my-library

creation of library with useful work ( in the making).

All files should be totally "Run All" reproducible. Data needed is called directly within the notebooks.

Files starting with "R" are in R, if no languages mentionned, files are in Python.

R files are using Google Colab Jupiter Notebooks and links to Google Colab are at the beginning of each files.

if any assistance is needed, if a link is broken or if you have any questions please reach me to my email adress : [email protected]

Name		Name	Last commit message	Last commit date
Latest commit History 17 Commits
.ipynb_checkpoints		.ipynb_checkpoints
assets		assets
causality		causality
classifiers		classifiers
finance		finance
readings		readings
visualisation		visualisation
.DS_Store		.DS_Store
Prophet_time_series_prediction.ipynb		Prophet_time_series_prediction.ipynb
README.md		README.md
R_simpson_paradox.ipynb		R_simpson_paradox.ipynb
etf_seasonality_market_timing.ipynb		etf_seasonality_market_timing.ipynb
merge_daily_csv.ipynb		merge_daily_csv.ipynb
multiple_linear_regression.ipynb		multiple_linear_regression.ipynb
my_covid_analysis.ipynb		my_covid_analysis.ipynb
non-linear-regression.ipynb		non-linear-regression.ipynb
polynomial-regression.ipynb		polynomial-regression.ipynb
power-quantile-transforms.ipynb		power-quantile-transforms.ipynb
recommendation-systems-collaborative-filtering.ipynb		recommendation-systems-collaborative-filtering.ipynb
recommendation-systems-content-based.ipynb		recommendation-systems-content-based.ipynb
seasonality_timeseries_analysis_catboost_feature_VIF_seaborn.ipynb		seasonality_timeseries_analysis_catboost_feature_VIF_seaborn.ipynb
simple_linear_regression.ipynb		simple_linear_regression.ipynb
simple_naive_bayes.ipynb		simple_naive_bayes.ipynb
teleCust1000t.csv		teleCust1000t.csv

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