Machine Learning - Level 1 Module 3 - Harshita Srinivas

Technical Areas:

  • Used pandas and NumPy to organize the data in an ordered set. Then used pandas dataframe functions to perform data manipulation and clean the data.
  • Gained additional insight into vectorization and cosine similarity
  • Understood the mechanism of the recommender system
  • Implemented a movie recommender system from scratch based on these learnings


  • Jupyter Notebook
  • Spacy, Pandas, NumPy, CSV, NLTK
  • Classifiers used: K-nearest neighbours, Cosine Similarity
  • Git / GitHub

Soft Skills:

  • Gained insight into ML concepts and functions using tutorials and online videos
  • Explored pandas data frame functions and debugged code
  • Utilized new libraries and read their documentation throughly

3 Achievement Highlights:

  • Created basic recommender system with cosine similarity
  • Understood the logic behind recommender systems and ML algorithms

Tasks Completed:

  • Removed stopwords and punctuations from the text data
  • Implemented a basic movie recommender system using cosine similarity (as recommended)
  • Categorized the movies based on genres and descriptions to determine 10 most similar movies

Link to project: