Overview of things learned:
- Technical Area:
- Used pandas to organize and clean the scraped data
- Used Rake, NumPy, and sklearn to create a simple recommender system
- Gained an understanding of cosine similarity and applied it in my model
- Used matplotlib to plot some graphs about the data and learned about balancing data
- Tools:
- Jupyter Notebooks
- Pandas, numpy, csv, matplotlib
- Soft Skills:
- Explored many new libraries and modules
- Enhanced my knowledge about data frames
- Collaborated with my teammates for EDA and making the model
Achievements:
- Built a simple post recommender that uses Bag of Words and cosine similarity
- Gained a decent understanding of the logic and algorithms behind ML
- Understood what to do next from the observations about accuracy and the unbalanced data