Vehicle Performance Analysis

Skills, Tech Stack, and Libraries

1. Skills: Data Wrangling, Predictive Analytics, Statistical Analysis, Data Visualization

2. Tech Stack: Power BI, Tableau, SQL, Python

3. Libraries: Pandas, NumPy, Matplotlib, Scikit-learn, Seaborn

Description and Approach

Objective:

I developed an advanced analytics system to monitor vehicle performance by analyzing telematics data. The project aimed to optimize fuel consumption, predict maintenance needs, and provide actionable insights for improving overall fleet efficiency.

Approach:

1. Data Collection and Cleaning:Acquired telematics data from vehicle sensors (e.g., GPS, fuel sensors, engine diagnostics) stored in SQL databases. Cleaned and processed the data using Pandas to handle inconsistencies, missing values, and outliers.

2. Exploratory Data Analysis (EDA):Conducted EDA with Matplotlib and Seaborn to analyze key metrics such as fuel efficiency, average speed, and engine load. Identified patterns and anomalies in vehicle performance across different routes and conditions.

3. Feature Engineering:Derived advanced features such as fuel efficiency per trip, idle time, and maintenance-critical metrics using Python. Grouped data by time intervals and vehicle types for granular insights.

4. Predictive Modeling:Implemented a machine learning model using Scikit-learn to predict maintenance needs and identify vehicles at risk of breakdown. The model used historical performance data, operating conditions, and maintenance history as inputs.

Dashboard Design:

Designed an advanced dashboard using Tableau to present:

1. Fleet-level insights like fuel efficiency trends and overall engine health.

2. Predictive maintenance alerts for specific vehicles.

3. Route-specific performance metrics for optimized scheduling and cost savings.

Automation:

Built an automated pipeline to ingest and process daily telematics data, ensuring the dashboard remained up-to-date for real-time decision-making.

Code Flow:

1. Ingest data using Python from SQL databases and flat files.

2. Preprocess and clean data with Pandas and NumPy.

3. Conduct EDA and feature engineering to derive performance metrics.

4. Train and validate predictive models in Scikit-learn.

5. Export processed data to Tableau for visualization and deploy the dashboard.

Results

The project delivered an advanced vehicle performance monitoring system that:

• Increased fleet fuel efficiency by 15% by identifying inefficient driving behaviors and routes.

• Reduced vehicle downtime by 25% with predictive maintenance alerts.

• Provided route-specific optimization insights, leading to a 10% reduction in operating costs.

• Empowered fleet managers with a real-time dashboard, enabling data-driven decisions for maintenance and route planning.

This project showcased the power of advanced analytics in streamlining fleet operations and reducing costs.

Git Link

For more information and code, visit the Git link.