Data_Science_Projects

This repository contains data science tutorials and pet-projects. It is created only for self-learning purpose.

• 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.

• 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

• 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.

• Recommendation_Engine and Recommender_Systems_Surprise - is a study on Recommendation engines and their metrics. A popular library surprise was used.