implementation_plan.md

Implementation Plan - Student Marks Prediction using RNN

This document outlines the steps to build an end-to-end Recurrent Neural Network (RNN) model to predict student marks based on the number of courses and study time.

1. Data Exploration & Preprocessing

• Load Student_Marks.csv.
• Inspect data quality and statistics.
• Normalize features (number_courses, time_study) and target (Marks) using MinMaxScaler or StandardScaler.
• Split the dataset into training (80%) and testing (20%) sets.
• RNN Reshaping: Reshape the input data to (samples, time_steps, features). Since this is a simple tabular dataset, we will use time_steps = 1.

2. Model Architecture

• Input Layer: Shape (1, 2).
• RNN Layer: Use SimpleRNN or LSTM with 64 units.
• Dense Layer: Hidden layer with 32 units, ReLU activation.
• Output Layer: Single neuron for regression (predicted Marks).
• Compile: Use Adam optimizer and Mean Squared Error (MSE) loss.

3. Training

• Train for 100 epochs (adjustable).
• Use a validation split to monitor overfitting.

4. Evaluation & Visualization

• Evaluate the model on the test set.
• Plot training and validation loss curves.
• Compare predicted values with actual values.

5. Inference

• Create a script to make predictions on new data.