I will record my learnings on different topics and share reproducible ML projects I've worked on as I continue my journey in data science.
- Retail company's catalog sales prediction: Predict top spending customers and their purchase amounts by building a logistic regression model to predict customers' purchasing likelihood and a multiple regression model to predict customers' expenditures. [R folder]
Text Analytics
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Regex101: Basic Regex knowledge and pattern finding. [Python folder]
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Text classifiation with Naive Bayes: Build a simple Naive Bayes classifier to predict tag of text. [Python folder]
- La Place Smoothing | Multinomial Naive Bayes
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Movie review sentiment analysis: Experimented variations of feature engineering and trained language models with Naive Bayes classifier to predict whether a movie review is positive or negative sentiment. [Python folder]
- TF-IDF | Multinomial Naive Bayes
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Spam Words detection: Trained a MNB model to classify spam/ham msg and explore further insights on spamminess of tokens. [Python folder]
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Introduction to LDA and Topic Modeling: Introduce the concept of LDA model followed by a model demontration and a summary on Airbnb's application of LDA to classify message intents. [Python folder]
- LDA | Topic Modeling
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Discover News Topics: Apply LDA with different techniques to ABC news articles titles [Python folder]
- LDA | Topic Modeling