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NaN.

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Pandas: Apply functions and GroupByby Wiki

So far in the course, we have learnt quite a bit about DataFrames. In particular, we learnt about using various boolean and arithmetic operations on DataFrame columns, and also about indexing to select and modify various subsets of a DataFrame.

In this tutorial we will learn another method for doing operations on and also modifying a DataFrame using DataFrame methods like apply() and applymap(). These methods allow us to *apply* a function over an entire DataFrame.

Let's get started!

As in the previous tutorials, let us load the Pandas and Numpy libraries at the beginning.

type=codeblock|id=pd_func_import|autocreate=datascience|show_output=0

Read more…(1655 words)

Category: Machine Learning

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Correlation Analysis: Multivariable [Under Construction]

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Category: Data Science and Big Data

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Correlation Analysis: Two Variables

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Pandas: Indexing and Slicing

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Category: Machine Learning

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Polynomial Regression and Feature Engineering

Polynomial features are higher power polynomial terms of the original features, which are added to the feature space of a model.

Let us understand this with a few examples.

Suppose we have a dataset with features x_1, x_2 and target variable y. A multivariable linear regression model for this set of data would be:

y_{pred}=w_1x_1+w_2x_2+b

Polynomial features are higher ordered values of x_1 and x_2 which we can add to this model, for eg. x_1^2, x_1^3, x_2^2 , etc.

Our new model would look like this:

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Category: Machine Learning

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Feature Scaling

Feature scaling is an important part of data pre-processing.

Often, numeric variables in a dataset have very different scales. For example, let's say we have a dataset which includes the area of a house (in square feet) and its corresponding price (in US dollars). Typically, the area of the house will be in the range 500 - 5000 square feet, but the price will range from $100,000 - $5,000,000. As you can see, the scale of the features are very different. In this case, the price is almost 1000x square feet area.

In this tutorial, we will first talk about how having all the variables be in a similar scale helps us. Then, we will talk about various methods to perform scaling.

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Category: Machine Learning

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tutorial

Categorical Features

In statistical modeling and machine learning, a *categorical* *variable* is a variable which can only have a fixed set of values. Some examples of categorical variables are nationality, size of clothes (small, medium, large), day of the week, genre of music, educational qualification (doctorate, graduate, diploma), etc.

**Categorical Feature labels**

As opposed to a continuous numerical variable such as height, age, and distance, the above variables are not intrinsically represented by continuous numbers. Instead they represented by *labels*. Each unique value in a categorical variable is known as a label.

For example, if the categorical variable was Size of clothing**, **the labels would be small, medium and large*. *For the categorical feature Educational qualification, some of the possible labels would be doctorate, graduate, diploma*, *etc.

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Category: Machine Learning

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Generalization and Overfitting

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Category: Machine Learning

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10.

tutorial

Pandas: Apply functions and GroupBy

So far in the course, we have learnt quite a bit about DataFrames. In particular, we learnt about using various boolean and arithmetic operations on DataFrame columns, and also about indexing to select and modify various subsets of a DataFrame.

In this tutorial we will learn another method for doing operations on and also modifying a DataFrame using DataFrame methods like apply() and applymap(). These methods allow us to *apply* a function over an entire DataFrame.

Let's get started!

As in the previous tutorials, let us load the Pandas and Numpy libraries at the beginning.

type=codeblock|id=pd_func_import|autocreate=datascience|show_output=0

Read more…(1655 words)

Category: Machine Learning

Contributed 59%

11.

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Pandas: Modifying DataFrames

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Category: Machine Learning

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