Gain insights into data science with easy-to-follow, hands-on explanations. Explore essential concepts quickly and efficiently, even without prior statistics knowledge, for a career boost.

GrokkingDataScientist.tar.gz

jupyter_job

Master the skills that can get you a $100K+ salary even if you bunked your statistics classes. 

No need to waste hours and hours on browsing from one article to the next and piecing together the info you need to grasp important topics. No need to get overwhelmed by the information overload. Find easy to follow, hands-on, and fun explanations of all the essential topics in one place so you can quickly and efficiently learn what you need to thrive as a data scientist.

"Is this course right for me?" Continue to read to decide for yourself!

- "I want to understand this data science concept. Let me Google it". Then after hours of surfing, reading random articles, and invoking the heavens, you are more confused than before.
- "Data science is the sexiest and highest paying job of the 21st century. I want to become a data scientist too".
- "I have a basic knowledge of Python, willingness to learn, and commitment to become a great data scientist."

Is that you? If yes, you are at the right place.

Grokking Data Science

## 3. Transformation Pipelines

As you can see, from imputing missing values to feature scaling to handling categorical attributes, we have many data transformation steps that need to be executed in the right order. Fortunately, Scikit-Learn is here to make our life easier: Scikit-Learn provides the `Pipeline` class to help with such sequences of transformations.

 >📌 **Note**: Creating transformation pipelines is optional. It is handy when dealing with a large number of attributes, so it is a good-to-know feature of Scikit-Learn. In fact, at this point we could directly move on to create our machine learning model. However, for learning how things are done, we are going to look at working with pipelines.

**Some Scikit-Learn terminology**:

- **Estimators**: An object that can estimate some parameters based on a dataset, e.g., an imputer is an estimator). The estimation itself is performed by simply calling the `fit()` method.

- **Transformers**: Some estimators (such as an imputer) can also transform a dataset; these are called transformers. The transformation is performed by the handy and easy to use `transform()` method with the dataset to transform as a parameter. 

- **Predictors**: Some estimators are capable of making predictions given a dataset; they are called predictors. For example, the LinearRegression model is a predictor. A predictor has a `predict()` method that takes a dataset of new instances and returns a dataset of corresponding predictions. It also has a `score()` method that measures the quality of the predictions given a test set.

Based on some of the data preparation steps we have identified so far, we are going to create a transformation pipeline based on `SimpleImputer`(*) and `StandardScalar` classes for the numerical attributes and `OneHotEncoder` for dealing with categorical attributes. 

(*)Scikit-Learn provides a very handy class, `SimpleImputer` to take care of missing values. You just tell it the type of imputation, e.g. by median, and voila, the job is done. We have already talked about the other two classes.

First, we will look at a simple example pipeline to impute and scale numerical attributes. Then we will create a full pipeline to handle both numerical and categorical attributes in one go.

The numerical pipeline:

# 3. Transformation Pipelines

As you can see, from imputing missing values to feature scaling to handling categorical attributes, we have many data transformation steps that need to be executed in the right order. Fortunately, Scikit-Learn is here to make our life easier: Scikit-Learn provides the `Pipeline` class to help with such sequences of transformations.

 >📌 **Note**: Creating transformation pipelines is optional. It is handy when dealing with a large number of attributes, so it is a good-to-know feature of Scikit-Learn. In fact, at this point we could directly move on to create our machine learning model. However, for learning how things are done, we are going to look at working with pipelines.

**Some Scikit-Learn terminology**:

- **Estimators**: An object that can estimate some parameters based on a dataset, e.g., an imputer is an estimator). The estimation itself is performed by simply calling the `fit()` method.

- **Transformers**: Some estimators (such as an imputer) can also transform a dataset; these are called transformers. The transformation is performed by the handy and easy to use `transform()` method with the dataset to transform as a parameter. 

- **Predictors**: Some estimators are capable of making predictions given a dataset; they are called predictors. For example, the LinearRegression model is a predictor. A predictor has a `predict()` method that takes a dataset of new instances and returns a dataset of corresponding predictions. It also has a `score()` method that measures the quality of the predictions given a test set.

Based on some of the data preparation steps we have identified so far, we are going to create a transformation pipeline based on `SimpleImputer`(*) and `StandardScalar` classes for the numerical attributes and `OneHotEncoder` for dealing with categorical attributes. 

(*)Scikit-Learn provides a very handy class, `SimpleImputer` to take care of missing values. You just tell it the type of imputation, e.g. by median, and voila, the job is done. We have already talked about the other two classes.

First, we will look at a simple example pipeline to impute and scale numerical attributes. Then we will create a full pipeline to handle both numerical and categorical attributes in one go.

The numerical pipeline:

Python Fundamentals for Data Science

The Fundamentals of Statistics

Machine Learning 101

End-to-End Machine Learning Project

The Real Talk

Kaggle Challenge - Data Transformation

3. Transformation Pipelines