Analysis of the Academic Success dataset for Classification. This folder include:

1. EDA and Preprocessing This is a basic EDA of the Classification Academic Success Dataset, followed by Data pre-processing and Target visualization. The full data description can be find on: https://archive.ics.uci.edu/dataset/697/predict+students+dropout+and+academic+success kaggle original competition: https://www.kaggle.com/competitions/playground-series-s4e6/overview

2. Feature Selection Univariate and multivariate Feature Selection with different models for Classification.

3. Partitioning and Model_Selection Partitioning twice to 80-20% train test, then test-dev. Model selection based on the Accuracy metric, according to Kaggle specifications.