- Created a tool that estimates data science salaries (MAE ~ $ 11K) to help data scientists negotiate their income when they get a job.
- Scraped over 1000 job descriptions from glassdoor using python and selenium
- Engineered features from the text of each job description to quantify the value companies put on python, excel, aws, and spark.
- Optimized Linear, Lasso, and Random Forest Regressors using GridsearchCV to reach the best model.
- Built a client facing API using flask
For this example project I built a ball classifier to identify balls from different sports. This could be useful for someone who is new to sports from a certain country. They could take a picture of a ball and an app could serve them some information about the history and rules of the game. This is the underlying model for building something with those capabilities.
I was able to get the model to predict the sport of the ball with 94% accuracy after minimal tuning. For most of the cases this would meet the need of an end user of the app. To get these results I used transfer learning on a CNN trained on resnet34. This created time efficiencies and solid results.