Movie Recommendation Program

This is a simple movie recommendation program that utilizes a movie rating dataset to provide movie recommendations based on user preferences. The program uses Python and pandas library for data processing and analysis.

Dataset

The program uses two datasets:

• ratings.dat: Contains information about movie ratings given by users. The dataset has columns like userId, movieId, rating, and timestamp.
• movies.dat: Contains information about movies, including their titles and genres. The dataset has columns like movieId, title, and genres.

Both datasets are downloaded from the following URLs:

• ratings.dat: https://raw.githubusercontent.com/Arnavsmayan/Movie-Recommender/main/ratings.dat
• movies.dat: https://raw.githubusercontent.com/Arnavsmayan/Movie-Recommender/main/movies.dat

Program Flow

1. The program starts by importing the necessary libraries: pandas and numpy.
2. The program reads the ratings.dat file using pd.read_csv() function and assigns column names: userId, movieId, rating, and timestamp.
3. It reads the movies.dat file using pd.read_csv() function and assigns column names: movieId, title, and genres.
4. The program merges the rating and movie dataframes using the pd.merge() function based on the common movieId column.
5. Unnecessary columns (timestamp and movieId) are dropped from the merged dataframe using the drop() function.
6. The program calculates the average rating and the number of reviews for each movie using the groupby() and mean() functions.
7. The program displays the first few rows of the resulting dataframe, showing the average rating and the number of reviews for each movie.

Outputs

The program displays two outputs:

1. The first output is a dataframe that shows the average rating and the number of reviews for each movie. It includes columns: title, Avg. Rating, and No. of Reviews.
2. The second output is a dataframe that shows the average rating and the number of reviews for a subset of movies. It includes columns for each movie title and userId.

Note: The second output dataframe only shows a few movie titles and userId columns for brevity. The actual dataframe includes columns for all movies and userId.

Please refer to the code cells in the notebook for the actual implementation and execution of the program.