Python Mini Program Series

Univariate and Multivariate analysis of the data is an important step during Exploratory Data Analysis phase of the data science project. We can target analyzing numerical and categorical variables separately before performing multivariate analysis. More we know the data, better it becomes during Model Building phase. We are  analyzing health insurance dataset in this case study.  You can follow  this  post to know how to get started. #CodeWithUs to find out more and do it your self!!  You can find the code at  Python Code - GitHub

Data Cleaning and Exploratory Data Analysis are crucial parts in any data science project. It is rightly said that if we give junk input to the model, we will get a junk output. The dataset may have missing values or outliers which should be identified and treated during data cleaning phase. These are only a few examples of un-clean data. A data analyst find hundreds of anomalies in the dataset while analyzing. Exploratory data analysis is done to get the hidden insights from the data. Data Analysts perform univariate and multi-variate analysis to understand the pattern in the dataset.  We are  analyzing health insurance dataset in this case study.  You can follow this post to know how to get started. #CodeWithUs to find out more and do it your self!!  You can find the code at  Python Code - GitHub