85% of the project done

projet_en_groupe/algorithme_netflix.py

@@ -4,28 +4,6 @@ import pandas as pd
 import tabulate
 import os
 
-# allow us to display maximum things
-#pd.set_option('display.max_rows', None) 
-
-"""file_path_1 = "/content/drive/MyDrive/Coding_project_2023/netflix_titles-2.csv
-
-
-data_1 = pd.read_csv(file_path_1)"""
-
-#display(data_1)
-
-"""from google.colab import drive
-drive.mount('/content/drive')"""
-
-
-
-# afficher le second document
-
-
-"""file_path_2 = "/content/drive/MyDrive/Coding_project_2023/ratings.csv
-
-data_2 = pd.read_csv(file_path_2)"""
-
 #display(data_2)
 
 
@@ -354,6 +332,7 @@ def parental_code(data_1):
     print("Here are the parental codes: ")
     print(code_list)
     save_to_csv(code_list)
+    return
 
 
 
@@ -386,6 +365,7 @@ def directors_nationality(data_1):
     for director, info in sorted_directors:
         print(f"{director}: {', '.join(info['nationalities'])} - {info['count']} movies/series")
     save_to_csv(sorted_directors)
+    return
 
 
 
@@ -490,6 +470,93 @@ def save_to_csv(data, default_filename='output.csv'):
 
 
 
+# début de l'algorithme de recommandation 
+
+# Load the CSV file
+
+def print_matrix(data_1):
+    # Initialize the category matrix with zeros
+    unique_categories = set(category for categories in data_1['listed_in'].str.split(', ') for category in categories)
+    category_matrix = {category: {other_category: 0 for other_category in unique_categories} for category in unique_categories}
+
+    # Fill the matrix with the number of movies/series for each category pair
+    for categories in data_1['listed_in'].str.split(', '):
+        for category1 in categories:
+            for category2 in categories:
+                category_matrix[category1][category2] += 1
+
+    category_df = pd.DataFrame(category_matrix)
+
+    # Save the DataFrame to an Excel file
+    output_file = 'category_matrix.xlsx'
+    category_df.to_excel(output_file, index_label='Categories')
+
+    print(f"Category matrix saved to {output_file}")
+    return category_matrix
+
+
+def recommend_movies(user_ratings, category_matrix, unique_categories, num_recommendations=5):
+    # Calculate the weighted sum of category matrix based on user ratings
+    weighted_sum = {category: 0 for category in unique_categories}
+    for category, rating in user_ratings.items():
+        if category != 'title':
+            for other_category in unique_categories:
+                weighted_sum[other_category] += rating * category_matrix[category][other_category]
+
+    # Find top N categories
+    top_categories = sorted(weighted_sum, key=weighted_sum.get, reverse=True)[:num_recommendations]
+
+    # Find movies in top categories not rated by the user
+    recommended_movies = []
+    for category in top_categories:
+        for i, row in user_ratings.iterrows():
+            if row['title'] not in user_ratings['title'] and category in row.values:
+                recommended_movies.append(row['title'])
+
+    return recommended_movies[:num_recommendations]
+
+
+def print_user_matrix(data_1, data_2, user_id_to_recommend, category_matrix=None, num_recommendations=5):
+    # Load the category matrix if not provided
+    if category_matrix is None:
+        category_matrix = print_matrix(data_1)
+
+    linked_base = pd.merge(data_1, data_2, on='show_id')
+    
+    data_of_users = data_2.drop(['show_id', 'appreciation (%)'], axis=1)
+    titles = linked_base['title']
+    data_of_users.insert(0, 'title', titles)
+
+    # Pivot the DataFrame
+    user_item_matrix = data_of_users.set_index('title').transpose()
+
+    unique_categories = set(category for categories in data_1['listed_in'].str.split(', ') for category in categories)
+
+    # Get user ID from the user_id_to_recommend variable
+    user_id = linked_base.loc[linked_base['user_id'] == user_id_to_recommend, 'show_id'].values[0]
+
+    # Get user ratings
+    user_ratings = data_of_users.loc[user_id]
+
+    # Recommend movies for the specified user
+    recommendations = recommend_movies(user_ratings, category_matrix, unique_categories, num_recommendations)
+
+    print(f"Top {num_recommendations} recommendations for user {user_id_to_recommend}:")
+    for movie_title in recommendations:
+        print(movie_title)
+
+    # Save the transposed user-item matrix to an Excel file
+    user_item_matrix.to_excel("user_item_matrix.xlsx", index_label='user_id')
+
+    print("Transposed User-item matrix saved to user_item_matrix.xlsx")
+    return
+
+
+
+
+
+
+
 
 # Création du menu
 def action() :
@@ -548,6 +615,11 @@ def action() :
     directors_nationality(data_1)
   elif command == "17" :
     basic_statistics(data_1)
+  elif command == "A" :
+      print_matrix(data_1)
+  elif command == "B" :
+      user_id_to_recommend = str(input("Quel utilisateur es-tu ? "))
+      print_user_matrix(data_1,data_2, user_id_to_recommend)
   elif command == "..." :
     return False