jtarakram / end-to-end-ml_project Goto Github PK

This project is an end-to-end machine learning project that aims to predict the performance of students based on various factors such as demographics, family background, and academic history. The project uses a dataset of student information that includes both numerical and categorical data.

The project is built using Python programming language and several popular machine learning libraries such as Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, etc. The project also utilizes Jupyter Notebook for data exploration, analysis, and visualization.

Dataset

The dataset used in this project is the "Student Performance" dataset, which contains 1,000 student records with 8 attributes. The dataset can be found in the student-performance.csv file, which is included in the project directory.

The attributes in the dataset are:

• gender: student's gender (binary: "male" or "female")
  
• race/ethnicity: student's race/ethnicity (categorical: "group A" to "group E")
  
• parental level of education: parental education level (categorical: high school, some college, associate's degree, bachelor's degree, or master's degree)
  
• lunch: student's lunch type (binary: standard or free/reduced)
  
• test preparation course: whether the student completed a test preparation course (binary: completed or none)
  
• math score: student's math score (numerical: 0-100)
  
• reading score: student's reading score (numerical: 0-100)
  
• writing score: student's writing score (numerical: 0-100)
  

Project Structure
The project directory contains the following files:

• stud.csv: the dataset file
  
• Student Performance - End to End Machine Learning Project.ipynb: the Jupyter Notebook that contains the project code
  
• README.md: this file, which provides an overview of the project
  

How to Run the Project

To run the project, you need to have Python and Jupyter Notebook installed on your machine. You can follow these steps:

• Clone the project repository or download the project files
  
• Open a terminal window and navigate to the project directory
  
• Create a virtual environment using virtualenv or conda
  
• Activate the virtual environment
  
• Install the required libraries using pip install -r requirements.txt
  
• Launch Jupyter Notebook using jupyter notebook
  
• Open the Student Performance - open the notebook and run the code cells
  
• Alternatively, you can run the project in a cloud environment such as Google Colab or Kaggle.
  

Conclusion

This project demonstrates the end-to-end process of a machine learning project, including data preparation, data exploration, data visualization, model selection, model training, model evaluation, and model deployment. The project can be used as a template for similar machine learning projects in education or other domains.