A detailed exploritoray analysis covering which countries have been vaccinated, which manufactures vaccinated which countries and how much each vaccine was used by countries.

trend analysis as well as seasonality, using naive method, Exponential smoothing models as well as ARIMA to best predict the number of airline passenger. ETS is the model of selection, with an sd of 0.0392 or 39 Passengers compared to that of the other 2 models which are in, the 1000s when it comes to average prediction miss

clustering and segmentation of mall customers

Data cleaning, exploritory data analysis, modeling, RandomForests, as well as Model training

Statistical Modeling of BankChurners using six different types of Modelling techniques to find the best Accurate model to best classify the Attrited Customers model accuracy=97.07%

modelling of bank data to classiy and predict attrited clients model accuracy 96%

Modeling Customer data to predict and classify Customers who are likely to churn.Modeling and model verification Model accuracy 78%

This data is about direct markerting case from an insurance sector which was trying to predict policy ownership. It is about predicting who would be interested in buying a caravan insurance policy. model accuracy=94%

exploratory data analysis on credit card defaults

comparing two categories at the same time, descriptives and visuals. model training and decision trees

comparison between two classes of juice, Minute Maid and Citrus Hill.

Wages data set with 3000 obs. linear modeling and predictions, graphicals with ggplot comparing 3 variables one being the sector variable. discriptives and modell validation

cleaning, loading, graphicals and explanatory data analysis

Food price index data combining and graphical plots as well as descriptives and convetions of charecters to factors using R programming

Data analysis and visualization usin regression modelling techinques and principal component analyses to help with variable selction and model reduction while still retaining the most infomation about the data with fewer variables

some basic histograms,plots and formating of graphs as well as some descriptive statistics

Binding and merging 2 or more Data sets into one easy to use data frame.

loading data thats big and breaking them into chunks using R