housing-price-prediction

This repository contains the machine learning model to predict the sale price of a house based on the dataset aquired from kaggle

Dataset

The dataset contains the below attributes:-

• SalePrice - the property's sale price 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 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 baths 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 garage in car capacity
• GarageArea: Size of 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

Data Preprocessing

I used the below data preprocessing techniques:-

• dropped the columns that have more than 70% values as null.
• identified columns that have same value in more than 80% of the records, so that I could delete them, as they do not add any meaningful value to the model.
• replaced null values in numerical fields with the mean.
• replaced null values in categorical fields with the mode.
• deleted the independent variables with high correlation between themselves.
• lebel encoded categorial values

Models

I built two models to compare the performance and efficiency - Random Forests and XGBoost, using cross-validation technique for determining the optimal hyperparameters for both of them. The model evaluation metrics can be found in the notebook.

Initial Feature Engineering and Modeling

The initial feature engineering and modeling processes are documented in the Jupyter notebook house_price_prediction_regression.ipynb. This notebook contains exploratory data analysis (EDA), data preprocessing steps, feature engineering techniques, and initial model building. It serves as a comprehensive guide to understanding the initial stages of the project.

Finalized Model

The finalized model, along with tuned hyperparameters, is implemented in the Python project. To execute the model, follow these steps:

1. Ensure you have installed all dependencies by running: pip install -r requirements.txt

2. Run the main Python script main.py: python main.py

Executing main.py will load the finalized model, apply it to the test data, and produce the desired output.

This modular approach allows for a clear separation between the exploratory and development phases in the Jupyter notebook and the production-ready model in the Python project.