Sprint Challenge: Predict Steph Curry's Shots

Sprint Challenge Overview

The Sprint Challenge is your opportunity to demonstrate the tree-based modeling concepts you've learned throughout this sprint. You'll apply decision trees, random forests, cross-validation, and classification metrics to build a model that predicts whether Steph Curry will make his next shot using 10 years of historical data.

Challenge Setup

To get started with the Sprint Challenge, follow these steps:

1. Access the Jupyter notebook using the link below.
2. Complete all tasks in the notebook, demonstrating your understanding of the sprint concepts.
3. You can complete the assignment locally or in Google Colab (make sure to Copy to your Google Drive).

Challenge Expectations

The Sprint Challenge is designed to test your mastery of the following key concepts:

• Decision trees: Building and interpreting tree-based classification models
• Random forests: Implementing ensemble methods and feature importance analysis
• Cross-validation: Using k-fold validation and hyperparameter optimization
• Classification metrics: Evaluating models with confusion matrices, precision, and recall
• Data preprocessing: Handling categorical encoding and missing values for tree-based models

What to Expect

In this sprint challenge, you'll apply everything you've learned about tree-based models to build a predictive model for basketball shots. This challenge will test your ability to:

• Perform exploratory data analysis on sports statistics
• Clean and preprocess data for machine learning
• Build decision tree and random forest models
• Fine-tune model hyperparameters using cross-validation
• Evaluate model performance using appropriate classification metrics
• Interpret feature importances and model results
• Present your findings and model results clearly

Remember to demonstrate your understanding of the concepts from all four modules in this sprint!

Sprint Challenge Resources

Tree-Based Models Documentation

• Scikit-learn: Decision Trees
• Scikit-learn: Random Forests
• Scikit-learn: DecisionTreeClassifier
• Scikit-learn: RandomForestClassifier

Model Evaluation and Tuning

• Scikit-learn: Cross-validation
• Scikit-learn: Hyperparameter Tuning
• Scikit-learn: Classification Metrics
• Scikit-learn: Encoding Categorical Features