Customer Segmentation using machine learning in R

Dashboard for Global Meat Consumption, Data Visualization Assignment 3

In this project, we will critique an already available online data and try to improve it using ggplot2 library of R

In this project we are Cleaning large set of Data. We have handled Outliers and Missing values by processing operation to clean data. Various functions such as Impute, sum, group-by has been used. All major data tidying functions were used in this project. The data was combined using gather, after which it was spreaded, mutated, filtered and joined using join function as per requirement. Tukey's method has been used to check the outliers. Special values were handled using appropriate methods.

The project is to understand about the price wars between two supermarkets coles and Woolworths. I have collected 95 samples and perform various statistical tests and done hypothesis testing on them to find whether the price difference is significant or not between coles and woolworths.

Cleaning and inspection of data of google app store. I have create sub-set of the data and have create new data frames to clean the raw data.

In general, there is a difference in male and female hip size, in this problem we will try to analyze the hip size of both the genders using available data which was provided by RMIT course teacher teaching introduction to statistics.

Performing Statistical tests on limited data to understand relation between gender and alcohol consumption.