1"""
 2[Title]
 3
 4Movie Recommendation System
 5
 6[Description]
 7
 8This project involves developing an application that recommends movies based on user input. When a user enters
 9the name of a movie, the system will provide a list of similar movies using machine learning techniques.
10The goal is to create an effective recommendation algorithm that helps users discover new films that they will like.
11
12[Functionalities]
13
14- Users can input the name of a movie.
15- The application provides a list of recommended movies similar to the input.
16- Utilizes machine learning algorithms to determine movie similarities.
17- Displays basic information and images (posters) of the recommended movies.
18
19[Milestones]
20
211. Data Collection and Preprocessing
22   - Gather movie data, including features like genres, cast, crew, ratings, and images.
23   - Preprocess the data to prepare it for machine learning models.
24   - Analyze the data
25
262. Development of the Recommendation Algorithm
27   - Implement content-based filtering using movie metadata and collaborative filtering based on user ratings.
28   - Combine both methods into a hybrid recommendation system for improved accuracy.
29
303. User Interface Development
31   - Create a simple application interface where users can type the name of a movie.
32   - Display the recommended movies along with their images (posters) and basic information.
33   - Ensure the interface is user-friendly and intuitive.
34
354. Evaluation and Optimization
36   - Evaluate the recommendation system using appropriate metrics (e.g., precision, recall).
37   - Optimize the algorithm's performance through hyperparameter tuning.
38
39[Estimate in man-hours]
40
41Total: 55 man-hours
42
43- Data collection, preprocessing and analysis: 20 hours
44- Development of the recommendation algorithm: 25 hours
45- User interface development: 10 hours
46
47[Usage of technologies]
48
49- Programming Language: Python (implemented using Jupyter Notebook)
50- Libraries:
51  - Pandas and NumPy for data manipulation
52  - Scikit-learn for implementing machine learning algorithms
53  - Surprise library for collaborative filtering techniques
54  - Using Flask to build make the web design or just something like Streamlit that doesn't need html and css
55- Data Sources:
56  - MovieLens dataset or TMDb API for movie data and images
57"""