supervised ML

Student Scores Prediction using Simple Linear Regression

Project Overview

This project aims to predict the scores of students based on the number of hours they studied. A simple linear regression model is used to build the prediction, where the independent variable is the number of study hours, and the dependent variable is the score achieved.

Dataset

The dataset consists of two columns:

1. Hours: Number of hours a student studied.
2. Scores: Scores achieved by the student.

Sample data:

Hours	Scores
2.5	21
5.1	47
3.2	27
8.5	75
3.5	30
1.5	20

The dataset is loaded from a CSV file.

Project Workflow

1. Data Preprocessing:

   • Load and visualize the dataset.
   • Plot the relationship between hours studied and scores to understand the data distribution.

2. Model Training:

   • The dataset is split into training and testing sets in an 80:20 ratio.
   • A linear regression model is trained on the training data.

3. Model Evaluation:

   • The model's performance is evaluated using the Mean Absolute Error (MAE).
   • The regression line is plotted to visualize the model's predictions.

4. Prediction:

   • Predict scores for test data and compare them with actual scores.
   • Predict the score for a student who studied for 9.25 hours.

Key Findings

• Training Set MAE: The Mean Absolute Error (MAE) on the training set was approximately 5.19.
• Test Set MAE: The Mean Absolute Error (MAE) on the test set was approximately 4.18, indicating good model performance.
• Prediction for 9.25 hours of study: The model predicts that a student who studies for 9.25 hours would score approximately 93.69.

Technologies Used

• Python: The programming language used to build the model.
• Libraries:
  • pandas for data handling and manipulation.
  • numpy for numerical computations.
  • matplotlib for data visualization.
  • scikit-learn for machine learning algorithms.

How to Run the Project

1. Install the required dependencies by running:

   pip install pandas numpy matplotlib scikit-learn

2. Open the Jupyter notebook my_assignment1.ipynb and run the cells step-by-step to see the workflow.

Results Visualization

The above graph shows the relationship between the number of hours studied and the scores obtained by students.

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