Movie Genre Recommendations

This project aims to build a movie recommendation system using Machine Learning (ML) techniques. The system will analyze user data like ratings, watch history, and preferences to suggest personalized movie choices, helping users navigate the vast film catalog efficiently.

Movie Recommended Systems: -

1. Content-Based Recommended System:- This system recommends movies based on the similarity of their content (e.g., genre, actors, plot) to the user's past preferences. It analyzes the features of movies a user has liked and suggests similar ones.
2. Popularity-Based Recommended System:- This system recommends movies based on their overall popularity among all users, typically measured by ratings, views, or sales. It suggests the most widely liked or trending movies at the moment.
3. Collaborative-Based Recommended System:- This system recommends movies based on the preferences of users with similar tastes. It identifies users who have liked the same films and suggests movies that other like-minded users have enjoyed.

Used by applications like: -

• Netflix
• Disney+Hotstar
• Amazon Prime

WORKFLOW

• Data
• Data Preprocessing
• Feature Extractions
• User Input
• Cosine Similarity Algorithm
• List of Movies

Cosine Similarity Algorithm:-

The cosine similarity algorithm is used in machine learning and information retrieval to measure the similarity between two non-zero vectors. It calculates the cosine of the angle between the vectors, which gives a value between -1 and 1, where 1 indicates that the vectors are identical, 0 means they are orthogonal (dissimilar), and -1 signifies that they are diametrically opposite.

The cosine similarity is beneficial for determining the similarity between two documents or text data, where each document is represented as a vector of word frequencies or embeddings.

It is widely used in recommendation systems, text analysis, and clustering algorithms.

Dataset:-https://drive.google.com/file/d/1cCkwiVv4mgfl20ntgY3n4yApcWqqZQe6/view

Github:- https://github.com/dvvansia1805/Movie_Genre_Recommendation_Model/tree/main

import numpy as np
import pandas as pd
import difflib
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

movies_data = pd.read_csv("D:\College Work\AI\Movie Genre Reommendation\movies.csv")

#Print the dataset
print(movies_data.head())

#Number of rows and columns in the dataset
print(movies_data.shape)

#Selecting the relevant features for recommendation
selected_features= ['genres', 'keywords', 'tagline', 'cast', 'director']
print(selected_features)

#Replacing the null values with null stirngs
for feature in selected_features:
    movies_data[feature] = movies_data[feature].fillna('')

#Combining all the 5 selected features
combined_features= movies_data['genres']+''+movies_data['keywords']+''+movies_data['tagline']+''+movies_data['cast']+''+movies_data['director']
print(combined_features)

#Conerting the text data to feature vectors
vectorizer= TfidfVectorizer()
feature_vectors= vectorizer.fit_transform(combined_features)
print(feature_vectors)

#Cosine Similarity
#Getting the similarity scores using the cosine similarity
similarity = cosine_similarity(feature_vectors)
print(similarity)
print(similarity.shape)

#Getting the movie name from the user
movie_name = input('Enter your favourite movie name :')

#Creating list with all the movie names given in the dataset
list_of_all_titles = movies_data['title'].tolist()
print(list_of_all_titles)

#Finding the close match for the movie name given by the user
find_close_match = difflib.get_close_matches(movie_name,list_of_all_titles)
print(find_close_match)

close_match = find_close_match[0]
print(close_match)

#Finding the index of the movie with the title
index_of_the_movie = movies_data[movies_data.title == close_match]['index'].values[0]
print(index_of_the_movie)

#Getting a list of similar movies 
similarity_score = list(enumerate(similarity[index_of_the_movie]))
print(similarity_score)

len(similarity_score)

#Sorting the movies based on their similarity score
sorted_similar_movies = sorted(similarity_score,key= lambda x:x[1], reverse= True)
print(sorted_similar_movies)

#Print the name of similar movies based on the index
print('Movie Suggested for you : \n')

i = 1

for movie in sorted_similar_movies:
    index = movie[0]
    tital_from_index = movies_data[movies_data.index==index]['title'].values[0]
    if (i<30):
        print(i,'.',tital_from_index)
        i+=1