🏠 House Price Prediction Project

Predicting house prices using advanced regression techniques. We explore, clean, engineer, and model data from the famous Kaggle House Prices dataset.

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Project Highlights

• Predictive Modeling: Deep dive into Linear, Ridge, and Lasso regression algorithms.
• Advanced EDA: Comprehensive visual analysis of 79 features using Seaborn and Matplotlib.
• Feature Engineering: Creating insightful features like TotalSF, HouseAge, and TotalBaths.
• Skewness Handling: Log-transforming target variables to optimize linear model performance.

Workflow Directory

Step	description
EDA	Understanding data distributions, correlations, and identifying missing values.
Data Cleaning	Handling NaN values with mode/median/none strategies for robustness.
Modeling	Training 3 distinct regression models and hyperparameter tuning with Alpha.
Evaluation	Comparing RMSE and R² scores to pick the champion model.

Quick Start

Launch your data science journey in minutes!

Prerequisites

• Python 3.8+
• Jupyter Notebook / VS Code
• Kaggle Dataset: Download Train & Test CSVs.

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Local Installation

1. Clone & Navigate:

   git clone https://github.com/SubashSK777/House-Price-Prediction.git
   cd House-Price-Prediction

2. Install Libraries:

   pip install pandas numpy matplotlib seaborn scikit-learn jupyter

3. Prepare Data: Place train.csv and test.csv in the root directory.

4. Launch Notebook:

   jupyter notebook house_price.ipynb

Model Performance

Model	RMSE (Log)	R² Score
Linear Regression	0.1254	0.892
Ridge Regression (L2)	0.1248	0.895
Lasso Regression (L1)	0.1261	0.889

Note: Results may vary slightly based on feature selection and random seed.

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