Unit tests should focus on individual components of our system: Data Processing: Test the functions responsible for handling and processing the input data for accuracy and reliability. Machine Learning Models: Ensure the machine learning models are correctly implemented and can process input data to provide meaningful recommendations. Feature Matching: Verify that the system can accurately match vehicle features with user preferences. Data Integration: Test the integration of various data sources (e.g., demographic, geographic, and economic data). Model Integration: Check that the machine learning models are properly integrated with the data processing components. User Interface: Verify that the user interface correctly displays the recommended vehicle features based on the input data. Load Testing: Evaluate how the system performs under high load conditions (e.g., multiple users inputting data simultaneously). Response Time: Measure the time taken by the system to process input data and provide recommendations. Pilot Testing: Implement the system in a small, controlled environment to identify any potential issues. A/B Testing: Compare the performance of the system against a control group to measure its impact on sales and visibility. Test Reports: Create detailed reports of all tests conducted, including outcomes and any identified issues. Issue Tracking: Maintain a log of any issues or bugs found during testing and track their resolution. Sample Test Cases Test Case 1: Country-Specific Preferences Input: Data from a country with snowy winters. Expected Output: Recommendations for vehicles with features suitable for snowy conditions (e.g., all-wheel drive, heated seats). Test Case 2: City-Specific Preferences Input: Data from a city with a preference for sporty vehicle designs. Expected Output: Recommendations for vehicles with aggressive front grilles, sharp lines, and vibrant colors. Test Case 3: Individual Preferences Input: Data from an individual who prefers eco-friendly vehicles. Expected Output: Recommendations for vehicles with electric or hybrid engines. Test Case 4: Load Testing Input: Simultaneous data input from 1000 users. Expected Output: System handles the load without significant performance degradation. By following these steps and test cases, we can thoroughly test our project to ensure it meets its objectives and performs effectively in real-world scenarios.
About
The purpose of this project is to prevent overproduction in the automotive sector by considering various criteria, thereby increasing the sales and visibility of a brand by producing custom designs for the country, region, city, and even individuals in the future. In our study, using the data we obtained and assumed, the machine learning system is trained with features such as the color, engine capacity, vehicle type, fuel type, etc., of the produced vehicles. When tested with new user data, the system recommends the most suitable features based on the desired criteria. As a result, the system generates a visually appealing and unique vehicle model based on these results.
