Movie Recommendation system
About this project-> This dataset contains metadata for 45,000 movies released on or before July 2017, all listed in the MovieLens database. This project embarks on a journey to create a personalized movie recommender system, transforming this vast dataset into a tool that can predict and suggest movies tailored to individual tastes and preferences.
The main objective of this project is to develop a robust and accurate movie recommender system. The system aims to:
Enhance User Experience: Provide users with personalized movie recommendations based on their viewing history and preferences.
Improve Content Discovery: Help users discover new movies that they might not have found on their own.
Increase User Engagement: Encourage users to spend more time on the platform by continuously suggesting relevant and interesting content.
Approach
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Data Cleaning and Preprocessing:
Handling missing values. Formatting and normalizing data for consistency.
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Exploratory Data Analysis (EDA):
Understanding the distribution of genres, ratings, and other key features. Identifying patterns and trends in the data. 3. Feature Engineering:
Creating meaningful features such as average ratings, popularity scores, and user preferences. 4. Building the Recommender System:
A Basic Recommendation System Based on weigthed rating Collaborative Filtering: Using user-item interaction data to recommend movies based on similar user preferences. Content-Based Filtering: Recommending movies similar to those a user has liked based on movie metadata such as genres, cast, and plot summaries.