This is an analysis of the “Bike Sharing Dataset” from the UCI Machine Learning Repository, using the Prophet Package for time series data. This dataset contains the daily count of rented bikes during 2011-2012.

This is an analysis of the census data set, a classification set where I will determine whether members of the poulation has an income amount of under $50,000.00 or over $50,000.00. This type of analysis may have many types of real-world uses in the business world.

The goalis to predict the type of wine based on the following variables: fixed acidity, volatile acidity, citric acid, residual sugar, chlorides, free sulfur dioxide, total sulfur dioxide, density, pH, sulphates and alcohol. Use these prediction techniques: * Logistic regression * Lasso logistic regression * Linear discriminant analysis * Lquadratic discriminant analysis * Naïve Bayes estimation * K nearest neighbor * Support vector machine

The goal in this exercise is to Predict wines type (type) using classification trees. Use the boosting technique to choose the best predictors from the following: fixed acidity, volatile acidity, citric acid, residual sugar, chlorides, free sulfur dioxide, total sulfur dioxide, density, pH, sulphates#and alcohol.

The goal in this exercise Create an OLS regression model to predict the relative CPU performance (prp) based on the following variables: myct, mmin, mmax, cach, chmin, chmax. Validate your model using both the validation set method and the k-fold cross-validation method.

An analysis of the Boston Housing data set using best subset regression, forward and backward step wise regression, ridge regression, lasso regression, and partial least squares regression

This is an analysis of a data set containing 6 variables, and 1000 observations. The response variable of this data set is "churn", which describes whether a customer will leave the company based on the other variables which are "predictors".