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Demystifying machine learning for beginners
To start off, I presume that you would like to learn machine learning for the following reasons:
In this tutorial we're going to be making a python script, that will:
First, let's import the required libraries:
import pandas
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.neighbors import KNeighborsClassifier
from sklearn import preprocessingIf you don't have some of these installed, you can install them by using
pip install or pip3 installNext, we're going to load-in the dataset which we're going to be using for this project:
import pandas
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.neighbors import KNeighborsClassifier
from sklearn import preprocessing
df = pandas.read_csv('IRIS.csv')For this project, we're going to be using the classic iris dataset which you can download here
Add these lines of code to your python script:
model = KNeighborsClassifier(n_neighbors=3)
features = list(zip(df["sepal_length"], df["sepal_width"]))
model.fit(features,df["species"])Let me explain...
Before, we start predicting new pieces of data, let's graph our dataset using a scatter graph. In our graph, the X axis will be representing the "sepal_length" and the Y axis will be representing the "sepal_width". We're also going to color code the different species of Iris flowers by adding
hue='species'. and then finally we'll define the data that we're going to be graphing as our Iris dataset by adding data=df to the end:sns.scatterplot(x='sepal_length', y='sepal_width',
hue='species', data=df, )
# Placing Legend outside the Figure
plt.legend(bbox_to_anchor=(1, 1), loc=1)
plt.show()Here's how the scatter graph should look:
To start classifying new pieces of data, first comment out the last code snippet like so:
import pandas
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.neighbors import KNeighborsClassifier
from sklearn import preprocessing
df = pandas.read_csv('IRIS.csv')
model = KNeighborsClassifier(n_neighbors=3)
features = list(zip(df["sepal_length"], df["sepal_width"]))
model.fit(features,df["species"])
"""sns.scatterplot(x='sepal_length', y='sepal_width',
hue='species', data=df, )
# Placing Legend outside the Figure
plt.legend(bbox_to_anchor=(1, 1), loc=1)
plt.show()
"""Then add these 2 lines of code to the end of your script:
predicted = model.predict([[4.6,5.8]])
print(predicted)This will simply predict which species of Iris flower is one that has a sepal_length of 4.6 and a sepal_width of 5.8.
['Iris-setosa']This means that our new mystery Iris flower has been classified as an "Iris-setosa".
You've made your first machine learning project!
You can now experiment with this code as well as try some new datasets(you can find lots of great ones on https://www.kaggle.com/).
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Byeeeee👋
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