This repository contains data science tutorials and pet-projects. It is created only for self-learning purpose.
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Bike_Sharing, Hotel_Clicks_Prediction, Hotel_Price_Prediction - pet - projects which were aimer to master
EDA
,Data Imputation
,Feature Engineering
,Modeling
in order to get valuable insights from the data and make predictions with different Regression models. -
Website_Hits_Estimation - is also a pet-project that continues the previous workflow, but now focusing more on boosting models, particularly LightGBM and Catboost.
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Conversion_rates, Cluster_Grocery, Pricing_Test and Employee_retention - are solution to some exercises from the book: "A Collection of Data Science Take-Home Challenges". Other useful resources: https://github.com/JifuZhao/DS-Take-Home and https://github.com/stasi009/TakeHomeDataChallenges
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Imbalance_Data_Tutorials and Missing_Data_Imputation - are tutorial/Kaggle kernels in order to have a glimpse at how to deal with missing entries in the data and what can be done in case of the imbalanced dataset.
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Recommendation_Engine and Recommender_Systems_Surprise - is a study on Recommendation engines and their metrics. A popular library
surprise
was used.