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Project overview
In this project, you’ll assume the role of a data scientist tasked with predicting market prices of cars based on various characteristics like body style, engine type and horsepower. You’ll practice the complete machine learning workflow using the k-nearest neighbors algorithm in R.
Using a dataset of car features and prices, you’ll explore relationships between predictors, split the data into training and test sets, and experiment with different models through cross-validation and hyperparameter tuning. You’ll evaluate your final models on the test set to gauge real-world performance.
Objective: Apply the machine learning workflow in R to build optimized k-nearest neighbors models for predicting car prices from features.
Key skill required
To complete this project, it's recommended to build these foundational skills in R
- Identifying key steps in a machine learning workflow
- Implementing k-nearest neighbors for prediction
- Employing the caret library for machine learning in R
- Evaluating model performance using error metrics and k-fold cross validation
Projects steps
Step 1: Introduction
Step 2: Examining Relationships Between Predictors
Step 3: Setting Up the Train-Test Split
Step 4: Cross-validation and Hyperparameter Optimization
Step 5: Experimenting With Different Models
Step 6: Final Model Evaluations
Step 7: Next Steps
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