Recommendation system Real World Projects using Python

Import and preprocess data with NumPy and Seaborn, read CSV datasets into dataframes, and prepare movies and credits data to build a real-world Python recommendation system.

Prepare and clean two data frames, align on a common id, join them, drop nonessential features, and export the cleaned dataset to csv for a real-world Python recommendation project.

Apply the average weighted rating technique to real world movie recommendations in Python, computing r, v, and c to rank top titles and visualize the results with a bar plot.

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.

Build a hybrid movie recommendation model by normalizing features like popularity, creating a score mix with weighted components, and visualizing top results using interactive bar charts in Python.

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.

Apply tf-idf to text data, build a vocabulary, remove stop words, and generate feature vectors to prepare for measuring vector similarities in a real-world recommendation system.

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.

Learn to extract meaningful features for a content-based recommendation system by building a feature extraction function, mapping roles to names, and handling data cleaning and missing values.

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.

Create metadata as a key feature by transforming and joining keywords into an important feature, then vectorize data and apply natural language processing steps in Python to improve recommendation quality.

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.

Explore how to create a pivot table to analyze movie ratings, map user IDs to movie titles, and interpret threshold filters for building a basic recommendation system.

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.