Project Title : Movie Recommendation System

Objective:
To build an AI-based system that recommends movies to users based on their preferences, viewing history, or similarities with other users.

Technologies Used:

Programming Language: Python

Libraries/Frameworks: Pandas, NumPy, Scikit-learn, Surprise, TensorFlow (optional for deep learning)

Tools: Jupyter Notebook, Streamlit (for GUI, optional)

Dataset: MovieLens, IMDb, or any open movie dataset

Approaches Implemented:

Content-Based Filtering:
Recommends movies similar to what the user has liked in the past, based on features like genre, director, or cast.

Collaborative Filtering:

User-User Filtering: Finds similar users and recommends movies they liked.

Item-Item Filtering: Recommends movies similar to what the user rated highly.

Hybrid Model (optional):
Combines both content-based and collaborative filtering for better accuracy.

Steps Involved:

Data Collection & Preprocessing

Load dataset, clean missing values, normalize data.

Feature Engineering

Extract genres, keywords, ratings, and tags.

Model Building

Implement similarity algorithms (cosine similarity, Pearson correlation).

Recommendation Engine

Generate top-N recommendations per user.

Evaluation

Use RMSE, MAE, or precision-recall to test the model's performance.

Deployment (Optional):

Simple web interface using Streamlit or Flask.

Outcome:
A smart AI system that personalizes movie suggestions, improving user experience similar to platforms like Netflix or Amazon Prime.