Data Science and Analytics Tools - Predictive Maintenance for Equipment Using Random Forest

 Notes:

• What problem does it solve?
  Predicts when machinery or equipment is likely to fail, helping businesses schedule maintenance proactively to avoid costly downtime.

• How can businesses or users benefit from customizing the code?
  Businesses can customize it by incorporating additional sensor data or adjusting the features based on specific equipment types.

• How can businesses or users adopt the solution further, if needed?
  The model can be integrated with real-time IoT sensor systems to predict failures and automatically trigger maintenance requests.

Actual Python Code:

import pandas as pd

from sklearn.ensemble import RandomForestClassifier

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score

# Load maintenance data (assumed to have 'Sensor1', 'Sensor2', ..., and 'Failure' column)

data = pd.read_csv('equipment_data.csv')

# Prepare features (sensor data) and target variable (failure status)

X = data[['Sensor1', 'Sensor2', 'Sensor3']]  # Add more sensors as needed

y = data['Failure']

# Split the data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Train a Random Forest model

model = RandomForestClassifier(n_estimators=100, random_state=42)

model.fit(X_train, y_train)

# Make predictions

y_pred = model.predict(X_test)

# Evaluate the model

accuracy = accuracy_score(y_test, y_pred)

print(f'Accuracy: {accuracy * 100:.2f}%')