Movie Recommendation Using Python

By MD ANAS

The "Movie Recommendation Using Python" project is a system that provides personalized movie recommendations to users based on their preferences.

The "Movie Suggestions with Python" project is a system that offers movie recommendations to users based on their interests. It uses machine learning technology and data analysis to find the view based on the user's needs. Here is a brief description of the project: Data Collection: Collect video files from various online sources like google, github and other sources where data can be found. The file should contain information such as movie titles, genres, ratings, and user reviews. Data Preprocessing: Cleans and preprocesses the collected data by removing duplicates, processing missing values, and making necessary changes to suit the analysis.Feature Extraction: Extract related features from video files. These functions will be used to create videos and user profiles. User Preference Modeling: Creating user preference models by analyzing data and user needs. This can be done using various methods such as eigenvectors and the like. Suggestion Generation: Uses input from the user based on similarity calculations to generate a list of recommended videos.The system can recommend the best videos, favorite videos or similar videos with high user count. Evaluation and Evaluation: Evaluation of proposals and evaluation of quality of proposals by providing sample input. Measure metrics such as accuracy, precision, recall, or user satisfaction. Cosine similarity is the cosine of the angle between two n-dimensional vectors in n-dimensional space. It is the dot product of two vectors divided by the product of the length (or size) of the two vectors.