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.