Group_Name : SmartSparks

Group Members: Nirajan Subedi, Pratik Neupane, Sadip Tamang, Umang Manandhar

Project Name: Attendance Rate and Its Influence on Final Grades

Overview

This Python script generates a pseudo dataset to analyze the influence of attendance rate on final grades and evaluates the performance of a logistic regression model in classifying students as 'High Achievers' or 'Struggling Students'.

Dataset Generation

• Number of Students: 400
• Attendance Rate: Randomly generated between 0% and 100%.
• Final Grade: Determined based on whether the student is a high achiever or struggling, with added noise to make the relationship less direct.

Code Breakdown

1. Dataset Creation:

   • attendance_rate: Uniformly distributed between 0 and 100.
   • probability_high_achiever: Probability derived from attendance rate.
   • is_high_achiever: Randomly assigned based on the probability.
   • noise_factor: Normally distributed noise added to final grades.
   • final_grade: Calculated based on achievement status and noise.

2. Model Preparation:

   • Features: Attendance Rate
   • Target: High Achiever vs. Struggling Student (encoded as 1 and 0).
   • Train-Test Split: 70% training, 30% testing.
   • Standardization: Features scaled using StandardScaler.
   • Model: Logistic Regression trained on standardized features.

3. Evaluation:

   • Classification Report: Displays precision, recall, F1-score, and support.
   • Confusion Matrix: Heatmap visualizing prediction performance.
   • Scatter Plot: Shows the relationship between Attendance Rate and Final Grade, colored by category.
   • Histograms: Distribution of Attendance Rate and Final Grade, categorized by student type.

Tech Stack Used

• numpy
• pandas
• scikit-learn
• seaborn
• matplotlib

Instructions

1. Install Stacks: Ensure all necessary packages are installed.
2. Run the Script: Execute the script to generate the dataset, train the model, and produce visualizations.
3. Review Results: Analyze the output metrics and plots to understand the influence of attendance on student performance.

Example Output

• Confusion Matrix: Visual representation of classification performance.
• Scatter Plot: Visualization of how attendance relates to final grades.
• Histograms: Distribution of attendance rates and final grades based on student categories.

This script provides insights into how attendance affects academic performance and evaluates a logistic regression model's ability to classify students based on their final grades.