Housing Case Study: Multiple Linear Regression

Problem Statement:

This project involves a case study of a real estate company with a dataset containing property prices in the Delhi region. The goal is to optimize the sale prices of properties based on important factors such as area, bedrooms, parking, etc.

Objectives:

• Identify Key Variables: Determine the variables affecting house prices, such as area, number of rooms, bathrooms, and more.
• Develop a Linear Model: Create a multiple linear regression model that quantitatively relates house prices with variables like the number of rooms, area, and number of bathrooms.
• Assess Model Accuracy: Evaluate the accuracy of the model to understand how well these variables can predict house prices.

Dataset:

The dataset includes various features related to the properties in the Delhi region, such as area, number of bedrooms, number of bathrooms, parking spaces, and the corresponding prices.

Key Components:

Code: Scripts for data preprocessing, analysis, and model building.

Data: The dataset used for the analysis.

Documentation: Detailed explanation of the steps