Predicting Car Prices
- Guided Project
- 0 hours
- Advanced
- R
Practice the machine learning workflow using k-nearest neighbors in R to predict car prices.
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.
What You'll Learn
- ✓ Implement the machine learning workflow on a new dataset
- ✓ Employ caret to carry out the entire machine learning workflow
- ✓ Explore making models
Before You Start
- ✓ 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
Project Steps
7 steps
- 1 Introduction
- 2 Examining Relationships Between Predictors
- 3 Setting Up the Train-Test Split
- 4 Cross-validation and Hyperparameter Optimization
- 5 Experimenting With Different Models
- 6 Final Model Evaluations
- 7 Next Steps
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