This repository contains a data analytics project that aims to analyze and gain insights from a given dataset. The project utilizes various data analytics techniques and tools to extract meaningful information and make data-driven decisions.
- Data preprocessing: The project includes data cleaning, transformation, and normalization techniques to ensure the data is in a suitable format for analysis.
- Exploratory data analysis (EDA): EDA techniques are applied to understand the dataset's structure, identify patterns, and uncover relationships between variables.
- Statistical analysis: The project employs statistical methods to analyze the dataset, including hypothesis testing, correlation analysis, and regression analysis.
- Data visualization: Visualizations such as charts, graphs, and plots are created to present the analyzed data in a clear and intuitive manner.
- Reporting: The project generates comprehensive reports summarizing the findings, insights, and recommendations derived from the data analysis.
To run this project locally, follow these steps:
- Clone the repository:
git clone https://github.com/your-username/data-analytics-project.git - Install the required dependencies:
pip install -r requirements.txt - Place the dataset file in the project's root directory.
- Run the main script:
python main.py
- Modify the configuration file (
config.yaml) to specify the dataset file, analysis techniques, and desired output. - Execute the main script to perform the data analysis:
python main.py - Explore the generated visualizations and reports in the designated output directory.
- Customize the project according to your specific requirements by modifying the codebase.
Contributions to this project are welcome. If you encounter any issues or have suggestions for improvements, please open an issue or submit a pull request.
This project is licensed under the MIT License.
For any inquiries or further information, please contact your-name.
Feel free to explore and utilize this data analytics project for your own analysis needs. Happy analyzing!