K-Means clustering is an unsupervised machine learning model. Unsupervised learning is where we do not provide the model with the actual outputs from the data. Unsupervised learning aims to model the underling structure or distribution […]
Category: ML
Working with date time features in your ML models
Having a date in our dataset can really improve our predictions. Imagine an ice cream company, their data has a seasonal aspect (more sales in summer), so predicting sales information without dates in our data […]
ML Scenario Part 3: Reducing dimensionality to improve accuracy
Reducing dimensionality can sometimes help to drastically improve your model accuracy. In the case of our sample dataset, I we don’t really expect it to – as there are not many features in the first […]
How does machine learning work?
Machine learning uses statistical techniques to give computer systems the ability to ‘learn’ rather than being explicitly programmed. By learning from historical inputs. we’re able to achieve far greater accuracy in our predictions & constantly […]
What are the types of machine learning?
Supervised learning Supervised learning is where we provide the model with the actual outputs from the data. This let’s it build a picture of the data and form links between the historic parameters (or features) […]
So, what is a Random Forest?
A random forest is a grouping of decision trees. Data is run through each tree within a forest and provides a classification. It’s important that we do this, because, while decision trees are great & […]
ML Scenario Part 2: Tuning our model hyper parameters
In the last article, we built a simple machine learning model which predicted the quality of wine based on a number of features. In this article, we’re going to tune that model. Our starting point […]
Simple Machine Learning Scenario: predicting quality
In this article, I am going to walk through a really simple machine learning scenario, using data from Kaggle. In future articles, I will look at tuning this model to improve the accuracy and see […]
Using Gridsearch for Hyper Parameter Tuning
Hyperparameter tuning can be quite the task. It can be heavily manual and rely on gut feel as to what the value for that parameter should roughly be. If we did it in a completely […]
Validating error estimation of our models with Cross Validation
Let’s say we have the below dataset. It’s a list of patients, with some health KPI’s and a boolean field which says whether they have diabetes or not. If you had thousands of patients, you […]
Dimensionality Reduction for Machine Learning using Principal Component Analysis (PCA)
Principal Component Analysis (PCA) is a linear transformation algorithm which sets out to simplify complex, highly dimensional data into fewer features; while retaining the trends and patterns in the data that we require for our […]
Machine Learning Data Prep: Standardization & Encoding
There are so many things to consider when getting your data ready for machine learning models. In this article, I am going to cover encoding and standardization (or scaling) our data. Standardization / scaling to […]
