This code is performing several steps involving data exploration, visualization,...

August 24, 2025 at 06:05 PM

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris

iris = load_iris()
#print(iris.feature_names)
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target
#print(df.head())

df0 = df[df['target'] == 0]
df1 = df[df['target'] == 1]
#df2 = df[df['target'] == 2]

"""plt.xlabel('sepal length (cm)')
plt.ylabel('sepal width (cm)')
plt.scatter(df0['sepal length (cm)'], df0['sepal width (cm)'], color='red', marker='+')
plt.scatter(df1['sepal length (cm)'], df1['sepal width (cm)'], color='blue', marker='*')
#plt.scatter(df2['sepal length (cm)'], df2['sepal width (cm)'], color='green', marker='o')
plt.show()"""

"""plt.xlabel('petal length (cm)')
plt.ylabel('petal width (cm)')
plt.scatter(df0['petal length (cm)'], df0['petal width (cm)'], color='red', marker='+')
plt.scatter(df1['petal length (cm)'], df1['petal width (cm)'], color='blue', marker='*')
plt.show()"""

from sklearn.model_selection import train_test_split

x = df.drop('target', axis='columns')
y = df['target']

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)

from sklearn.svm import SVC

model = SVC()
model.fit(x_train, y_train)
print(model.score(x_test, y_test))
print(model.get_params())

This code is performing several steps involving data exploration, visualization, and machine learning using the Iris dataset from the sklearn library. Here's what it does step by step:

Libraries and Dataset Setup

Imports necessary libraries:
- pandas for data handling.
- numpy for numerical operations.
- matplotlib.pyplot for data visualization.
- sklearn.datasets to load the Iris dataset.
Loads the Iris dataset using load_iris() and converts it into a Pandas DataFrame where columns correspond to the dataset's feature names. It also adds a target column representing the class labels of the Iris species (0, 1, 2).

Data Filtering

Filters the dataset:
- The rows of the DataFrame are grouped based on the target class (e.g., 0, 1).
- Separate DataFrames (df0 and df1) are created for target classes 0 and 1. (Code for class 2 is commented out.)

Data Visualization (Commented Out)

The visualization code (commented out) creates scatter plots for feature relationships:
- One plot uses sepal length and sepal width as features.
- Another uses petal length and petal width.
The scatter plots distinguish between class 0 and class 1 using different colors and markers. Note that these visualizations are currently commented out, so they will not execute.

Preparing Data for Classification

Splits the data into features (x) and target values (y):
- x contains all feature columns.
- y contains the target column (the class labels).
Splits the dataset into training and testing sets:
- Uses train_test_split with an 80-20 split (80% for training, 20% for testing).
- x_train and y_train are used for training, while x_test and y_test are reserved for testing.

Building and Training the Model

Trains a Support Vector Classifier (SVC):
- The SVC model from sklearn is instantiated and trained on the training data (using x_train and y_train).
- The classifier learns to classify between the target classes.
Evaluates the trained model:
- Computes and prints the accuracy of the classifier on the test data using model.score(x_test, y_test).
Prints the model's hyperparameters:
- Uses model.get_params() to print the parameters of the SVC.

End Results

The program outputs two things to the console:
1. The accuracy of the SVC on the test set (a value between 0 and 1).
2. A dictionary containing the current hyperparameters of the SVC model.

Summary

In short, this code:

Loads the Iris dataset.
Filters and prepares the data for analysis and machine learning.
(Optionally) visualizes the data in scatter plots (currently commented out).
Trains a Support Vector Classifier to classify the Iris species based on the provided four features.
Evaluates its performance and reports the model accuracy and configuration.

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