Student performance and grade prediction using Machine learning

The updated Streamlit application for predicting a student's performance index features a sleek, dark-themed design with a central focus on usability and aesthetics. It uses a pre-trained Random Forest model, loaded via joblib, to predict performance based on user inputs such as hours studied, previous scores, extracurricular activities, and more. Inputs are collected through a form with sliders and select boxes, and predictions are displayed with rounded values and categorized grades. Custom CSS enhances the UI with contrasting colors, borders, and padding to create a visually appealing and user-friendly interface.

Data Set

The dataset link is are as follows :-https://www.kaggle.com/datasets/nikhil7280/student-performance-multiple-linear-regression

on this dataset, below processing are performed :

1. featue scaling and column reinitialization
2. errors and outliers removal using box plot
3. remove na,missing values , regularization etc
4. Drop duplicates , normalization , column dropping

(all this works ar depicted in student_performance_predictionn.ipynb file)

Methodology

The project follows the below structured methodology ranging from data preprocessing pipeline to feature engineering model training, evaluation and deployment :-

1. Data Preprocessing and feature enginnering

2. Exploratory Data Analysis (EDA): after Data preprocessing the next step is Exploratory data analysis using different plotting libraries like matplotlib,pandas,seaborn and plotly.following plots were plotted in this step:-

   1. Pie charts
   2. violen plots
   3. box plot of numerical features
   4. count plots
   5. histogram
   6. model comparison graphs
   7. confusion matrix
   8. Correlation metrices

3. Model Training and evaluation: The four machine learning model Decision Tree ,SVM (linear kernel) ,Random Forest, Multiple linear regression are selected for model training over the inputed processed data:

   The most accurate Random Forest model is then loaded into streamlit application after installing and using joblib library.

4. Inference: Deployed the model with the help streamlit web application to predict the student marks and grade using ML.

Libraries Used

1. Joblib: For downloading the RF model
2. Scikit learn: For machine learning processing and operations
3. Matplotlib: For plotting and visualizing the detection results.
4. Pandas: For Data manipulation.
5. NumPy: For efficient numerical operations.
6. Seaborn : for advanced data visualizations
7. plotly : for 3D data visualizations .
8. Streamlit : for creating gui of the web application.
9. requests : requests for creating Htttp requests

How to Use

1. Clone the Repository:

   git clone url_to_this_repository

2. Install Dependencies:

   pip install -r requirements.txt

3. Download the model and Run the Model:

   link : https://drive.google.com/file/d/1PFrcz_8IhXIudi5h-uxJo6VDZE7r058d/view?usp=sharing

   streamlit run app.py

Demo :

webapp.mp4