Clustering with K-Means
In this exercise you’ll explore our first unsupervised learning technique for creating features
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kaggle
Creating Featues
In this exercise you’ll start developing the features
Mutual Information
Mutual information describes relationships in terms of uncertainty.
Machine Learning Competitions
You will create and submit predictions for a Kaggle competition.
Random Forest
Random Forest is going to be an easy win.
Underfitting and Overfitting
optimize the size of the tree to make better predictions.
Mode Validation
You will test how good your model is.
Your First Machine Learning Model
Select the target variable, which corresponds to the sales price.
Explore Your Data
Filter the data, build model, and improve model.
Distributions
Create histograms and density plots
Scatter Plots
Leverage the coordinate plane to explore relationships between variables
Line Charts
Visualize trends over time
Hello, Seaborn
Your first introduction to coding for data visualization
IntermediateML data-leakage
You will learn what data leakage is and how to prevent it.
IntermediateML XGBoost
you will learn how to build and optimize models with gradient boosting
IntermediateML cross-validation
you will learn how to use cross-validation for better measures of model performance.
IntermediateML pipelines
This notebook is an exercise in the Intermediate Machine Learning course. You can reference the tutorial at this link.
IntermediateML categorical-variables
Notice that the dataset contains both numerical and categorical variables.
IntermediateML missing-values
You’ll obtain a more comprehensive understanding of the missing values in the dataset.
IntermediateML introduction
Now it’s time to go through the modeling process and make predictions.
Pandas Renaming and Combining
Run the following cell to load your data and some utility functions.
Pandas Data-types and Missing
Run the following cell to load your data and some utility functions.
Pandas grouping and Sorting
In these exercises we’ll apply groupwise analysis to our dataset.
Pandas Summary, Functions and Maps
Now you are ready to get a deeper understanding of your data.
Pandas Indexing, Selecting and assigning
In this set of exercises we will work with the Wine Reviews dataset.
Pandas Creating, Reading and Writing
The first step in most data analytics projects is reading the data file.
AI
Pipelines
Pipelines are a simple way to keep your data preprocessing and modeling code organized.
Building Your Model
The steps to building and using a model are: Define: What type of model will it be? A decison tree? Some other type of model? Some other parameters...
data
Tips for Creating Features
Tips from kaggle’s instructor ‘Ryan Holbrook’.
Save Your Time
introduce useful Pandas Snippets
visualization
Bar Charts and Heatmaps
Use color or length to compare categories in a dataset
barplot
Bar Charts and Heatmaps
Use color or length to compare categories in a dataset
heatmap
Bar Charts and Heatmaps
Use color or length to compare categories in a dataset