Project Objectives
Build a model which accurately predicts the performance of students
Deploy the model using web app interface
Project Phases & Timeline
Data Cleaning & Analysis (Week 1)
Handling null values, fixing data types, data inconsistencies
EDA, understanding distributions, outliers, relationships of features with target variable
Feature Selection using correlation analysis, ANOVA tests, F-test, etc.
Feature Engineering & Model Selection (Week 2 & 3)
Building new features, one hot encoding, feature scaling
Handling outliers through statistical and heuristic methods
Normalisation or Standardisation of input features is the ML algorithm in the pipeline requires it
Model training, comparison and selection
Model evaluation and Optimization using K-fold cross validation, hyperparameter tuning
Deployment (Week 4)
Building a streamlit app
Deploying model to app
Deploying app to streamlit cloud
Link to GitHub: https://github.com/SuperDataScience-Community-Projects/SDS-CP015-student-performance-pred