Housing Case Study

Problem Statement:

Consider a real estate company that has a dataset containing the prices of properties in the Delhi region. It wishes to use the data to optimise the sale prices of the properties based on important factors such as area, bedrooms, parking, etc.

Essentially, the company wants —

• To identify the variables affecting house prices, e.g. area, number of rooms, bathrooms, etc.
• To create a linear model that quantitatively relates house prices with variables such as number of rooms, area, number of bathrooms, etc.
• To know the accuracy of the model, i.e. how well these variables can predict house prices

Steps followed:

• Reading and Understanding the Data
• Visualising the Data
• Data Preparation
• Splitting the Data into Training and Testing Sets
• Building a linear model
• Residual Analysis of the train data
• Making Predictions Using the Final Model
• Model Evaluation