projects_location.md

Projects Location

There are many implemented projects stored in different folders, I need to record their locations when I am looking for them.

• Lime, Visualization, Visualizaed Feature Importance

  • https://github.com/hanhanwu/Hanhan_Data_Science_Practice/blob/master/Better4Industry/lime_interpretable_ML.ipynb

• Lime, Visualization, MultiClass

  • https://marcotcr.github.io/lime/tutorials/Tutorial%20-%20continuous%20and%20categorical%20features.html
  • All the features have to be int or float, but you can indicate categorical features

• SHAP, Feature Importance, Visualization

  • https://github.com/hanhanwu/Hanhan_Data_Science_Practice/blob/master/Better4Industry/Feature_Selection_Collection/try_shap_xgboost.ipynb
  • It also supports multiclass now:
    • How to generate multi-class output: shap/shap#367
    • shap/shap#367

• Multi-Class, Confusion Matrix

  • https://github.com/hanhanwu/Hanhan_Break_the_Limits/blob/master/attack_signals_recommendation_system/generate_rules.ipynb

• Simplified User Trained Search Engine - NN, Neural Network, Interactive

  • https://github.com/hanhanwu/Hanhan_NLP/blob/master/short_context_search.py

• p-value, t-test to compare 2 lists of values

  • https://github.com/hanhanwu/Hanhan_Applied_DataScience/blob/master/Simple%20Production%20Solutions.ipynb

• Find Optimal Thresholds of the Model Prediction

  • https://github.com/hanhanwu/Hanhan_Applied_DataScience
  • You can use whatever metrics you want to use, but by changing thresholds, it will show you which threshold will get the optimal metrics value

• geckodriver errors

  • On Windows: https://github.com/hanhanwu/Hanhan_Break_the_Limits/blob/master/Ideas_Spark/track_siliconvalley_vancouver_trend.ipynb

• The definition of "Lift"

  • In frequent pattern mining: https://github.com/hanhanwu/Hanhan_Data_Science_Practice/tree/master/Frequent_Pattern_Mining
    • lift = confidence/expected confidence

• Ployly API Key, PyOD

  • https://github.com/hanhanwu/Hanhan_Data_Science_Practice/blob/master/Outliers_and_Clustering/try_PyOD.ipynb

• Multi Media

  • How to read multi-media: https://www.analyticsvidhya.com/blog/2017/03/read-commonly-used-formats-using-python/?utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+AnalyticsVidhya+%28Analytics+Vidhya%29
  • PDF
    • PyPDF library: https://github.com/mstamy2/PyPDF2/tree/master/Sample_Code
  • Excel
    • Python read, write Excel: https://github.com/hanhanwu/Basic_But_Useful/blob/master/python_excel.py

• IPython JS display, python tricks

  • https://github.com/hanhanwu/Basic_But_Useful/blob/master/python_tricks.ipynb

• Virtual Environment, IPython Kernel

  • How to details: https://github.com/hanhanwu/Hanhan_Data_Science_Practice/blob/master/AI_Experiments/ReadMe.md

• PCA, Dimensional Reduction, Reconstruction

  • [Python] https://github.com/hanhanwu/Hanhan_Data_Science_Practice/blob/master/make_sense_dimension_reduction.ipynb
    • How to make PCA dimensional reduction visualization makes sense
    • How to keep original features based on PCA results
  • [R] https://github.com/hanhanwu/Hanhan_Data_Science_Practice/blob/master/AI_Experiments/autoencoder_dimensional_reduction.Rmd
    • PCA, autoencoder reconstruction error
  • How to plot PCA/t-SNE converted 2D data with original labels: https://github.com/hanhanwu/Hanhan_COLAB_Experiemnts/blob/master/Try_DeepWalk.ipynb
  • t-SNE vs PCA vs LDA: https://github.com/hanhanwu/Hanhan_Data_Science_Resources2
    • Search for "t-SNE"

• For Industry Suggestions

  • Overall 1: https://github.com/hanhanwu/Hanhan_Data_Science_Practice/tree/master/Better4Industry
  • Overall 2: https://github.com/hanhanwu/Hanhan_Applied_DataScience
  • Learning Notes: https://github.com/hanhanwu/Hanhan_Applied_DataScience/blob/master/Learning_Notes.md
  • Experience Notes: https://github.com/hanhanwu/Hanhan_Data_Science_Practice/blob/master/Better4Industry/ExperienceNotes.md
  • Feature Selection Collection: https://github.com/hanhanwu/Hanhan_Data_Science_Practice/tree/master/Better4Industry/Feature_Selection_Collection
  • Model Evaluation: https://github.com/hanhanwu/Hanhan_Data_Science_Practice/tree/master/Better4Industry/Model_Evaluation
  • Deployment Reminder: https://github.com/hanhanwu/Hanhan_Data_Science_Practice/blob/master/Better4Industry/Deployment_Experience.md
  • Prototype Toolkit: https://github.com/hanhanwu/Hanhan_Applied_DataScience/tree/master/Prototype_Toolkit