Dynamic Streamlit Data Science Project

This project aims to provide an interactive web application using Streamlit for dynamic data analysis and machine learning model building. It offers flexibility in choosing datasets, classifiers, and tuning hyperparameters, enabling users to perform comprehensive data exploration and model evaluation.

Website link: https://kebab-kun-dynamic-streamlit-data-science-project-main-yfnws1.streamlit.app/

Table of Contents

• Overview
• Features
• Getting Started
• Usage
• Contributing
• Contact
• License

Overview

Dynamic Streamlit Data Science Project is designed to streamline the process of data analysis and machine learning model development. The web application built with Streamlit allows users to select from various datasets, choose classifiers, and tune hyperparameters without writing extensive code. It facilitates interactive data exploration, model training, and evaluation, making it suitable for both beginners and experienced data scientists.

Features

• Dynamic Dataset Selection: Choose from multiple datasets, including Breast Cancer, for analysis.
• Model Selection: Select classifiers such as KNN, SVM, or Naive Bayes to build predictive models.
• Hyperparameter Tuning: Utilize GridSearchCV for hyperparameter optimization and improved model performance.
• Interactive Visualization: Explore data insights through interactive plots, heatmaps, and scatter plots.
• Easy-to-Use Interface: Navigate through the web application with a user-friendly interface and intuitive controls.

Getting Started

To get started with the project, follow these steps:

1. Clone the repository:

   git clone https://github.com/Kebab-kun/Dynamic-Streamlit-Data-Science-Project.git

2. Install dependencies:

   pip install -r requirements.txt

3. Run the main.py file in command prompt with streamlit:

   streamlit run main.py

4. Access the Streamlit web application by opening a browser and navigating to the provided URL.

Usage

1. Select Dataset: Choose a dataset from the dropdown menu on the sidebar.
2. Select Classifier: Choose a classifier (KNN, SVM, or Naive Bayes) for the selected dataset.
3. Tune Hyperparameters: Adjust hyperparameters using the sliders or radio buttons on the sidebar.
4. Explore Results: View model performance metrics, including accuracy, F1 score, precision, recall, and confusion matrix.

Contributing

Contributions to this project are welcome! If you encounter any bugs, issues, or have suggestions for improvements, feel free to open an issue or submit a pull request.

Contact

For inquiries or collaborations related to this project, please contact [email protected]. You can also connect with me on LinkedIn for further discussions.

License

This project is licensed under the MIT License.