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"}}},{"cell_type":"markdown","source":"### --- Data fields present in the dataset ---\n\n###### SalePrice - the property's sale price in dollars. This is the target variable that you're trying to predict\n###### MSSubClass: Identifies the type of dwelling involved in the sale.\t\n\t\n        20\t1-STORY 1946 & NEWER ALL STYLES\n        30\t1-STORY 1945 & OLDER\n        40\t1-STORY W/FINISHED ATTIC ALL AGES\n        45\t1-1/2 STORY - UNFINISHED ALL AGES\n        50\t1-1/2 STORY FINISHED ALL AGES\n        60\t2-STORY 1946 & NEWER\n        70\t2-STORY 1945 & OLDER\n        75\t2-1/2 STORY ALL AGES\n        80\tSPLIT OR MULTI-LEVEL\n        85\tSPLIT FOYER\n        90\tDUPLEX - ALL STYLES AND AGES\n       120\t1-STORY PUD (Planned Unit Development) - 1946 & NEWER\n       150\t1-1/2 STORY PUD - ALL AGES\n       160\t2-STORY PUD - 1946 & NEWER\n       180\tPUD - MULTILEVEL - INCL SPLIT LEV/FOYER\n       190\t2 FAMILY CONVERSION - ALL STYLES AND AGES\n\n###### MSZoning: Identifies the general zoning classification of the sale.\n\t\t\n       A\tAgriculture\n       C\tCommercial\n       FV\tFloating Village Residential\n       I\tIndustrial\n       RH\tResidential High Density\n       RL\tResidential Low Density\n       RP\tResidential Low Density Park \n       RM\tResidential Medium Density\n\t\n\t\n###### LotFrontage: Linear feet of street connected to property\n\n###### LotArea: Lot size in square feet\n\n###### Street: Type of road access to property\n\n       Grvl\tGravel\t\n       Pave\tPaved\n       \t\n###### Alley: Type of alley access to property\n\n       Grvl\tGravel\n       Pave\tPaved\n       NA \tNo alley access\n\t\t\n###### LotShape: General shape of property\n\n       Reg\tRegular\t\n       IR1\tSlightly irregular\n       IR2\tModerately Irregular\n       IR3\tIrregular\n       \n###### LandContour: Flatness of the property\n\n       Lvl\tNear Flat/Level\t\n       Bnk\tBanked - Quick and significant rise from street grade to building\n       HLS\tHillside - Significant slope from side to side\n       Low\tDepression\n\t\t\n###### Utilities: Type of utilities available\n\t\t\n       AllPub\tAll public Utilities (E,G,W,& S)\t\n       NoSewr\tElectricity, Gas, and Water (Septic Tank)\n       NoSeWa\tElectricity and Gas Only\n       ELO\tElectricity only\t\n\t\n###### LotConfig: Lot configuration\n\n       Inside\tInside lot\n       Corner\tCorner lot\n       CulDSac\tCul-de-sac\n       FR2\tFrontage on 2 sides of property\n       FR3\tFrontage on 3 sides of property\n\t\n###### LandSlope: Slope of property\n\t\t\n       Gtl\tGentle slope\n       Mod\tModerate Slope\t\n       Sev\tSevere Slope\n\t\n###### Neighborhood: Physical locations within Ames city limits\n\n       Blmngtn\tBloomington Heights\n       Blueste\tBluestem\n       BrDale\tBriardale\n       BrkSide\tBrookside\n       ClearCr\tClear Creek\n       CollgCr\tCollege Creek\n       Crawfor\tCrawford\n       Edwards\tEdwards\n       Gilbert\tGilbert\n       IDOTRR\tIowa DOT and Rail Road\n       MeadowV\tMeadow Village\n       Mitchel\tMitchell\n       Names\tNorth Ames\n       NoRidge\tNorthridge\n       NPkVill\tNorthpark Villa\n       NridgHt\tNorthridge Heights\n       NWAmes\tNorthwest Ames\n       OldTown\tOld Town\n       SWISU\tSouth & West of Iowa State University\n       Sawyer\tSawyer\n       SawyerW\tSawyer West\n       Somerst\tSomerset\n       StoneBr\tStone Brook\n       Timber\tTimberland\n       Veenker\tVeenker\n\t\t\t\n###### Condition1: Proximity to various conditions\n\t\n       Artery\tAdjacent to arterial street\n       Feedr\tAdjacent to feeder street\t\n       Norm\tNormal\t\n       RRNn\tWithin 200' of North-South Railroad\n       RRAn\tAdjacent to North-South Railroad\n       PosN\tNear positive off-site feature--park, greenbelt, etc.\n       PosA\tAdjacent to postive off-site feature\n       RRNe\tWithin 200' of East-West Railroad\n       RRAe\tAdjacent to East-West Railroad\n\t\n###### Condition2: Proximity to various conditions (if more than one is present)\n\t\t\n       Artery\tAdjacent to arterial street\n       Feedr\tAdjacent to feeder street\t\n       Norm\tNormal\t\n       RRNn\tWithin 200' of North-South Railroad\n       RRAn\tAdjacent to North-South Railroad\n       PosN\tNear positive off-site feature--park, greenbelt, etc.\n       PosA\tAdjacent to postive off-site feature\n       RRNe\tWithin 200' of East-West Railroad\n       RRAe\tAdjacent to East-West Railroad\n\t\n###### BldgType: Type of dwelling\n\t\t\n       1Fam\tSingle-family Detached\t\n       2FmCon\tTwo-family Conversion; originally built as one-family dwelling\n       Duplx\tDuplex\n       TwnhsE\tTownhouse End Unit\n       TwnhsI\tTownhouse Inside Unit\n\t\n###### HouseStyle: Style of dwelling\n\t\n       1Story\tOne story\n       1.5Fin\tOne and one-half story: 2nd level finished\n       1.5Unf\tOne and one-half story: 2nd level unfinished\n       2Story\tTwo story\n       2.5Fin\tTwo and one-half story: 2nd level finished\n       2.5Unf\tTwo and one-half story: 2nd level unfinished\n       SFoyer\tSplit Foyer\n       SLvl\tSplit Level\n\t\n###### OverallQual: Rates the overall material and finish of the house\n\n       10\tVery Excellent\n       9\tExcellent\n       8\tVery Good\n       7\tGood\n       6\tAbove Average\n       5\tAverage\n       4\tBelow Average\n       3\tFair\n       2\tPoor\n       1\tVery Poor\n\t\n###### OverallCond: Rates the overall condition of the house\n\n       10\tVery Excellent\n       9\tExcellent\n       8\tVery Good\n       7\tGood\n       6\tAbove Average\t\n       5\tAverage\n       4\tBelow Average\t\n       3\tFair\n       2\tPoor\n       1\tVery Poor\n\t\t\n###### YearBuilt: Original construction date\n\n###### YearRemodAdd: Remodel date (same as construction date if no remodeling or additions)\n\n###### RoofStyle: Type of roof\n\n       Flat\tFlat\n       Gable\tGable\n       Gambrel\tGabrel (Barn)\n       Hip\tHip\n       Mansard\tMansard\n       Shed\tShed\n\t\t\n###### RoofMatl: Roof material\n\n       ClyTile\tClay or Tile\n       CompShg\tStandard (Composite) Shingle\n       Membran\tMembrane\n       Metal\tMetal\n       Roll\tRoll\n       Tar&Grv\tGravel & Tar\n       WdShake\tWood Shakes\n       WdShngl\tWood Shingles\n\t\t\n###### Exterior1st: Exterior covering on house\n\n       AsbShng\tAsbestos Shingles\n       AsphShn\tAsphalt Shingles\n       BrkComm\tBrick Common\n       BrkFace\tBrick Face\n       CBlock\tCinder Block\n       CemntBd\tCement Board\n       HdBoard\tHard Board\n       ImStucc\tImitation Stucco\n       MetalSd\tMetal Siding\n       Other\tOther\n       Plywood\tPlywood\n       PreCast\tPreCast\t\n       Stone\tStone\n       Stucco\tStucco\n       VinylSd\tVinyl Siding\n       Wd Sdng\tWood Siding\n       WdShing\tWood Shingles\n\t\n###### Exterior2nd: Exterior covering on house (if more than one material)\n\n       AsbShng\tAsbestos Shingles\n       AsphShn\tAsphalt Shingles\n       BrkComm\tBrick Common\n       BrkFace\tBrick Face\n       CBlock\tCinder Block\n       CemntBd\tCement Board\n       HdBoard\tHard Board\n       ImStucc\tImitation Stucco\n       MetalSd\tMetal Siding\n       Other\tOther\n       Plywood\tPlywood\n       PreCast\tPreCast\n       Stone\tStone\n       Stucco\tStucco\n       VinylSd\tVinyl Siding\n       Wd Sdng\tWood Siding\n       WdShing\tWood Shingles\n\t\n###### MasVnrType: Masonry veneer type\n\n       BrkCmn\tBrick Common\n       BrkFace\tBrick Face\n       CBlock\tCinder Block\n       None\tNone\n       Stone\tStone\n\t\n###### MasVnrArea: Masonry veneer area in square feet\n\n###### ExterQual: Evaluates the quality of the material on the exterior \n\t\t\n       Ex\tExcellent\n       Gd\tGood\n       TA\tAverage/Typical\n       Fa\tFair\n       Po\tPoor\n\t\t\n###### ExterCond: Evaluates the present condition of the material on the exterior\n\t\t\n       Ex\tExcellent\n       Gd\tGood\n       TA\tAverage/Typical\n       Fa\tFair\n       Po\tPoor\n\t\t\n###### Foundation: Type of foundation\n\t\t\n       BrkTil\tBrick & Tile\n       CBlock\tCinder Block\n       PConc\tPoured Contrete\t\n       Slab\tSlab\n       Stone\tStone\n       Wood\tWood\n\t\t\n###### BsmtQual: Evaluates the height of the basement\n\n       Ex\tExcellent (100+ inches)\t\n       Gd\tGood (90-99 inches)\n       TA\tTypical (80-89 inches)\n       Fa\tFair (70-79 inches)\n       Po\tPoor (<70 inches\n       NA\tNo Basement\n\t\t\n###### BsmtCond: Evaluates the general condition of the basement\n\n       Ex\tExcellent\n       Gd\tGood\n       TA\tTypical - slight dampness allowed\n       Fa\tFair - dampness or some cracking or settling\n       Po\tPoor - Severe cracking, settling, or wetness\n       NA\tNo Basement\n\t\n###### BsmtExposure: Refers to walkout or garden level walls\n\n       Gd\tGood Exposure\n       Av\tAverage Exposure (split levels or foyers typically score average or above)\t\n       Mn\tMimimum Exposure\n       No\tNo Exposure\n       NA\tNo Basement\n\t\n###### BsmtFinType1: Rating of basement finished area\n\n       GLQ\tGood Living Quarters\n       ALQ\tAverage Living Quarters\n       BLQ\tBelow Average Living Quarters\t\n       Rec\tAverage Rec Room\n       LwQ\tLow Quality\n       Unf\tUnfinshed\n       NA\tNo Basement\n\t\t\n###### BsmtFinSF1: Type 1 finished square feet\n\n###### BsmtFinType2: Rating of basement finished area (if multiple types)\n\n       GLQ\tGood Living Quarters\n       ALQ\tAverage Living Quarters\n       BLQ\tBelow Average Living Quarters\t\n       Rec\tAverage Rec Room\n       LwQ\tLow Quality\n       Unf\tUnfinshed\n       NA\tNo Basement\n\n###### BsmtFinSF2: Type 2 finished square feet\n\n###### BsmtUnfSF: Unfinished square feet of basement area\n\n###### TotalBsmtSF: Total square feet of basement area\n\n###### Heating: Type of heating\n\t\t\n       Floor\tFloor Furnace\n       GasA\tGas forced warm air furnace\n       GasW\tGas hot water or steam heat\n       Grav\tGravity furnace\t\n       OthW\tHot water or steam heat other than gas\n       Wall\tWall furnace\n\t\t\n###### HeatingQC: Heating quality and condition\n\n       Ex\tExcellent\n       Gd\tGood\n       TA\tAverage/Typical\n       Fa\tFair\n       Po\tPoor\n\t\t\n###### CentralAir: Central air conditioning\n\n       N\tNo\n       Y\tYes\n\t\t\n###### Electrical: Electrical system\n\n       SBrkr\tStandard Circuit Breakers & Romex\n       FuseA\tFuse Box over 60 AMP and all Romex wiring (Average)\t\n       FuseF\t60 AMP Fuse Box and mostly Romex wiring (Fair)\n       FuseP\t60 AMP Fuse Box and mostly knob & tube wiring (poor)\n       Mix\tMixed\n\t\t\n###### 1stFlrSF: First Floor square feet\n \n###### 2ndFlrSF: Second floor square feet\n\n###### LowQualFinSF: Low quality finished square feet (all floors)\n\n###### GrLivArea: Above grade (ground) living area square feet\n\n###### BsmtFullBath: Basement full bathrooms\n\n###### BsmtHalfBath: Basement half bathrooms\n\n###### FullBath: Full bathrooms above grade\n\n###### HalfBath: Half baths above grade\n\n###### Bedroom: Bedrooms above grade (does NOT include basement bedrooms)\n\n###### Kitchen: Kitchens above grade\n\n###### KitchenQual: Kitchen quality\n\n       Ex\tExcellent\n       Gd\tGood\n       TA\tTypical/Average\n       Fa\tFair\n       Po\tPoor\n       \t\n###### TotRmsAbvGrd: Total rooms above grade (does not include bathrooms)\n\n###### Functional: Home functionality (Assume typical unless deductions are warranted)\n\n       Typ\tTypical Functionality\n       Min1\tMinor Deductions 1\n       Min2\tMinor Deductions 2\n       Mod\tModerate Deductions\n       Maj1\tMajor Deductions 1\n       Maj2\tMajor Deductions 2\n       Sev\tSeverely Damaged\n       Sal\tSalvage only\n\t\t\n###### Fireplaces: Number of fireplaces\n\n###### FireplaceQu: Fireplace quality\n\n       Ex\tExcellent - Exceptional Masonry Fireplace\n       Gd\tGood - Masonry Fireplace in main level\n       TA\tAverage - Prefabricated Fireplace in main living area or Masonry Fireplace in basement\n       Fa\tFair - Prefabricated Fireplace in basement\n       Po\tPoor - Ben Franklin Stove\n       NA\tNo Fireplace\n\t\t\n###### GarageType: Garage location\n\t\t\n       2Types\tMore than one type of garage\n       Attchd\tAttached to home\n       Basment\tBasement Garage\n       BuiltIn\tBuilt-In (Garage part of house - typically has room above garage)\n       CarPort\tCar Port\n       Detchd\tDetached from home\n       NA\tNo Garage\n\t\t\n###### GarageYrBlt: Year garage was built\n\t\t\n###### GarageFinish: Interior finish of the garage\n\n       Fin\tFinished\n       RFn\tRough Finished\t\n       Unf\tUnfinished\n       NA\tNo Garage\n\t\t\n###### GarageCars: Size of garage in car capacity\n\n###### GarageArea: Size of garage in square feet\n\n###### GarageQual: Garage quality\n\n       Ex\tExcellent\n       Gd\tGood\n       TA\tTypical/Average\n       Fa\tFair\n       Po\tPoor\n       NA\tNo Garage\n\t\t\n###### GarageCond: Garage condition\n\n       Ex\tExcellent\n       Gd\tGood\n       TA\tTypical/Average\n       Fa\tFair\n       Po\tPoor\n       NA\tNo Garage\n\t\t\n###### PavedDrive: Paved driveway\n\n       Y\tPaved \n       P\tPartial Pavement\n       N\tDirt/Gravel\n\t\t\n###### WoodDeckSF: Wood deck area in square feet\n\n###### OpenPorchSF: Open porch area in square feet\n\n###### EnclosedPorch: Enclosed porch area in square feet\n\n###### 3SsnPorch: Three season porch area in square feet\n\n###### ScreenPorch: Screen porch area in square feet\n\n###### PoolArea: Pool area in square feet\n\n###### PoolQC: Pool quality\n\t\t\n       Ex\tExcellent\n       Gd\tGood\n       TA\tAverage/Typical\n       Fa\tFair\n       NA\tNo Pool\n\t\t\n###### Fence: Fence quality\n\t\t\n       GdPrv\tGood Privacy\n       MnPrv\tMinimum Privacy\n       GdWo\tGood Wood\n       MnWw\tMinimum Wood/Wire\n       NA\tNo Fence\n\t\n###### MiscFeature: Miscellaneous feature not covered in other categories\n\t\t\n       Elev\tElevator\n       Gar2\t2nd Garage (if not described in garage section)\n       Othr\tOther\n       Shed\tShed (over 100 SF)\n       TenC\tTennis Court\n       NA\tNone\n\t\t\n###### MiscVal:  $Value of miscellaneous feature\n\n###### MoSold: Month Sold (MM)\n\n###### YrSold: Year Sold (YYYY)\n\n###### SaleType: Type of sale\n\t\t\n       WD \t    Warranty Deed - Conventional\n       CWD\t    Warranty Deed - Cash\n       VWD\t    Warranty Deed - VA Loan\n       New\t    Home just constructed and sold\n       COD\t    Court Officer Deed/Estate\n       Con\t    Contract 15% Down payment regular terms\n       ConLw\tContract Low Down payment and low interest\n       ConLI\tContract Low Interest\n       ConLD\tContract Low Down\n       Oth\t    Other\n\t\t\n###### SaleCondition: Condition of sale\n\n       Normal\t Normal Sale\n       Abnorml\t Abnormal Sale -  trade, foreclosure, short sale\n       AdjLand\t Adjoining Land Purchase\n       Alloca\t Allocation - two linked properties with separate deeds, typically condo with a \n       garage unit\t\n       Family\t Sale between family members\n       Partial\t Home was not completed when last assessed (associated with New Homes)","metadata":{}},{"cell_type":"code","source":"## Importing the Libraries\n\nimport pandas as pd ## pandas is used to manupulate the dataframe\nimport numpy as np ## numpy is used to do scientific calculations\nimport matplotlib.pyplot as plt ## matplotlib used for visualization\nimport seaborn as sns ## seaborn used for visualization \nimport missingno as msno ## used to visualize missing values\nimport warnings ## used to remove warnings\nwarnings.filterwarnings('ignore')","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:47.027905Z","iopub.execute_input":"2022-07-21T07:56:47.028288Z","iopub.status.idle":"2022-07-21T07:56:47.034610Z","shell.execute_reply.started":"2022-07-21T07:56:47.028260Z","shell.execute_reply":"2022-07-21T07:56:47.033373Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## Importing the data\n\ndata=pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:47.039949Z","iopub.execute_input":"2022-07-21T07:56:47.040283Z","iopub.status.idle":"2022-07-21T07:56:47.065245Z","shell.execute_reply.started":"2022-07-21T07:56:47.040255Z","shell.execute_reply":"2022-07-21T07:56:47.064141Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## Getting the data\n\ndata","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:47.084806Z","iopub.execute_input":"2022-07-21T07:56:47.085365Z","iopub.status.idle":"2022-07-21T07:56:47.595471Z","shell.execute_reply.started":"2022-07-21T07:56:47.085333Z","shell.execute_reply":"2022-07-21T07:56:47.594071Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###### There are 1460 rows and 81 columns in this dataset","metadata":{}},{"cell_type":"code","source":"## Basic Checks\n\ndata.head()  ## getting first five columns","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:47.597588Z","iopub.execute_input":"2022-07-21T07:56:47.598083Z","iopub.status.idle":"2022-07-21T07:56:47.628017Z","shell.execute_reply.started":"2022-07-21T07:56:47.598044Z","shell.execute_reply":"2022-07-21T07:56:47.626903Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.tail() ## getting last five columns","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:47.629448Z","iopub.execute_input":"2022-07-21T07:56:47.629870Z","iopub.status.idle":"2022-07-21T07:56:47.655171Z","shell.execute_reply.started":"2022-07-21T07:56:47.629836Z","shell.execute_reply":"2022-07-21T07:56:47.654005Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.describe() ## getting the descriptive satistical  details ","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:47.657361Z","iopub.execute_input":"2022-07-21T07:56:47.657799Z","iopub.status.idle":"2022-07-21T07:56:47.754789Z","shell.execute_reply.started":"2022-07-21T07:56:47.657763Z","shell.execute_reply":"2022-07-21T07:56:47.753778Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.info() ## getting the information from the dataframe","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:47.756277Z","iopub.execute_input":"2022-07-21T07:56:47.756779Z","iopub.status.idle":"2022-07-21T07:56:47.777405Z","shell.execute_reply.started":"2022-07-21T07:56:47.756741Z","shell.execute_reply":"2022-07-21T07:56:47.776261Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## checking if there is any duplicate data\n\ndata.duplicated().sum()","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:47.779043Z","iopub.execute_input":"2022-07-21T07:56:47.779381Z","iopub.status.idle":"2022-07-21T07:56:47.799925Z","shell.execute_reply.started":"2022-07-21T07:56:47.779352Z","shell.execute_reply":"2022-07-21T07:56:47.798680Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###### There is no duplicate data","metadata":{}},{"cell_type":"code","source":"## Analysing if there is any null values\n\npd.options.display.max_rows=None ## shows all the rows\ndata.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:47.801521Z","iopub.execute_input":"2022-07-21T07:56:47.802176Z","iopub.status.idle":"2022-07-21T07:56:47.816131Z","shell.execute_reply.started":"2022-07-21T07:56:47.802131Z","shell.execute_reply":"2022-07-21T07:56:47.814804Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###### We have null values in the dataset\n###### There are 19 columns with null values","metadata":{}},{"cell_type":"code","source":"#pd.reset_option('max_columns')\npd.reset_option('max_rows')","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:47.817804Z","iopub.execute_input":"2022-07-21T07:56:47.818147Z","iopub.status.idle":"2022-07-21T07:56:47.827901Z","shell.execute_reply.started":"2022-07-21T07:56:47.818117Z","shell.execute_reply":"2022-07-21T07:56:47.826748Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Handling the missing values and Feature selection","metadata":{}},{"cell_type":"markdown","source":"###### Handling missing values for LotFrontage\n\n","metadata":{}},{"cell_type":"code","source":"data.LotFrontage.value_counts()","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:47.829420Z","iopub.execute_input":"2022-07-21T07:56:47.829873Z","iopub.status.idle":"2022-07-21T07:56:47.847180Z","shell.execute_reply.started":"2022-07-21T07:56:47.829834Z","shell.execute_reply":"2022-07-21T07:56:47.846051Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## distribution plot for LotFrontage\n \nsns.distplot(data.LotFrontage)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:47.851826Z","iopub.execute_input":"2022-07-21T07:56:47.852170Z","iopub.status.idle":"2022-07-21T07:56:48.136816Z","shell.execute_reply.started":"2022-07-21T07:56:47.852140Z","shell.execute_reply":"2022-07-21T07:56:48.135993Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data['LotFrontage'].mean()  ## getting the mean","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:48.138051Z","iopub.execute_input":"2022-07-21T07:56:48.138569Z","iopub.status.idle":"2022-07-21T07:56:48.144051Z","shell.execute_reply.started":"2022-07-21T07:56:48.138536Z","shell.execute_reply":"2022-07-21T07:56:48.143283Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## Checking the percentage of missing values for LotFrontage\n\nprint('The Percentage of data missing in LotFrontage is ',data.LotFrontage.isnull().sum()/len(data)*100)","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:48.145532Z","iopub.execute_input":"2022-07-21T07:56:48.145852Z","iopub.status.idle":"2022-07-21T07:56:48.156347Z","shell.execute_reply.started":"2022-07-21T07:56:48.145825Z","shell.execute_reply":"2022-07-21T07:56:48.155372Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.loc[data['LotFrontage'].isnull()==True]","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:48.157765Z","iopub.execute_input":"2022-07-21T07:56:48.158332Z","iopub.status.idle":"2022-07-21T07:56:48.190885Z","shell.execute_reply.started":"2022-07-21T07:56:48.158273Z","shell.execute_reply":"2022-07-21T07:56:48.190117Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## Replacing the nan values with mean for LotFrontage\n\ndata.loc[data['LotFrontage'].isnull()==True,'LotFrontage']=70.0","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:48.192105Z","iopub.execute_input":"2022-07-21T07:56:48.192539Z","iopub.status.idle":"2022-07-21T07:56:48.199175Z","shell.execute_reply.started":"2022-07-21T07:56:48.192508Z","shell.execute_reply":"2022-07-21T07:56:48.198309Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###### Handling the missing values for Alley,PoolQC,Fence,MiscFeature\n","metadata":{}},{"cell_type":"code","source":"## Percentage of missing values in Alley,PoolQC,Fence,MiscFeature\n \nd1=data[['Alley','PoolQC','Fence','MiscFeature']]\na=d1.isnull().sum()/len(d1)*100\na\n","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:48.200638Z","iopub.execute_input":"2022-07-21T07:56:48.201151Z","iopub.status.idle":"2022-07-21T07:56:48.215893Z","shell.execute_reply.started":"2022-07-21T07:56:48.201122Z","shell.execute_reply":"2022-07-21T07:56:48.214768Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"##### More than 80% of the data are missing in Alley,PoolQC,Fence,MiscFeature","metadata":{}},{"cell_type":"code","source":"## removing the features\n\ndata.drop(['Alley','PoolQC','Fence','MiscFeature'],axis=1,inplace=True)","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:48.217264Z","iopub.execute_input":"2022-07-21T07:56:48.218370Z","iopub.status.idle":"2022-07-21T07:56:48.228040Z","shell.execute_reply.started":"2022-07-21T07:56:48.218325Z","shell.execute_reply":"2022-07-21T07:56:48.227174Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###### Handling the missing values for BsmtQual,BsmtQual,BsmtCond,BsmtExposure,BsmtFinType1,BsmtFinType2","metadata":{}},{"cell_type":"code","source":"## Percentage of missing values\n\nd2=data[['BsmtQual','BsmtCond','BsmtExposure','BsmtFinType1','BsmtFinType2']]\nb=d2.isnull().sum()/len(d2)*100\nb","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:48.229454Z","iopub.execute_input":"2022-07-21T07:56:48.229776Z","iopub.status.idle":"2022-07-21T07:56:48.251224Z","shell.execute_reply.started":"2022-07-21T07:56:48.229748Z","shell.execute_reply":"2022-07-21T07:56:48.250386Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## Replacing the missing values with 'NA'\n\ndata['BsmtQual']=data['BsmtQual'].fillna('NA')\ndata['BsmtCond']=data['BsmtCond'].fillna('NA')\ndata['BsmtExposure']=data['BsmtExposure'].fillna('NA')\ndata['BsmtFinType1']=data['BsmtFinType1'].fillna('NA')\ndata['BsmtFinType2']=data['BsmtFinType2'].fillna('NA')\n","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:48.252181Z","iopub.execute_input":"2022-07-21T07:56:48.253056Z","iopub.status.idle":"2022-07-21T07:56:48.263996Z","shell.execute_reply.started":"2022-07-21T07:56:48.253024Z","shell.execute_reply":"2022-07-21T07:56:48.263178Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###### Replacing missing values for MasVnrType","metadata":{}},{"cell_type":"code","source":"data.MasVnrType.value_counts()","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:48.265132Z","iopub.execute_input":"2022-07-21T07:56:48.265626Z","iopub.status.idle":"2022-07-21T07:56:48.276926Z","shell.execute_reply.started":"2022-07-21T07:56:48.265596Z","shell.execute_reply":"2022-07-21T07:56:48.276182Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## Replacing the missing values with mode\n\ndata.loc[data['MasVnrType'].isnull()==True,'MasVnrType']='None'","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:48.278193Z","iopub.execute_input":"2022-07-21T07:56:48.278731Z","iopub.status.idle":"2022-07-21T07:56:48.286077Z","shell.execute_reply.started":"2022-07-21T07:56:48.278702Z","shell.execute_reply":"2022-07-21T07:56:48.284994Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###### Replacing missing values for MasVnrArea","metadata":{}},{"cell_type":"code","source":" sns.distplot(data.MasVnrArea)","metadata":{"scrolled":true,"execution":{"iopub.status.busy":"2022-07-21T07:56:48.287417Z","iopub.execute_input":"2022-07-21T07:56:48.287732Z","iopub.status.idle":"2022-07-21T07:56:48.571027Z","shell.execute_reply.started":"2022-07-21T07:56:48.287705Z","shell.execute_reply":"2022-07-21T07:56:48.569889Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## Finding the median\n\ndata['MasVnrArea'].median()","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:48.572672Z","iopub.execute_input":"2022-07-21T07:56:48.573107Z","iopub.status.idle":"2022-07-21T07:56:48.582848Z","shell.execute_reply.started":"2022-07-21T07:56:48.573066Z","shell.execute_reply":"2022-07-21T07:56:48.580631Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## Replacing the missing value by median\ndata.loc[data['MasVnrArea'].isnull()==True,'MasVnrArea']=0.0","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:48.584698Z","iopub.execute_input":"2022-07-21T07:56:48.585011Z","iopub.status.idle":"2022-07-21T07:56:48.590529Z","shell.execute_reply.started":"2022-07-21T07:56:48.584985Z","shell.execute_reply":"2022-07-21T07:56:48.589620Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###### Handling missing values for GarageType,GarageFinish,GarageQual,GarageCond ","metadata":{}},{"cell_type":"code","source":"## Percentage of missing values\nd3=data[['GarageType','GarageYrBlt','GarageFinish','GarageQual','GarageCond' ]]\nc=d3.isnull().sum()/len(d3)*100\nc\n","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:48.592088Z","iopub.execute_input":"2022-07-21T07:56:48.593055Z","iopub.status.idle":"2022-07-21T07:56:48.607818Z","shell.execute_reply.started":"2022-07-21T07:56:48.593013Z","shell.execute_reply":"2022-07-21T07:56:48.606730Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## Replacing the missing values for GarageType,GarageFinish,GarageQual,GarageCond  by NA\n\ndata['GarageType']=data['GarageType'].fillna('NA')\ndata['GarageFinish']=data['GarageFinish'].fillna('NA')\ndata['GarageQual']=data['GarageQual'].fillna('NA')\ndata['GarageCond']=data['GarageCond'].fillna('NA')\n\n","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:48.609451Z","iopub.execute_input":"2022-07-21T07:56:48.609795Z","iopub.status.idle":"2022-07-21T07:56:48.620873Z","shell.execute_reply.started":"2022-07-21T07:56:48.609765Z","shell.execute_reply":"2022-07-21T07:56:48.619838Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###### Handling the missing value for GarageYrBlt","metadata":{}},{"cell_type":"code","source":"sns.distplot(data.GarageYrBlt)","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:48.622649Z","iopub.execute_input":"2022-07-21T07:56:48.622991Z","iopub.status.idle":"2022-07-21T07:56:48.849693Z","shell.execute_reply.started":"2022-07-21T07:56:48.622957Z","shell.execute_reply":"2022-07-21T07:56:48.848629Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.GarageYrBlt.median()","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:48.851162Z","iopub.execute_input":"2022-07-21T07:56:48.851480Z","iopub.status.idle":"2022-07-21T07:56:48.858142Z","shell.execute_reply.started":"2022-07-21T07:56:48.851452Z","shell.execute_reply":"2022-07-21T07:56:48.857141Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.loc[data['GarageYrBlt'].isnull()==True,'GarageYrBlt']=1980.0","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:48.866812Z","iopub.execute_input":"2022-07-21T07:56:48.867186Z","iopub.status.idle":"2022-07-21T07:56:48.873598Z","shell.execute_reply.started":"2022-07-21T07:56:48.867156Z","shell.execute_reply":"2022-07-21T07:56:48.872349Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###### Handling the missing value for Electrical          ","metadata":{}},{"cell_type":"code","source":"data.Electrical.value_counts()","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:48.875398Z","iopub.execute_input":"2022-07-21T07:56:48.875748Z","iopub.status.idle":"2022-07-21T07:56:48.888182Z","shell.execute_reply.started":"2022-07-21T07:56:48.875721Z","shell.execute_reply":"2022-07-21T07:56:48.886757Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## Replacing the null values with mode\ndata.loc[data['Electrical'].isnull()==True,'Electrical']='SBrkr'","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:48.889810Z","iopub.execute_input":"2022-07-21T07:56:48.890966Z","iopub.status.idle":"2022-07-21T07:56:48.899649Z","shell.execute_reply.started":"2022-07-21T07:56:48.890922Z","shell.execute_reply":"2022-07-21T07:56:48.898470Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###### Handling the missing values for FireplaceQu","metadata":{}},{"cell_type":"code","source":"data.FireplaceQu.value_counts()","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:48.901085Z","iopub.execute_input":"2022-07-21T07:56:48.902156Z","iopub.status.idle":"2022-07-21T07:56:48.915674Z","shell.execute_reply.started":"2022-07-21T07:56:48.902119Z","shell.execute_reply":"2022-07-21T07:56:48.914314Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## Replacing the missing values with NA\ndata.loc[data['FireplaceQu'].isnull()==True,'FireplaceQu']='NA'","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:48.916946Z","iopub.execute_input":"2022-07-21T07:56:48.917277Z","iopub.status.idle":"2022-07-21T07:56:48.926640Z","shell.execute_reply.started":"2022-07-21T07:56:48.917247Z","shell.execute_reply":"2022-07-21T07:56:48.925508Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:48.927767Z","iopub.execute_input":"2022-07-21T07:56:48.928593Z","iopub.status.idle":"2022-07-21T07:56:48.945019Z","shell.execute_reply.started":"2022-07-21T07:56:48.928557Z","shell.execute_reply":"2022-07-21T07:56:48.943698Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###### There is no null values now.We have cleared all the null values.","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(25,15))\nsns.heatmap(data.drop('SalePrice',axis=1).corr(),cmap=\"BuPu\",annot=True)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:48.946169Z","iopub.execute_input":"2022-07-21T07:56:48.947168Z","iopub.status.idle":"2022-07-21T07:56:54.302928Z","shell.execute_reply.started":"2022-07-21T07:56:48.947129Z","shell.execute_reply":"2022-07-21T07:56:54.302080Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Exploratory Data Analysis","metadata":{}},{"cell_type":"markdown","source":"### Univarient Analysis","metadata":{}},{"cell_type":"code","source":"## Getting the features with object datatype\ndtype_objects = list(columns for columns in data.select_dtypes([object]).columns)\n","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:54.304016Z","iopub.execute_input":"2022-07-21T07:56:54.304882Z","iopub.status.idle":"2022-07-21T07:56:54.311808Z","shell.execute_reply.started":"2022-07-21T07:56:54.304844Z","shell.execute_reply":"2022-07-21T07:56:54.310737Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dtype_objects","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:54.313237Z","iopub.execute_input":"2022-07-21T07:56:54.313604Z","iopub.status.idle":"2022-07-21T07:56:54.332238Z","shell.execute_reply.started":"2022-07-21T07:56:54.313577Z","shell.execute_reply":"2022-07-21T07:56:54.331023Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len(dtype_objects)","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:54.333613Z","iopub.execute_input":"2022-07-21T07:56:54.333942Z","iopub.status.idle":"2022-07-21T07:56:54.345272Z","shell.execute_reply.started":"2022-07-21T07:56:54.333914Z","shell.execute_reply":"2022-07-21T07:56:54.344188Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## countplot for datatype with objects\n\nplt.figure(figsize=(15,150))\nplotnumber=1\nfor c in dtype_objects:\n    ax=plt.subplot(20,2,plotnumber)\n    b= sns.countplot(x=data[c],palette='Set2')\n    plt.xticks(rotation=70)\n    plotnumber+=1\n    for bar in b.patches:\n        b.annotate(format(bar.get_height()),\n            (bar.get_x() + bar.get_width() / 2,\n            bar.get_height()), ha='center', va='center',\n            size=10, xytext=(0, 6),textcoords='offset points')\nplt.show() ","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:56:54.346882Z","iopub.execute_input":"2022-07-21T07:56:54.347205Z","iopub.status.idle":"2022-07-21T07:57:00.594321Z","shell.execute_reply.started":"2022-07-21T07:56:54.347175Z","shell.execute_reply":"2022-07-21T07:57:00.593117Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###### Count plot for the datatype object has been plotted","metadata":{}},{"cell_type":"code","source":"## Getting the features with float datatype\n\ndtype_float =list(columns for columns in data.select_dtypes([float]).columns)","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:00.595677Z","iopub.execute_input":"2022-07-21T07:57:00.596477Z","iopub.status.idle":"2022-07-21T07:57:00.602941Z","shell.execute_reply.started":"2022-07-21T07:57:00.596441Z","shell.execute_reply":"2022-07-21T07:57:00.601787Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dtype_float","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:00.604796Z","iopub.execute_input":"2022-07-21T07:57:00.605138Z","iopub.status.idle":"2022-07-21T07:57:00.616658Z","shell.execute_reply.started":"2022-07-21T07:57:00.605108Z","shell.execute_reply":"2022-07-21T07:57:00.615490Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## Distribution plot \n\nplt.figure(figsize=(15,150))\nplotnumber=1\nfor a in dtype_float:\n    ax=plt.subplot(20,2,plotnumber)\n    sns.distplot(x=data[a],color='purple')\n    plt.xticks(rotation=70)\n    plotnumber+=1\nplt.show() ","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:00.618582Z","iopub.execute_input":"2022-07-21T07:57:00.618893Z","iopub.status.idle":"2022-07-21T07:57:01.313002Z","shell.execute_reply.started":"2022-07-21T07:57:00.618866Z","shell.execute_reply":"2022-07-21T07:57:01.311805Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###### From the graph LotFrontage gives normal distribution but MasVnrArea and GarageYrBlt is skewed","metadata":{}},{"cell_type":"code","source":"## Pie plot for CentralAir\n\nplt.figure(figsize=(5,5))\nlabels=['Y','N']\nsize=data['CentralAir'].value_counts()\ncolors=['lightgreen','lightslategray']\nexplode=[0,0.3]\nplt.pie(size,labels=labels,colors=colors,explode=explode,autopct='%.2f%%',shadow = True,startangle = -30,\nwedgeprops= {'edgecolor':'white','linewidth':1})\nplt.legend(labels,loc=\"upper right\",title='Category') ## used to label at the side\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:01.314575Z","iopub.execute_input":"2022-07-21T07:57:01.315018Z","iopub.status.idle":"2022-07-21T07:57:01.467280Z","shell.execute_reply.started":"2022-07-21T07:57:01.314975Z","shell.execute_reply":"2022-07-21T07:57:01.465969Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###### From the graph 93.49% houses has CentralAir and 6.51% houses has no CentralAir","metadata":{}},{"cell_type":"code","source":"## Donut chart for GarageFinish\n\nplt.figure(figsize=(5,5))\nlabels=['Fin','RFn','Unf','NA']\nsize=data['GarageFinish'].value_counts()\ncolors=['purple','lightblue','pink','yellow']\nexplode=[0,0.1,0,0]\nplt.pie(size,labels=labels,colors=colors,explode=explode,autopct='%.2f%%',shadow=True)\ncircle = plt.Circle( (0,0),0.8, color='white')\np=plt.gcf()\np.gca().add_artist(circle)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:01.469315Z","iopub.execute_input":"2022-07-21T07:57:01.470119Z","iopub.status.idle":"2022-07-21T07:57:01.629918Z","shell.execute_reply.started":"2022-07-21T07:57:01.470059Z","shell.execute_reply":"2022-07-21T07:57:01.628523Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###### From the graph for 41.44% of the houses the interior finish of the garage is finished.","metadata":{}},{"cell_type":"markdown","source":"### Bivarient and Multivarient Analysis","metadata":{}},{"cell_type":"code","source":"## Countplot for CentralAir and BedroomAbvGr\n\nplt.figure(figsize=(15,9))\nsns.countplot(x='CentralAir',hue='BedroomAbvGr',palette='terrain',data=data).set(title=\"BedroomAbvGr vs CentralAir\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:01.632267Z","iopub.execute_input":"2022-07-21T07:57:01.633182Z","iopub.status.idle":"2022-07-21T07:57:01.935672Z","shell.execute_reply.started":"2022-07-21T07:57:01.633127Z","shell.execute_reply":"2022-07-21T07:57:01.934500Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###### The houses with 3 bedrooms  has more central air conditioning than others.","metadata":{}},{"cell_type":"code","source":"## Scatter plot for GarageArea and GarageYrBlt\n\nsns.scatterplot(x='GarageArea',y='GarageYrBlt',data=data,color='lightcoral')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:01.937323Z","iopub.execute_input":"2022-07-21T07:57:01.937881Z","iopub.status.idle":"2022-07-21T07:57:02.141917Z","shell.execute_reply.started":"2022-07-21T07:57:01.937848Z","shell.execute_reply":"2022-07-21T07:57:02.140808Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###### We can see there are some outliers present while comparing garage area and garage year built","metadata":{}},{"cell_type":"code","source":"## barplot for saleprice and the data type with objects\n\nplt.figure(figsize=(20,150),facecolor='white')\nplotnumber=1\nfor c in dtype_objects:\n    ax=plt.subplot(20,2,plotnumber)\n    sns.barplot(x=data[c],y=data.SalePrice,palette='Set3')\n    plotnumber+=1\n    plt.xticks(rotation=70)\nplt.show() ","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:02.143818Z","iopub.execute_input":"2022-07-21T07:57:02.144159Z","iopub.status.idle":"2022-07-21T07:57:13.226100Z","shell.execute_reply.started":"2022-07-21T07:57:02.144128Z","shell.execute_reply":"2022-07-21T07:57:13.224629Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## bar plot for Electrical,MSSubClass,CentralAir\n\nplt.figure(figsize=(15,9))\nsplot=sns.barplot(x='Electrical',y='MSSubClass',hue='CentralAir',palette='nipy_spectral',data=data)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:13.228042Z","iopub.execute_input":"2022-07-21T07:57:13.228649Z","iopub.status.idle":"2022-07-21T07:57:13.647163Z","shell.execute_reply.started":"2022-07-21T07:57:13.228575Z","shell.execute_reply":"2022-07-21T07:57:13.645926Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###### Building with 2-STORY 1946 & NEWER and Standard Circuit Breakers & Romex use more central air conditioning than others.\n\n","metadata":{}},{"cell_type":"code","source":"## Getting the continuous data features\n\nbox=data[['LotArea','YearBuilt','BsmtFinSF1','1stFlrSF','2ndFlrSF','LotFrontage', 'MasVnrArea', 'GarageYrBlt']]","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:13.649013Z","iopub.execute_input":"2022-07-21T07:57:13.649367Z","iopub.status.idle":"2022-07-21T07:57:13.655166Z","shell.execute_reply.started":"2022-07-21T07:57:13.649334Z","shell.execute_reply":"2022-07-21T07:57:13.654372Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(15,15),facecolor='white')\n\nplotnum=1 #counter\n\nfor c in box:\n    if(plotnum<9):\n        a=plt.subplot(4,2,plotnum)#plotting 8 graph\n        sns.distplot(box[c])#to know distribution\n    plotnum+=1#increment counter\nplt.tight_layout()    ","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:13.656166Z","iopub.execute_input":"2022-07-21T07:57:13.656518Z","iopub.status.idle":"2022-07-21T07:57:15.754208Z","shell.execute_reply.started":"2022-07-21T07:57:13.656481Z","shell.execute_reply":"2022-07-21T07:57:15.753392Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Finding the outliers","metadata":{}},{"cell_type":"code","source":"## getting the continuous data features\n\nbox=data[['LotArea','YearBuilt','BsmtFinSF1','1stFlrSF','2ndFlrSF','LotFrontage', 'MasVnrArea', 'GarageYrBlt']]","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:15.755350Z","iopub.execute_input":"2022-07-21T07:57:15.756275Z","iopub.status.idle":"2022-07-21T07:57:15.762589Z","shell.execute_reply.started":"2022-07-21T07:57:15.756238Z","shell.execute_reply":"2022-07-21T07:57:15.761170Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"box","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:15.763921Z","iopub.execute_input":"2022-07-21T07:57:15.764503Z","iopub.status.idle":"2022-07-21T07:57:15.788925Z","shell.execute_reply.started":"2022-07-21T07:57:15.764468Z","shell.execute_reply":"2022-07-21T07:57:15.787859Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## boxplot to find the outliers\n\nplt.figure(figsize=(10,30),facecolor='white')\nplotnumber=1\nfor c in box:\n    ax=plt.subplot(8,1,plotnumber)\n    sns.boxplot(data[c],color='green')\n    plotnumber=plotnumber + 1 \nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:15.790596Z","iopub.execute_input":"2022-07-21T07:57:15.791427Z","iopub.status.idle":"2022-07-21T07:57:16.480938Z","shell.execute_reply.started":"2022-07-21T07:57:15.791381Z","shell.execute_reply":"2022-07-21T07:57:16.479745Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Handling the skewness","metadata":{}},{"cell_type":"code","source":"from scipy.stats import skew\n\nnumerical_features = data.dtypes[data.dtypes != 'object'].index\n\n# checking the skewness in all the numerical features\nskewed_features = data[numerical_features].apply(lambda x: skew(x.dropna())).sort_values(ascending = False)\n\n# converting the features into a dataframe\nskewness = pd.DataFrame({'skew':skewed_features})\n\n# checking the head of skewness dataset\nskewness","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:16.482658Z","iopub.execute_input":"2022-07-21T07:57:16.482985Z","iopub.status.idle":"2022-07-21T07:57:16.510636Z","shell.execute_reply.started":"2022-07-21T07:57:16.482957Z","shell.execute_reply":"2022-07-21T07:57:16.509088Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"##applying box-cox transformations\n\nskewness = skewness[abs(skewness > 0.8)]\n\n# printing how many features are to be box-cox transformed\nprint(\"There are {} skewed numerical features to box cox transform\".format(skewness.shape[0]))\n\n# importing box-cox1p\nfrom scipy.special import boxcox1p\n\n# defining skewed features\nskewed_features = skewness.index\n\nlamda = 0.15\nfor features in skewed_features:\n    data[features] += 1\n    data[features] = boxcox1p(data[features], lamda)\ndata[skewed_features] = np.log1p(data[skewed_features])\nprint('Skewness has been Handled using Box Cox Transformation')","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:16.512129Z","iopub.execute_input":"2022-07-21T07:57:16.514852Z","iopub.status.idle":"2022-07-21T07:57:16.558345Z","shell.execute_reply.started":"2022-07-21T07:57:16.514804Z","shell.execute_reply":"2022-07-21T07:57:16.557062Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Changing categorical data to numerical data","metadata":{}},{"cell_type":"code","source":"#getting all the categorical feature\n\ndata_object = data.select_dtypes(include = \"object\").columns\nprint (data_object)","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:16.559791Z","iopub.execute_input":"2022-07-21T07:57:16.560152Z","iopub.status.idle":"2022-07-21T07:57:16.568209Z","shell.execute_reply.started":"2022-07-21T07:57:16.560123Z","shell.execute_reply":"2022-07-21T07:57:16.567384Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Label Encoding to convert categorical data to numerical data\n\nfrom sklearn.preprocessing import LabelEncoder\nle = LabelEncoder()\n\nfor features in data_object:\n    data[features] = le.fit_transform(data[features].astype(str))\n\nprint (data.info())","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:16.569434Z","iopub.execute_input":"2022-07-21T07:57:16.570482Z","iopub.status.idle":"2022-07-21T07:57:16.691249Z","shell.execute_reply.started":"2022-07-21T07:57:16.570443Z","shell.execute_reply":"2022-07-21T07:57:16.690050Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Feature Scaling","metadata":{}},{"cell_type":"code","source":"## Scaling the features\n## Spliting the variables\n\nx=data.drop(['Id','SalePrice'],axis=1) ## all the features\ny=data['SalePrice']  ## target variable","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:16.692701Z","iopub.execute_input":"2022-07-21T07:57:16.693488Z","iopub.status.idle":"2022-07-21T07:57:16.702584Z","shell.execute_reply.started":"2022-07-21T07:57:16.693435Z","shell.execute_reply":"2022-07-21T07:57:16.701490Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#using minmax scaler to scale all the datas\n\nfrom sklearn.preprocessing import MinMaxScaler\nmc=MinMaxScaler()\nscaled_x=mc.fit_transform(x)","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:16.704032Z","iopub.execute_input":"2022-07-21T07:57:16.705021Z","iopub.status.idle":"2022-07-21T07:57:16.716745Z","shell.execute_reply.started":"2022-07-21T07:57:16.704987Z","shell.execute_reply":"2022-07-21T07:57:16.715612Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Model Creation","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:16.718023Z","iopub.execute_input":"2022-07-21T07:57:16.718566Z","iopub.status.idle":"2022-07-21T07:57:16.779658Z","shell.execute_reply.started":"2022-07-21T07:57:16.718534Z","shell.execute_reply":"2022-07-21T07:57:16.778660Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"x_train,x_test,y_train,y_test=train_test_split(scaled_x,y,test_size=0.20,random_state=0)","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:16.783045Z","iopub.execute_input":"2022-07-21T07:57:16.783680Z","iopub.status.idle":"2022-07-21T07:57:16.791048Z","shell.execute_reply.started":"2022-07-21T07:57:16.783642Z","shell.execute_reply":"2022-07-21T07:57:16.789940Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"x_train.shape  ## number of rows and columns  given for training ","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:16.792520Z","iopub.execute_input":"2022-07-21T07:57:16.793026Z","iopub.status.idle":"2022-07-21T07:57:16.801105Z","shell.execute_reply.started":"2022-07-21T07:57:16.792997Z","shell.execute_reply":"2022-07-21T07:57:16.800357Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"x_test.shape  ## number of row and columns given for testing","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:16.802192Z","iopub.execute_input":"2022-07-21T07:57:16.803251Z","iopub.status.idle":"2022-07-21T07:57:16.812186Z","shell.execute_reply.started":"2022-07-21T07:57:16.803210Z","shell.execute_reply":"2022-07-21T07:57:16.810954Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Model Evaluation","metadata":{}},{"cell_type":"markdown","source":"## Linear Regression","metadata":{}},{"cell_type":"code","source":"## importing the library\n\nfrom sklearn.linear_model import LinearRegression","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:16.813541Z","iopub.execute_input":"2022-07-21T07:57:16.814023Z","iopub.status.idle":"2022-07-21T07:57:16.891742Z","shell.execute_reply.started":"2022-07-21T07:57:16.813994Z","shell.execute_reply":"2022-07-21T07:57:16.890781Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"LR=LinearRegression()\nLR.fit(x_train,y_train)  ## fitting the training data\n\nx_test_pred_LR=LR.predict(x_test)  ## predicted x test","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:16.892837Z","iopub.execute_input":"2022-07-21T07:57:16.893609Z","iopub.status.idle":"2022-07-21T07:57:16.923695Z","shell.execute_reply.started":"2022-07-21T07:57:16.893575Z","shell.execute_reply":"2022-07-21T07:57:16.922171Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"x_test_pred_LR","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:16.926255Z","iopub.execute_input":"2022-07-21T07:57:16.928180Z","iopub.status.idle":"2022-07-21T07:57:16.952420Z","shell.execute_reply.started":"2022-07-21T07:57:16.928125Z","shell.execute_reply":"2022-07-21T07:57:16.951031Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_test ## tested y","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:16.954906Z","iopub.execute_input":"2022-07-21T07:57:16.956665Z","iopub.status.idle":"2022-07-21T07:57:16.976043Z","shell.execute_reply.started":"2022-07-21T07:57:16.956611Z","shell.execute_reply":"2022-07-21T07:57:16.974572Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"x_train_pred_LR=LR.predict(x_train) ##predicted x train\n\nx_train_pred_LR","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:16.978655Z","iopub.execute_input":"2022-07-21T07:57:16.980529Z","iopub.status.idle":"2022-07-21T07:57:17.000607Z","shell.execute_reply.started":"2022-07-21T07:57:16.980455Z","shell.execute_reply":"2022-07-21T07:57:16.999271Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_train  ## trained y","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:17.003129Z","iopub.execute_input":"2022-07-21T07:57:17.004934Z","iopub.status.idle":"2022-07-21T07:57:17.023839Z","shell.execute_reply.started":"2022-07-21T07:57:17.004880Z","shell.execute_reply":"2022-07-21T07:57:17.022590Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('Linear Regression trainind score is',LR.score(x_train,y_train))\n","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:17.025718Z","iopub.execute_input":"2022-07-21T07:57:17.026534Z","iopub.status.idle":"2022-07-21T07:57:17.037391Z","shell.execute_reply.started":"2022-07-21T07:57:17.026482Z","shell.execute_reply":"2022-07-21T07:57:17.036078Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('Linear Regression testing score is',LR.score(x_test,y_test))\n","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:17.039609Z","iopub.execute_input":"2022-07-21T07:57:17.040591Z","iopub.status.idle":"2022-07-21T07:57:17.049268Z","shell.execute_reply.started":"2022-07-21T07:57:17.040527Z","shell.execute_reply":"2022-07-21T07:57:17.047987Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Evaluation Metrics for Linear Regression","metadata":{}},{"cell_type":"code","source":"from sklearn.metrics import r2_score","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:17.051414Z","iopub.execute_input":"2022-07-21T07:57:17.052353Z","iopub.status.idle":"2022-07-21T07:57:17.058406Z","shell.execute_reply.started":"2022-07-21T07:57:17.052286Z","shell.execute_reply":"2022-07-21T07:57:17.057148Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_score=r2_score(y_train,x_train_pred_LR)\nprint('Linear Regression r2_score for training is',train_score)","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:17.060256Z","iopub.execute_input":"2022-07-21T07:57:17.061016Z","iopub.status.idle":"2022-07-21T07:57:17.072725Z","shell.execute_reply.started":"2022-07-21T07:57:17.060975Z","shell.execute_reply":"2022-07-21T07:57:17.071260Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_score=r2_score(y_test,x_test_pred_LR)\nprint('Linear Regression r2_score for testing is',test_score)","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:17.074942Z","iopub.execute_input":"2022-07-21T07:57:17.075871Z","iopub.status.idle":"2022-07-21T07:57:17.084444Z","shell.execute_reply.started":"2022-07-21T07:57:17.075816Z","shell.execute_reply":"2022-07-21T07:57:17.083225Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###### Training score is more than testing score so the model is underfitting.","metadata":{}},{"cell_type":"markdown","source":"## Random Forest Regressor","metadata":{}},{"cell_type":"code","source":"## Importing the library\n\nfrom sklearn.ensemble import RandomForestRegressor","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:17.086459Z","iopub.execute_input":"2022-07-21T07:57:17.087202Z","iopub.status.idle":"2022-07-21T07:57:17.239448Z","shell.execute_reply.started":"2022-07-21T07:57:17.087159Z","shell.execute_reply":"2022-07-21T07:57:17.238398Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"RF=RandomForestRegressor()\nRF.fit(x_train,y_train) ## fitting the data\n\nx_test_pred_RF=RF.predict(x_test)  ## predicted x test","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:17.240827Z","iopub.execute_input":"2022-07-21T07:57:17.241292Z","iopub.status.idle":"2022-07-21T07:57:18.876142Z","shell.execute_reply.started":"2022-07-21T07:57:17.241264Z","shell.execute_reply":"2022-07-21T07:57:18.875011Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"x_test_pred_RF","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:18.877405Z","iopub.execute_input":"2022-07-21T07:57:18.877724Z","iopub.status.idle":"2022-07-21T07:57:18.887027Z","shell.execute_reply.started":"2022-07-21T07:57:18.877695Z","shell.execute_reply":"2022-07-21T07:57:18.885839Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_test   ## y test","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:18.888785Z","iopub.execute_input":"2022-07-21T07:57:18.889151Z","iopub.status.idle":"2022-07-21T07:57:18.902667Z","shell.execute_reply.started":"2022-07-21T07:57:18.889121Z","shell.execute_reply":"2022-07-21T07:57:18.901377Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"x_train_pred_RF=LR.predict(x_train)  ## predicted x train\nx_train_pred_RF","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:18.904241Z","iopub.execute_input":"2022-07-21T07:57:18.904687Z","iopub.status.idle":"2022-07-21T07:57:18.920949Z","shell.execute_reply.started":"2022-07-21T07:57:18.904647Z","shell.execute_reply":"2022-07-21T07:57:18.919660Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_train  ## y train","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:18.922680Z","iopub.execute_input":"2022-07-21T07:57:18.923102Z","iopub.status.idle":"2022-07-21T07:57:18.932508Z","shell.execute_reply.started":"2022-07-21T07:57:18.923051Z","shell.execute_reply":"2022-07-21T07:57:18.931400Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Random Foresr Regressor Score","metadata":{}},{"cell_type":"code","source":"print('Training score for Random Forest Regressor is',RF.score(x_train,y_train))","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:18.934150Z","iopub.execute_input":"2022-07-21T07:57:18.934826Z","iopub.status.idle":"2022-07-21T07:57:19.003329Z","shell.execute_reply.started":"2022-07-21T07:57:18.934786Z","shell.execute_reply":"2022-07-21T07:57:19.002030Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('Testing score for Random Forest Regressor is',RF.score(x_test,y_test))","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:19.009207Z","iopub.execute_input":"2022-07-21T07:57:19.012362Z","iopub.status.idle":"2022-07-21T07:57:19.043087Z","shell.execute_reply.started":"2022-07-21T07:57:19.012282Z","shell.execute_reply":"2022-07-21T07:57:19.042036Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###### Training score is more than Testing score,so the Random Forest Regressor model is underfitting.So we can do hyper parametric tuning.","metadata":{}},{"cell_type":"markdown","source":"### Hyper parametric Tuning -- Random Forest Regressor","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import RandomizedSearchCV","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:19.053752Z","iopub.execute_input":"2022-07-21T07:57:19.054135Z","iopub.status.idle":"2022-07-21T07:57:19.059889Z","shell.execute_reply.started":"2022-07-21T07:57:19.054100Z","shell.execute_reply":"2022-07-21T07:57:19.058356Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"random_grid = {'n_estimators': [100,200,300,400,500,600],  ## no. of trees\n               'max_features': ['auto', 'sqrt'],  \n               'max_depth': [10, 15,20,25], ## maxinum number of levels in trees \n               'min_samples_split':  [2, 5, 10], ## minimum number of samples required to split a node\n               'min_samples_leaf': [1, 2, 4], ## Minimum number of samples required at each leaf node\n               'bootstrap': [True, False]}  ##  Method of selecting samples for training each tree","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:19.061517Z","iopub.execute_input":"2022-07-21T07:57:19.062889Z","iopub.status.idle":"2022-07-21T07:57:19.070684Z","shell.execute_reply.started":"2022-07-21T07:57:19.062841Z","shell.execute_reply":"2022-07-21T07:57:19.069634Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"hyper_tuning=RandomizedSearchCV(estimator=RF,param_distributions=random_grid,n_iter=10,cv=5,verbose=5,random_state=2)","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:19.071701Z","iopub.execute_input":"2022-07-21T07:57:19.072330Z","iopub.status.idle":"2022-07-21T07:57:19.081058Z","shell.execute_reply.started":"2022-07-21T07:57:19.072276Z","shell.execute_reply":"2022-07-21T07:57:19.079964Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"hyper_tuning.fit(x_train,y_train)","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:57:19.082335Z","iopub.execute_input":"2022-07-21T07:57:19.082792Z","iopub.status.idle":"2022-07-21T07:58:33.401197Z","shell.execute_reply.started":"2022-07-21T07:57:19.082760Z","shell.execute_reply":"2022-07-21T07:58:33.399916Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"hyper_tuning.best_params_  ## getting the best parameters","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:33.402772Z","iopub.execute_input":"2022-07-21T07:58:33.403686Z","iopub.status.idle":"2022-07-21T07:58:33.409765Z","shell.execute_reply.started":"2022-07-21T07:58:33.403650Z","shell.execute_reply":"2022-07-21T07:58:33.408695Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"RF_hyper_tuning=RandomForestRegressor(n_estimators=600,min_samples_split=2,min_samples_leaf= 1,max_features='sqrt',max_depth=30,bootstrap= False)  ## implementing the best parameters\n\n","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:33.411177Z","iopub.execute_input":"2022-07-21T07:58:33.411539Z","iopub.status.idle":"2022-07-21T07:58:33.421200Z","shell.execute_reply.started":"2022-07-21T07:58:33.411510Z","shell.execute_reply":"2022-07-21T07:58:33.420042Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"RF_hyper_tuning.fit(x_train,y_train)","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:33.422925Z","iopub.execute_input":"2022-07-21T07:58:33.423374Z","iopub.status.idle":"2022-07-21T07:58:36.085273Z","shell.execute_reply.started":"2022-07-21T07:58:33.423331Z","shell.execute_reply":"2022-07-21T07:58:36.084481Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"RF_hyper_pred_test=RF_hyper_tuning.predict(x_test) ## predicted hyperparametric tuning x test\n\nRF_hyper_pred_test","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:36.086676Z","iopub.execute_input":"2022-07-21T07:58:36.087248Z","iopub.status.idle":"2022-07-21T07:58:36.191319Z","shell.execute_reply.started":"2022-07-21T07:58:36.087218Z","shell.execute_reply":"2022-07-21T07:58:36.190237Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_test","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:36.192931Z","iopub.execute_input":"2022-07-21T07:58:36.193366Z","iopub.status.idle":"2022-07-21T07:58:36.202561Z","shell.execute_reply.started":"2022-07-21T07:58:36.193324Z","shell.execute_reply":"2022-07-21T07:58:36.201078Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"RF_hyper_pred_train=RF_hyper_tuning.predict(x_train)  ## predicted hyper parametric tuning x train\n\nRF_hyper_pred_train","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:36.204343Z","iopub.execute_input":"2022-07-21T07:58:36.204852Z","iopub.status.idle":"2022-07-21T07:58:36.412627Z","shell.execute_reply.started":"2022-07-21T07:58:36.204817Z","shell.execute_reply":"2022-07-21T07:58:36.411566Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Score for Random Forest Regressor Hyper Parametric Tuning","metadata":{}},{"cell_type":"code","source":"print('Training score for Random Forest Regressor Hyper Parametric Tuning is ',RF_hyper_tuning.score(x_train,y_train))","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:36.413867Z","iopub.execute_input":"2022-07-21T07:58:36.414186Z","iopub.status.idle":"2022-07-21T07:58:36.618436Z","shell.execute_reply.started":"2022-07-21T07:58:36.414157Z","shell.execute_reply":"2022-07-21T07:58:36.617353Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('Testing score for Random Forest Regressor Hyper Parametric Tuning is ',RF_hyper_tuning.score(x_test,y_test))","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:36.620035Z","iopub.execute_input":"2022-07-21T07:58:36.620550Z","iopub.status.idle":"2022-07-21T07:58:36.719251Z","shell.execute_reply.started":"2022-07-21T07:58:36.620515Z","shell.execute_reply":"2022-07-21T07:58:36.718206Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Decision Tree Regressor","metadata":{}},{"cell_type":"code","source":"## Importing the library\n\nfrom sklearn.tree import DecisionTreeRegressor","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:36.720646Z","iopub.execute_input":"2022-07-21T07:58:36.720947Z","iopub.status.idle":"2022-07-21T07:58:36.726020Z","shell.execute_reply.started":"2022-07-21T07:58:36.720921Z","shell.execute_reply":"2022-07-21T07:58:36.724885Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"DT=DecisionTreeRegressor()\nDT.fit(x_train,y_train)","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:36.727728Z","iopub.execute_input":"2022-07-21T07:58:36.728012Z","iopub.status.idle":"2022-07-21T07:58:36.764318Z","shell.execute_reply.started":"2022-07-21T07:58:36.727988Z","shell.execute_reply":"2022-07-21T07:58:36.763234Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"x_test_pred_DT=DT.predict(x_test) ## predicted x test\n\nx_test_pred_DT  ## predicted x test","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:36.765702Z","iopub.execute_input":"2022-07-21T07:58:36.765992Z","iopub.status.idle":"2022-07-21T07:58:36.774169Z","shell.execute_reply.started":"2022-07-21T07:58:36.765966Z","shell.execute_reply":"2022-07-21T07:58:36.773409Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_test ## y test","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:36.775607Z","iopub.execute_input":"2022-07-21T07:58:36.775928Z","iopub.status.idle":"2022-07-21T07:58:36.786706Z","shell.execute_reply.started":"2022-07-21T07:58:36.775901Z","shell.execute_reply":"2022-07-21T07:58:36.785746Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"x_train_pred_DT=DT.predict(x_train)  ## predicted x train\n\nx_train_pred_DT","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:36.787813Z","iopub.execute_input":"2022-07-21T07:58:36.788104Z","iopub.status.idle":"2022-07-21T07:58:36.797342Z","shell.execute_reply.started":"2022-07-21T07:58:36.788077Z","shell.execute_reply":"2022-07-21T07:58:36.796287Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_train ## y train","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:36.798914Z","iopub.execute_input":"2022-07-21T07:58:36.799219Z","iopub.status.idle":"2022-07-21T07:58:36.810136Z","shell.execute_reply.started":"2022-07-21T07:58:36.799192Z","shell.execute_reply":"2022-07-21T07:58:36.809194Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('Training score for Decision Tree Regressor is',DT.score(x_train,y_train))","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:36.811655Z","iopub.execute_input":"2022-07-21T07:58:36.811969Z","iopub.status.idle":"2022-07-21T07:58:36.820435Z","shell.execute_reply.started":"2022-07-21T07:58:36.811942Z","shell.execute_reply":"2022-07-21T07:58:36.819374Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('Testing score for Decision Tree Regressor is',DT.score(x_test,y_test))","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:36.822387Z","iopub.execute_input":"2022-07-21T07:58:36.822816Z","iopub.status.idle":"2022-07-21T07:58:36.830093Z","shell.execute_reply.started":"2022-07-21T07:58:36.822774Z","shell.execute_reply":"2022-07-21T07:58:36.829222Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###### Training score is more than the Testing score,so the decision tree model is underfitting,So we can do hyper parametric tuning.","metadata":{}},{"cell_type":"markdown","source":"### Hyper parametric Tuning-- Decision Tree","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import RandomizedSearchCV","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:36.831330Z","iopub.execute_input":"2022-07-21T07:58:36.832049Z","iopub.status.idle":"2022-07-21T07:58:36.838851Z","shell.execute_reply.started":"2022-07-21T07:58:36.832020Z","shell.execute_reply":"2022-07-21T07:58:36.837740Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"parameters={\"splitter\":[\"best\",\"random\"],\n            \"max_depth\" : [2,4,6,8],\n           \"min_samples_leaf\":[1,2,3,4,5,],\n           \"max_features\":[\"auto\",\"sqrt\"],\n           \"max_leaf_nodes\":[5,10,15] }\n","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:36.839737Z","iopub.execute_input":"2022-07-21T07:58:36.840465Z","iopub.status.idle":"2022-07-21T07:58:36.848113Z","shell.execute_reply.started":"2022-07-21T07:58:36.840434Z","shell.execute_reply":"2022-07-21T07:58:36.846960Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"hyper_tuning_DT = RandomizedSearchCV(estimator=DT, param_distributions = parameters,\n                               cv = 2, n_iter = 10, n_jobs=-1)","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:36.849370Z","iopub.execute_input":"2022-07-21T07:58:36.850123Z","iopub.status.idle":"2022-07-21T07:58:36.857974Z","shell.execute_reply.started":"2022-07-21T07:58:36.850094Z","shell.execute_reply":"2022-07-21T07:58:36.857241Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"hyper_tuning_DT.fit(x_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:36.860219Z","iopub.execute_input":"2022-07-21T07:58:36.861321Z","iopub.status.idle":"2022-07-21T07:58:38.547014Z","shell.execute_reply.started":"2022-07-21T07:58:36.861257Z","shell.execute_reply":"2022-07-21T07:58:38.546098Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"hyper_tuning_DT.best_params_","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:38.548755Z","iopub.execute_input":"2022-07-21T07:58:38.549235Z","iopub.status.idle":"2022-07-21T07:58:38.557699Z","shell.execute_reply.started":"2022-07-21T07:58:38.549191Z","shell.execute_reply":"2022-07-21T07:58:38.556344Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"hypertuning_DT = DecisionTreeRegressor(splitter= 'best',min_samples_leaf=5,max_leaf_nodes=15,max_features='sqrt',max_depth=6)\n\nhypertuning_DT","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:38.559608Z","iopub.execute_input":"2022-07-21T07:58:38.560540Z","iopub.status.idle":"2022-07-21T07:58:38.567928Z","shell.execute_reply.started":"2022-07-21T07:58:38.560492Z","shell.execute_reply":"2022-07-21T07:58:38.567227Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"hypertuning_DT.fit(x_train,y_train)","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:38.569351Z","iopub.execute_input":"2022-07-21T07:58:38.569647Z","iopub.status.idle":"2022-07-21T07:58:38.580776Z","shell.execute_reply.started":"2022-07-21T07:58:38.569620Z","shell.execute_reply":"2022-07-21T07:58:38.579692Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"DT_hyper_pred_xtest=hypertuning_DT.predict(x_test)\n\nDT_hyper_pred_xtest","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:38.582315Z","iopub.execute_input":"2022-07-21T07:58:38.582903Z","iopub.status.idle":"2022-07-21T07:58:38.593860Z","shell.execute_reply.started":"2022-07-21T07:58:38.582874Z","shell.execute_reply":"2022-07-21T07:58:38.592871Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_test","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:38.594973Z","iopub.execute_input":"2022-07-21T07:58:38.595875Z","iopub.status.idle":"2022-07-21T07:58:38.603835Z","shell.execute_reply.started":"2022-07-21T07:58:38.595845Z","shell.execute_reply":"2022-07-21T07:58:38.602742Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"DT_hyper_pred_xtrain=hypertuning_DT.predict(x_train)\n\nDT_hyper_pred_xtrain","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:38.606543Z","iopub.execute_input":"2022-07-21T07:58:38.607584Z","iopub.status.idle":"2022-07-21T07:58:38.614253Z","shell.execute_reply.started":"2022-07-21T07:58:38.607541Z","shell.execute_reply":"2022-07-21T07:58:38.613606Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_train","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:38.615202Z","iopub.execute_input":"2022-07-21T07:58:38.615803Z","iopub.status.idle":"2022-07-21T07:58:38.625772Z","shell.execute_reply.started":"2022-07-21T07:58:38.615759Z","shell.execute_reply":"2022-07-21T07:58:38.624763Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('Training score for hyper parametric tuning of Decision tree regressor is',hypertuning_DT.score(x_train,y_train))","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:38.627184Z","iopub.execute_input":"2022-07-21T07:58:38.627664Z","iopub.status.idle":"2022-07-21T07:58:38.637618Z","shell.execute_reply.started":"2022-07-21T07:58:38.627636Z","shell.execute_reply":"2022-07-21T07:58:38.636666Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('Testing score for hyper parametric tuning of Decision tree regressor is',hypertuning_DT.score(x_test,y_test))","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:38.638838Z","iopub.execute_input":"2022-07-21T07:58:38.639316Z","iopub.status.idle":"2022-07-21T07:58:38.646829Z","shell.execute_reply.started":"2022-07-21T07:58:38.639273Z","shell.execute_reply":"2022-07-21T07:58:38.645788Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Xtreme Gradient Boosting","metadata":{}},{"cell_type":"code","source":"from xgboost import XGBRegressor","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:38.648277Z","iopub.execute_input":"2022-07-21T07:58:38.648994Z","iopub.status.idle":"2022-07-21T07:58:38.778484Z","shell.execute_reply.started":"2022-07-21T07:58:38.648948Z","shell.execute_reply":"2022-07-21T07:58:38.777442Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"XGB=XGBRegressor()\nXGB.fit(x_train,y_train) \n\nxtest_XGB_pred=XGB.predict(x_test) ## predicted x test","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:38.780022Z","iopub.execute_input":"2022-07-21T07:58:38.780919Z","iopub.status.idle":"2022-07-21T07:58:39.484435Z","shell.execute_reply.started":"2022-07-21T07:58:38.780874Z","shell.execute_reply":"2022-07-21T07:58:39.483552Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"xtest_XGB_pred","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:39.486031Z","iopub.execute_input":"2022-07-21T07:58:39.486721Z","iopub.status.idle":"2022-07-21T07:58:39.496786Z","shell.execute_reply.started":"2022-07-21T07:58:39.486685Z","shell.execute_reply":"2022-07-21T07:58:39.495917Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"xtrain_XGB_pred=XGB.predict(x_train)\n","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:39.498257Z","iopub.execute_input":"2022-07-21T07:58:39.499148Z","iopub.status.idle":"2022-07-21T07:58:39.515251Z","shell.execute_reply.started":"2022-07-21T07:58:39.499117Z","shell.execute_reply":"2022-07-21T07:58:39.513956Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"xtrain_XGB_pred","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:39.516581Z","iopub.execute_input":"2022-07-21T07:58:39.517712Z","iopub.status.idle":"2022-07-21T07:58:39.525128Z","shell.execute_reply.started":"2022-07-21T07:58:39.517667Z","shell.execute_reply":"2022-07-21T07:58:39.524167Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('Training score for XGB is',XGB.score(x_train,y_train))","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:39.526551Z","iopub.execute_input":"2022-07-21T07:58:39.526859Z","iopub.status.idle":"2022-07-21T07:58:39.547194Z","shell.execute_reply.started":"2022-07-21T07:58:39.526833Z","shell.execute_reply":"2022-07-21T07:58:39.546234Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('Training score for XGB is',XGB.score(x_test,y_test))","metadata":{"execution":{"iopub.status.busy":"2022-07-21T07:58:39.548716Z","iopub.execute_input":"2022-07-21T07:58:39.549203Z","iopub.status.idle":"2022-07-21T07:58:39.561753Z","shell.execute_reply.started":"2022-07-21T07:58:39.549168Z","shell.execute_reply":"2022-07-21T07:58:39.560712Z"},"trusted":true},"execution_count":null,"outputs":[]}]}