365 Learning Data Challenge: Insights & Machine Learning to Understand Student Behavior

Overview

This repository is the culmination of my participation in the "365 Learning Data Challenge," where the task was to create a dashboard and a machine learning model for "365 Data Science" to better understand student behavior on their educational platform. The challenge might be over, but the journey has just begun!

Table of Contents

1. Datasets
2. Key Insights
3. Tools Used
4. Machine Learning Model
5. Dashboard Features
6. Installation
7. Usage
8. Contributing

Datasets

The project employs several datasets captured between January to October 2022 to provide a holistic view of the student behavior:

1. Course Ratings: Contains ratings for each course on the platform.
2. Student Engagement: Includes metrics for onboarded students and their activities.
3. Student Learning: Details the number of minutes watched for every course by each student.
4. Student Purchases: Showcases purchase types, which are Monthly, Quarterly, and Annual.
5. Course Info: Contains the Course ID and corresponding course title.
6. Student Hub Questions: Holds the ID of each question asked in the student hub, the student ID, and the date it was asked.

Key Insights

• Peak in August: The month of August recorded the highest student engagement across the platform.
• Annual Subscription Preference: Annual purchase plans are the most popular among students.
• Global Variation: Significant geographical differences in onboarded students, suggesting an opportunity for localized marketing strategies.

Tools Used

Streamlit: For dashboard creation and visualization. Plotly & Matplotlib: For data visualization. Pandas: For data manipulation. Scikit-Learn (sklearn): For machine learning models. Optuna: For hyperparameter optimization. Langchain: For creating a chatbot.

Machine Learning Model

The Random Forest Classifier model is used to differentiate between free-tier and paid-tier students. This model achieved an impressive f1-score of 97%.

Dashboard Features

• General Analytics: A complete overview of key performance indicators.
• Time Series Analysis: Temporal distribution of key metrics.
• Ask ChatGPT: A unique feature where you can ask additional questions about the data used in the machine learning model training.

Installation

Clone the repository git clone https://github.com/AryehRotberg/365-Learning-Data-Challenge.git

Navigate to the directory cd 365-Learning-Data-Challenge

Install dependencies pip install -r requirements.txt

Usage

To run the Streamlit app: streamlit run app/Dashboard.py

Contributing

Feel free to fork the project and submit a pull request with your changes!

For a live demo, check out the Streamlit Dashboard.

Happy Learning! 🎓📊