House Price Prediction

🏡 House Price Prediction using XGBoost

An end-to-end Machine Learning project to predict house prices using the XGBoost Regressor. This project includes comprehensive Exploratory Data Analysis (EDA), data visualization, correlation analysis, and predictive modeling.

📌 Project Overview

Predicting real estate prices is a classic regression problem. This repository demonstrates how to load, clean, analyze, and train a high-performance gradient boosting model (XGBoost) on housing datasets to achieve accurate price predictions.

🛠️ Tech Stack & Libraries

The project is built using Python and the following data science libraries:

Data Manipulation: pandas, numpy

Data Visualization: matplotlib, seaborn

Machine Learning: scikit-learn

Gradient Boosting Model: xgboost

📊 Dataset & Features

The project utilizes housing datasets (such as the Boston Housing Dataset and California Housing Dataset) which include key features that influence house prices:

crim: Per capita crime rate by town.

rm: Average number of rooms per dwelling.

tax: Full-value property-tax rate.

lstat: Percentage of lower status of the population.

medv / target: Median value of owner-occupied homes (Target Variable).

🚀 Key Steps in the Notebook

1. Data Loading & Preprocessing

Importing datasets directly via web URLs and sklearn.datasets.

Handling missing values and checking data shapes.

2. Exploratory Data Analysis (EDA)

Statistical summary using df.describe().

Investigating correlation between features using df.corr().

Visualizing features correlation using Seaborn's Heatmap to identify key price drivers.

3. Model Training & Evaluation

Splitting data into training and testing sets using train_test_split.

Training an XGBoost Regressor model.

Evaluating model performance using regression metrics (MAE, R-squared).

💻 How to Run the Project Local

Follow these steps to run the notebook on your local machine:

1. Clone the repository

cd house-price-prediction