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  

Data Science domain has grown rapidly since past few years with industries realizing that the data stored can be put to best use, to help them grow their business. People who understands the data, can play around with the data and build insights from them are in great demands. You must have heard about roles like Data Scientist, Data Analysts and Machine learning engineers to name a few. Today, we will learn how to analyze the data and to build predictive analytics models using Python.  Here, we have come up with simple and easy to understand tutorial for analyzing health insurance dataset. #CodeWithUs to find out more and do it your self!!  You can find the code at  Python Code - GitHub               

I am sure most of us have scraped data to store the result in excel or csv for analysis. We explored an interesting option with scraping. Instead of scraping the data part, we have scraped the image and downloaded into our local folder. Does this excite you? If yes, go through the video and don't forget to browse the code on Github. You can use the same concept to scrape images from other sites as well. #CodeWithUs to find out more and do it your self!!  You can find the code at  Python Code - GitHub You can also follow us on  Facebook - In It To Learn