This project focuses on predicting home prices using the power of data manipulation and analysis with Python. We leverage the capabilities of the Pandas and NumPy libraries to clean, explore, and preprocess the data, and then build a predictive model.

• Installation
• Usage
• Data
• Workflow
• Dependencies
• Contributing
• License

Clone the repository to your local machine:

git clone https://github.com/AryanMurthy/Home-Price-Prediction.git
cd Home-Price-Prediction

Create a virtual environment and install the required packages:

python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
pip install -r requirements.txt

1. Ensure you have activated your virtual environment (source venv/bin/activate).
2. Run the Jupyter notebook or Python script for home price prediction.

jupyter notebook Home_Price_Prediction.ipynb
# or
python home_price_prediction.py

Follow the instructions in the notebook or script to execute the analysis and prediction.

The dataset used for this project is located in the data directory. It includes various features such as square footage, number of bedrooms, and location, which will be used to predict home prices.

1. Data Cleaning and Exploration: Use Pandas for loading the dataset, handling missing values, and exploring the distribution of features.

2. Data Preprocessing: Perform necessary preprocessing tasks, such as encoding categorical variables, scaling numerical features, and splitting the dataset into training and testing sets.

3. Model Building: Utilize machine learning algorithms to build a predictive model. Common models include linear regression, decision trees, or ensemble methods.

4. Model Evaluation: Evaluate the performance of the model using metrics such as Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE).

5. Prediction: Use the trained model to make predictions on new data or test set.

• Python 3.x
• Jupyter Notebook
• Pandas
• NumPy
• Scikit-learn

Install the required dependencies using the provided requirements.txt file.

pip install -r requirements.txt

Feel free to contribute to this project. You can open issues, submit pull requests, or provide suggestions for improvement.