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: 4 months 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 (12 months ago)
- Default Branch: main
- Last Pushed: 2024-10-23T00:31:46.000Z (8 months ago)
- Last Synced: 2025-01-08T07:41:49.690Z (5 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
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Movie Recommendation System - Content-Based Recommendation System
### OverviewThis 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