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In the context of the retail company’s objective, what are the differences between artificial intelligence and machine learning?
In the context of the retail company’s objective, what are the differences between artificial intelligence and machine learning?
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Log in to answerBest Answer · By JanBask Data Science Expert
Answered on Mar 6, 2024
In the context of data science, there is a minute difference between AI and ML. Here are the differences given:-
Artificial intelligence (AI)
Artificial intelligence refers to the simulation of human intelligence in the form of a machine that is used in performing tasks and actions which mainly requires the intelligence of humans to understand natural language, recognize patterns, and make decisions.
In the context of the retail company artificial intelligence can be used in the development of chatbots which can help in customer service, and analyze customer sentiment from social media.
Machine learning (ML)
It is a subset of artificial intelligence which can focus on the development of algorithms and models which would allow you to enable the computers to learn from and make predictions or decisions based on the data you provide.
In the context of a retail company, it can be used in predicting the preference and recommended personalized products, and forecasts demands for the specific items.
Here is a simplified Python script given which would illustrate how a simple machine learning approach for demanding forecasting in retail:-
Import pandas as pd
From sklearn.model_selection import train_test_split
From sklearn.ensemble import RandomForestRegressor
From sklearn.metrics import mean_squared_error
# Load historical sales data
Sales_data = pd.read_csv(‘sales_data.csv’)
# Split data into features (X) and target variable (y)
X = sales_data.drop(columns=[‘date’, ‘sales’])
Y = sales_data[‘sales’]
# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Initialize and train a random forest regressor model
Model = RandomForestRegressor(n_estimators=100, random_state=42)
Model.fit(X_train, y_train)
# Make predictions on the test set
Predictions = model.predict(X_test)
# Evaluate model performance
Mse = mean_squared_error(y_test, predictions)
Print(“Mean Squared Error:”, mse)Free tutorials and interview questions from industry experts — learn the skill, then get ready to prove it.
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