This project used my assigned NLP project as a starting point to determine if I could improve the model. Resampling provided the greatest improvement in prediction while accuracy increased only 3%, the average F1 score increased 14%.

• Scraped 1K GitHub repositories with Harry Potter in the search
• Filtered out those without a readme file or program language
• Filtered for only the top 4 represented program languages: JavaScript, Java, HTML, Python
• Cleaned and prepared data using regex and toktoktokenizer
• Removed common english stop words and additional words found to create noise in dataset
• Vetorized data using TF-IDF
• Initial attempts to improve model by adding data, reducing noise, and testing multiple algorithms did not significantly increase accuracy
• Determined imbalanced dataset might be contributing factor and Resampled data with SMOTE
• This only improved the average model accuracy by 3%, but improved the average F1 score by 14%

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