Description

AI in the retail industry enhances customer experience, optimizes inventory management, enables dynamic pricing, and personalizes marketing strategies. From virtual assistants to predictive analytics, AI empowers retailers to better understand their customers and streamline operations.

Examples (Code)

Below is the sample code

# AI-powered product recommendation system using cosine similarity
from sklearn.metrics.pairwise import cosine_similarity
import pandas as pd

# Sample customer-item purchase matrix
data = {
    'Product_A': [1, 0, 1],
    'Product_B': [0, 1, 1],
    'Product_C': [1, 1, 0]
}
df = pd.DataFrame(data, index=['User1', 'User2', 'User3'])

# Calculate similarity between products
similarity = cosine_similarity(df.T)
similarity_df = pd.DataFrame(similarity, index=df.columns, columns=df.columns)

print("Product Similarities:\n", similarity_df)

Real-World Applications

Where topic Is Applied

Resources

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