House Price Prediction Using Python

The house price prediction competition is a amazing place to start.

House Prices: Advanced Regression Techniques

This Blog is for reference for anyone who want to start with Kaggle competition (beginner friendly).

Requirements

• Python3 (3.5 or 3.6 recommended)
• jupyter
• Packages (pandas, numpy, matplotlib, seaborn, scikit-learn.)

For Data Cleaning we should understand our Data set.

Data Fields:

Here’s a brief version of what you’ll find in the data description file.

• 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.