### ***TASK 3:***
To prepare the car for the grid, you must first conduct a rigorous structural inspection of the dataset to ensure it can withstand the pressures of the race. Begin by checking the air pressure in the tires by identifying and resolving all missing values, either by refilling gaps with statistical averages or replacing incomplete records entirely. Next, strip away unnecessary weight by scanning for damaged or duplicate spare parts, ensuring that no redundant data rows compromise the car’s efficiency. To complete the initial setup, upgrade the onboard electronics by verifying that all technical readings are correctly formatted, converting every relevant performance metric into a precise numeric value to ensure the telemetry systems are fully operational. Once the foundation is secure, focus on performance optimization to ensure the car handles perfectly at high speeds. Fine-tune the aerodynamics by detecting and mitigating extreme outliers that could destabilize the vehicle’s performance, carefully smoothing out anomalies without discarding essential structural components that define the car’s integrity. Be cautious not to over-clean, as maintaining the core features is vital for the car’s overall strength. Finally, balance the engine power by normalizing the data, bringing all performance metrics into a stable, comparable range. With these calibrations complete, the dataset is refined, balanced, and ready to be submitted for final race approval.

You are a Formula-1 race analyst responsible for identifying the championship contender and predicting the ultimate race winner. Using the provided dataset, select relevant features such as starting grid, qualifying position, driver experience update, constructor rank, total laps, race time, laps completed, laps led, average speed, finish position, and championship points to represent driver skill, car performance, and race dynamics. Additionally, add additional features that you think are relevant to predicting the race winner based on logical reasoning and domain understanding. Train a machine learning model to predict the race winner, for example whether a driver takes first place or not. The model is evaluated on unseen race data using the accuracy score, where a higher score indicates a more accurate and reliable system for spotting the driver who takes the checkered flag.