Housing Price Prediction using Machine Learning

📌 Project Overview

This project focuses on predicting housing prices using machine learning techniques.
The goal is to build an end-to-end ML pipeline that includes data preprocessing, model training, evaluation, and comparison of multiple regression models.

The project is implemented using scikit-learn and follows good ML engineering practices such as pipelines, cross-validation, and proper train-test splitting.

---

📊 Dataset

• Dataset: Housing dataset (CSV)
• Target variable: median_house_value
• Features include numerical and categorical attributes such as income, location, and housing characteristics.

---

⚙️ Data Preprocessing

The following preprocessing steps are applied:

• Handling missing values using median imputation
• Feature scaling using StandardScaler
• Encoding categorical variables using OneHotEncoder
• Stratified train-test split based on income categories

All preprocessing is handled using scikit-learn Pipelines and ColumnTransformer.

---

🧠 Models Implemented

The following models were trained and evaluated:

• Linear Regression
• Decision Tree Regressor
• Random Forest Regressor

Model performance is evaluated using Root Mean Squared Error (RMSE) with cross-validation.

---

📈 Evaluation Metric

• RMSE (Root Mean Squared Error) is used to compare model performance.
• Cross-validation ensures reliable and unbiased evaluation.

---

📁 Project Structure

project1/ ├── data_preprocessing.ipynb # Data cleaning and feature engineering ├── train_and_evaluate.py # Model training and evaluation ├── model.py # Final training and model saving ├── .gitignore # Ignore model artifacts and outputs