Easy

Machine learning

Prediction

Customer behavior

An artist’s illustration of artificial intelligence (AI). This image depicts a look inside how AI microchips are designed. It was created by Champ Panupong Techawongthawon as part of the Visualising AI project launched by Google DeepMind.

Create a Machine Learning Model for Predicting Purchases

Build a machine learning model to predict the likelihood of a customer making a purchase based on the time of day and their browsing history

In this project, we will be creating a machine learning model to predict the likelihood of a customer making a purchase based on the time of day and their browsing history. This can be useful for businesses to optimize their marketing and sales efforts and better understand customer behavior.

Project Checklist

  • Gather and preprocess data on customer purchases and browsing history
  • Train and evaluate a machine learning model using the data
  • Implement a system for making predictions using the trained model

Bonus Project Checklist Items

  • Improve the performance of the model by experimenting with different algorithms or hyperparameter settings
  • Implement a system for updating the model with new data as it becomes available
  • Integrate the prediction system with a real-time data stream or API to make predictions in near real-time

Inspiration (Any companies/libraries similar)

  • Amazon Personalize
  • Google Cloud Prediction API
  • Azure Machine Learning

Hint/Code snippet to start

To get started, you can use the following code snippet to set up a basic machine learning model using Python and the scikit-learn library:
# Import necessary
libraries from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection
import train_test_split

# Load and preprocess the data

X = ...
y = ...
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and fit the model

model = RandomForestClassifier()
model.fit(X_train, y_train)

# Evaluate the model

accuracy = model.score(X_test, y_test)
print(f"Model accuracy: {accuracy:.2f}")