Machine Learning (5)
Principal Components Analysis (PCA)
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Math, Data Science, Machine Learning, Unsupervised Learning
Principal component analysis (PCA) is a classic multivariate analysis method and a technique based on orthogonal transformation. Reduce the dimension while preserving the feature of the data as much as possible. Often used for data visualization and dealing with multicollinearity issues.
Customer Personality Modeling and Evaluation
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Math, Data Science, Machine Learning, Portfolio, Collaboration, R Language
This study will model customer personalities and address common issues in financial data, such as multicollinearity and outliers, by introducing robust logistic regression analysis to predict whether a customer will participate in an event. Due to the imbalance in the predicted target, the commonly used accuracy metric is abandoned in favor of developing a profit-based model evaluation metric to maximize profit as the model selection criterion. Finally, based on principal component selection of variables, the study analyzes and explores the market positioning behind the event and identifies the characteristics of potential customers.
Latent Dirichlet Allocation (LDA)
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Math, Data Science, Machine Learning, Text Mining, Unsupervised Learning, Latent Dirichlet Allocation (LDA)
Performing natural language processing through hierarchies, introducing latent Dirichlet allocation (LDA).
Classification and Regression Tree (CART)
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Math, Data Science, Machine Learning, Supervised Learning
A tree-based method of building decision trees for predicting categorical variables and can also be used for non-linear prediction
Simple Linear Regression
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Math, Data Science, Machine Learning, Supervised Learning
Introducing single variable linear regression.