House Price Prediction
This is a free machine learning project designed to predict house prices in California using a trained Linear Regression model. The application was built as part of the “Complete Machine Learning and Data Science Course” by Priya Bhatia Ma’am from GeeksforGeeks.
It features a clean, interactive web UI built using Streamlit — allowing users to input house data and instantly receive price predictions.
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📋 Project Summary Table
| Attribute | Details |
|---|---|
| Project Name | Predicting House Prices |
| Language/s Used | Python |
| Framework | Streamlit |
| Database | Not Used |
| Type | Machine Learning Web App |
| Developer | UPDATEGADH |
📘 About the Project
This beginner-friendly project trains and uses a Linear Regression model on the California Housing Dataset. It showcases the complete ML workflow from model building to deployment using Streamlit, making it ideal for students and learners who want to build real-world ML apps.
📊 Dataset Used
The application uses the California Housing Dataset, which includes:
- Median income
- House age
- Average number of rooms
- Average number of bedrooms
- Population
- Latitude
- Longitude
- Target: Median house value
🛠️ Technologies Used
- Python 🐍
- Streamlit 🌐
- Scikit-learn
- Pandas
- NumPy
- Joblib (for model serialization)
✅ Available Features
- Streamlit-based user interface
- Input sliders and fields for key housing features
- Pre-trained Linear Regression model
- Instant house price prediction
- Lightweight and easy to run locally
- Professionally structured Python codebase
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