Tag: Machine Learning
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Will it Rain Tomorrow?
Code and data used can be found here: Repository In this episode we will be expanding on Logistic Regression in Python, implementing much more data pre-processing steps on a larger data set that contains both numerical and categorical data (words). Objective Construct a logistic regression model to predict if it will rain tomorrow in a…
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Logistic Regression in Python
You can view and use the code and data used in this episode here: Link Consider reading Episode 7.1 before continuing, which explains how logistic regression works. How to set up your programming environment can be found at the start of : Episode 4.3 Objective Predict whether it will rain tomorrow in Albury, Australia given the…
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Logistic Regression Explained
Logistic Regression can be thought of as an extension of Linear Regression. With Linear Regression our final output for our model took a single value, however, with logistic regression, we apply an extra function to Linear Regression that puts our final value output into a group i.e. 1 or 0. What is Logistic Regression? Logistic…
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Predicting Weather Data
Code and data used can be found here: Repository This episode combines knowledge from all previous episodes to build, evaluate and improve a ridge regression model that makes predictions for weather data in Hungary, Szeged. Objective Construct a regression model that makes reasonable predictions for Humidity given the follow data: Link Our model should take…
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Cross Validation Explained
So far, when implementing all of our regression models in python, we have been using all of our data to construct our model: This, however, often leads to models which overfit our data and it becomes very difficult to evaluate and make improvements to our model. To address this problem, before creating our model, we…
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Solving Underfitting and Overfitting
Explaining and solving bad models Underfitting and overfitting are both common problems data scientists come across when evaluating their model. It is important you are aware of these issues and what we can do resolve them. Definitions Underfitting: Occurs when our model fails to capture the underlying trend in our data: Models which underfit our…
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Polynomial Regression in Python
Code and data used can be found here: Repository Importing our Data The first step is to import our data into python. We can do that by going on the following link: Data Click on “code” and download ZIP. Locate WeatherDataP.csv and copy it into your local disc under a new file called ProjectData Note: WeatherData.csv…
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Understanding Polynomial Regression
This Article expands on Simple Linear Regression and Multiple Linear Regression, ensure you have a good understanding of these two topic areas before continuing. What is Polynomial Regression? Polynomial Regression is used to capture non-linear relationships between variables. For example: For linear relationships we use Linear Regression. Overview Take a look at the following graph…
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Multiple Linear Regression in Python
Code and data used can be found here: Repository This episode expands on Implementing Simple Linear Regression In Python. We extend our simple linear regression model to include more variables. Setting up your programming environment can be found in the first section of Ep 4.3. Importing our Data The first step is to import our data…
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Understanding Multiple Linear Regression
We have taken a look at Simple Linear Regression in Episode 4.1 where we had one variable x to predict y, but what if now we have multiple variables, not just x, but 𝑥₁,𝑥₂ and 𝑥₃ … to predict y — how would we approach this problem? I hope to explain in this article. Simple Linear Regression Recap…
The Data Series 