Creating Featues
In this exercise you’ll start developing the features
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- one_hot_encoding 1
- one-hot-encoding 1
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- strategy 1
- cross-validation 1
- xgboost 1
- data-leakage 1
- target-leakage 1
- train-test contamination 1
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- scatterplot 1
- regplot 1
- lmplot 1
- swarmplot 1
- distplot 1
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- review 1
- target 1
- prediction 1
- test 1
- split 1
- fit 1
- Underfitting 1
- Overfitting 1
- random forest 1
- competition 1
- submission 1
- Housing price 1
- mutual 1
- transforms 1
- counts 1
- building features 1
- breaking-down features 1
- clustering 1
- k-means 1
data
Tips for Creating Features
Tips from kaggle’s instructor ‘Ryan Holbrook’.
Mutual Information
Mutual information describes relationships in terms of uncertainty.
Explore Your Data
Filter the data, build model, and improve model.
Pipelines
Pipelines are a simple way to keep your data preprocessing and modeling code organized.
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.
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...
python
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.
Save Your Time
introduce useful Pandas Snippets
pandas
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.
Save Your Time
introduce useful Pandas Snippets
feature
Clustering with K-Means
In this exercise you’ll explore our first unsupervised learning technique for creating features
Creating Featues
In this exercise you’ll start developing the features
Tips for Creating Features
Tips from kaggle’s instructor ‘Ryan Holbrook’.
Mutual Information
Mutual information describes relationships in terms of uncertainty.
IntermediateML pipelines
This notebook is an exercise in the Intermediate Machine Learning course. You can reference the tutorial at this link.
visualization
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
ml
Your First Machine Learning Model
Select the target variable, which corresponds to the sales price.
IntermediateML cross-validation
you will learn how to use cross-validation for better measures of model performance.
optimize
Underfitting and Overfitting
optimize the size of the tree to make better predictions.
IntermediateML XGBoost
you will learn how to build and optimize models with gradient boosting
lineplot
Line Charts
Visualize trends over time
Hello, Seaborn
Your first introduction to coding for data visualization
modeling
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...
kaggle
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...
snippet
Save Your Time
introduce useful Pandas Snippets
missing_values
IntermediateML missing-values
You’ll obtain a more comprehensive understanding of the missing values in the dataset.
one_hot_encoding
IntermediateML categorical-variables
Notice that the dataset contains both numerical and categorical variables.
one-hot-encoding
IntermediateML pipelines
This notebook is an exercise in the Intermediate Machine Learning course. You can reference the tutorial at this link.
pipeline
IntermediateML pipelines
This notebook is an exercise in the Intermediate Machine Learning course. You can reference the tutorial at this link.
strategy
IntermediateML pipelines
This notebook is an exercise in the Intermediate Machine Learning course. You can reference the tutorial at this link.
cross-validation
IntermediateML cross-validation
you will learn how to use cross-validation for better measures of model performance.
xgboost
IntermediateML XGBoost
you will learn how to build and optimize models with gradient boosting
data-leakage
IntermediateML data-leakage
You will learn what data leakage is and how to prevent it.
target-leakage
IntermediateML data-leakage
You will learn what data leakage is and how to prevent it.
train-test contamination
IntermediateML data-leakage
You will learn what data leakage is and how to prevent it.
seaborn
Hello, Seaborn
Your first introduction to coding for data visualization
label
Line Charts
Visualize trends over time
scatterplot
Scatter Plots
Leverage the coordinate plane to explore relationships between variables
regplot
Scatter Plots
Leverage the coordinate plane to explore relationships between variables
lmplot
Scatter Plots
Leverage the coordinate plane to explore relationships between variables
swarmplot
Scatter Plots
Leverage the coordinate plane to explore relationships between variables
distplot
Distributions
Create histograms and density plots
kdeplot
Distributions
Create histograms and density plots
review
Explore Your Data
Filter the data, build model, and improve model.
target
Your First Machine Learning Model
Select the target variable, which corresponds to the sales price.
prediction
Your First Machine Learning Model
Select the target variable, which corresponds to the sales price.
test
Mode Validation
You will test how good your model is.
split
Mode Validation
You will test how good your model is.
fit
Mode Validation
You will test how good your model is.
Underfitting
Underfitting and Overfitting
optimize the size of the tree to make better predictions.
Overfitting
Underfitting and Overfitting
optimize the size of the tree to make better predictions.
random forest
Random Forest
Random Forest is going to be an easy win.
competition
Machine Learning Competitions
You will create and submit predictions for a Kaggle competition.
submission
Machine Learning Competitions
You will create and submit predictions for a Kaggle competition.
Housing price
Machine Learning Competitions
You will create and submit predictions for a Kaggle competition.
mutual
Mutual Information
Mutual information describes relationships in terms of uncertainty.
transforms
Creating Featues
In this exercise you’ll start developing the features
counts
Creating Featues
In this exercise you’ll start developing the features
building features
Creating Featues
In this exercise you’ll start developing the features
breaking-down features
Creating Featues
In this exercise you’ll start developing the features
clustering
Clustering with K-Means
In this exercise you’ll explore our first unsupervised learning technique for creating features
k-means
Clustering with K-Means
In this exercise you’ll explore our first unsupervised learning technique for creating features