House Price Prediction 🏠💰

This project predicts house prices based on various features. It involves data cleaning, feature engineering, and model training.

Steps 📋

1. Data Cleaning 🧹

   • Import libraries and load data.
   • Drop unnecessary columns.
   • Handle missing values.

2. Feature Engineering 🛠️

   • Convert categorical data to numerical.
   • Create new features.

3. Model Training 🤖

   • Train models like Linear Regression.
   • Evaluate model performance.

Usage 🚀

1. Clone the repo.
2. Install dependencies.
3. Run the Jupyter Notebook.

Results 📊

The model predicts house prices with reasonable accuracy. The final model can be used for further predictions.

Files 📂

• House_Price_Prediction.ipynb: Main notebook with code and explanations.
• data: Folder containing the dataset file.

Contact 📬

For any queries, reach out at [email protected].

Feel free to contribute and improve the project! 🎉