https://github.com/rahul-404/mlproject
https://github.com/rahul-404/mlproject
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/rahul-404/mlproject
- Owner: Rahul-404
- Created: 2024-03-04T17:42:25.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-13T17:31:05.000Z (5 months ago)
- Last Synced: 2025-02-01T22:13:54.883Z (4 months ago)
- Language: Jupyter Notebook
- Size: 1010 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# **Student Performance Prediction - Machine Learning Project**
This project predicts students' performance based on multiple factors such as **gender**, **race/ethnicity**, **parental level of education**, **lunch**, **test preparation course**, and scores in **math**, **reading**, and **writing**. It is an end-to-end machine learning pipeline, from data preprocessing to model evaluation.
---
## **Table of Contents**
- [Installation](#installation)
- [Project Structure](#project-structure)
- [How to Run the Project](#how-to-run-the-project)
- [Features](#features)
- [Modeling](#modeling)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
---
## **Installation**
Follow these steps to install and run the project:
1. **Clone the repository**:
```bash
git clone https://github.com/Rahul-404/mlproject.git
cd mlproject
```
2. **Create and activate a virtual environment** (recommended):
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. **Install dependencies**:
```bash
pip install -r requirements.txt
```
4. **Set up environment variables** (if required for external APIs, etc.).
---
## **Project Structure**
The project is organized as follows:
```
mlproject/
│
├── src/
│ └── mlproject/
│ ├── __init__.py # Initialization of the project package
│ ├── components/
│ │ ├── __init__.py # Component initialization
│ │ ├── data_ingestion.py # Data ingestion logic
│ │ ├── data_transformation.py # Data transformation/feature engineering
│ │ ├── model_trainer.py # Model training pipeline
│ │ └── model_monitoring.py # Model monitoring and evaluation
│ ├── pipeline/
│ │ ├── __init__.py # Pipeline initialization
│ │ ├── training_pipeline.py # Training pipeline logic
│ │ └── prediction_pipeline.py # Prediction pipeline logic
│ ├── exception.py # Custom exceptions
│ ├── logger.py # Logging utilities
│ ├── utils.py # Utility functions
├── app.py # Main script for running the app
├── Dockerfile # Docker configuration
├── requirements.txt # Python dependencies
├── setup.py # Setup script for packaging
├── artifacts/ # Directory for saving trained models and outputs
└── README.md # Project documentation
```
- **`src/{project_name}/`**: The main source code directory where all the core project files are located.
- **`components/`**: Contains the logic for individual components of the machine learning pipeline, including data ingestion, transformation, training, and monitoring.
- **`pipeline/`**: Contains the pipeline scripts that orchestrate the data flow and model training/prediction processes.
- **`exception.py`**: Contains custom exceptions for error handling.
- **`logger.py`**: Logging utilities to keep track of the application's execution and errors.
- **`utils.py`**: Utility functions used throughout the project for tasks such as metrics calculation and data loading.
- **`app.py`**: Main entry point for running the application and interacting with the trained model.
- **`Dockerfile`**: Configuration file to containerize the application using Docker.
- **`requirements.txt`**: Contains a list of dependencies required to run the project.
- **`setup.py`**: Setup script for packaging and installing the project.
- **`artifacts/`**: Directory for storing models, data, and outputs generated during training and prediction.
---
## **How to Run the Project**
### 1. **Run the Application**
To start the application and interact with the model, use the following command:
```bash
python app.py
```
This will launch the application and allow you to make predictions based on input data such as student scores and personal information.
---
## **Features**
- **Data Ingestion**: Loads raw data from external sources (e.g., CSV, database).
- **Data Transformation**: Preprocesses the data by encoding categorical features, handling missing values, and scaling numerical features.
- **Model Training**: Trains machine learning models (e.g., Logistic Regression, Random Forest, SVM) to predict student performance.
- **Model Monitoring**: Monitors the performance of the model after deployment, with tools to track accuracy and other metrics.
- **Prediction Pipeline**: Provides functionality to make predictions on new data based on the trained model.
---
## **Modeling**
This project explores multiple machine learning models to predict student performance:
- **Logistic Regression**
- **Random Forest Classifier**
- **Support Vector Machine (SVM)**
The models are evaluated using a **train-test split** (typically 80/20) and **cross-validation** techniques to ensure robust results. The best-performing model is selected for final predictions.
### **Model Training Pipeline**:
1. **Preprocessing**: Handle missing data, encode categorical features (e.g., one-hot encoding for gender), and scale numerical features (e.g., using StandardScaler).
2. **Model Evaluation**: Evaluate different models on the training data using cross-validation and metrics like accuracy, precision, and recall.
3. **Final Model**: Save the best-performing model in the `artifacts/` directory for later predictions.
---
## **Usage**
1. **Run the application**:
- Open the terminal and run the `app.py` script to interact with the trained model.
- The app will load the trained model and accept inputs like gender, test scores, etc., to predict student performance.
2. **Input Data**:
- Provide input in the form of a dictionary with features such as `gender`, `math_score`, `reading_score`, etc.
- The model will output a predicted performance score.
3. **Predictions**:
- The model predicts student performance based on various features, which can help in identifying students at risk of underperforming.
---
## **Contributing**
We welcome contributions! If you'd like to contribute to this project, please follow these steps:
1. Fork the repository.
2. Clone your fork to your local machine.
3. Create a new branch for your feature or bugfix.
4. Make your changes and test them locally.
5. Push your changes to your fork.
6. Open a pull request with a clear description of your changes.
---
## **License**
This project is licensed under the **MIT License**. See the [LICENSE](LICENSE) file for more information.
---
## **Acknowledgments**
- **Dataset**: The data used for this project can be sourced from [Kaggle](https://www.kaggle.com/datasets) or similar datasets related to student performance.
- **Libraries Used**:
- **Scikit-learn** for model building and evaluation.
- **Pandas** and **NumPy** for data processing.
- **Matplotlib** and **Seaborn** for visualizations.