Machine Learning- Level 1 Module 3- Shreya Vora


  • Using bag of words to convert the useful information (word vector)
  • Calculated the cosine similarity (needs to be close to 1)
  • Depending on the cosine similarity, I found the top 10 different recommendations
  • Trained classification models on my data
    • Calculated metrics: Accuracy, Precision, and Recall
  • Tested the model on the data collected


  • Google Colab
  • Python Libraries:
    • NLP Libraries: spacy, and gensim
    • Matplotlib, seaborn, sklearn, pandas, etc
  • Youtube

Soft Skills:

  • When I came across some issues I looked through different youtube videos to see all the different approaches people tried.
  • I was also able to find more effective ways to debug my code.


  • I was able to make a basic recommender system where I was able to give the 10 recommended posts
  • Was able to use a visual representation to analyse the results
  • Clean the data so that I can use it in my recommender system
  • Became more comfortable with using google colab


  • Cleaned the data so that it can be used for my model (data pre-processing)
  • Was able to understand and use cosine similarity to define my recommender system
  • Trained and tested my model and data


  • I had a hard time making the recommender system so I looked at different videos to figure out the different ideas that I could use.
  • The cosine similarity idea was a little difficult for me to conceptualize, but eventually I was able to understand it by watching different videos.