Student Grades Prediction

This repository contains a Python code that demonstrates a simple data science approach to predicting students' final grades based on various attributes. The code utilizes popular data science libraries such as pandas, numpy, scikit-learn, and matplotlib.

Dataset

The code uses the student_mat.csv dataset, which contains information about students' academic performance and other relevant attributes. The dataset includes the following columns:

• G1: First period grade (numeric: from 0 to 20)
• G2: Second period grade (numeric: from 0 to 20)
• G3: Final grade (numeric: from 0 to 20, output target)
• studytime: Weekly study time (numeric: 1 = <2 hours, 2 = 2 to 5 hours, 3 = 5 to 10 hours, 4 = >10 hours)
• absences: Number of school absences (numeric)
• failures: Number of past class failures (numeric)
• schoolsup: Extra educational support (categorical: "yes" or "no")
• famsup: Family educational support (categorical: "yes" or "no")

Code Explanation

The code follows the following steps:

1. Importing the necessary libraries:

   • pandas is used for data manipulation and analysis.
   • numpy provides support for numerical operations.
   • scikit-learn offers machine learning algorithms and model evaluation tools.
   • matplotlib is utilized for data visualization.

2. Importing and preprocessing the dataset:

   • The dataset is loaded into a pandas DataFrame, specifying the separator as ";".
   • Only relevant attributes (columns) are selected for analysis.
   • Categorical attributes (schoolsup and famsup) are encoded into binary values for further processing.

3. Data cleaning and preparation:

   • Null entries are checked for and displayed.
   • Column names are cleaned and renamed for clarity and consistency.

4. Model training and evaluation:

   • The dataset is split into training and testing sets using scikit-learn's train_test_split function.
   • A linear regression model is instantiated and trained using the training data.
   • The accuracy of the model is calculated using the testing data.
   • The best-performing model i.e the model with the highest accuracy is saved using pickle serialization.

5. Model analysis and predictions:

   • The saved model is loaded from the pickle file.
   • Model coefficients (weights) and intercept are displayed.
   • The model is used to predict the final grades for the testing data.
   • Predicted grades, corresponding features, and actual labels are printed for analysis.

6. Data visualization:

   • Scatter plots are created to visualize the relationship between independent variables and the final grade.

   • Each plot represents the relationship between a specific attribute and the final grade.

     

Conclusion

The linearity assumption is not met by some variables so linear regression may not be the best model for this data.

Instructions

To run the code and reproduce the results:

1. Ensure that you have Python installed on your machine.
2. Install the required libraries by running the following command: pip install pandas numpy scikit-learn matplotlib
3. Download the dataset file student_mat.csv and place it in the same directory as the code file.
4. Execute the code using a Python interpreter.
5. Review the output for model performance, predictions, and data visualization.