E-commerce Automation and Optimization - Dynamic Product Recommendation Engine

 

Notes:

• Problem: E-commerce sites often lack personalized product recommendations.

• Benefit: Increases sales by recommending relevant products to users based on past purchases.

• Adoption: Customize recommendations to reflect user behavior, product trends, etc.

Code:

import pandas as pd

from sklearn.metrics.pairwise import cosine_similarity

# Load user and product data

user_data = pd.read_csv('user_data.csv')

product_data = pd.read_csv('product_data.csv')

# Create similarity matrix

product_features = product_data.drop('product_id', axis=1)

similarity_matrix = cosine_similarity(product_features)

# Recommend products

def recommend_products(user_id):

    user_purchases = user_data[user_data['user_id'] == user_id]['purchased_products']

    recommended = []

    for product in user_purchases:

        similar_products = similarity_matrix[product]

        recommended.extend(similar_products)

    return recommended[:5]

print(recommend_products(1))  # Recommend products for user with ID 1