Data Analysis Projects in Python, R, and Tableau

Python: 

During my IBM certification class, we completed multiple projects introducing machine learning concepts. We started with linear regression models, then moved into classification techniques such as K-Nearest Neighbors, logisitic regression, decision trees, support vector machines, and K-Means Clustering. The class ended with a brief introduction to non-linear predictive techniques such as neural networks and transformer models. We also reviewed assessment techniques of these models including error calculations and confusion matrices. 

R:

One of the last classes in my major is SOM 485: Decision Support Systems. In this class we used the R programming language to build models to derive useful information from large datasets. For my final project in that class I built a logistic regression model that would produce the probability of a patient's susceptibility to heart disease based off a dataset that included demographic information and predispositions.

Another project consisted of building a Logisitic regression model that calculated the likelihood of a customer staying with their subscription to an internet provider based off of customer traits and behavior.  I evaluated the model's accuracy using a confusion matrix as well as the sensitivity and specificity of the model to particular variables. Below is the code from that project.