At General Assembly, I completed 7 comprehensive projects that showcase my understanding of data science and machine learning on real world problems. Each project folder contains a separate README.md, which serves as a primer for how I approached the project and its outcomes.
- Project 1: SAT Scores
- Project 2: Titanic-Lusitania Sleuthing
- Project 3: Iowa Liquor Sales
- Project 4: Salary Prediction for Indeed.com
- Project 5: Exploring the Madelon Data Set
- Project 6: Pseudo Wikipedia API
- Project 7: Airport Delays
- P1: Exploratory Data Analysis, Data Visualization
- P2: Data Cleaning/Munging, Summary Statistics
- P3: Expoloratory Data Analysis, Pivot Tables, Linear Regression
- P4: Logistic Regression with StatsModels and Sci-Kit Learn
- P5: Feature Selection, Logistic Regression, KNearest Neighbors, GridSearchCV
- P6: SQL/PostgreSQL, Extract Transform Load (ETL), TF-IDF
- P7: Principal Component Analysis, KMeans Clustering