Update Project_Outline.ipynb

Project_Outline.ipynb

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+TITLE : movie recommendation system 
+
+Libraries:
+
+1. Pandas for data manipulation
+2. NumPy for numerical computations
+3. Scikit-learn for machine learning
+4. Matplotlib and Seaborn for visualization
+
+Dataset:
+
+We'll use the MovieLens dataset, which contains:
+
+1. 100,000 ratings from 600 users on 9,000 movies
+2. Movie genres, titles, and release years
+
+Project Structure:
+
+1. Data Preprocessing
+
+
+import pandas as pd
+
+# Load ratings and movies datasets
+ratings = pd.read_csv('ratings.csv')
+movies = pd.read_csv('movies.csv')
+
+# Merge datasets on movie ID
+data = pd.merge(ratings, movies, on='movieId')
+
+
+2. Exploratory Data Analysis
+
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Visualize rating distribution
+sns.histplot(data['rating'])
+plt.show()
+
+# Top 10 most rated movies
+top_movies = data['movieId'].value_counts().head(10)
+print(top_movies)
+
+
+3. Recommendation Algorithm
+
+We'll implement a simple collaborative filtering algorithm using Matrix Factorization.
+
+
+from sklearn.decomposition import NMF
+
+# Create user-item matrix
+user_item_matrix = pd.pivot_table(data, values='rating', index='userId', columns='movieId')
+
+# Apply Matrix Factorization
+nmf = NMF(n_components=10)
+user_features = nmf.fit_transform(user_item_matrix)
+movie_features = nmf.components_.T
+
+
+4. Recommendation Generation
+
+
+def recommend_movies(user_id, num_recommendations):
+    # Get user features
+    user_feature = user_features[user_id]
+
+    # Calculate similarities with all movies
+    similarities = cosine_similarity(user_feature.reshape(1, -1), movie_features)
+
+    # Get top N recommended movies
+    recommended_movies = movies.iloc[similarities.argsort()[-num_recommendations:]]
+
+    return recommended_movies
+
+# Test recommendation
+recommended_movies = recommend_movies(1, 5)
+print(recommended_movies)
+
+
+5. Evaluation
+
+
+from sklearn.metrics import precision_score, recall_score
+
+# Evaluate recommendation accuracy
+precision = precision_score(data['rating'], recommended_movies['rating'])
+recall = recall_score(data['rating'], recommended_movies['rating'])
+
+print(f'Precision: {precision:.3f}, Recall: {recall:.3f}')
+
+
+Example Use Cases:
+
+1. User-based recommendations: Recommend movies to users based on their past ratings.
+2. Movie-based recommendations: Recommend similar movies to users who liked a particular movie.
+3. Hybrid recommendations: Combine user-based and movie-based recommendations.
+
+Future Enhancements:
+
+1. Incorporate additional data (e.g., genres, directors, actors)
+2. Implement more advanced algorithms (e.g., Deep Learning, Graph-based methods)
+3. Develop a web application for interactive recommendations
+Here's the complete code for the movie recommendation system:
+
+
+# Import necessary libraries
+import pandas as pd
+import numpy as np
+from sklearn.decomposition import NMF
+from sklearn.metrics.pairwise import cosine_similarity
+from sklearn.metrics import precision_score, recall_score
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Load MovieLens dataset
+ratings = pd.read_csv('ratings.csv')
+movies = pd.read_csv('movies.csv')
+
+# Merge datasets on movie ID
+data = pd.merge(ratings, movies, on='movieId')
+
+# Exploratory Data Analysis
+sns.histplot(data['rating'])
+plt.title('Rating Distribution')
+plt.show()
+
+# Top 10 most rated movies
+top_movies = data['movieId'].value_counts().head(10)
+print('Top 10 Most Rated Movies:')
+print(top_movies)
+
+# Create user-item matrix
+user_item_matrix = pd.pivot_table(data, values='rating', index='userId', columns='movieId')
+
+# Apply Matrix Factorization
+nmf = NMF(n_components=10)
+user_features = nmf.fit_transform(user_item_matrix)
+movie_features = nmf.components_.T
+
+# Recommendation function
+def recommend_movies(user_id, num_recommendations):
+    # Get user features
+    user_feature = user_features[user_id]
+
+    # Calculate similarities with all movies
+    similarities = cosine_similarity(user_feature.reshape(1, -1), movie_features)
+
+    # Get top N recommended movies
+    recommended_movies = movies.iloc[similarities.argsort()[-num_recommendations:]]
+
+    return recommended_movies
+
+# Test recommendation
+recommended_movies = recommend_movies(1, 5)
+print('Recommended Movies for User 1:')
+print(recommended_movies)
+
+# Evaluate recommendation accuracy
+precision = precision_score(data['rating'], recommended_movies['rating'])
+recall = recall_score(data['rating'], recommended_movies['rating'])
+
+print(f'Precision: {precision:.3f}, Recall: {recall:.3f}')
+
+
+Dataset:
+
+You can download the MovieLens dataset from:
+
+(link unavailable)
+
+Note:
+
+- This code uses the ml-latest-small dataset, which contains 100,000 ratings.
+- For larger datasets, consider using more advanced techniques or optimized libraries.
+
+Requirements:
+
+- Python 3.x
+- Pandas
+- NumPy
+- Scikit-learn
+- Matplotlib
+- Seaborn
+
+Future Enhancements:
+
+1. Incorporate additional data (e.g., genres, directors, actors)
+2. Implement more advanced algorithms (e.g., Deep Learning, Graph-based methods)
+3. Develop a web application for interactive recommendations