The code uses Python syntax and libraries like pandas for data manipulation and numpy for numerical operations. It involves basic Python operations like variable assignment, function calls, and control flow statements. Data Analysis:
The code loads and processes data from files using pandas. It performs data aggregation and filtering operations, such as calculating mean ratings, counting occurrences, and filtering by specific criteria.
It uses only python, Machine learning not yet implemented. we can implement Collaborative Filtering
Code Example: import pandas as pd from scipy.spatial.distance import cosine
Load data ratings = pd.read_csv('ratings.csv')
Create user-item matrix user_item_matrix = ratings.pivot_table(index='user_id', columns='item_id', values='rating')
Calculate user similarities user_similarities = 1 - cosine(user_item_matrix.fillna(0).values)
Recommend items to a user def recommend(user_id, top_n=10): similar_users = user_similarities[user_id].argsort()[::-1][1:] recommendations = user_item_matrix.iloc[similar_users].mean(axis=0) return recommendations.sort_values(ascending=False).head(top_n)
Code Example:
import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer
Load data items = pd.read_csv('items.csv')
Create item features tfidf_vectorizer = TfidfVectorizer(stop_words='english') item_features = tfidf_vectorizer.fit_transform(items['description'])
Calculate item similarities item_similarities = 1 - cosine(item_features.toarray())
Recommend items to a user based on their preferences def recommend(user_preferences, top_n=10): recommendations = item_similarities[user_preferences.nonzero()[0]].mean(axis=0) return recommendations.argsort()[::-1][:top_n]