https://github.com/devprnvk/realestateml

This Python program analyzes a dataset (HousePricePrediction.xlsx) containing information about house prices. It utilizes pandas for data manipulation, matplotlib for plotting, and seaborn for visualizing correlations and distributions.
https://github.com/devprnvk/realestateml

data-science data-visualization datasets houses npm plotting prediction-model seaborn

Last synced: 8 months ago
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This Python program analyzes a dataset (HousePricePrediction.xlsx) containing information about house prices. It utilizes pandas for data manipulation, matplotlib for plotting, and seaborn for visualizing correlations and distributions.

• Host: GitHub
• URL: https://github.com/devprnvk/realestateml
• Owner: devprnvk
• Created: 2024-06-28T00:00:50.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-06-28T00:16:51.000Z (over 1 year ago)
• Last Synced: 2025-05-30T08:36:38.817Z (9 months ago)
• Topics: data-science, data-visualization, datasets, houses, npm, plotting, prediction-model, seaborn
• Language: Python
• Homepage:
• Size: 198 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# House Price Prediction Analysis

This Python program analyzes a dataset (`HousePricePrediction.xlsx`) containing information about house prices. It utilizes pandas for data manipulation, matplotlib for plotting, and seaborn for visualizing correlations and distributions.

## Features

- **Data Loading and Exploration**: Loads the dataset (`HousePricePrediction.xlsx`) using pandas and displays the first 5 records.

  

- **Numeric and Categorical Analysis**: Identifies numeric and categorical columns in the dataset and performs analysis accordingly.

  

- **Correlation Heatmap**: Plots a heatmap to visualize correlations among numeric features.

  

- **Unique Values and Distribution**: Displays the number of unique values and distribution plots for categorical features.

## Requirements

- Python 3.x

- pandas

- matplotlib

- seaborn

## Installation

1. Clone the repository:

''git clone https://github.com/your-username/house-price-prediction.git

2. Install the required dependencies:

pip install pandas matplotlib seaborn openpyxl

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## Usage

1. Navigate to the directory containing `analyze.py`.

2. Run the script:

python analyze.py

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This will execute the program and display visualizations in matplotlib and seaborn.

## Example Output

- **Correlation Heatmap**:

![Correlation Heatmap](images/correlation_heatmap.png)

- **Unique Values of Categorical Features**:

![Unique Values](images/unique_values.png)

- **Distribution of Categorical Features**:

![Distribution](images/categorical_distribution.png)

## License

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

## Contributing

1. Fork the repository.

2. Create a new branch (`git checkout -b feature-new-analysis`).

3. Make changes and commit (`git commit -am 'Add new analysis feature'`).

4. Push to the branch (`git push origin feature-new-analysis`).

5. Create a new Pull Request.