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Developing machine learning applications require substantial steps. Gathering the data, doing data cleaning, exploring the data through exploratory data analysis, feature engineering, feature selection, modeling, evaluation, and interpretation. Interpretation is one of the crucial parts of developing a machine learning... [Read More]

Big data has become ubiquitous, as every action we undertake generates data that holds the potential for valuable insights. For instance, we can improve our health management activities based on our sports activities tracked on our smartphones or we can... [Read More]

This article delves into the practice of A/B testing. I assume that the reader comprehend the building blocks of statistics such as control and treatment, p-values, confidence interval, statistical significance, practical significance, randomization, sample, and population in order to get... [Read More]

Data Visualization has been playing an important part in understanding data in any data science projects. The way we present our visualization will determine how the audience will comprehend the data that we would like to present. A famous idiom... [Read More]