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Genre Vectorization Using TF-IDF

Genre Vectorization Using TF-IDF

Machine learning models operate on numerical data, not text. Since movie genres are stored as text, they must be converted into numerical form before similarity calculations can be performed.

TF-IDF is used to convert genre text into numerical vectors that represent the importance of each genre for a movie.

Code:

from sklearn.feature_extraction.text import TfidfVectorizer

tfidf = TfidfVectorizer(token_pattern='[a-zA-Z0-9]+')

tfidf_matrix = tfidf.fit_transform(movies['genres'])

After this step, each movie is represented as a vector of numbers. These vectors capture genre composition and allow the system to measure similarity between movies mathematically.

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Movie Recommendation System Project Using Content-Based Filtering

J
Jebasta

Movie Recommendation System Project Using Content-Based Filtering

MODULE 1 : Introduction
lessonWhat Is Data Science?
lessonWhat Is a Movie Recommendation System?
lessonAim of the Project
MODULE 2 : Pre-requisites and Tech Stack Used
lessonBasic Requirements
lessonTech Stack Used
lessonInput
lessonOutput
MODULE 3 : Necessary Concepts
lessonContent-Based Filtering
lessonCollaborative Filtering Signal
lessonTF-IDF Vectorization
lessonK-Nearest Neighbors Algorithm
lessonHybrid Recommendation Strategy
MODULE 4 : Step-by-Step Implementation
lessonDataset Download and Environment Setup
lessonLoading the Dataset
lessonData Auditing and Initial Inspection
lessonExploratory Data Analysis (EDA)
lessonData Preprocessing
lessonGenre Vectorization Using TF-IDF
lessonAdding Average Ratings as a Popularity Signal
lessonBuilding the KNN Similarity Model
lessonCreating the Recommendation Function
lessonTesting the Recommendation System
MODULE 5 : Deployment
lessonInstalling and Importing Gradio
lessonCreating the Interface Function
lessonBuilding the Gradio Interface
lessonLaunching the Application
lessonWhy Gradio Is Used
MODULE 6 : Conclusion
lessonLearning Outcome
lessonLive Link and Output Overview
lessonMCQs
lessonResources

Movie Recommendation System Project Using Content-Based Filtering

MODULE 1 : Introduction
lessonWhat Is Data Science?
lessonWhat Is a Movie Recommendation System?
lessonAim of the Project
MODULE 2 : Pre-requisites and Tech Stack Used
lessonBasic Requirements
lessonTech Stack Used
lessonInput
lessonOutput
MODULE 3 : Necessary Concepts
lessonContent-Based Filtering
lessonCollaborative Filtering Signal
lessonTF-IDF Vectorization
lessonK-Nearest Neighbors Algorithm
lessonHybrid Recommendation Strategy
MODULE 4 : Step-by-Step Implementation
lessonDataset Download and Environment Setup
lessonLoading the Dataset
lessonData Auditing and Initial Inspection
lessonExploratory Data Analysis (EDA)
lessonData Preprocessing
lessonGenre Vectorization Using TF-IDF
lessonAdding Average Ratings as a Popularity Signal
lessonBuilding the KNN Similarity Model
lessonCreating the Recommendation Function
lessonTesting the Recommendation System
MODULE 5 : Deployment
lessonInstalling and Importing Gradio
lessonCreating the Interface Function
lessonBuilding the Gradio Interface
lessonLaunching the Application
lessonWhy Gradio Is Used
MODULE 6 : Conclusion
lessonLearning Outcome
lessonLive Link and Output Overview
lessonMCQs
lessonResources

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HCL GUVI (Grab Your Vernacular Imprint) Geek Network Private Limited is a leading online learning and skills development company, incubated by IIT Madras and IIM Ahmedabad.

Established in 2014 and acquired by the HCL Group in 2022, HCL GUVI is dedicated to providing effective and high-quality learning and skilling programs that transcend language barriers in technology education. HCL GUVI today is trusted by over 3 million learners and 2000+ corporate partners.

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Refer & Earn

HCL GUVI (Grab Your Vernacular Imprint) Geek Network Private Limited is a leading online learning and skills development company, incubated by IIT Madras and IIM Ahmedabad.

Established in 2014 and acquired by the HCL Group in 2022, HCL GUVI is dedicated to providing effective and high-quality learning and skilling programs that transcend language barriers in technology education. HCL GUVI today is trusted by over 3 million learners and 2000+ corporate partners.

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