PClassifier.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Dec  5 16:45:08 2019

@author: eleanorezimah
"""
# Load required libraries
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import Perceptron
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import numpy as np
# Load the iris dataset
iris = datasets.load_iris()

# Create our X and y data
X = iris.data
y = iris.target
# View the first five observations of our y data
y[:5]
# View the first five observations of our x data.
# Notice that there are four independent variables (features)
X[:5]
# Split the data into 70% training data and 30% test data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
# Train the scaler, which standarizes all the features to have mean=0 and unit variance
sc = StandardScaler()
sc.fit(X_train)
# Apply the scaler to the X training data
X_train_std = sc.transform(X_train)

# Apply the SAME scaler to the X test data
X_test_std = sc.transform(X_test)
# Create a perceptron object with the parameters: 40 iterations (epochs) over the data, and a learning rate of 0.1
ppn = Perceptron(eta0=0.1, random_state=0)

# Train the perceptron
ppn.fit(X_train_std, y_train)
# Apply the trained perceptron on the X data to make predicts for the y test data
y_pred = ppn.predict(X_test_std)
# View the predicted y test data
y_pred
# View the true y test data
y_test
# View the accuracy of the model, which is: 1 - (observations predicted wrong / total observations)
print('Accuracy: %.2f' % accuracy_score(y_test, y_pred))