HelloWorld.py

 # Import necessary modules

from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
import numpy as np
import matplotlib.pyplot as plt

irisData = load_iris()

# Create feature and target arrays
X = irisData.data
y = irisData.target

# Split into training and test set
X_train, X_test, y_train, y_test = train_test_split(
			X, y, test_size = 0.2, random_state=42)

neighbors = np.arange(1, 9)
train_accuracy = np.empty(len(neighbors))
test_accuracy = np.empty(len(neighbors))

# Loop over K values
for i, k in enumerate(neighbors):
	knn = KNeighborsClassifier(n_neighbors=k)
	knn.fit(X_train, y_train)
	
	# Compute traning and test data accuracy
	train_accuracy[i] = knn.score(X_train, y_train)
	test_accuracy[i] = knn.score(X_test, y_test)

# Generate plot
plt.plot(neighbors, test_accuracy, label = 'Testing dataset Accuracy')
plt.plot(neighbors, train_accuracy, label = 'Training dataset Accuracy')

plt.legend()
plt.xlabel('n_neighbors')
plt.ylabel('Accuracy')
plt.show()

created 3 years ago by pavan

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Tutorial & Syntax help

Loops

1. If-Else:

When ever you want to perform a set of operations based on a condition IF-ELSE is used.

if conditional-expression
    #code
elif conditional-expression
    #code
else:
    #code

Note:

Indentation is very important in Python, make sure the indentation is followed correctly

2. For:

For loop is used to iterate over arrays(list, tuple, set, dictionary) or strings.

Example:

mylist=("Iphone","Pixel","Samsung")
for i in mylist:
    print(i)

3. While:

While is also used to iterate a set of statements based on a condition. Usually while is preferred when number of iterations are not known in advance.

while condition  
    #code

Collections

There are four types of collections in Python.

1. List:

List is a collection which is ordered and can be changed. Lists are specified in square brackets.

Example:

mylist=["iPhone","Pixel","Samsung"]
print(mylist)

2. Tuple:

Tuple is a collection which is ordered and can not be changed. Tuples are specified in round brackets.

Example:

myTuple=("iPhone","Pixel","Samsung")
print(myTuple)

Below throws an error if you assign another value to tuple again.

myTuple=("iPhone","Pixel","Samsung")
print(myTuple)
myTuple[1]="onePlus"
print(myTuple)

3. Set:

Set is a collection which is unordered and unindexed. Sets are specified in curly brackets.

Example:

myset{"iPhone","Pixel","Samsung"}
print{myset}

4. Dictionary:

Dictionary is a collection of key value pairs which is unordered, can be changed, and indexed. They are written in curly brackets with key - value pairs.

Example:

mydict = {
    "brand" :"iPhone",
    "model": "iPhone 11"
}
print(mydict)

Supported Libraries

Following are the libraries supported by OneCompiler's Python compiler

Name	Description
NumPy	NumPy python library helps users to work on arrays with ease
SciPy	SciPy is a scientific computation library which depends on NumPy for convenient and fast N-dimensional array manipulation
SKLearn/Scikit-learn	Scikit-learn or Scikit-learn is the most useful library for machine learning in Python
Pandas	Pandas is the most efficient Python library for data manipulation and analysis
Matplotlib	Matplotlib is a cross-platform, data visualization and graphical plotting library for Python programming and it's numerical mathematics extension NumPy
DOcplex	DOcplex is IBM Decision Optimization CPLEX Modeling for Python, is a library composed of Mathematical Programming Modeling and Constraint Programming Modeling