Web App for EDA, Visualization, and Classification

This web app is built using Streamlit and provides a user-friendly interface for performing Exploratory Data Analysis (EDA), visualizations, and training classification models on your dataset. It allows you to upload your own dataset, explore its features, visualize relationships, and evaluate various classification algorithms.

Features

The web app offers the following features:

1. EDA: Perform Exploratory Data Analysis on your uploaded dataset, including summary statistics, missing values, and data distribution.

2. Visualization: Generate visualizations such as pair plots to explore relationships between features, histograms, scatter plots, and more.

3. Model Training and Testing: Train and test classification models on your dataset using the following algorithms:

   • K-Nearest Neighbors (KNN)
   • Support Vector Classifier (SVC)
   • Naive Bayes
   • Logistic Regression
   • Decision Tree

4. About Me: Get to know the developer behind this web app. The "About Me" section includes links to their GitHub, LinkedIn, and Twitter profiles.

Installation

To run this web app locally, follow these steps:

1. Clone the repository: git clone https://github.com/Vic3sax/streamlit-web-app.git

2. Change into the project directory: cd streamlit-web-app

3. Install the required dependencies using pip: pip install -r requirements.txt

4. Run the Streamlit app: streamlit run app.py

5. Open your browser and visit http://localhost:8501 to access the web app.

Dependencies

The following libraries are used in this project:

• Streamlit
• Pandas
• Scikit-learn
• Matplotlib

You can install them by running the following command: pip install -r requirements.txt

Usage

1. Launch the web app by running the app.py file with Streamlit.

2. Upload your dataset using the provided file upload feature.

3. Explore the EDA section to gain insights into your dataset.

4. Navigate to the Visualization section to generate visualizations based on your data.

5. Proceed to the Model section to select and train classification algorithms on your dataset.

6. Evaluate the performance of the trained models and compare their results.

7. If needed, repeat the above steps with different datasets or tweak the model parameters.

Contributing

Contributions to this project are welcome! If you encounter any issues, have suggestions, or would like to add new features, please submit an issue or a pull request.

License

This project is licensed under the MIT License.

Contact

For any questions or inquiries, please reach out to me via the following channels:

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