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🏠 House Price Regression Project

This project focuses on analyzing housing features (square footage, number of rooms, location, building age, etc.) and building machine learning models to predict house prices.

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📌 Project Overview

Accurately predicting house prices and identifying key value drivers is essential in real estate and financial analytics. In this notebook:

• Data Preprocessing: Handling missing values, outlier detection.
• Exploratory Data Analysis (EDA): Visualizing feature distributions, correlations, and key relationships with target house prices.
• Model Development: Training multiple machine learning regression algorithms and evaluating their predictive performance.

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🛠️ Tech Stack & Libraries

• Language: Python 3.14
• Data Manipulation: pandas, numpy
• Data Visualization: matplotlib, seaborn
• Machine Learning: scikit-learn
• Environment: Jupyter Notebook (.ipynb)

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📁 Repository Structure

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├── 6- HousePriceRegression.ipynb   # Main notebook containing EDA, preprocessing & modeling
└── README.md                       # Project documentation