Classifier for Flower Species

Objectives

We will use iris dataset to create a classifier that can classify the various species of flowers

Here is a list of Tasks we’ll be doing:

  1. Use Pandas to load data sets.
  2. Identify the target and features.
  3. Use Logistic Regression to build a classifier.
  4. Use metrics to evaluate the model.
  5. Make predictions using a trained model.

Setup

We will be using the following libraries:

  • pandas for managing the data.
  • sklearn for machine learning and machine-learning-pipeline related functions.

Install Libraries

# All Libraries required for this lab are listed below. Already installed so this code block is commented out
pip install pandas==1.3.4
pip install scikit-learn==1.0.2

Suppress Warnings

To suppress warnings generated by our code, we’ll use this code block

# To suppress warnings generated by the code
def warn(*args, **kwargs):
    pass
import warnings
warnings.warn = warn
warnings.filterwarnings('ignore')

Import Libraries

import pandas as pd
from sklearn.linear_model import LogisticRegression

Data - Task 1

Load

# the data set is available at the url below.
URL = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-BD0231EN-SkillsNetwork/datasets/iris.csv"

# using the read_csv function in the pandas library, we load the data into a dataframe
df = pd.read_csv(URL)
# you can sample the data with df.sample(5)

SepalLengthCm		 SepalWidthCm PetalLengthCm PetalWidthCm Species
66 5.6 3.0 4.5 1.5 Iris-versicolor
75 6.6 3.0 4.4 1.4 Iris-versicolor
73 6.1 2.8 4.7 1.2 Iris-versicolor
126 6.2 2.8 4.8 1.8 Iris-virginica
49 5.0 3.3 1.4 0.2 Iris-setosa 
df.shape
(150, 5)

Plot Data

  • You can see that there are 3 species and 50 flowers for each type of species.
df.Species.value_counts().plot.bar()

Define Targets/Features - Task 2

Target

In Classification models, the Target is the value our machine learning model needs to classify

So, in this example we are trying to classify the species

Features

The feature(s) is/are the data columns we will provide our model as input from which our model learns from

In our example let’s provide the model with these Features, and see how accurate it will be in predicting the species

  • SepalLengthCm
  • SepalWidethCm
  • PetalLengthCm
  • PetalWidthCm
target = df["Species"]
features = df[["SepalLengthCm","SepalWidthCm","PetalLengthCm","PetalWidthCm"]]

Build & Train Classifier - Task 3

Logistic Regression Model

Create a Logistic Regression model

classifier = LogisticRegression()

Train Logistic Regression Model

Response will be:

  • LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True, intercept_scaling=1, max_iter=100, multi_class=‘warn’, n_jobs=None, penalty=‘l2’, random_state=None, solver=‘warn’, tol=0.0001, verbose=0, warm_start=False)
classifier.fit(features,target)
LogisticRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Evaluate Model - Task 4

Now that the model has been trained on the data/features provided above, let’s evaluate it

Score

The higher the better

#Higher the score, better the model.
classifier.score(features,target)
0.9733333333333334

Predict

Let’s make some predictions:

  • Let’s predict the species of a flower with
  • SepalLengthCm = 5.4
  • SepalWidthCm = 2.6
  • PetalLengthCm = 4.1
  • PetalWidthCm = 1.3
classifier.predict([[5.4,2.6,4.1,1.3]])
array(['Iris-versicolor'], dtype=object)