Project Overview

This project focuses on predicting real estate prices using advanced regression techniques. By leveraging various regression algorithms and feature engineering strategies, the goal is to build a model that can accurately estimate property prices based on several real estate features. The project was developed as part of a Kaggle competition, where data from historical sales and property features were provided for analysis

Problem Statement

In the real estate industry, predicting house prices is crucial for both buyers and sellers. House prices depend on various factors such as location, size, number of rooms, amenities, and more. The challenge is to create a machine learning model that can predict house prices based on these features.

Objective

The main objective of this project is to develop a regression model capable of accurately predicting house prices. To achieve this, we will:

Analyze the relationship between various features and house prices. Apply advanced regression techniques, including regularization methods, to improve prediction accuracy. Fine-tune the model to minimize prediction error.

Dataset

The dataset used in this project consists of several features such as:

Lot Area, Year Built, Overall Quality, Location (Neighborhood), Number of Bathrooms, Garage Size, Condition of the Property etc. The dataset contains both numerical and categorical features that require preprocessing before being fed into the regression model. The dataset is divided into training and test sets for evaluation.