This Streamlit application analyzes the performance of students in Maths for the academic year 2023-24 and provides predictions for their first-year engineering results based on their academic scores.

This Streamlit app performs the following tasks:

• Data loading and preprocessing: Loads the student performance data from a CSV file and preprocesses it by handling categorical data and dropping unnecessary columns.
• Data exploration: Displays the loaded dataset and summary statistics to provide an overview of the data.
• Data visualization: Visualizes the relationship between different academic scores (10th, 12th, and CET marks) and semester 1 performance using bar charts.
• Model training: Trains a Random Forest classifier using the preprocessed data to predict student results.
• Model evaluation: Evaluates the trained model's performance using accuracy, confusion matrix, and classification report metrics.
• Feature importance analysis: Displays the feature importance plot to show the importance of different input features in predicting student results.
• Prediction: Allows users to input their academic scores and predicts their first-year engineering result using the trained model.

To run this Streamlit app locally, follow these steps:

1. Clone this repository to your local machine:

   git clone https://github.com/sojith29034/maths_mp_23-24.git
   

2. Navigate to the project directory:

   cd maths_mp_23-24
   

3. Install the required dependencies using pip:

   pip install -r requirements.txt
   

4. Run the Streamlit app:

   streamlit run maths_mp.py
   

5. Access the Streamlit app in your web browser at http://localhost:8501.

• Upon running the Streamlit app, you'll see the different sections for data exploration, visualization, model training, and prediction.
• Use the sliders or input fields to input your academic scores for 10th, 12th, and CET marks.
• Click the "Predict" button to see the predicted result for your first-year engineering.

The Streamlit app relies on the following Python libraries:

• streamlit: For building and running the web application.
• matplotlib: For data visualization and plotting.
• pandas: For data manipulation and analysis.
• scikit-learn: For machine learning model training and evaluation.

These dependencies are listed in the requirements.txt file for easy installation.

Contributions are welcome! If you find any bugs or have suggestions for improvements, please open an issue or submit a pull request.