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Showing posts from September, 2020

Building ML Pipelines using Scikit Learn and Hyper Parameter Tuning

Data Scientists often build Machine learning pipelines which involves preprocessing (imputing null values, feature transformation, creating new features), modeling, hyper parameter tuning. There are many transformations that need to be done before modeling in a particular order. Scikit learn provides us with the Pipeline class to perform those transformations in one go. Pipeline serves multiple purposes here (from documentation ): Convenience and encapsulation : You only have to call fit and predict once on your data to fit a whole sequence of estimators. Joint parameter selection : You can grid search over parameters of all estimators in the pipeline at once (hyper-parameter tuning/optimization). Safety : Pipelines help avoid leaking statistics from your test data into the trained model in cross-validation, by ensuring that the same samples are used to train the transformers and predictors. In this article, I will show you How to build a complete pi

A year of experience as a Data Scientist

On June 3rd 2019, I joined ZS Associates as a Data Scientist after graduating from IIIT SriCity. It was my first job and was very happy to get placed as a Data Scientist through lateral hiring. If you haven’t read my Data Science journey, please read it here :) After joining, I had some awesome moments that I never experienced since childhood. I got a chance to stay in a 4 star or 5 star hotel multiple times. I got a chance to travel by flight. I travelled to Pune, Delhi and Bangalore. I saw Vizag, Pune, Delhi and Bangalore airports in less than six months. I loved it. A few office parties, outings during Diwali and New year celebrations. Above are some of the moments that I can never forget in my life. My first job allowed me to experience these first time moments. Enjoying life is more important than anything. If you don’t enjoy your life, you cannot achieve anything big. Okay, let’s go into the main topic in detail. Me (inner voice during BTech):