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Chu-Yun (Evelyn) Hsiao's Projects

breast-cancer-prediction icon breast-cancer-prediction

I perform Nested Cross Validation among four models(Decision Tree, Logistic Regression, KNN, SVM) to find the best model. And the Built the final model based on the result of nested CV to predict the diagnosis of breast cancer.

consumer-spending-prediction icon consumer-spending-prediction

I built several predictive models to predict consumer spending, and conducted GridSearch to find the best hyperparameter for every models. Models I used including: Ensemble Stacking/ Deep Neural Network/ Support Vector Regressor/ ElasticNet/ Regression Tree/ KNN Regressor

customer_churn_prediction_using_pyspark icon customer_churn_prediction_using_pyspark

This project aims to leverage big data technologies to help OTT platforms predict churn at a customer level in real-time. We use Amazon S3 as the data storage, Databricks for data processing, spark MLlib for machine learning, and Amazon Quicksight for visualization.

movie-recommendation-system icon movie-recommendation-system

We create movie recommendation system through demographic filtering, content-based filtering, collaborative filtering, and hybrid engine.

nytimes-sentiment-analysis icon nytimes-sentiment-analysis

I implemented sentiment analysis on textual data and compared media volume of two president candidates by Web-scrapping NYTimes articles published two weeks before 2020 US president election day

spam-email-detection icon spam-email-detection

I built several classification models to detect whether an email is spam or not. I built models and found the best performance using Nested CV and GridSearch under two scenarios: Accuracy-Based & Cost-Sensitive Based. The models I used including Support Vector Classifier/ XGBoost/ LightGBM/ Deep Neural Network/ Decision Tree/ Logistic Regression

walmart-weekly-sales-forecast icon walmart-weekly-sales-forecast

We created four models, such as SARIMAX, Prophet, and Ensemble Model(Prophet+XGBoost), to forecast Walmart 45 stores' weekly sales.

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