This document outlines the development of two predictive maintenance models focusing on demographic and sensor data to enhance vehicle maintenance strategies. It highlights the challenges in automotive maintenance, details the implementation and analysis processes, and emphasizes the use of advanced machine learning techniques like XGBoost. The findings aim to provide actionable insights for vehicle service managers and individual owners by predicting maintenance needs effectively.

2. Predictive Maintenance: A holistic
approach to predict vehicle
maintenance using Demographic
and sensor data
Manikandan Prasad

3. Project Scope
The scope involves developing two separate predictive maintenance models, each
tailored to a specific type of data: demographic data and sensor data.These models
will independently predict maintenance needs and provide actionable insights for
vehicle service managers and individual owners.This dual-model approach allows for
comprehensive analysis while maintaining modularity and focus on distinct data types.

4. Problem Statement
The fundamental problem in the automotive maintenance domain is the lack of proactive, predictive
maintenance strategies

5. Drawbacks of Existing Systems

6. Advantages of the Proposed System

7. DATA COLLECTION:
ACQUISITION OF INPUT
PARAMETERS THROUGH
DEMOGRAPHIC DATA.
Implementation Strategy
SELECTION OF INPUT
PARAMETERS
DATA PREPROCESSING:
ADDRESSED MISSING VALUES, NAN
VALUES BY REPLACING THEM WITH
MEAN VALUES.
Z-SCORE TO REMOVE OUTLIERS
MODEL 1:
XG BOOST CLASSIFIER:
Excels with complex feature-target
relationships
POST-PROCESSING:
PROBABILITY BASED
MAINTENANCE ESTIMATION

8. Exploratory Data Analysis
(EDA)

9. Approach using Demographic dataset

10. Vehicle Maintenance Data Analysis Framework

13. Univariate Analysis

14. Key Insights on Univariate Analysis

16. Summary of Correlation Analysis
The correlation analysis reveals significant insights, particularly strong positive correlations
between maintenance needs and reported issues. Many negative correlations appear weak,
suggesting minimal interdependencies between variables like service history and odometer
readings. These findings provide valuable guidance for refining vehicle maintenance strategies
and prioritizing further investigations.

17. Chi-Squared Analysis of Maintenance Factors

18. Machine Learning Model Development
XG Boost (Extreme Gradient Boosting):
• Type: Ensemble learning method, specifically a boosting algorithm.
• Concept: XG Boost is an advanced gradient boosting algorithm known for its
efficiency and performance. It builds decision trees sequentially, focusing on
minimizing the loss function. It uses gradient descent for optimization.

19. Vehicle Maintenance Prediction Model Comparison

20. Accuracy-XGBoost Analysis

21. Results

22. Approach using Sensor data

23. Data Overview

24. DATA COLLECTION:
ACQUISITION OF INPUT
PARAMETERS THROUGH SENSOR
DATA.
Implementation Strategy
SELECTION OF INPUT
PARAMETERS
DATA PREPROCESSING:
ADDRESSED MISSING VALUES, NAN
VALUES BY REPLACING THEM WITH
MEAN VALUES.
Z-SCORE TO REMOVE OUTLIERS
MODEL 1:
Gradient Boosting Classifier:
Excels with complex feature-target
relationships
POST-PROCESSING:
PROBABILITY BASED
MAINTENANCE ESTIMATION

25. Result

26. Vehicle Maintenance Predictive Analytics: Model Comparative
Analysis

27. Model Comparative Insights

28. Model Deployment Lifecycle

29. Conclusion

30. Business Recommendations

31. Questions ?

32. Thank You !