Image Classifier - TensorFlow

This was the second project in the Introduction to Machine Learning with TensorFlow Nanodegree Program from Udacity. In this project, I'll work through a Jupyter notebook first to implement an image classifier with TensorFlow to recognize different species of flowers, then it will be converted into a command line application.

Finding Donors for CharityML

This was the first project in the Introduction to Machine Learning with TensorFlow Nanodegree Program from Udacity. In this project, I will employ several supervised algorithms to accurately model individuals' income using data collected from the 1994 U.S. Census. Then I will choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. The goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000.

Communicate Data Findings
(Ford GoBike System Data in 2019)

This was the fifth project in Data Analyst Nanodegree from Udacity. This project has two parts that demonstrate the importance and value of data visualization techniques in the data analysis process. In the first part, Python visualization libraries were used to systematically explore a selected dataset, starting from plots of single variables and building up to plots of multiple variables. In the second part, a short presentation was produced that illustrates interesting properties, trends, and relationships that were discovered in the selected dataset. The primary method of conveying the findings was through transforming the exploratory visualizations from the first part into polished, explanatory visualizations.

Wrangle and Analyze Data

This was the fourth project in Data Analyst Nanodegree from Udacity. In this project, I will be wrangling and analyzing the tweet archive of Twitter user @dog_rates, also known as WeRateDogs. In addition to the Twitter archive file, two pieces of data needs to be gathered:

- The tweet image predictions, i.e., what breed of dog (or other object, animal, etc.) is present in each tweet according to a neural network. This file (image_predictions.tsv) is hosted on Udacity's servers and should be downloaded programmatically.

- Each tweet's retweet count and favorite count at minimum, and any additional interesting data via Twitter's API.

Analyze A/B Test Results

This was the third project in Data Analyst Nanodegree from Udacity. In this project, I will be working to understand the results of an A/B test run by an e-commerce website. The company has developed a new web page in order to try and increase the number of users who "convert" meaning the number of users who decide to pay for the company's product. The goal is to work through the notebook to help the company understand if they should implement this new page, keep the old page, or perhaps run the experiment longer to make their decision.

Investigate a Dataset
(Medical Appointment No Shows)

This was the second project in Data Analyst Nanodegree from Udacity. In this project, I analyzed Medical Appointment No Shows Dataset and communicated my findings about it using Python libraries NumPy, pandas, Matplotlib, and seaborn. The dataset collects information from 110k medical appointments in Brazil and is focused on the question of whether or not patients show up for their appointment.

Explore US Bikeshare Data

This was the second project in Programming for Data Science with Python Nanodegree from Udacity. In this project, Python was used to explore data related to bike share systems for three major cities in the United States: Chicago, New York City, and Washington. Interesting questions about the data were answered by computing descriptive statistics.

Investigate a Relational Database

This was the first project in Programming for Data Science with Python Nanodegree from Udacity. In this project, SQL has been used to explore a database related to movie rentals and answer interesting questions about the database. Also, visualizations were created to showcase the output of the queries.