The dataset contains a list of ingredients used to make red wine. Every ingredient has an effect on the taste and quality of the wine. Through this analysis, I tried to understand the effect of various ingredients as which one is the most important one in the wine using a machine learning model of Random Forest. The accuracy of each variable in this model helps us understand the wine quality.

The analysis is based upon the world happiness index-2017 report. I plotted the variables to understand the relation between them if any. Secondly, I plotted a KNN model to predict the nest number of clusters in analyzing the dataset. Finally, I plotted the clusters showing the countries with happiness index on similar kind of variables compared to another cluster which has a different set of countries.

A dataset on the customer purchase history of date and the dollar amount of purchase per transaction. A prediction was made based on the actual gender and predicted gender values. A confusion matrix was built to analyze the quality of predictions made on gender.

An exploratory and graphical analysis of 3 major world population indicators: Birth Rate, Infant Mortality Rate, Life Expectancy. The resulting graph indicates the trend in growth/decline of the rates in these 3 measures across the regions of the world.

A comparison of mobile usage between the year 1998 and 2016 along with the trend of changing Life Expectancy, Birth Rate, and Infant Mortality Rate in the different regions of the world.

Phase 1 of extracting data from WDI and visualizing the variables on a world map