🏠 California Housing Price Prediction

A complete end-to-end machine learning regression project using the California Housing Dataset, focusing on predicting median house values based on multiple features. This project was developed as a task for the Machine Learning Internship at Arch Technologies.

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📌 Project Summary

This project demonstrates:

• Data preprocessing & visualization
• Building regression models:
  • Linear Regression
  • Decision Tree Regressor
  • Random Forest Regressor
• Cross-validation evaluation
• Hyperparameter tuning using:
  • GridSearchCV
  • RandomizedSearchCV
• Final model testing and RMSE evaluation

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📁 Dataset

• Source: Built-in California housing dataset from sklearn.datasets
• Features:
  • MedInc: Median income in block group
  • HouseAge: Median house age
  • AveRooms: Average number of rooms
  • AveBedrms: Average number of bedrooms
  • Population: Block group population
  • AveOccup: Average number of household members
  • Latitude & Longitude
• Target:
  • MedHouseVal: Median house value

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🛠️ Technologies & Libraries

• Python 3.x
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Scikit-learn
• SciPy

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📊 Exploratory Data Analysis

• Visualized distributions of features using histograms
• Heatmap correlation matrix to identify feature relationships
• Scatter plot between MedInc and MedHouseVal

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🧪 Model Training & Evaluation

📌 Models Implemented:

1. Linear Regression
2. Decision Tree Regressor
3. Random Forest Regressor

📌 Evaluation Metric:

• Root Mean Squared Error (RMSE) using 10-fold Cross-Validation

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👨‍💻 Developed By

Abdul Rafay
📚 BS Software Engineering | 🎯 AI & ML Enthusiast
🔗 LinkedIn

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📜 License

This repository is licensed under the MIT License.

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🌟 Support & Contribution

If you found this helpful:

• ⭐ Star the repo
• 🍴 Fork it and contribute
• 📢 Share on LinkedIn and tag me!

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🔍 Accurate detection. 🎯 Precise segmentation. 🚀 Built with passion.