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Machine Learning

This repository contains a collection of machine learning projects.

  1. Books-Classifier

    • Use XGBoost to classify books.
  2. GBDT-XGBoost-LightGBM

    • Use XGBoost and LightGBM to predict whether a bicycle has a fault.
  3. SVM-KNN-DT-RF-LR-NB-AdaBoost-Ensemble

    This folder contains implementations of several key machine learning algorithms and an ensemble method that combines these models.

    • Support Vector Machine (SVM)
    • K-Nearest Neighbors (KNN)
    • Decision Tree (DT)
    • Random Forest (RF)
    • Logistic Regression (LR)
    • Naive Bayes (NB)
    • Adaptive Boosting (AdaBoost)
    • Ensemble

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A collection of statistical machine learning implementations and application projects. Features core algorithms (SVM, KNN, RF) and ensemble methods (GBDT, XGBoost, LightGBM).

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