Applied basic regression analysis in Python to predict next day S&P 500 values. Involved cleaning invalid data; use of linear regression class in the sckit-learn package to predict values; measuring the mean squared error, root mean squared error and mean absolute error of the model; and visualizing the regression model.

Daily S&P 500 index price from 2005 to 2015. [link]

Use of Python to create a classification model utilizing binary discrimination. The model was used to optimize bank profit from credit card approvals and required the application of techniques such as calculation of the model's predictive power, sensitivity, specificity and fallout, and computation of ROC curves, and precision and recall curves.

Sample credit score data for a collection of individuals, including binary data on whether the individual has paid-off their credit in the past and a score of probability of being approved for future credit.

Created a linear regression model using Python and Sklearn to predict if an applicant will be admitted to a US University. Techniques such as logistic regression, determination of the model's predictive power, computation of the ROC curve and interpretation of results were used to achieve this outcome.

Data for 1,000 University applicants, including Graduate Record Exam (GRE) score, Grade Point Average (GPA) and whether the applicant was/was not admitted.

Applied one-versus-all multiclass classification techniques using Python and Sklearn to create a logistic regression model and predict the origin of a vehicle. Used techniques such as classification matricies and confusion matrcies; calculated average accuracy, precision and recall; and measured the F-score of the model.

Attribute data for 398 automobiles from the StatLib library, including fuel consumption, number of cylinders, displacement, and origin. [link]

Applied linear regression, r-squared and t-statistics using Python and scipy to estimate the leaning rate of the Leaning Tower of Pisa.

Yearly data recorded from 1975 to 1987 measuing the lean angle of the Leaning Tower of Pisa.

Applied Scikit-learn tools using Python to run and visualize the results of a robust K-means implementation, and as a result segment NBA players into groups with similar traits.

NBA players data (e.g. player name, position and points per game) from the 2013-2014 season. [link]

Use of Python and Sklearn to normalize data, fit data to a linear model and apply a gradient descent algorithm in order to predict the accuracy of a golfer's drive using the distance of the drive.

Professional golfers' driving statistics, measuring driving distance and accuracy. [link]

Use of Python and Sklearn to apply neural network theory, backpropagation and splitting data techniques to predict the species of iris flowers.

Iris flower dataset, including flower sepal length, sepal width, petal length, petal width and species. [link]