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https://github.com/abhirajp595/python

Data Science Project using Python
https://github.com/abhirajp595/python

data-analysis data-science data-visualization eda jyputer-notebook numpy pandas statistics

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Data Science Project using Python

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README 

        
# Python



Project Statement:

While searching for the dream house, the buyer looks at various factors, not just at the height of the basement ceiling or the proximity to an east-west railroad.

Using the dataset, find the factors that influence price negotiations while buying a house.

There are 79 explanatory variables describing every aspect of residential homes in Ames, Iowa.



Dataset Description:



Variable: 	Description



SalePrice	: The property's sale price is in dollars. This is the target variable that you're trying to predict.



MSSubClass	: The building class



MSZoning	: The general zoning classification



LotFrontage	 : Linear feet of street connected to property



LotArea	: Lot size in square feet



Street	: Type of road access



Alley	Type : of alley access



LotShape	: General shape of property



LandContour : Flatness of the property



Utilities	: Type of utilities available



LotConfig	: Lot configuration



LandSlope	: Slope of property



Neighborhood	: Physical locations within Ames city limits



Condition1	: Proximity to main road or railroad



Condition2	: Proximity to main road or railroad (if a second is present)



BldgType	: Type of dwelling



HouseStyle	: Style of dwelling



OverallQual	: Overall material and finish quality



OverallCond	: Overall condition rating



YearBuilt	: Original construction date



YearRemodAdd	: Remodel date



RoofStyle	: Type of roof



RoofMatl	: Roof material



Exterior1st	: Exterior covering on house



Exterior2nd	: Exterior covering on house (if more than one material)



MasVnrType	: Masonry veneer type



MasVnrArea	: Masonry veneer area in square feet



ExterQual	: Exterior material quality



ExterCond	: Present condition of the material on the exterior



Foundation	: Type of foundation



BsmtQual	: Height of the basement



BsmtCond	: General condition of the basement



BsmtExposure	: Walkout or garden level basement walls



BsmtFinType1	: Quality of the basement finished area



BsmtFinSF1	: Type 1 finished square feet



BsmtFinType2	: Quality of second finished area (if present)



BsmtFinSF2	: Type 2 finished square feet



BsmtUnfSF : Unfinished square feet of basement area



TotalBsmtSF	: Total square feet of basement area



Heating	: Type of heating



HeatingQC	: Heating quality and condition



CentralAir	: Central air conditioning



Electrical	: Electrical system



1stFlrSF	: First Floor square feet



2ndFlrSF	: Second floor square feet



LowQualFinSF	: Low quality finished square feet (all floors)



GrLivArea	: Above grade (ground) living area square feet



BsmtFullBath	: Basement full bathrooms



BsmtHalfBath	: Basement half bathrooms



FullBath	: Full bathrooms above grade



HalfBath	: Half bathrooms above grade



Bedroom	: Number of bedrooms above basement level



Kitchen	: Number of kitchens



KitchenQual :	Kitchen quality



TotRmsAbvGrd	: Total rooms above grade (does not include bathrooms)



Functional	: Home functionality rating



Fireplaces	: Number of fireplaces



FireplaceQu	: Fireplace quality



GarageType	: Garage location



GarageYrBlt	: Year garage was built



GarageFinish	: Interior finish of the garage



GarageCars	: Size of the garage in car capacity



GarageArea	: Size of the garage in square feet



GarageQual	: Garage quality



GarageCond	: Garage condition



PavedDrive	: Paved driveway



WoodDeckSF	: Wood deck area in square feet



OpenPorchSF	: Open porch area in square feet



EnclosedPorch	: Enclosed porch area in square feet



3SsnPorch	: Three season porch area in square feet



ScreenPorch	: Screen porch area in square feet



PoolArea	: Pool area in square feet



PoolQC	: Pool quality



Fence	: Fence quality



MiscFeature	: Miscellaneous feature not covered in other categories



MiscVal	: $Value of miscellaneous feature



MoSold	: Month Sold



YrSold	: Year Sold



SaleType	: Type of sale



SaleCondition	: Condition of sale



Perform the following steps:

1.	Understand the dataset:

a.	Identify the shape of the dataset

b.	Identify variables with null values

c.	Identify variables with unique values



2.	Generate a separate dataset for numerical and categorical variables

3.	EDA of numerical variables:

a.	Missing value treatment

b.	Identify the skewness and distribution

c.	Identify significant variables using a correlation matrix 

d.	Pair plot for distribution and density

4.	EDA of categorical variables

a.	Missing value treatment

b.	Count plot for bivariate analysis

c.	Identify significant variables using p-values and Chi-Square values

5.	Combine all the significant categorical and numerical variables

6.	Plot box plot for the new dataset to find the variables with outliers

Note: The last two points are performed to make the new dataset ready for training and prediction.