ML_Exercises

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description: No description available. — — toc: false —

Machine Learning Project

This repository contains a collection of machine learning notebooks and resources, organized into different categories. Below is an overview of the project structure and the contents of each folder.

Project Structure

01-supervised/
    decision_trees.ipynb
    gradient_boosting.ipynb
    linear_regression.ipynb
    logistic_regression.ipynb
    svm_digits.ipynb
    usa_housing_model.pkl
02-unsupervised/
    hierarchical_dbscan.ipynb
    kmeans_clustering.gif
    kmeans_iris.ipynb
    tsne_visualization.ipynb
03-preprocessing-eda/
    eda_titanic.ipynb
    feature_selection.ipynb
    preprocessing_pipeline.ipynb
04-evaluation-tuning/
    crossval_gridsearch.ipynb
    model_metrics.ipynb
05-advanced/
    ensemble_comparison.ipynb
    nlp_sentiment_analysis.ipynb
    time_series_forecasting.ipynb
LICENSE
README.md

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Folder Details

01-supervised/

This folder contains notebooks related to supervised learning techniques:

• decision_trees.ipynb: Implementation of decision trees with feature importance visualization.
• gradient_boosting.ipynb: Comparison of XGBoost and LightGBM with ROC curve analysis.
• linear_regression.ipynb: Linear regression for predicting housing prices.
• logistic_regression.ipynb: Logistic regression with metrics like accuracy, confusion matrix, and ROC-AUC.
• svm_digits.ipynb: Support Vector Machines for digit classification using the MNIST dataset.

02-unsupervised/

This folder focuses on unsupervised learning techniques:

• hierarchical_dbscan.ipynb: HDBSCAN and DBSCAN clustering with visualization of linkage and condensed trees.
• kmeans_clustering.gif: Animated visualization of K-Means clustering.
• kmeans_iris.ipynb: K-Means clustering applied to the Iris dataset.
• tsne_visualization.ipynb: t-SNE for dimensionality reduction and visualization.

03-preprocessing-eda/

This folder includes preprocessing and exploratory data analysis (EDA) notebooks:

• eda_titanic.ipynb: EDA on the Titanic dataset with visualizations, feature engineering, and correlation heatmaps.
• feature_selection.ipynb: Feature selection using ANOVA F-test, mutual information, and Random Forest importance.
• preprocessing_pipeline.ipynb: End-to-end preprocessing pipeline with feature scaling, encoding, and model evaluation.

04-evaluation-tuning/

This folder contains notebooks for model evaluation and hyperparameter tuning:

• crossval_gridsearch.ipynb: Cross-validation and grid search for hyperparameter optimization.
• model_metrics.ipynb: Evaluation metrics such as accuracy, precision, recall, F1-score, and ROC-AUC.

05-advanced/

This folder explores advanced machine learning topics:

• ensemble_comparison.ipynb: Comparison of ensemble methods like Random Forest, Gradient Boosting, and Extra Trees.
• nlp_sentiment_analysis.ipynb: Sentiment analysis using Naive Bayes on the NLTK movie reviews dataset.
• time_series_forecasting.ipynb: Time series forecasting with models like ARIMA and Holt-Winters.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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How to Use

1. Clone the repository:

   git clone https://github.com/alireza-astane/ML_Exercises.git
   cd ml-project
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Explore the notebooks in the notebooks/ directory.

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