Python: Modeling Your Data

Name: Python: Modeling Your Data
Author: Briana Brownell

Learn how to model data

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Course: On-Demand

Hi, I'm Brianna Brownell, and in these lessons, we're going to talk about how to use a number of different techniques to model your data. In this lesson, we'll cover the most common modeling technique, linear regression. So I have my colab file in a similar state as I've had it before, but I've got one slightly different thing here. Instead of using our GSS file, instead, I'm uploading a dataset that looks at a time series of happiness through time. This is collected every time the general social survey is run, and there is data from several decades that we can look at. So if I load it and look at the head of the data file, I see that these are all of the sort of possible answer options. So what I want to do first is to create some new variables so that I can look at the percentage of individuals who indicated they are very happy, pretty happy, or not too happy in each year. So I'm simply doing that by creating a number of new variables with a formula. So what I would like to do is I would like to run a linear regression to see whether the trend of individuals who are very happy is changing through time. So in order to do that, I am bringing in the linear regression module from sklearn. The first thing that I'm doing is I need to reshape the values just slightly, because in this case, I have only one input variable that I want to use in my linear regression. In this case, I'm reshaping it because the only variable that I want to use to predict the percentage of individuals who are very happy is the year in my linear regression. So I have a simpler case than you may have if you're trying to do a prediction using many different input variables to see how well a number of them predict a certain outcome variable. So let's see how that looks. So you can see two things here. I've got a scatter plot that shows the data from the survey through time. And this line here is the linear regression formula. And so you can see that there is definitely a downward trend through time. From here, I can get the exact values of the trend line. And I can also use it to predict values in the future. Another common way of doing a regression is by using stochastic gradient descent. Now, this method is gaining a lot of popularity because the way in which it calculates the coefficients of the model can at times be more efficient and faster than linear regression. So we set it up in a very similar way, except in this time, we need to first standardize our variables in order for it to work. We also need to indicate the maximum number of iterations that the Regressor will go through to calculate the coefficients and make the prediction. So let's see how it does. So here you can see that we have a slightly different result. And this is because the stochastic gradient descent is using a different method compared to our method in order to calculate the coefficients for the regression. Linear models are useful in many different areas and are one of the go-to modeling methods that you will encounter when doing data analysis. In the next lesson, we'll talk about another type of regression analysis, logistic regression. Thanks for watching.

Beginner Provider : Briana Brownell 8 Lessons · 41m in English

Course Description

This module covers various machine learning methods for modeling data, including supervised and unsupervised methods. The course starts with linear regression, where you will learn how to build and evaluate linear regression models in Python, including using the techniques of least squares and gradient descent. You will learn how to build and evaluate logistic regression models, a method used for modeling data with a binary outcome.

The course also covers other supervised machine learning methods, such as decision trees and support vector machines, and how to implement these models in Python. Additionally, you will learn about ensemble methods, which combine the predictions of multiple models to improve performance, including using bagging and boosting techniques. You will also learn about dimension reduction, which is a technique for reducing the number of dimensions in a dataset using techniques such as principal component analysis. This will help you to understand how to reduce the complexity of your data and make it easier to model.

The course also covers clustering methods, including hierarchical clustering and K-means, which are used to group data into clusters based on similarities. Additionally, you will learn about neural networks and deep learning, including an overview of neural networks and how they can be used to model data, as well as some of the challenges using them. Finally, the module covers other common modeling methods including Association Rules and Anomaly detection. Overall, this module will provide you with a comprehensive understanding of how to effectively model data using various machine learning methods, and will equip you with the knowledge and skills needed to analyze and make predictions based on your data.

What You'll Learn

Build and evaluate linear regression models in Python using least squares and gradient descent
Build and evaluate logistic regression models for data with a binary outcome
Implement decision trees and support vector machines in Python
Apply ensemble methods using bagging and boosting to improve model performance
Reduce data complexity with dimension reduction techniques such as principal component analysis
Group data into clusters using hierarchical clustering and K-means

Key Takeaways

The module covers both supervised and unsupervised machine learning methods for modeling data.
Linear regression models can be built and evaluated using least squares and gradient descent, while logistic regression handles binary outcomes.
Ensemble methods combine the predictions of multiple models, using bagging and boosting techniques, to improve performance.
Dimension reduction techniques such as principal component analysis reduce the number of dimensions in a dataset to lower its complexity.
The course also introduces neural networks and deep learning, along with other modeling methods including Association Rules and Anomaly detection.

Frequently Asked Questions

What topics does this course cover?

It covers supervised and unsupervised machine learning methods, including linear and logistic regression, decision trees, support vector machines, ensemble methods, dimension reduction, clustering (hierarchical and K-means), neural networks and deep learning, and other methods such as Association Rules and Anomaly detection.

What will I be able to do after completing this course?

You will understand machine learning methods, be able to implement regression models, and know how to improve the performance of data models.

Which machine learning methods are taught for modeling data with a binary outcome?

The course covers logistic regression, a method used for modeling data with a binary outcome.

Does this course use a specific programming language?

Yes, the course teaches how to build, evaluate, and implement these modeling methods in Python.

What skills does this course help develop?

It develops skills in Data Mining, Data Mining Methods, Machine Learning, Machine Learning Algorithms, Machine Learning Methods, and Machine Learning Model Training.