I successfully implemented a hybrid recommendation system that combines content-based and collaborative filtering, benefiting from the strengths of both approaches for a more robust and accurate recommendation system.

The project involved web scraping to gather information on over 5000+ movies, including details like genre, IMDb ratings, directors, runtime, and release years from https://justwatch.com

IMDb API was used to fill missing information in different streaming platforms.

Install my-project with streamit

steps to deploy

1. Data Extraction 2. Exploratory Data Analysis(EDA) 3. Feature Engineering 4. Model building 5. Building a streamlit app 6. Pushing code to Github 7. Connecting to the streamlit url 8. Deploy App

link : https://recommender-app-lulquhbn9mgavyjaatwpyh.streamlit.app/

Python

Machine learning

Model Deployment

Data Cleaning

Data Collection

Hello, I'm Benjamin, a dedicated Data Scientist with a passion for developing impactful machine learning models and creating insightful visualizations. My focus is on leveraging data science to make a meaningful contribution to society.

👩‍💻 I'm currently working on building a recommendation system

🧠 I'm currently learning deep learning with tensor flow

👯‍♀️ I'm looking to collaborate on SQL

💬 Ask me about anything DS/ML

📫 How to reach me nwandu@gmail.com

😄 Pronouns He/Him

⚡️ Fun fact I love movies