🏠 House Price Prediction

• A Machine Learning based web application that predicts house prices based on different features like location, rooms, income and population.

🚀 Project Overview

• This project uses Data Analysis, Feature Engineering and Machine Learning to predict house prices.

• The model is trained on the California Housing Dataset and deployed using Streamlit for real-time predictions.

🛠 Technologies Used

• Python

• Pandas

• NumPy

• Seaborn

• Matplotlib

• Scikit-learn

• Streamlit

• Joblib

🤖 Machine Learning Models Used

• Linear Regression

• Random Forest Regressor

• GridSearchCV (Hyperparameter Tuning)

📊 Dataset Details

• Dataset: California Housing Dataset

• Problem Type: Regression

• Target Variable: median_house_value

🔍 Methodology

1️⃣ Data Cleaning

• Checked dataset info

• Handled missing values using dropna()

• Removed null records

2️⃣ Feature Engineering

Log transformation on skewed data:

• total_rooms

• total_bedrooms

• population

• households

Created new features:

• bedroom_ratio = bedrooms / rooms

• household_rooms = rooms / households

One-Hot Encoding for:

• ocean_proximity

3️⃣ Exploratory Data Analysis (EDA)

• Histogram plots for feature distribution

• Correlation heatmap

• Scatter plot (latitude vs longitude with price)

📸 Project Screenshots

📈 Model Evaluation

Evaluation Metrics Used:

• R² Score

• Mean Squared Error (MSE)

• Root Mean Squared Error (RMSE)

🏆 Model Performance

• Linear Regression applied

• Random Forest gave better accuracy

• GridSearchCV used for best parameter tuning

• Final model selected based on performance

💡 Key Insights

• Location (latitude & longitude) strongly affects house prices

• Median income has high impact on prediction

• Engineered features improved model accuracy

• Log transformation helped normalize skewed data

🌐 Deployment

• Model saved using Joblib

• Scaler also saved

• Integrated into a Streamlit Web App

Users can:

• Enter house details

• Click Predict

• Get estimated house price instantly

▶️ How to Run the Project

##Install dependencies

• pip install -r requirements.txt

##Run Streamlit app

• streamlit run app.py

🔮 Future Improvements

• Add more advanced models (XGBoost, Gradient Boosting)

• Improve feature engineering

• Add map-based visualization

• Create better UI/UX