Category: Python

Machine Learning – Logistic Regression

Logistic Regression

Logistic regression aims to solve classification problems. It does this by predicting categorical outcomes, unlike linear regression that predicts a continuous outcome.

In the simplest case there are two outcomes, which is called binomial, an example of which is predicting if a tumor is malignant or benign. Other cases have more than two outcomes to classify, in this case it is called multinomial. A common example for multinomial logistic regression would be predicting the class of an iris flower between 3 different species.

Here we will be using basic logistic regression to predict a binomial variable. This means it has only two possible outcomes. Continue reading Machine Learning – Logistic Regression

Machine Learning – Hierarchical Clustering

Hierarchical Clustering

Hierarchical clustering is an unsupervised learning method for clustering data points. The algorithm builds clusters by measuring the dissimilarities between data. Unsupervised learning means that a model does not have to be trained, and we do not need a “target” variable. This method can be used on any data to visualize and interpret the relationship between individual data points.

Here we will use hierarchical clustering to group data points and visualize the clusters using both a dendrogram and scatter plot. Continue reading Machine Learning – Hierarchical Clustering

Machine Learning – Confusion Matrix

What is a confusion matrix?

It is a table that is used in classification problems to assess where errors in the model were made.

The rows represent the actual classes the outcomes should have been. While the columns represent the predictions we have made. Using this table it is easy to see which predictions are wrong. Continue reading Machine Learning – Confusion Matrix

Machine Learning – Decision Tree

Decision Tree

In this chapter we will show you how to make a “Decision Tree”. A Decision Tree is a Flow Chart, and can help you make decisions based on previous experience.

In the example, a person will try to decide if he/she should go to a comedy show or not.

Luckily our example person has registered every time there was a comedy show in town, and registered some information about the comedian, and also registered if he/she went or not. Continue reading Machine Learning – Decision Tree

Machine Learning – Train/Test

Evaluate Your Model

In Machine Learning we create models to predict the outcome of certain events, like in the previous chapter where we predicted the CO2 emission of a car when we knew the weight and engine size.

To measure if the model is good enough, we can use a method called Train/Test.

What is Train/Test

Train/Test is a method to measure the accuracy of your model.

It is called Train/Test because you split the data set into two sets: a training set and a testing set.

80% for training, and 20% for testing.

You train the model using the training set.

You test the model using the testing set.

Train the model means create the model.

Test the model means test the accuracy of the model.

Continue reading Machine Learning – Train/Test

Machine Learning – Scale

Scale Features

When your data has different values, and even different measurement units, it can be difficult to compare them. What is kilograms compared to meters? Or altitude compared to time?

The answer to this problem is scaling. We can scale data into new values that are easier to compare.

Take a look at the table below, it is the same data set that we used in the multiple regression chapter, but this time the volume column contains values in liters instead of cm3 (1.0 instead of 1000). Continue reading Machine Learning – Scale

Machine Learning – Multiple Regression

Multiple Regression

Multiple regression is like linear regression, but with more than one independent value, meaning that we try to predict a value based on two or more variables.

Take a look at the data set below, it contains some information about cars.

Car Model Volume Weight CO2
Toyota Aygo 1000 790 99
Mitsubishi Space Star 1200 1160 95
Skoda Citigo 1000 929 95
Fiat 500 900 865 90
Mini Cooper 1500 1140 105
VW Up! 1000 929 105
Skoda Fabia 1400 1109 90
Mercedes A-Class 1500 1365 92
Ford Fiesta 1500 1112 98
Audi A1 1600 1150 99
Hyundai I20 1100 980 99
Suzuki Swift 1300 990 101
Ford Fiesta 1000 1112 99
Honda Civic 1600 1252 94
Hundai I30 1600 1326 97
Opel Astra 1600 1330 97
BMW 1 1600 1365 99
Mazda 3 2200 1280 104
Skoda Rapid 1600 1119 104
Ford Focus 2000 1328 105
Ford Mondeo 1600 1584 94
Opel Insignia 2000 1428 99
Mercedes C-Class 2100 1365 99
Skoda Octavia 1600 1415 99
Volvo S60 2000 1415 99
Mercedes CLA 1500 1465 102
Audi A4 2000 1490 104
Audi A6 2000 1725 114
Volvo V70 1600 1523 109
BMW 5 2000 1705 114
Mercedes E-Class 2100 1605 115
Volvo XC70 2000 1746 117
Ford B-Max 1600 1235 104
BMW 2 1600 1390 108
Opel Zafira 1600 1405 109
Mercedes SLK 2500 1395 120

We can predict the CO2 emission of a car based on the size of the engine, but with multiple regression we can throw in more variables, like the weight of the car, to make the prediction more accurate. Continue reading Machine Learning – Multiple Regression

Machine Learning – Polynomial Regression

Polynomial Regression

If your data points clearly will not fit a linear regression (a straight line through all data points), it might be ideal for polynomial regression.

Polynomial regression, like linear regression, uses the relationship between the variables x and y to find the best way to draw a line through the data points.

How Does it Work?

Python has methods for finding a relationship between data-points and to draw a line of polynomial regression. We will show you how to use these methods instead of going through the mathematic formula.

In the example below, we have registered 18 cars as they were passing a certain tollbooth.

We have registered the car’s speed, and the time of day (hour) the passing occurred.

The x-axis represents the hours of the day and the y-axis represents the speed: Continue reading Machine Learning – Polynomial Regression

Machine Learning – Linear Regression

Regression

The term regression is used when you try to find the relationship between variables.

In Machine Learning, and in statistical modeling, that relationship is used to predict the outcome of future events.

Linear Regression

Linear regression uses the relationship between the data-points to draw a straight line through all them.

This line can be used to predict future values.

In Machine Learning, predicting the future is very important. Continue reading Machine Learning – Linear Regression

Machine Learning – Scatter Plot

Scatter Plot

A scatter plot is a diagram where each value in the data set is represented by a dot.

The Matplotlib module has a method for drawing scatter plots, it needs two arrays of the same length, one for the values of the x-axis, and one for the values of the y-axis:

x = [5,7,8,7,2,17,2,9,4,11,12,9,6]

y = [99,86,87,88,111,86,103,87,94,78,77,85,86]

The x array represents the age of each car.

The y array represents the speed of each car. Continue reading Machine Learning – Scatter Plot