• Text Classification
  • Analyse the data and create new features
    
  • Transform text data into Term Frequency - Inverse Document Frequency, select the best feature with f_classif and fit the transformed data to Bayesian algorithm
    
  • Transform text data into Word Embedding, select the best feature with f_classif and fit the transformed data to Bayesian algorithm
    
  • Word Embedding + Bayes improves 8% accuracy from 86% (baseline) to 94% Meanwhile TF-IDF + Bayes improve 5% accuracy from 86% to 91%
    
  • Text Classification with TF-IDF, Word Embedding and Naive Bayes
    

• Wine Clustering
  

  • Clustering wine color from their chemical properties. Using KMeans as feature engineering in Classification
    Exploring how hyperparameter tuning and cross validation impact to the improvement
    KMeans - Clustering Method Part 1

• Customer Segmentation
  

  • Explore the variation of different customers and predict the segmentation throught their channel products and spending
    Dimensionality Reduction with Principal Component Analysis

• Movie Recommendation

  • Recommend movies to users using collaborative filtering and content based techinques
  • Collaborative filtering: Recommendation list is generated based on the most similar items to a user's already-rated items
  • Content based Model: Recommend movies with similar contents : genres, actors, actresses, crew
  • Techniques: Data Cleaning, Data Visualization, NLP

• Sentiment Mining

  • Explore Logistic Regression Classifier with postive/negative/neutral product's reviews
  • Extrac topics in customer's review with Laten Dirichlet Allocation (LDA)
  • Generate postitive/negative/neutral reviews by implementing Marko Chain Text Generator
  • Techniques: Data Visualization, Data Cleaning, Classification, Topic Modeling

• Portfolio Investment Optimization

  • Collect historical data of 20 stocks of S&P 500 in 5 years
  • Use the Principle Component Analysis from Sklearn to structure the eigenvector features of covariance matrix of stocks
  • Calculate the weights of each portfolio in PCA components
  • Compute the sharpe ratio, annual return and annual volatility of each portfolio
  • Techniques: Data scrapping, Data Visualization, Principal Components Analysis