Mini Program 15 - Health Insurance Data Analysis & Model building using Python - Part 2

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

 

Comments

  1. Sowmya26 November 2020 at 21:48

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