/Presentation-At-Udacity

Primary LanguageJupyter Notebook

Udacity Meeting (San Jose)

Winner Algorithm (XGBoost)

Pourya Ayria

Udacity Meeting (03/03/2018)

OutLine:

  1. How XGBoost Works

  2. Multi CLassification(Iris dataset)

  1. Problem Definition

  2. Load the Dataset

  3. Analyze Data

    3.1. Descriptive Statistics

    3.2. Data Prepration: Reduce the size of data, Label Encoding

    3.3. Data Visualization

    3.4. Checking Target Distribution

  4. Modeling

    4.1. Feature Importance

    4.2. Kfold Cross Validation

    4.3. XGBoost vs. Logistic Regression

    4.4 Tuning: GridSearchCV, RandomizedSearchCV

    4.5 Prediction

  5. Saving the Model