3142-4a8f-b0fe-ff98dec8f270", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1460, 81)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "houses.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b216ea63-947d-42af-921c-aee50c67a946", + "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": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "houses.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4140e819-d7cc-4ecf-8f88-acc8d5ed7a60", + "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": [ + "houses.info()" + ] + }, + { + "cell_type": "markdown", + "id": "fcf1f217-f18a-4600-95d2-2a80e82dff60", + "metadata": {}, + "source": [ + "### Después analizaremos los valores nulos de cada columna para saber cuáles no nos servirán en el modelo" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c6d3941d-d9c4-48ae-a152-cf7eda1bcac3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PoolQC 1453\n", + "MiscFeature 1406\n", + "Alley 1369\n", + "Fence 1179\n", + "FireplaceQu 690\n", + " ... \n", + "ExterQual 0\n", + "Exterior2nd 0\n", + "Exterior1st 0\n", + "RoofMatl 0\n", + "SalePrice 0\n", + "Length: 81, dtype: int64" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "houses.isnull().sum().sort_values(ascending = False)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6a630dfb-8f7f-4d9a-bc68-446d8212aaba", + "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": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nulos_columnas = houses.isnull().sum()\n", + "nulos_columnas" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "34293624-0eaf-4c1b-96a7-bf65c773f50f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LotFrontage 259\n", + "Alley 1369\n", + "MasVnrType 8\n", + "MasVnrArea 8\n", + "BsmtQual 37\n", + "BsmtCond 37\n", + "BsmtExposure 38\n", + "BsmtFinType1 37\n", + "BsmtFinType2 38\n", + "Electrical 1\n", + "FireplaceQu 690\n", + "GarageType 81\n", + "GarageYrBlt 81\n", + "GarageFinish 81\n", + "GarageQual 81\n", + "GarageCond 81\n", + "PoolQC 1453\n", + "Fence 1179\n", + "MiscFeature 1406\n", + "dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nulos_columnas[nulos_columnas > 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0692b021-889c-40e8-a26e-2597a832d4bc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PoolQC 0.995205\n", + "MiscFeature 0.963014\n", + "Alley 0.937671\n", + "Fence 0.807534\n", + "FireplaceQu 0.472603\n", + "LotFrontage 0.177397\n", + "GarageType 0.055479\n", + "GarageYrBlt 0.055479\n", + "GarageFinish 0.055479\n", + "GarageQual 0.055479\n", + "GarageCond 0.055479\n", + "BsmtExposure 0.026027\n", + "BsmtFinType2 0.026027\n", + "BsmtFinType1 0.025342\n", + "BsmtCond 0.025342\n", + "BsmtQual 0.025342\n", + "MasVnrArea 0.005479\n", + "MasVnrType 0.005479\n", + "Electrical 0.000685\n", + "dtype: float64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "porcentaje_nulos = nulos_columnas[nulos_columnas > 0]/houses.shape[0]\n", + "porcentaje_nulos.sort_values(ascending = False)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8b9a39f8-8db0-4c7d-bf8a-be7d46bf5bd1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['LotFrontage', 'Alley', 'MasVnrType', 'MasVnrArea', 'BsmtQual',\n", + " 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2',\n", + " 'Electrical', 'FireplaceQu', 'GarageType', 'GarageYrBlt',\n", + " 'GarageFinish', 'GarageQual', 'GarageCond', 'PoolQC', 'Fence',\n", + " 'MiscFeature'],\n", + " dtype='object')" + ] + }, + "execution_count": 11, + "metadata": {}, 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" + ], + "text/plain": [ + " LotFrontage Alley MasVnrType MasVnrArea BsmtQual BsmtCond BsmtExposure \\\n", + "0 65.0 NaN BrkFace 196.0 Gd TA No \n", + "1 80.0 NaN None 0.0 Gd TA Gd \n", + "2 68.0 NaN BrkFace 162.0 Gd TA Mn \n", + "3 60.0 NaN None 0.0 TA Gd No \n", + "4 84.0 NaN BrkFace 350.0 Gd TA Av \n", + "... ... ... ... ... ... ... ... \n", + "1455 62.0 NaN None 0.0 Gd TA No \n", + "1456 85.0 NaN Stone 119.0 Gd TA No \n", + "1457 66.0 NaN None 0.0 TA Gd No \n", + "1458 68.0 NaN None 0.0 TA TA Mn \n", + "1459 75.0 NaN None 0.0 TA TA No \n", + "\n", + " BsmtFinType1 BsmtFinType2 Electrical FireplaceQu GarageType GarageYrBlt \\\n", + "0 GLQ Unf SBrkr NaN Attchd 2003.0 \n", + "1 ALQ Unf SBrkr TA Attchd 1976.0 \n", + "2 GLQ Unf SBrkr TA Attchd 2001.0 \n", + "3 ALQ Unf SBrkr Gd Detchd 1998.0 \n", + "4 GLQ Unf SBrkr TA Attchd 2000.0 \n", + "... ... ... ... ... ... ... \n", + "1455 Unf Unf SBrkr TA Attchd 1999.0 \n", + "1456 ALQ Rec SBrkr TA Attchd 1978.0 \n", + "1457 GLQ Unf SBrkr Gd Attchd 1941.0 \n", + "1458 GLQ Rec FuseA NaN Attchd 1950.0 \n", + "1459 BLQ LwQ SBrkr NaN Attchd 1965.0 \n", + "\n", + " GarageFinish GarageQual GarageCond PoolQC Fence MiscFeature \n", + "0 RFn TA TA NaN NaN NaN \n", + "1 RFn TA TA NaN NaN NaN \n", + "2 RFn TA TA NaN NaN NaN \n", + "3 Unf TA TA NaN NaN NaN \n", + "4 RFn TA TA NaN NaN NaN \n", + "... ... ... ... ... ... ... \n", + "1455 RFn TA TA NaN NaN NaN \n", + "1456 Unf TA TA NaN MnPrv NaN \n", + "1457 RFn TA TA NaN GdPrv Shed \n", + "1458 Unf TA TA NaN NaN NaN \n", + "1459 Fin TA TA NaN NaN NaN \n", + "\n", + "[1460 rows x 19 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "houses_only_nulls = houses[porcentaje_nulos.index]\n", + "houses_only_nulls" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b1988056-06c9-4285-a05f-6edc1e49556c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1460 entries, 0 to 1459\n", + "Data columns (total 19 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 LotFrontage 1201 non-null float64\n", + " 1 Alley 91 non-null object \n", + " 2 MasVnrType 1452 non-null object \n", + " 3 MasVnrArea 1452 non-null float64\n", + " 4 BsmtQual 1423 non-null object \n", + " 5 BsmtCond 1423 non-null object \n", + " 6 BsmtExposure 1422 non-null object \n", + " 7 BsmtFinType1 1423 non-null object \n", + " 8 BsmtFinType2 1422 non-null object \n", + " 9 Electrical 1459 non-null object \n", + " 10 FireplaceQu 770 non-null object \n", + " 11 GarageType 1379 non-null object \n", + " 12 GarageYrBlt 1379 non-null float64\n", + " 13 GarageFinish 1379 non-null object \n", + " 14 GarageQual 1379 non-null object \n", + " 15 GarageCond 1379 non-null object \n", + " 16 PoolQC 7 non-null object \n", + " 17 Fence 281 non-null object \n", + " 18 MiscFeature 54 non-null object \n", + "dtypes: float64(3), object(16)\n", + "memory usage: 216.8+ KB\n" + ] + } + ], + "source": [ + "houses_only_nulls.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "c3e63d2b-1770-4973-b8e1-779637655fe3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['LotFrontage', 'Alley', 'FireplaceQu', 'PoolQC', 'Fence',\n", + " 'MiscFeature'],\n", + " dtype='object')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "columnas_eliminar = porcentaje_nulos[porcentaje_nulos > .15].index #eliminaré las columnas con nulos mayor al 15% del total\n", + "columnas_eliminar" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "79d89230-22ac-4608-bf71-893cb41e4e15", + "metadata": {}, + "outputs": [], + "source": [ + "houses_clean = houses.drop(columns = columnas_eliminar)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "f9fd1005-0b37-49c0-b04a-e67fa6f0a47a", + "metadata": {}, + "outputs": [], + "source": [ + "houses_clean = houses_clean.drop(columns=['Id']) #también se eliminará la columna Id ya que no aporta al precio de la casa" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "b840aef6-268c-4a6b-9d35-bcbec1be241b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1460, 74)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "houses_clean.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "e2ba268c-f6ac-47bf-b119-6d1838cc54d8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MSSubClassMSZoningLotAreaStreetLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhood...EnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
060RL8450PaveRegLvlAllPubInsideGtlCollgCr...0000022008WDNormal208500
120RL9600PaveRegLvlAllPubFR2GtlVeenker...0000052007WDNormal181500
260RL11250PaveIR1LvlAllPubInsideGtlCollgCr...0000092008WDNormal223500
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3 rows × 74 columns

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" + ], + "text/plain": [ + " MSSubClass MSZoning LotArea Street LotShape LandContour Utilities \\\n", + "0 60 RL 8450 Pave Reg Lvl AllPub \n", + "1 20 RL 9600 Pave Reg Lvl AllPub \n", + "2 60 RL 11250 Pave IR1 Lvl AllPub \n", + "\n", + " LotConfig LandSlope Neighborhood ... EnclosedPorch 3SsnPorch ScreenPorch \\\n", + "0 Inside Gtl CollgCr ... 0 0 0 \n", + "1 FR2 Gtl Veenker ... 0 0 0 \n", + "2 Inside Gtl CollgCr ... 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", + "\n", + "[3 rows x 74 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "houses_clean.head(3)" + ] + }, + { + "cell_type": "markdown", + "id": "c34b6d3d-7d7a-49ab-9de8-4542c95f71c2", + "metadata": {}, + "source": [ + "### Ya que se tiene el dataframe limpio de nulos analizaré la distribución de la variable a estudiar, que en este caso es el precio de la casa" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "c4db31ca-a077-4761-9812-b8abc33cbe37", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "houses_clean.SalePrice.hist(bins = 100);" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "32557667-aaf2-4001-8da8-45b3236fb6e3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# La distribución podría ser más equilibrada de los dos lados, por lo tanto intentaré formas de normalizarla, primero con raíz cuadrada\n", + "\n", + "np.sqrt(houses_clean['SalePrice']).hist(bins = 100);" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "ee7d9aa0-2ab3-4377-861e-5d08a1255eb2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Después con elevarla al cuadrado\n", + "\n", + "(((houses_clean['SalePrice'])) ** 2).hist(bins = 100);" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "0c441165-bcb0-436d-82a1-c125973e1c3b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Y por último con logaritmo, aquí se ve que es la gráfica más balanceada\n", + "\n", + "np.log(houses_clean['SalePrice']).hist(bins = 100);" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "718a548f-520b-4894-9412-86cd0f88a96a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " MSSubClass MSZoning LotArea Street LotShape LandContour Utilities \\\n", + "0 60 RL 8450 Pave Reg Lvl AllPub \n", + "1 20 RL 9600 Pave Reg Lvl AllPub \n", + "\n", + " LotConfig LandSlope Neighborhood ... 3SsnPorch ScreenPorch PoolArea \\\n", + "0 Inside Gtl CollgCr ... 0 0 0 \n", + "1 FR2 Gtl Veenker ... 0 0 0 \n", + "\n", + " MiscVal MoSold YrSold SaleType SaleCondition SalePrice SalePriceLog \n", + "0 0 2 2008 WD Normal 208500 12.247694 \n", + "1 0 5 2007 WD Normal 181500 12.109011 \n", + "\n", + "[2 rows x 75 columns]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "houses_clean['SalePriceLog'] = np.log(houses_clean['SalePrice']) #agregamos esa nueva columna al dataframe\n", + "houses_clean.head(2)" + ] + }, + { + "cell_type": "markdown", + "id": "03a4bbc3-cc5b-4406-a0ce-c93393109f0a", + "metadata": {}, + "source": [ + "### Aún quedan bastantes columnas, así que después de leer la descripción de cada una, elegí las más importantes y agrupé de una manera que me pareció lógica" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "8ac9bb46-b29b-4c8b-bea4-ce98555b3e94", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['MSSubClass', 'MSZoning', 'LotArea', 'Street', 'LotShape',\n", + " 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood',\n", + " 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'OverallQual',\n", + " 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'RoofStyle', 'RoofMatl',\n", + " 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'MasVnrArea', 'ExterQual',\n", + " 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure',\n", + " 'BsmtFinType1', 'BsmtFinSF1', 'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF',\n", + " 'TotalBsmtSF', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical',\n", + " '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath',\n", + " 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr',\n", + " 'KitchenQual', 'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'GarageType',\n", + " 'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n", + " 'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n", + " 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal',\n", + " 'MoSold', 'YrSold', 'SaleType', 'SaleCondition', 'SalePrice',\n", + " 'SalePriceLog'],\n", + " dtype='object')" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "houses_clean.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "1901a280-1e9b-49b2-94af-4d8bffa48b3f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/qn/d5_7tb1x561dlhpvhyh3k0rr0000gn/T/ipykernel_92497/2551492073.py:3: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " houses_small_c['rooms_area'] = houses_clean['GrLivArea'] + houses_clean['GarageArea'] + houses_clean['TotalBsmtSF'] + houses_clean['WoodDeckSF'] + houses_clean['OpenPorchSF'] + houses_clean['EnclosedPorch'] + houses_clean['3SsnPorch']+ houses_clean['ScreenPorch'] + houses_clean['PoolArea']\n", + "/var/folders/qn/d5_7tb1x561dlhpvhyh3k0rr0000gn/T/ipykernel_92497/2551492073.py:5: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " houses_small_c['total_num_rooms'] = houses_clean.BedroomAbvGr + houses_clean.KitchenAbvGr + houses_clean.FullBath + houses_clean.HalfBath + houses_clean.BsmtFullBath + houses_clean.BsmtHalfBath\n", + "/var/folders/qn/d5_7tb1x561dlhpvhyh3k0rr0000gn/T/ipykernel_92497/2551492073.py:7: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " houses_small_c['quality_condition'] = houses_clean['OverallCond'] + houses_clean['OverallQual']\n", + "/var/folders/qn/d5_7tb1x561dlhpvhyh3k0rr0000gn/T/ipykernel_92497/2551492073.py:9: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " houses_small_c['years_since_built'] = 2022 - houses_clean.YearBuilt\n", + "/var/folders/qn/d5_7tb1x561dlhpvhyh3k0rr0000gn/T/ipykernel_92497/2551492073.py:11: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " houses_small_c['years_since_remod'] = 2022 - houses_clean.YearRemodAdd\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Neighborhood LotArea rooms_area total_num_rooms quality_condition \\\n", + "0 CollgCr 8450 3175 8 12 \n", + "1 Veenker 9600 3282 7 14 \n", + "2 CollgCr 11250 3356 8 12 \n", + "\n", + " years_since_built years_since_remod \n", + "0 19 19 \n", + "1 46 46 \n", + "2 21 20 " + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "houses_small_c = houses_clean[['Neighborhood', 'LotArea']]\n", + "\n", + "houses_small_c['rooms_area'] = houses_clean['GrLivArea'] + houses_clean['GarageArea'] + houses_clean['TotalBsmtSF'] + houses_clean['WoodDeckSF'] + houses_clean['OpenPorchSF'] + houses_clean['EnclosedPorch'] + houses_clean['3SsnPorch']+ houses_clean['ScreenPorch'] + houses_clean['PoolArea']\n", + " \n", + "houses_small_c['total_num_rooms'] = houses_clean.BedroomAbvGr + houses_clean.KitchenAbvGr + houses_clean.FullBath + houses_clean.HalfBath + houses_clean.BsmtFullBath + houses_clean.BsmtHalfBath\n", + "\n", + "houses_small_c['quality_condition'] = houses_clean['OverallCond'] + houses_clean['OverallQual']\n", + "\n", + "houses_small_c['years_since_built'] = 2022 - houses_clean.YearBuilt\n", + "\n", + "houses_small_c['years_since_remod'] = 2022 - houses_clean.YearRemodAdd\n", + "\n", + "houses_small_c.head(3)" + ] + }, + { + "cell_type": "markdown", + "id": "c4336875-66a2-4e9f-9f1a-c47547b80102", + "metadata": {}, + "source": [ + "### Después convertimos a valor numérico todas las variables, la de Vecindario la mejor manera de hacerlo sería con dummies pero dado que son demasiados valores únicos se agregan demasiadas columnas" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "fa13bbeb-b71a-4fe2-b412-35ddbca19857", + "metadata": {}, + "outputs": [], + "source": [ + "houses_numeric = houses_small_c._get_numeric_data()\n", + "\n", + "houses_numeric['Neighborhood_num'] = houses_small_c['Neighborhood'].astype('category').cat.codes" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "834ff0de-7183-4eb2-af36-2f022ca6b306", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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LotArearooms_areatotal_num_roomsquality_conditionyears_since_builtyears_since_remodNeighborhood_num
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1460 rows × 7 columns

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" + ], + "text/plain": [ + " LotArea rooms_area total_num_rooms quality_condition \\\n", + "0 8450 3175 8 12 \n", + "1 9600 3282 7 14 \n", + "2 11250 3356 8 12 \n", + "3 9550 3422 6 12 \n", + "4 14260 4455 9 13 \n", + "... ... ... ... ... \n", + "1455 7917 3100 7 11 \n", + "1456 13175 4464 7 12 \n", + "1457 9042 3804 7 16 \n", + "1458 9717 2874 5 11 \n", + "1459 9937 3592 7 11 \n", + "\n", + " years_since_built years_since_remod Neighborhood_num \n", + "0 19 19 5 \n", + "1 46 46 24 \n", + "2 21 20 5 \n", + "3 107 52 6 \n", + "4 22 22 15 \n", + "... ... ... ... \n", + "1455 23 22 8 \n", + "1456 44 34 14 \n", + "1457 81 16 6 \n", + "1458 72 26 12 \n", + "1459 57 57 7 \n", + "\n", + "[1460 rows x 7 columns]" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "houses_numeric" + ] + }, + { + "cell_type": "markdown", + "id": "14b34d20-3330-4e2f-aef0-46c9585dcda1", + "metadata": {}, + "source": [ + "### Después escalamos las variables para que estén en una escala similar" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "b1d70126-9508-472b-b293-d845316cd55c", + "metadata": {}, + "outputs": [], + "source": [ + "houses_final = StandardScaler().fit_transform(houses_numeric)\n", + "houses_final = pd.DataFrame(houses_final)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "30e8fb58-9552-4609-a710-b647fded943d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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LotArea_scaled-0.207142-0.0918860.073480
rooms_area_scaled-0.0528790.0500660.121262
total_num_rooms_scaled1.1589650.4591761.158965
quality_condition_scaled0.1921751.3735470.192175
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years_since_remod_scaled-0.8786680.429577-0.830215
Neighborhood_num_scaled-1.2062151.954302-1.206215
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" + ], + "text/plain": [ + " 0 1 2\n", + "LotArea_scaled -0.207142 -0.091886 0.073480\n", + "rooms_area_scaled -0.052879 0.050066 0.121262\n", + "total_num_rooms_scaled 1.158965 0.459176 1.158965\n", + "quality_condition_scaled 0.192175 1.373547 0.192175\n", + "years_since_built_scaled -1.050994 -0.156734 -0.984752\n", + "years_since_remod_scaled -0.878668 0.429577 -0.830215\n", + "Neighborhood_num_scaled -1.206215 1.954302 -1.206215" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "houses_final.rename(columns= {0:'LotArea_scaled', 1:'rooms_area_scaled', 2:'total_num_rooms_scaled', 3:'quality_condition_scaled', 4:'years_since_built_scaled', 5:'years_since_remod_scaled', 6:'Neighborhood_num_scaled'}, inplace = True)\n", + "houses_final.head(3).T" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "55b26c40-abd1-4591-a877-7c815057d2f5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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LotArea_scaledrooms_area_scaledtotal_num_rooms_scaledquality_condition_scaledyears_since_built_scaledyears_since_remod_scaledNeighborhood_num_scaled
count1.460000e+031.460000e+031.460000e+031.460000e+031.460000e+031.460000e+031.460000e+03
mean-4.202783e-17-1.106421e-161.447852e-166.387585e-17-1.126572e-16-4.212764e-17-3.414316e-17
std1.000343e+001.000343e+001.000343e+001.000343e+001.000343e+001.000343e+001.000343e+00
min-9.237292e-01-2.786217e+00-2.339977e+00-5.714684e+00-1.282839e+00-1.217843e+00-2.037929e+00
25%-2.969908e-01-6.775251e-01-9.404005e-01-3.985107e-01-9.516316e-01-9.271216e-01-8.735286e-01
50%-1.040633e-01-1.216693e-01-2.406122e-011.921752e-01-5.737148e-02-4.425864e-01-4.181361e-02
75%1.087080e-015.335238e-014.591762e-017.828611e-015.719226e-018.656586e-017.899013e-01
max2.051827e+011.051199e+015.357695e+004.326976e+003.287824e+001.689368e+001.954302e+00
\n", + "
" + ], + "text/plain": [ + " LotArea_scaled rooms_area_scaled total_num_rooms_scaled \\\n", + "count 1.460000e+03 1.460000e+03 1.460000e+03 \n", + "mean -4.202783e-17 -1.106421e-16 1.447852e-16 \n", + "std 1.000343e+00 1.000343e+00 1.000343e+00 \n", + "min -9.237292e-01 -2.786217e+00 -2.339977e+00 \n", + "25% -2.969908e-01 -6.775251e-01 -9.404005e-01 \n", + "50% -1.040633e-01 -1.216693e-01 -2.406122e-01 \n", + "75% 1.087080e-01 5.335238e-01 4.591762e-01 \n", + "max 2.051827e+01 1.051199e+01 5.357695e+00 \n", + "\n", + " quality_condition_scaled years_since_built_scaled \\\n", + "count 1.460000e+03 1.460000e+03 \n", + "mean 6.387585e-17 -1.126572e-16 \n", + "std 1.000343e+00 1.000343e+00 \n", + "min -5.714684e+00 -1.282839e+00 \n", + "25% -3.985107e-01 -9.516316e-01 \n", + "50% 1.921752e-01 -5.737148e-02 \n", + "75% 7.828611e-01 5.719226e-01 \n", + "max 4.326976e+00 3.287824e+00 \n", + "\n", + " years_since_remod_scaled Neighborhood_num_scaled \n", + "count 1.460000e+03 1.460000e+03 \n", + "mean -4.212764e-17 -3.414316e-17 \n", + "std 1.000343e+00 1.000343e+00 \n", + "min -1.217843e+00 -2.037929e+00 \n", + "25% -9.271216e-01 -8.735286e-01 \n", + "50% -4.425864e-01 -4.181361e-02 \n", + "75% 8.656586e-01 7.899013e-01 \n", + "max 1.689368e+00 1.954302e+00 " + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "houses_final.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "8061e81b-a343-4151-8404-328b4fed5c36", + "metadata": {}, + "source": [ + "## Análisis estadístico" + ] + }, + { + "cell_type": "markdown", + "id": "c3d06773-510f-43a9-bc7a-63e45f3ec7e7", + "metadata": {}, + "source": [ + "### Correlación" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "fdbad3b2-8f14-4110-8227-0d7b38aa4470", + "metadata": {}, + "outputs": [], + "source": [ + "houses_final['SalePriceLog'] = houses_clean['SalePriceLog']\n", + "houses_final['SalePrice'] = houses_clean['SalePrice']" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "3166db43-05c5-4fbb-8345-ea3ac7050957", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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SalePriceLogSalePrice
SalePriceLog1.0000000.948374
SalePrice0.9483741.000000
rooms_area_scaled0.8090900.807704
quality_condition_scaled0.6431170.594786
total_num_rooms_scaled0.5211200.470702
LotArea_scaled0.2573200.263843
Neighborhood_num_scaled0.2016850.210851
years_since_remod_scaled-0.565608-0.507101
years_since_built_scaled-0.586570-0.522897
\n", + "
" + ], + "text/plain": [ + " SalePriceLog SalePrice\n", + "SalePriceLog 1.000000 0.948374\n", + "SalePrice 0.948374 1.000000\n", + "rooms_area_scaled 0.809090 0.807704\n", + "quality_condition_scaled 0.643117 0.594786\n", + "total_num_rooms_scaled 0.521120 0.470702\n", + "LotArea_scaled 0.257320 0.263843\n", + "Neighborhood_num_scaled 0.201685 0.210851\n", + "years_since_remod_scaled -0.565608 -0.507101\n", + "years_since_built_scaled -0.586570 -0.522897" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "houses_final.corr()[['SalePriceLog', 'SalePrice']].sort_values(by = 'SalePriceLog', ascending = False)\n", + "\n", + "#La variable que tiene la correlación más alta es la del área total de los cuartos, seguida por la calidad y la condición de la casa" + ] + }, + { + "cell_type": "markdown", + "id": "f9e40298-8630-48fd-9bea-6bdc32d72ca9", + "metadata": {}, + "source": [ + "#### Las tres columnas con correlación más alta son la del área total de cuartos, seguida por la calidad y la condición de la casa y por último los años que tiene la casa tiene una alta correlación negativa" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "a5d5b26f-8adc-4399-b1b8-953f1327b3dc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Graficamos las tres variables con mayor correlación para ver su comportamiento\n", + "\n", + "houses_final.plot.scatter(x = 'rooms_area_scaled', y = 'SalePriceLog');" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "4f9d8952-a53e-4d70-acd7-0175eca2a800", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "houses_final.plot.scatter(x = 'quality_condition_scaled', y = 'SalePriceLog');" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "33812e53-55c2-4a48-9fda-48a7d83caab4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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sum_sqdfFPR(>F)
LotArea_scaled0.9023761.034.1785506.199997e-09
rooms_area_scaled23.8412111.0903.0139231.119567e-154
total_num_rooms_scaled1.2094201.045.8081881.886687e-11
quality_condition_scaled13.9384151.0527.9338826.706010e-100
years_since_built_scaled11.6612721.0441.6843917.914976e-86
years_since_remod_scaled0.2266501.08.5846313.443081e-03
Neighborhood_num_scaled0.0792011.02.9998348.348544e-02
Residual38.3354421452.0NaNNaN
\n", + "
" + ], + "text/plain": [ + " sum_sq df F PR(>F)\n", + "LotArea_scaled 0.902376 1.0 34.178550 6.199997e-09\n", + "rooms_area_scaled 23.841211 1.0 903.013923 1.119567e-154\n", + "total_num_rooms_scaled 1.209420 1.0 45.808188 1.886687e-11\n", + "quality_condition_scaled 13.938415 1.0 527.933882 6.706010e-100\n", + "years_since_built_scaled 11.661272 1.0 441.684391 7.914976e-86\n", + "years_since_remod_scaled 0.226650 1.0 8.584631 3.443081e-03\n", + "Neighborhood_num_scaled 0.079201 1.0 2.999834 8.348544e-02\n", + "Residual 38.335442 1452.0 NaN NaN" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "anova_table = sm.stats.anova_lm(model, typ=2)\n", + "anova_table" + ] + }, + { + "cell_type": "markdown", + "id": "5d3fe828-9b2f-40aa-8f0e-4175ea701a7d", + "metadata": {}, + "source": [ + "### Modelo OLS" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "bbfa96dc-6976-46e7-bf85-837e2584af49", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/marcosrubio/opt/anaconda3/lib/python3.9/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(houses_final[['LotArea_scaled', 'rooms_area_scaled', 'total_num_rooms_scaled', 'quality_condition_scaled', 'years_since_built_scaled', 'years_since_remod_scaled', 'Neighborhood_num_scaled']])\n", + "Y = houses_final.SalePriceLog" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "e3adcfea-82b0-47e5-963b-3bfb0522aa96", + "metadata": {}, + "outputs": [], + "source": [ + "model = sm.OLS(Y, X).fit()\n", + "predictions = model.predict(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "3fe826e1-8f23-4792-810a-8363c9401a3c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: SalePriceLog R-squared: 0.835\n", + "Model: OLS Adj. R-squared: 0.835\n", + "Method: Least Squares F-statistic: 1052.\n", + "Date: Fri, 29 Jul 2022 Prob (F-statistic): 0.00\n", + "Time: 16:26:47 Log-Likelihood: 585.42\n", + "No. Observations: 1460 AIC: -1155.\n", + "Df Residuals: 1452 BIC: -1113.\n", + "Df Model: 7 \n", + "Covariance Type: nonrobust \n", + "============================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "--------------------------------------------------------------------------------------------\n", + "const 12.0241 0.004 2827.550 0.000 12.016 12.032\n", + "LotArea_scaled 0.0264 0.005 5.846 0.000 0.018 0.035\n", + "rooms_area_scaled 0.1846 0.006 30.050 0.000 0.173 0.197\n", + "total_num_rooms_scaled 0.0349 0.005 6.768 0.000 0.025 0.045\n", + "quality_condition_scaled 0.1236 0.005 22.977 0.000 0.113 0.134\n", + "years_since_built_scaled -0.1164 0.006 -21.016 0.000 -0.127 -0.106\n", + "years_since_remod_scaled -0.0175 0.006 -2.930 0.003 -0.029 -0.006\n", + "Neighborhood_num_scaled 0.0075 0.004 1.732 0.083 -0.001 0.016\n", + "==============================================================================\n", + "Omnibus: 1321.606 Durbin-Watson: 1.967\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 159024.982\n", + "Skew: -3.703 Prob(JB): 0.00\n", + "Kurtosis: 53.589 Cond. No. 3.06\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" + ] + }, + { + "data": { + "text/plain": [ + "'\\nLa variable que más peso tiene es el área total de los cuartos porque tiene el mayor coeficiente \\ny las variables que elegimos explican el 83.5% de la variación de los datos\\n'" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print_model = model.summary()\n", + "print(print_model)\n", + "\n", + "\n", + "'''\n", + "La variable que más peso tiene es el área total de los cuartos porque tiene el mayor coeficiente \n", + "y las variables que elegimos explican el 83.5% de la variación de los datos por lo que fue una buena elección\n", + "\n", + "También podemos observar que el p-value del vecindario es relativamente grande por lo que puede no ser tan representativo (aunque el tema del\n", + "labeling también pudo haber influido)\n", + "'''" + ] + }, + { + "cell_type": "markdown", + "id": "17768b92-40df-453e-8a8d-e401c39fc594", + "metadata": {}, + "source": [ + "### Graficar regresión lineal de top 3 de variables" + ] + }, + { + "cell_type": "markdown", + "id": "73030a44-d70f-4a2b-831b-caf10032111a", + "metadata": {}, + "source": [ + "#### Área total de las habitaciones" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "20fbecdf-386c-4ed7-b73d-f19389a57e8a", + "metadata": {}, + "outputs": [], + "source": [ + "slope_1, intercept_1, r_value_1, p_value_1, std_err_1 = linregress(x = houses_final.rooms_area_scaled, y = houses_final.SalePriceLog)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "c3f561e1-08f2-4ce4-b0d3-e4088696a52a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(x = houses_final.rooms_area_scaled, y = houses_final.SalePriceLog)\n", + "\n", + "x = [houses_final.rooms_area_scaled.min(), houses_final.rooms_area_scaled.max()]\n", + "y = [houses_final.rooms_area_scaled.min() * slope_1 + intercept_1, houses_final.rooms_area_scaled.max() * slope_1 + intercept_1]\n", + "plt.plot(x,y, color='r')\n", + "plt.xlabel('rooms_area_scaled')\n", + "plt.ylabel('SalePriceLog');" + ] + }, + { + "cell_type": "markdown", + "id": "bd3e72be-27f4-46b8-887a-fda7273b7778", + "metadata": {}, + "source": [ + "#### Calidad y condición de la casa" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "200acd28-c993-4c92-86e6-5f578f90566f", + "metadata": {}, + "outputs": [], + "source": [ + "slope_2, intercept_2, r_value_2, p_value_2, std_err_2 = linregress(x = houses_final.quality_condition_scaled, y = houses_final.SalePriceLog)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "6b3ecb0a-39a0-4d13-89ca-d53f280658f7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(x = houses_final.quality_condition_scaled, y = houses_final.SalePriceLog)\n", + "\n", + "x = [houses_final.quality_condition_scaled.min(), houses_final.quality_condition_scaled.max()]\n", + "y = [houses_final.quality_condition_scaled.min() * slope_2 + intercept_2, houses_final.quality_condition_scaled.max() * slope_2 + intercept_2]\n", + "plt.plot(x,y, color='r')\n", + "plt.xlabel('quality_condition_scaled')\n", + "plt.ylabel('SalePriceLog');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2ccd6a1-ea26-4bf7-a323-8937120d69c0", + "metadata": {}, + "outputs": [], + "source": [ + "years_since_built_scaled" + ] + }, + { + "cell_type": "markdown", + "id": "5c04bb01-a2df-480f-98f1-7f39f39e156a", + "metadata": {}, + "source": [ + "#### Edad de la casa" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "9904ad9c-3dfb-41de-bd56-8d8e7f4e7e1e", + "metadata": {}, + "outputs": [], + "source": [ + "slope_3, intercept_3, r_value_3, p_value_3, std_err_3 = linregress(x = houses_final.years_since_built_scaled, y = houses_final.SalePriceLog)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "02b80c9b-c067-4af3-ba87-f716d432d381", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(x = houses_final.years_since_built_scaled, y = houses_final.SalePriceLog)\n", + "\n", + "x = [houses_final.years_since_built_scaled.min(), houses_final.years_since_built_scaled.max()]\n", + "y = [houses_final.years_since_built_scaled.min() * slope_3 + intercept_3, houses_final.years_since_built_scaled.max() * slope_3 + intercept_3]\n", + "plt.plot(x,y, color='r')\n", + "plt.xlabel('years_since_built_scaled')\n", + "plt.ylabel('SalePriceLog');" + ] + }, + { + "cell_type": "markdown", + "id": "8d8c03a8-b245-4e51-b93e-2efde5ca43e3", + "metadata": {}, + "source": [ + "# Conclusión: Dado que el objetivo del proyecto es \"... to identify the most important features of houses that affect the sale prices.\" La respuesta es el área total en pies cuadrados de las recámaras de la casa\n", + "\n" + ] + } + ], + "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 +}