Ex Summer Intern @HMSI || Pre-Final year student at TIET ||Data Science Enthusiast ||Front End Development
Movie Recommendation System Using TMDB Dataset Made a Machine Learning Project Project Overview This project involves building a movie recommendation system using a dataset of 5000 movies from The Movie Database (TMDB). The project is divided into several key stages: data processing, vectorization, model building, front-end development, and deployment. The final application is hosted on Streamlit Cloud. Key Stages 1. Data Processing Data Collection: Acquire the dataset containing details of 5000 movies from TMDB. Data Cleaning: Handle missing values, remove duplicates, and standardize data formats. Feature Engineering: Extract and create relevant features (e.g., genres, cast, director, keywords). 2. Vectorization Text Vectorization: Convert textual data (e.g., movie descriptions, genres) into numerical vectors using techniques like TF-IDF, Count Vectorizer, or Word2Vec. Similarity Calculation: Compute similarity scores between movies using cosine similarity or other relevant metrics. 3. Model Building Recommendation Algorithm: Implement a recommendation algorithm that suggests movies based on the similarity scores. Collaborative filtering and content-based filtering can be used. Evaluation: Test the model using appropriate metrics to ensure accuracy and relevance of recommendations. 4. Front-End Development Streamlit: Build an interactive web interface using Streamlit. User Input: Allow users to input a movie title to get recommendations. Display Results: Show recommended movies with details like title, genre, and poster. Additional Features: Include features such as filtering by genre, rating, or release year. 5. Deployment Streamlit Cloud: Deploy the Streamlit application on Streamlit Cloud for easy access and sharing. You can check this out at: https://lnkd.in/e33tzf_K The code for this project is available on my Github.
