Recommendation system Real World Projects using Python

Develop a hybrid recommendation model by normalizing weighted average and popularity score with a min-max scaler, then combine them for a balanced python-based system.

Explore content based recommendation for movie data by vectorizing the overview text with NLP techniques, handling missing values, and building models that use the overview to recommend items.

Explore how tf-idf converts text data into vector representations for machine learning by applying term frequency and inverse document frequency, with lowercase normalization and document-level math.

Explore the intuition behind tf-idf by computing inverse document frequency and demonstrate how multiplying tf and idf yields a final vector representation for machine learning.

Improve a content-based recommendation system by exploring key features such as keywords and data structures. Leverage Python AST and dictionary-based representations to extract insights from data from a business perspective.

Clean and prepare data by extracting names from a list of dictionaries, trimming spaces, normalizing to lowercase, and dropping missing values to create robust features for nlp in Python.

Learn to build a real world recommendation system through data preparation, feature extraction (user id, movie id, rating, title), dataset merging, aggregation, and exporting clean results.

Explore data preparation for a recommendation system by analyzing the total rating feature with pivot tables and box plots, using percentile thresholds and statistical approaches.

Build a collaborative filtering recommender by exploring data with Pandas, pivot tables, and CSR metrics to prepare a user–movie rating matrix for the nearest neighbours model.

Enhance collaborative filtering by turning distance-based movie similarities into user-friendly recommendations using pivot tables, index operations, and distance handling across platforms like YouTube, Amazon Prime, and Netflix.