House Price Prediction Project

House Price Prediction System | Machine Learning Project

This project aims to develop a predictive model for estimating house prices using machine learning techniques. The model is trained on structured housing data and leverages features such as property size, number of rooms, location, and additional attributes.

Key Steps:

Data Cleaning & Preprocessing

Exploratory Data Analysis (EDA)

Feature Engineering

Model Training (e.g., Linear Regression / Random Forest)

Model Evaluation (RMSE, MAE)

Objective:

To build an accurate and scalable model that helps users estimate property prices and supports real estate analysis and decision-making.