Objective:
The goal of this assignment is to develop a machine-learning model that predicts the reliability of different vehicle brands and models based on historical service and maintenance data. Reliability, in this context, is defined as the likelihood that a vehicle will require unscheduled maintenance or experience issues.
Dataset:
You are provided with a dataset containing information on service and maintenance records for various vehicles. The dataset includes the following features:
• Vehicle Brand
• Vehicle Model
• Year of Manufacture
• Mileage
• Type of Service/Repair (e.g., engine issues, brake problems, electrical faults)
• Service Date
• Whether the service was scheduled or unscheduled
• Number of previous services
The target variable is binary: 1 indicates a reliable vehicle, and 0 indicates an unreliable vehicle. Reliability is determined based on whether the vehicle has had unscheduled maintenance in the past.
Tasks:
Data Exploration:
• Load and explore the dataset to understand its structure and characteristics.
• Visualize the distribution of reliability across different vehicle brands and models.
• Identify any patterns or trends in the data.
Data Preprocessing:
• Handle missing values appropriately.
• Encode categorical variables.
• Explore and apply any necessary feature engineering.
Feature Selection:
• Select relevant features that contribute to predicting vehicle reliability.
• Justify your feature selection choices.
Model Development:
• Split the dataset into training and testing sets.
• Develop a machine learning model to predict vehicle reliability.
• Experiment with different algorithms (e.g., logistic regression, decision trees, random forests) and select the best-performing one.
Model Evaluation:
• Evaluate the performance of your model using appropriate metrics (accuracy, precision, recall, F1-score).
• Discuss the strengths and weaknesses of your model.
Insights and Recommendations:
• Provide insights into which vehicle brands and models are predicted to be the most reliable.
• Discuss any patterns or factors that contribute to vehicle reliability.
Documentation:
• Provide clear documentation for your code, explaining each step.
• Include comments to explain key decisions in your analysis.
Submission:
Submit a well-documented Jupyter Notebook or Python script containing your code, analysis, and findings. Include visualizations, model performance metrics, and any relevant insights gained from your analysis.
Note:
Ensure ethical use of the data, respecting privacy and any applicable terms of use. Dataset can be downloaded/ from Kagale, Datasets can be downloaded/created from different free providers like Kagale, dataset, databricks & etc.
Use appropriate data splitting techniques to avoid data leakage.
Feel free to use external datasets or resources to enhance your analysis if needed.
Type of Service/Repair
Below is an exhaustive list of potential types of service and repairs for vehicles. Keep in mind that the actual dataset might have variations or additional details, so this list is comprehensive to cover a wide range of possibilities:
• Engine Oil Change
• Transmission Fluid Change
• Brake System Inspection
• Brake Pad Replacement
• Brake Rotor Resurfacing/Replacement
• Tire Rotation
• Wheel Alignment
• Battery Replacement
• Spark Plug Replacement
• Air Filter Replacement
• Fuel Filter Replacement
• Coolant Flush
• Radiator Repair/Replacement
• Thermostat Replacement
• Timing Belt Replacement
• Serpentine Belt Replacement
• Alternator Repair/Replacement
• Starter Motor Repair/Replacement
• Exhaust System Repair/Replacement
• Oxygen Sensor Replacement
• Catalytic Converter Replacement
• Suspension System Inspection
• Shock Absorber/Strut Replacement
• Power Steering Fluid Flush
• Steering Gearbox Adjustment/Replacement
• Wheel Bearing Replacement
• Drive Belt Tensioner Replacement
• A/C System Inspection/Recharge
• Heater Core Replacement
• Windshield Wiper Blade Replacement
• Headlight Bulb Replacement
• Tail Light Bulb Replacement
• Ignition Coil Replacement
• Distributor Cap and Rotor Replacement
• EGR Valve Cleaning/Replacement
• PCV Valve Replacement
• Engine Diagnostics
• Transmission Diagnostics
• Wheel Bearing Replacement
• Fuel Injector Cleaning/Replacement
• Throttle Body Cleaning
• Intake Manifold Gasket Replacement
• Power Window Motor/Regulator Replacement
• Door Lock Actuator Replacement
• Transmission Fluid Leak Repair
• Oil Pan Gasket Replacement
• Engine Mount Replacement
• Transmission Mount Replacement
• Clutch Replacement
• Flywheel Resurfacing/Replacement
Impact on Reliability
It's important to note that reliability issues can stem from various types of services or repairs, and the impact can depend on the specific circumstances. However, certain types of service or repair activities may be associated with a higher likelihood of a vehicle being deemed unreliable. Here's a rating based on the potential impact on reliability:
High Impact on Reliability:
Transmission Diagnostics/Repair/Replacement: Transmission issues can significantly impact a vehicle's reliability.
Engine Diagnostics/Repair/Replacement: Engine problems are often major and can lead to frequent breakdowns.
Multiple Unscheduled Repairs: A history of multiple unscheduled repairs indicates underlying reliability issues.
Electrical System Repairs: Ongoing electrical problems may lead to unpredictable issues.
Moderate Impact on Reliability:
5. Suspension System Repairs: While important for safety, suspension repairs may not directly affect reliability unless recurring.
Brake System Repairs: Brake issues are critical for safety but may not always indicate overall reliability concerns.
Cooling System Repairs: Cooling system problems can lead to breakdowns but may not be as frequent as major engine issues.
Low Impact on Reliability:
8. Routine Maintenance Services (Oil changes, tire rotations, etc.): Proper maintenance contributes to reliability.
Wear-and-Tear Replacements (Brake Pads, Filters): Regular replacements are part of normal vehicle upkeep.
Note: The impact ratings are generalizations, and the actual impact can vary based on the specific circumstances, the vehicle's age, and maintenance history. Additionally, some repairs might be preventive and contribute positively to reliability.
In the context of the machine learning assignment, it's crucial to explore the dataset to understand patterns and correlations between types of service/repair and the reliability of vehicles. The rating here provides a starting point, but insights from the data analysis will refine the understanding of what contributes most to unreliability.