Data Analytics, Dashboarding
PyBer
2017
Project Description
In order to convince investors that a bike-sharing program in Des Moines is a solid business proposal, one of the key stakeholders requested to see a bike trip analysis.
For this analysis, Pandas was used to change the "tripduration" column from an integer to a datetime datatype. Then, using the converted datatype, a set of visualizations was created to:
- Show the length of time that bikes are checked out for all riders and genders
- Show the number of bike trips for all riders and genders for each hour of each day of the week
- Show the number of bike trips for each type of user and gender for each day of the week.
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Crypto Analysis
Unsupervised Machine Learning: Clustering
Credit Risk Analysis
The Easy Ensemble AdaBoost Classifier was best at predicting high risk credit applications in comparison to the other models. With a 0.925 accuracy and a 0.91 recall score, both Easy Ensemble AdaBoost and Random Forest Classifiers outperformed the Resampling techniques in accurately predicting high-risk credit card applicants.
Neural Network and Deep Learning
Overall results of the deep learning classification problem was 73% accuracy.
