House Price Prediction

The goal of this project is to develop a predictive model that estimates the prices of houses based on a set of features. The dataset used includes various attributes of houses, such as size, number of rooms, location, etc.

Getting Started

To run the code in this repository, follow these steps:

1. Clone the repository:

git clone https://github.com/HMNS19/house-price-prediction.git

2. Navigate to the project directory:

cd house-price-prediction

3. Install the required dependencies:

pip install pandas numpy matplotlib seaborn scikit-learn

4. Open the Jupyter Notebook:

jupyter notebook main.ipynb

5. Run the Notebook

Project Workflow

The project follows these main steps:

1. Data Loading:

Load the dataset into a pandas DataFrame.

2. Data Exploration:

Analyze the dataset to understand its structure and contents.

3. Data Preprocessing:

Handle missing values. Encode categorical variables. Normalize or standardize numerical features.

4. Feature Selection:

Select relevant features for model training.

5. Feature Engineering:

Generate additional features from existing ones

6. Model Building:

Train various regression models to predict house prices.

7. Prediction:

Use the trained model to make predictions on test data.

8. Model Evaluation:

Evaluate the performance of the models using appropriate metrics.

Contributing

We welcome contributions to this repository! Follow the steps below to get started:

Step 1: Fork the Repository

Click the Fork button in the top-right corner of the repository to create your copy.

Step 2: Clone Your Fork

Clone your forked repository to your local machine:

git clone https://github.com/<your-username>/house-price-prediction.git
cd house-price-prediction

Step 3: Set Up the Development Environment

Install the required dependencies:

pip install -r requirements.txt

Step 4: Create a Branch

Create a new branch for your feature or bug fix:

git checkout -b <branch-name>

Step 5: Make Changes

Implement your changes or additions in the appropriate files.

Step 6: Test Your Changes

Verify your changes work as intended. For example, rerun the Jupyter Notebook or relevant tests.

Step 7: Commit and Push

Commit your changes with a clear and descriptive message:

git add .
git commit -m "Description of the changes"
git push origin <branch-name>

Step 8: Submit a Pull Request

Navigate to the original repository on GitHub. Click on Pull Requests > New Pull Request. Select your branch and describe the changes in detail.

License

This project is licensed under the MIT License. See the LICENSE file for details