README.md

Tools for a Data Science projects

This repository is a compendium of notebooks and scripts to be used in my daily work for Data Science projects. I will try to use this landscape as reference:

Content

Tools

• Data understanding [0]
• Experiments mlflow [5]

Scripts

• Analysis [0]
  • ADA [5]
  • Decision tree for analysis [3]
    • Dataset files [0]
  • Errors [3]
• Plot [4]
• Datasets [8]
  • Tools [1]
  • Kaggle [0]
    • Titanic [1]
  • Summaries [0]
  • Data [0]
• Pipelines [3]
• Utils [6]
• Models [7]
  • Xgboost [5]
  • GAM [1]
• Dashboarding [1]
• Preprocessing [2]
  • Scalers [1]
  • Smoothers [1]
  • Outliers [3]
• Data [2]

Cheatsheets

• Statistics [0]
• Mathematics miscelaneous [0]
• Agile [1]
• Programming [0]
• Ds project management [0]
• Plots [0]

Data

Notebooks

• Statistics [0]
  • Probability [2]
  • Sampling [4]
    • pycache [1]
  • Hypotesis testing [4]
  • Multimodal distribution analysis [2]
  • Impurity measurements [2]
    • pycache [1]
• Anomaly detection [1]
• Pycaret [0]
  • Anomaly detection [1]
  • Supervised learning [1]
• Timeseries [2]
• Analysis [0]
  • Density estimation [1]
  • EDA [0]
    • Eda-tools [3]
    • Eda-autoeda libraries [7]
  • Analysis missing values [1]
  • Analysis dpi [1]
  • Factor analysis [2]
  • Analysis regression [2]
  • Analysis errors [1]
  • Analysis fourier [2]
  • Analysis clustering [2]
    • Src [2]
      • pycache [2]
  • Analysis decision tree [1]
  • Analysis non-linear correlation [3]
  • Analysis anomalies [4]
  • Analysis data quality [1]
  • Plots [0]
    • Scatter plots [1]
    • Hist kde [1]
    • Pie chart [1]
    • My final plots [6]
    • Bar plots [1]
• Data quality [4]
  • pycache [1]
• Synthetic data [2]
• Mlops [3]
  • Pyarrow dataset [1]
    • Data [0]
      • Sample dataset [0]
      • Parquet dataset partitioned [0] - Part=a [0] - Part=b [0] - Part=c [0]
      • Parquet dataset 2 [0]
      • Parquet dataset 3 [0] - Folder 2 [0] - Folder 1 [0]
      • Parquet dataset 4 [0]
      • Partitioned dataset [0] - C=1 [0] - C=2 [0]
      • Parquet dataset 1 [0]
  • Automated code style [1]
    • .ruff cache [0]
      • 0.6.9 [0]
    • .mypy cache [0]
      • 3.9 [0] - typeshed [0] - Sys [0] - Os [0] - Importlib [0] - Metadata [0] - Collections [0] - Email [0]
  • Automated documentation [0]
    • Docs [0]
      • Src [0] - Module2 [0] - Module1 [0]
    • Src [0]
      • Module2 [2] - pycache [2]
      • Module1 [2] - pycache [2]
  • Workflow [0]
    • Snakemake [0]
      • Paralel [1] - .snakemake [0] - Locks [0] - Conda [0] - Incomplete [0] - Shadow [0] - Conda-archive [0] - Singularity [0] - Log [0] - Metadata [0] - Auxiliary [0]
      • Introduction [1] - .snakemake [0] - Locks [0] - Conda [0] - Incomplete [0] - Shadow [0] - Conda-archive [0] - Singularity [0] - Log [0] - Metadata [0] - Auxiliary [0] - Data [0]
    • Experiments [1]
      • Experiments [0]
    • Pipelines [2]
  • Configs [2]
    • Conf [0]
      • Environment [0]
      • Experiment [0]
  • Documentation generators [0]
    • Library pdoc [1]
      • Html [0]
  • Make task with invoke [3]
  • Bigdata [1]
  • Testing with pytest [0]
    • .pytest cache [0]
      • V [0] - Cache [0]
    • Tests [1]
      • pycache [1]
      • Module1 [3] - pycache [3]
    • Src [1]
      • pycache [1]
      • Module1 [3] - pycache [5]
• Datasets [5]
• Similarity [1]
• Sampling [3]
• Learning semi supervised [1]
• Learning supervised [0]
  • Lib lightgbm [1]
  • Interpretability [1]
• Automl [1]
  • Extended eda [0]
  • Automl 1 [0]
    • Ensemble [0]
    • 6 default randomforest [0]
    • EDA [0]
    • 2 decisiontree [0]
    • 3 linear [0]
    • 1 baseline [0]
    • 5 default neuralnetwork [0]
    • 4 default xgboost [0]
• Model interpretability [4]
• Dashboards [0]
  • Panel [0]
    • 4-dashboard to html [1]
    • 2-power curve app v2 [1]
    • 3-nested selectors [1]
    • 0-first app [1]
    • 1-power curve app v1 [1]
• NLP [9]
• Feature engineering [0]
  • Missing values imputation [1]
  • Features selection [5]
  • Best practices for classification [1]
  • Auto data preparation [1]
• Algorithms selection [2]
  • Multiple algorithms evaluation with lazypredict [2]
• Learning un supervised [2]
• Data cleaning [2]
• Information theory [1]

Updated on 2024-10-12 21:06:37