What it does

How we built it

We first conducted EDA and found some problems in the original dataset. Then, we clean the data based on that. After that, we build our predictive machine learning model using random forest with a RMSE lower than 0.5. Also, we put our data into a database using python sqlite3, and then we only provided users with a query function

Challenges we ran into

Understanding the differential privacy theory, cleaning data, and applying the theory to our model.

Accomplishments that we're proud of

Data preprocessing, EDA, ML algorithm, application of DP theory, and the ppt.

What we learned

Differential privacy theory.

What's next for Differential Privacy Project based on yellow cab data

Improving our Differential Privacy Mechanism and providing more useful query API for users of our database.

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