https://github.com/akshaya13/recommendation-system

Content Based Recommendation system using tags!
https://github.com/akshaya13/recommendation-system

nltk scikit-learn similarity-search tmdb-database

Last synced: about 1 month ago
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Content Based Recommendation system using tags!

• Host: GitHub
• URL: https://github.com/akshaya13/recommendation-system
• Owner: akshaya13
• License: mit
• Created: 2024-06-21T19:36:12.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-10-23T00:31:46.000Z (over 1 year ago)
• Last Synced: 2025-07-16T17:06:23.950Z (11 months ago)
• Topics: nltk, scikit-learn, similarity-search, tmdb-database
• Language: Jupyter Notebook
• Homepage:
• Size: 12 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Movie Recommendation System - Content-Based Recommendation System

### Overview

This project implements a content-based movie recommendation system utilizing the TMDB 5000 dataset from Kaggle. The system analyzes various movie attributes to generate personalized recommendations based on user input.

### Dataset

Source: TMDB 5000 Dataset (https://www.kaggle.com/datasets/tmdb/tmdb-movie-metadata)

* Version 1: Stemming + Bag of Words + Similarity Search

* Version 2: Lemmatization + TFIDF + Similarity Search

### Steps

* Data Preprocessing: Clean and prepare the dataset for analysis.

* Exploratory Data Analysis (EDA): Analyze the dataset to understand its structure and key features.

* Feature Engineering: Extract meaningful features from the dataset to enhance recommendation accuracy.

* Tag Creation: Generate tags based on multiple columns including Genre, Overview, Keywords, Cast, and Crew.

* Text Processing: Apply stemming and lemmatization techniques, and remove stop words to refine the tags for better similarity matching.

* Cosine similarity is applied to find the similar movies