PyCaret

Load customer data into a Pandas DataFrame.

Initialize the setup using `setup(data = dataframe, target = 'churn')`.

Train and compare classification models using `compare_models()`.

Tune the best-performing model using `tune_model()`.

Deploy the finalized model to predict churn on new customers.

Load transaction data into a Pandas DataFrame.

Initialize the setup using `setup(data = dataframe, target = 'is_fraud')`.

Train and compare anomaly detection models using `compare_models()`.

Identify fraudulent transactions based on anomaly scores.

Implement alerts and actions to mitigate fraudulent activities.

Load historical sales data into a Pandas DataFrame.

Initialize the setup using `setup(data = dataframe, target = 'sales', session_id = 123)`.

Train and compare time series models using `compare_models()`.

Forecast future sales using the best-performing model.

Adjust inventory and resource plans based on the sales forecast.

Load customer data into a Pandas DataFrame.

Initialize the setup using `setup(data = dataframe)`.

Train and compare clustering models using `compare_models()`.

Segment customers based on cluster assignments.

Develop targeted marketing campaigns for each customer segment.

Load equipment sensor data into a Pandas DataFrame.

Initialize the setup using `setup(data = dataframe, target = 'failure')`.

Train and compare classification models using `compare_models()`.

Predict equipment failures based on sensor data.

Schedule maintenance based on failure predictions.

Prepare the dataset with images and corresponding labels.

Use PyCaret's integration with deep learning libraries to build and train an image classification model.

Evaluate the model's performance on a test dataset.

Deploy the model to classify new images.