Data Analytics Exercises and Machine Learning Models

This repository contains a collection of Jupyter Notebooks focused on data analytics and machine learning exercises. Each notebook demonstrates the application of various libraries for data preprocessing, exploratory data analysis, and predictive modeling tasks.

Directory Structure

• Notebooks/
  Contains multiple notebooks labeled as Esercizio1, Esercizio2, and so on. Each notebook explores different practices such as data cleaning, visualization, classification, and feature engineering. Some notebooks include updated or advanced versions of earlier exercises.

• Datasets/
  Stores dataset files in several subdirectories. These files are referenced by the notebooks for demonstration and practice of data loading, manipulation, and analysis.

Key Libraries and Tools

• Python 3
• pandas for data manipulation and analysis
• NumPy for numerical computations
• scikit-learn for machine learning (including classifiers, model evaluation, and metrics)
• matplotlib and seaborn for data visualization
• mlxtend for association rule mining and frequent pattern analysis
• imblearn (SMOTE) for handling imbalanced datasets

Usage

1. Open a notebook (e.g., Esercizio1.ipynb) in a Jupyter environment.
2. Inspect the cells to learn how data is loaded, processed, and analyzed.
3. Run the cells sequentially to reproduce results and experiment with different parameters.

Contributing

For modifications or additional exercises, please create a fork or branch, apply changes, and submit a pull request. Contributions that improve code clarity, performance, or documentation are appreciated.