Student Score Predictor

A machine learning project that predicts student scores based on study hours, built with Python and Jupyter Notebook. This project demonstrates the application of linear regression in educational analytics and showcases practical data science skills.

Project Overview

Student Score Predictor is designed to estimate the expected score of a student based on the number of hours studied. By inputting study hours, the model predicts the corresponding score, helping to illustrate the relationship between time spent studying and academic performance. This project is ideal for those learning about regression techniques and looking to apply machine learning to real-world educational data.

Key Features

• Predicts student scores from study hours using a linear regression model.
• Visualizes data and regression results using Matplotlib.
• Simple, readable code implemented in a Jupyter Notebook for easy experimentation and learning.
• Demonstrates the end-to-end process of data loading, visualization, model training, and prediction.

Technologies Used

• Python 3
• Jupyter Notebook
• scikit-learn (for Linear Regression)
• pandas (for data manipulation)
• matplotlib (for data visualization)
• numpy (for numerical operations)

Getting Started

Prerequisites

• Python 3.7 or above
• Jupyter Notebook (Anaconda recommended or install via pip)
• Required Python packages: pandas, numpy, matplotlib, scikit-learn

Installation

1. Clone the repository:

   git clone https://github.com/Gideon-tech/Student_score_predictor.git
   cd Student_score_predictor

2. Install dependencies:

   pip install pandas numpy matplotlib scikit-learn

3. Open the Jupyter Notebook:

   jupyter notebook student_scores_predictor.ipynb

Usage

• Run the notebook cells in order.
• Modify the input value for study hours to predict scores for different scenarios.
• Visualize the regression line and data points to understand the model's fit.

Example:

# Predict score for 6 study hours
predicted_score = model.predict([[6]])
print(f"If a student studies 6 hours, they will likely score: {predicted_score[0]:.2f}")

Project Value and Resume Highlight

This project demonstrates:

• Practical implementation of machine learning regression.
• Data visualization and exploratory data analysis.
• End-to-end workflow: data preprocessing, model training, evaluation, and prediction.
• Use of Jupyter Notebook for reproducible and shareable data science experiments.

Great for showcasing your data science and machine learning abilities on your resume.

License

This project is open-source. Please see the repository for licensing details.

Contact

• GitHub: Gideon-tech

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Feel free to fork, contribute, or use this project as a portfolio example!