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tutorial

Correlation Analysis: Multivariable [Under Construction]

Note: This tutorial is currently under construction. The final version is expected to be ready on or before June 15th 2019.

Correlation analysis is statistical evaluation method used to study the strength of relationship between two numerical variables. This type of analysis is useful when we want to check if there exist any positive or negative connections between the variables.

We will start by loading the wine_v2 , tips and questions_data datasets.

type=codeblock|id=load_data1|autocreate=datascience|show_output=0

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Category: Data Science and Big Data

Contributed 6%

3.

tutorial

Correlation Analysis: Two Variables

Correlation analysis can help us understand whether, and how strongly, a pair of variables are related.

In data science and machine learning, this can help us understand relationships between features/predictor variables and outcomes. It can also help us understand dependencies between different feature variables.

For example:

- How strong is the correlation between mental stress and cardiac issues?
- Is there a correlation between literacy rate and frequency of criminal activities?

This tutorial will help you learn the different tech...

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Category: Data Science and Big Data

Contributed 46%

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tutorial

Pro Content

Pandas: Indexing and Slicing

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Category: Machine Learning

Contributed 94%

5.

tutorial

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

Contributed 58%

6.

tutorial

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

Contributed 77%

7.

tutorial

Categorical Features

What is a categorical feature?

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 catego...

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Category: Machine Learning

Contributed 75%

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tutorial

Pro Content

Generalization and Overfitting

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Category: Machine Learning

Contributed 51%

9.

tutorial

Pro Content

Pandas: Apply functions and GroupBy

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Category: Machine Learning

Contributed 59%

10.

tutorial

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Pandas: Modifying DataFrames

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Category: Machine Learning

Contributed 42%

11.

tutorial

Pandas: Creating Series and DataFrames

In the previous tutorial, you have used **DataFrame** which is a 2-dimensional data structure supported by Pandas, which looks and behaves like a *table*.

In this tutorial, you will learn about **Series** which is a 1-dimensional data structure supported by Pandas. In fact, in Pandas, each column of a DataFrame is a Series.

We will also learn in depth about creating DataFrames, adding rows to a DataFrame and converting DataFrames to other formats, such as NumPy arrays or Python lists and dictionaries.

Before we start, let's import the Pandas and NumPy libraries, as we will need them throughout the tutorial.

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

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Category: Machine Learning

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