Student score prediction through study hours

Table of Contents

• Objective
• Data Source
• Procedure
• Usage

Objective

This is a project that I have created as a part of a selection competetion for FossMec club. Through this process I have created a ML model using PyTorch that will predict the marks scored by the a student using the number of hours studied.

Data

The dataset used for this project is synthetically generated. It can be found in here

Procedure

Exploratory Data Analysis

1. Checking for null values.
2. Identifying and handling duplicates.
3. Exploring data types and dataset entries.
4. Providing a statistical summary of the data.
5. Visualizing the relationship between hours studied and scores.

Model Training

The model was trained using PyTorch with L1Loss function and ADAM optimizer. The data was split into train and test by the ratio 4:1 where the number of hours studied is taken as features and score is taken as label.

Model Evaluation

The performance of the model is evaluated using the R-squared (R2) score, which provides insights into how well the model fits the data. The R2 score ranges from 0 to 1, with a higher score indicating a better fit. In this project, the R2 score is 0.95, demonstrating the model's capability to predict scores based on study hours.

This loss curve indicate that the model is stablized

Usage

The model was saved using pyTorch's save method which saves models in pickle format. It can be easily accessed using pyTorch's load method

How to Load and Use the Saved Model

After downloading the saved model file (student_scores_model.pth), you can load it in PyTorch and use it to make predictions. Follow the steps below:

1. Install PyTorch: Make sure you have PyTorch installed in your environment. If not, you can install it using pip:

pip install torch

2.Define the Model Architecture: You need to define the same model architecture that was used to train the model. Here is the code to do that:

import torch
import torch.nn as nn

class LinearRegressionModel_v0(nn.Module):
  def __init__(self):
    super().__init__()
    self.layer1 = nn.Linear(1,1)

  #Forward pass methof of our model
  def forward(self , x: torch.Tensor) -> torch.Tensor:
    return self.layer1(x)
model = LinearRegressionModel()

3.Load the Model's State Dictionary:

# Load the state dictionary from the saved model file
model.load_state_dict(torch.load("student_scores_model.pth"))

# Set the model to evaluation mode
model.eval()

4.Make Predictions: You can now use the loaded model to make predictions. For example, to predict the score for 9.5 hours of study:

# Example: Predicting score for 9.5 hours of study
hours_tensor = torch.tensor([[9.5]])
predicted_score = model(hours_tensor)
print(f"Predicted score for 9.5 hours of study: {predicted_score.item():.2f}")