Welcome to the Comprehensive Data Analysis Toolkit! This repository provides an all-in-one solution for Exploratory Data Analysis (EDA), Data Cleaning, Feature Engineering, Data Transformation, Model Building, and more. The toolkit is designed to simplify the data analysis process and make it accessible for data enthusiasts, data scientists, and machine learning practitioners.
- Data Cleaning: Handle missing values, remove duplicates, and convert data types with ease.
- Feature Engineering: Create new features, select relevant features, and generate polynomial features.
- Data Transformation: Apply log transformation, polynomial features, and binning to your data.
- Model Building: Train and evaluate machine learning models using built-in functions.
- Hyperparameter Tuning: Optimize model performance with Grid Search.
- Cross-Validation: Validate model performance using K-Fold Cross-Validation.
- Data Sampling: Split your data into training and testing sets.
- Interactive Visualizations: Generate insightful plots using Plotly, including histograms, scatter plots, bar charts, heatmaps, and pie charts.
- Download Modified Data: Easily download cleaned, feature-engineered, transformed, and sampled datasets.
This tool is built with Streamlit for a user-friendly web interface and Plotly for interactive and visually appealing plots.
- Clone the Repository:
git clone https://github.com/Divyansh0108/DataAnalysisToolkit.git
cd comprehensive-data-analysis-toolkit
- Install Dependencies: Create a virtual environment and install the required packages:
python -m venv env
env\Scripts\activate
pip install -r requirements.txt
- Run the Application: Start the Streamlit app by running:
streamlit run app.py
Upload Your Data: Open your browser to http://localhost:8501, upload your dataset, and start exploring!
Upload your dataset: The toolkit supports various file formats, including CSV, Excel, and JSON.
Explore the sidebar: The sidebar provides options for data cleaning, feature engineering, data transformation, model building, and visualization.
Customize your analysis: Adjust parameters and settings based on your specific needs and preferences.
Visualize your data: The toolkit generates interactive plots to help you gain insights into your data.
Download your modified data: After preprocessing and transforming your data, you can download the cleaned, feature-engineered, and sampled datasets for further analysis or model training. Contributing
If you encounter any issues, ensure that all dependencies are correctly installed and check the logs for error messages. For more help, please open an issue on my GitHub repository or contact me at divyanshpandey0108@gmail.com
Contributions are welcome! If you find any issues or have suggestions for improvements, please feel free to create a new issue or submit a pull request.
This project is licensed under the MIT License. MIT