Jason Fang - Projects

Churn Rate Analysis

In this project, I worked for a fintech company that wanted to minimize the churn rate for its app. Mainly focusing on the app features, I could help the company improve the app to keep its customers. To create the best model, I applied feature selection, data balancing, k-fold cross-validation and some other techniques. It is very hard to predict that whether people would churn or not because the churn rate is not balanced, only about 40% of people churned. In the end, I created a model with a 64% accuracy.

Fraud Detection

This is a preprocessed dataset. There are 28 features not interpretable because the personal information of our customers has been transformed and kept anonymous. The "Class" feature is whether customers have been defrauded or not, 0 = no, 1 = yes. I used undersampling and oversampling in this case because this dataset contains more than 280,000 observations, but only about 400 people have been defrauded, which just accounts for 0.14% of the dataset.

Loan E-Signing

Cat vs Dog Classifier

Classifying a cat or dog image sounds very easy, but nobody could have a 100% accuracy model nowadays. There is a famous meme about AI, which is that AI identifies a cat as a dog. In this project, I ended up with an 88% accuracy model in my testing dataset. The model is an example of how I created a Convolutional Neural Network model.

Echo Look

In this project, I used an image dataset that contains 58,000 images of apparel, such as T-shirts, trousers, pullovers, dresses, and so on. The images are in greyscale, and they are just 28*28 pixels. By using the convolutional neural network, I'm able to identify different types of apparel. It can be implemented in the real world and Echo Look is an example of it. This project just shows how I use CNN to classify apparel.

Churn Rate Analysis

Fraud Detection

Loan E-Signing

Cat vs Dog Classifier

Echo Look

Let's work together on your next project