Warranty management is a crucial aspect of maintaining customer trust and loyalty. However, traditional methods often struggle to keep up with the rapid pace of innovation in various industries.
Machine learning (ML) has emerged as an innovative solution for warranty management. By leveraging ML algorithms, organizations can analyze large amounts of data, identify patterns, and make predictions about customer behavior.
# Import necessary libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
# Generate sample warranty data
data = {
'Customer ID': [1, 2, 3, 4, 5],
'Warranty Type': ['Premium', 'Standard', 'Deluxe', 'Basic', 'Free'],
'Repair Time': [10, 15, 20, 25, 30],
'Customer Satisfaction': [80, 70, 60, 50, 40]
}
# Create a pandas dataframe
df = pd.DataFrame(data)
# Split data into training and testing sets
X = df[['Repair Time', 'Customer Satisfaction']]
y = df['Customer ID']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Train a logistic regression model on training data
model = LogisticRegression()
model.fit(X_train, y_train)
# Evaluate the model using accuracy and precision metrics
y_pred = model.predict(X_test)
print("Accuracy:", model.accuracy_score(y_test, y_pred))
print("Precision:", model.precision_score(y_test, y_pred))
# Use the trained model to make predictions on test data
new_data = pd.DataFrame({'Repair Time': [12], 'Customer Satisfaction': [90]})
predicted_id = model.predict(new_data)
print("Predicted Customer ID:", predicted_id[0])