🏡 Advanced House Regression

This project focuses on building advanced regression models to predict house prices using machine learning techniques. The dataset used is based on the popular Kaggle competition: House Prices - Advanced Regression Techniques, with the dataset sourced from Hands-On ML by Aurélien Géron.

🚀 Overview

The goal is to predict the final sale price of each house based on a rich set of features. We explore various regression models and preprocessing steps to achieve high accuracy.

🔍 Features

• Data cleaning and preprocessing (handling missing values, encoding categorical features, feature engineering)
• Feature scaling using StandardScaler
• Model training and evaluation using LinearRegression
• Batch prediction from CSV files and export of predicted results
• Interactive UI with Streamlit for real-time predictions

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📁 Project Structure

advanced-house-regression/
├── housing.csv                  # Dataset file
├── data_preprocessing.py       # Preprocesses and saves the training data
├── train_model.py              # Trains the model and saves the scaler/model
├── predict.py                  # Script to make a single prediction
├── batch_predict.py            # Predicts house prices from new_data.csv and exports predictions.csv
├── app.py                      # Streamlit app for web-based prediction
├── new_data.csv                # Sample new data for batch predictions
├── predictions.csv             # Output CSV with predictions
├── scaler.pkl                  # Saved StandardScaler object
├── linear_regression_model.pkl # Trained model file
├── prepared_data.pkl           # Scaled and split dataset
├── requirements.txt            # Python dependencies
└── README.md                   # Project documentation

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📅 Setup & Usage

⚡ Installation

pip install -r requirements.txt

🎮 Run Preprocessing and Training

python3 data_preprocessing.py
python3 train_model.py

💰 Predict Single Input (CLI)

python3 predict.py

📤 Batch Predict from CSV

python3 batch_predict.py

🌐 Run Streamlit App

streamlit run app.py

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📄 Dataset

Dataset used: housing.csv

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📃 Requirements

pandas
scikit-learn
joblib
streamlit

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📈 Example Output

Predicted House Price: $415,721
Predictions saved to predictions.csv