SIMPLE END TO END STUDENTS PERFORMANCE PREDICTOR

Project Overview

This end-to-end project involves the following stages:

• Data Collection: Gathering data from various sources including academic records, attendance, and extracurricular activities.
• Data Processing: Cleaning and preprocessing the data to make it suitable for analysis.
• Model Training: Developing and training machine learning models to predict student performance.
• Evaluation: Assessing the performance of the models and selecting the best one.
• Deployment: Deploying the model to make predictions on new data.

Installation

To set up the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/susil-123/end_to_end_students_performance_prediction.git
   cd end_to_end_students_performance_prediction
   

2. Create virtual enviroinment:

   conda create -p venv python==3.8
   conda activate venv
   

3. Install requirements:

   pip install -r requirements.txt
   

4. Run the application:

   python app.py