developed a personalized AI

In this project, I utilized a combination of Python, TensorFlow, and SQL. First, I collected user data, including viewing history, ratings, and demographic information. Then, I used Python to preprocess and clean the data, handling missing values and encoding categorical variables.

Based on the preprocessed data, I implemented a machine learning algorithm using TensorFlow. Leveraging supervised learning techniques such as collaborative filtering or matrix factorization models, I trained a personalized recommendation model.

Additionally, I utilized SQL to store and manage the data, as well as to perform efficient database queries during the recommendation process. By optimizing the database queries, I was able to retrieve the necessary data efficiently and provide real-time recommendations to users.