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Qingfeng-Liu / repository

Machine-Learning-Toolkit

This project presents a structured, end-to-end machine learning toolkit designed to support both educational and research objectives. The repository features a carefully organized collection of Jupyter notebooks (sequentially numbered 01-14) that guide users through complete ML workflows.

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README preview

README

Author: Qiingfeng Liu Email: qliu.econ@gmail.com Affiliation: Faculty of Science and Engineering, Hosei University

Project: Machine Learning Introduction Dataset: Original Generated Data

License: MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Requirements:

Python 3.8+
Jupyter Notebook
PyTorch
Transformers library
scikit-learn
pandas
numpy
matplotlib
optuna

For detailed package versions, see requirements.txt

Project Structure

ML_Toolkit/
    01_housing_regression.ipynb 📓
    02_matplotlib_masterclass.ipynb 📓
    03_wage_analysis_with_datasplit.ipynb 📓
    04_DNN_for_wage.ipynb 📓
    05_preprocessing_ML.ipynb 📓
    06_Pytorch_DNN_Optuna.ipynb 📓
    07_DNN_Classification_Binary.ipynb 📓
    08_Ordinal_Rating.ipynb 📓
    09_BERT_English.ipynb 📓
    10_BERT_Positive_Negative.ipynb 📓
    11_BERT_Japanese.ipynb 📓
    12_ChatGPT_API.ipynb 📓
    13_ChatGPT_LDA.ipynb 📓
    14_ChatGPT_LDA_Chinese.ipynb 📓
    LICENSE 📄
    README.md 📄
    rating_model.pth 🧠
    requirements.txt 📄
    scaler.pkl 🧠
    Appendix/
        Divide_word.ipynb 📓
        Dropout.ipynb 📓
        Pandas_Comprehensive_Tutorial_With_Exercises.ipynb 📓
        Python_Introduction.ipynb 📓
        Python_Introduction.py 🐍
        Rename_with_No.py 🐍
        Transformer_Attention_Chinese.ipynb 📓
        Transformer_Attention_English.ipynb 📓
        input.txt 📄
        jp_text_sentence_wordcount.ipynb 📓
        model_rss_comparison.png 📄
        nn_performance.png 📄
        train_val_comparison.png 📄
        wage_analysis_data_generation.ipynb 📓
        wage_prediction_performance.png 📄
    DATA/
        CAPM02.csv 📊
        DATA01.csv 📊
        dnn_intro_data.csv 📊
        ex_housing.csv 📊
        raw_regression_data.csv 📊
        regression_data.csv 📊
        wage_data.csv 📊
        wage_test_X.csv 📊
        wage_test_Xy.csv 📊
        wage_train_val.csv 📊
    Figures/
        code_01.pdf 📄
        code_01.png 📄
        pasted1.eps 📄
        pasted1.png 📄
    Notes/
        BERT_Embedding.pdf 📄
        BERT_Embedding.tex 📄
        BERT_and_transformer_Bearmer.pdf 📄
        BERT_and_transformer_Bearmer.tex 📄
        Binary_Selection.pdf 📄
        Binary_Selection.tex 📄
        How to Start PyCharm with Anaconda.docx 📄
        How to Start PyCharm with Anaconda.pdf 📄
        Ordinal_Rating_DNN.pdf 📄
        Ordinal_Rating_DNN.tex 📄
        Principal of Neural Network.pdf 📄
    tools/
        README.md 📄
        generate_structure.py 🐍

Auto-generated on 2025-08-03 19:08:34

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Machine-Learning-Toolkit

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