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Data Science Projects with Python/

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Splitting the Data: Training and Test Sets

Learn how to split the data for the model evaluation using scikit-learn.

We'll cover the following...

Evaluating binary classification with a train/test split
Train/test split in scikit-learn
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Introduction

Data Exploration and Cleaning

(Challenge) Exploring Remaining Financial Features in Dataset

Introduction to scikit-learn and Model Evaluation

Fake News Detection Using Scikit-learn

(Challenge) Logistic Regression and Precision-Recall Curve

Details of Logistic Regression and Feature Extraction

(Challenge) Logistic Regression Model and Coefficients

The Bias-Variance Trade-Off

(Challenge) Cross-Validation and Feature Engineering

Decision Trees and Random Forests

(Challenge) Cross-Validation Grid Search with Random Forest

Gradient Boosting, XGBoost, and SHAP Values

(Challenge) XGBoost and SHAP Explanation for Case Study Data

Predict Frog Toxicity with Python and XGBoost

Test Set Analysis, Financial Insights, and Delivery to the Client

(Challenge) Deriving Financial Insights

Appendix

Splitting the Data: Training and Test Sets