This project implements a movie recommendation system with Machine Learning using Python. The recommendation system utilizes collaborative filtering and content-based filtering techniques to provide personalized movie recommendations based on user preferences and movie attributes.

1. Introduction
2. Installation
3. Usage
4. Features

The ability to generate value for businesses by leveraging data and applying pertinent programming abilities is the fundamental component of both Data Science (DS) and Artificial Intelligence (AI). The way individuals can now access and enjoy products and services from the comfort of their homes with just a few clicks has been transformed by industry leaders like Netflix, Amazon, and Uber Eats. These platforms have used recommendation algorithms to improve the user experience. Users are catered to by these systems, which provide an abundance of customized options that are carefully crafted to suit their individual interests and tastes. Within this framework, Python is a vital resource that provides an adaptable and strong environment for creating and implementing state-of-the-art recommendation systems.

To run this project, you need Python 3 installed on your system along with the following dependencies:

pip install numpy pandas scikit-learn matplotlib seaborn

1. Clone the repository to your local machine:

git clone https://github.com/your-username/movie-recommendation-system.git

2. Navigate to the project directory:

cd movie-recommendation-system

3. Run the main Python script to execute the recommendation system:

python recommendation_system.py

Follow the on-screen instructions to input user preferences and receive movie recommendations.

• Collaborative filtering: Recommends items based on the similarity between users.
• Content-based filtering: Recommends items based on the similarity between items and user profiles.
• Hybrid techniques: Combines collaborative and content-based filtering for improved recommendations.
• User-item matrix creation: Constructs a sparse matrix representation of user-item interactions.
• Movie similarity analysis: Identifies similar movies using k-Nearest Neighbors (KNN) algorithm.
• Movie recommendation: Suggests movies for a user based on their preferences and highest-rated movie.