House Price Prediction using Multi-Linear Regression

Overview

Welcome to the House Price Prediction project, where we utilize Multi-Linear Regression to predict house prices. This statistical method allows us to analyze the relationship between multiple independent variables and house price.

Requirements

To run this project, ensure you have the following dependencies installed:

• Jupyter Notebook
• Libraries I used:
  • Pandas
  • NumPy
  • Scikit-learn
  • Matplotlib
  • Seaborn
  • BeautifulSoup

Model Building

We employ Multi-Linear Regression to build the predictive model. The model learns the relationship between the independent variables (features) and the dependent variable (house price) from the training data. We evaluate its performance using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error ( RMSE).

Evaluation and Validation

After training the model, we evaluate its performance on unseen data using techniques like train-test. This ensures the model's accuracy and reliability in predicting house prices.