Week -7 program: Implementation of KNN USING SKlinear

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Sun Jun 01 2025 14:41:15 GMT+0000 (Coordinated Universal Time)

Week -7 
Implementation of KNN USING SKlinear:
The K-Nearest Neighbors (KNN) algorithm is a simple, versatile machine learning method used for both classification and regression tasks. It makes predictions by finding the "k" closest data points (neighbors) to a new data point in a feature space and using their labels or values to make a prediction for the new point. 
# Import necessary libraries
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score, classification_report

# Load a sample dataset (Iris)
data = load_iris()
X = data.data        # Features
y = data.target      # Labels

# Split into train and test sets (80% train, 20% test)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize the KNN classifier with k=3
knn = KNeighborsClassifier(n_neighbors=3)

# Train the model
knn.fit(X_train, y_train)

# Predict on test data
y_pred = knn.predict(X_test)

# Evaluate the model
print("Accuracy:", accuracy_score(y_test, y_pred))
print("Classification Report:\n", classification_report(y_test, y_pred))

output:

Accuracy: 1.0
Classification Report:
               precision    recall  f1-score   support

           0       1.00      1.00      1.00        10
           1       1.00      1.00      1.00         9
           2       1.00      1.00      1.00        11

    accuracy                           1.00        30
   macro avg       1.00      1.00      1.00        30
weighted avg       1.00      1.00      1.00        30

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#python ##python #strings #comments

Create simple string along with variables

#assign a value to a variable:
types_of_people = 10 
# make a string using variable name:
X = f “there are {types_of_people} types of people.”

Output:
There are 10 types of people

##python #coding #python #programming #pydantic #uuid

how to make default uuid or id for pydantic model

from pydantic import BaseModel, Field
from uuid import UUID, uuid4

class item(BaseModel):
    name : str
    price : float
    item_id : UUID = Field(default_factory = uuid4)


items = []

for i in range(3):
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print(items)

##python #coding #python

How to Encrypt and Decrypt Data in SQLite with Python's Fernet

from cryptography.fernet import Fernet
import sqlite3

# Generate key to use it
#print(Fernet.generate_key())

key = b'rMqQ4gNSsvkubQnn9CmW25PTFDwNlQPUp7YN4qDVSts='
cipher_suite = Fernet(key)

db_name = 'mydb.db'

conn = sqlite3.connect(db_name)
cursor = conn.cursor()

# create a table
cursor.execute('create table if not exists items (name STRING, price INTEGER)')

def add_item(name, price):
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    price = cipher_suite.encrypt(str(price).encode())

    cursor.execute('insert into items (name, price) values (?, ?)', (name, price))

    conn.commit()

add_item('test', 50)

def get_items():
    results = cursor.execute('select * from items').fetchall()
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get_items()

#python ##python ##pythonlists

How to use for loop with range function.

  #use for loop and set the range
for index in range(10):
  Print (index)
           
           #when you run this program, the output will be:
               0 
               1 
               2 
               3 
               4 
               5
               6
               7
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##python ##python2.7 ##codecademy

Calculator For Best Shipping Option with Grounded Values

def ground_cost(weight):
  if weight <= 2:
    return weight * 1.50 + 20
  elif weight > 2 and weight <= 6:
    return weight * 3.00 + 20
  elif weight > 6 and weight <= 10:
    return weight * 4.00 + 20
  elif weight > 10:
    return weight * 4.75 + 20

premground_cost = 125.0

def drone_cost(weight):
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  elif weight > 2 and weight <= 6:
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  elif weight > 6 and weight <= 10:
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def pricecheck(weight):
  if ground_cost(weight) < premground_cost and ground_cost(weight) < drone_cost(weight):
    return "Ground shipping is the cheapest option for the selected weight. Your total cost will be $" + str(ground_cost(weight)) + "."
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Python virtualenv

# instala la libreria
pip install virtualenv

# genera el virtualenv env
virtualenv env

#activo el virtual env
source env/bin/activate

#guardar todo las librerias en un archivo de texto
pip freeze > requirements.txt

#instalar las librerias de un env a partir de requirements.txt
source env/bin/activate
pip install -r requirements.txt

##python ##outlook

Send an outlook email using python with out SMTP

import win32com.client
from win32com.client import Dispatch, constants

const=win32com.client.constants
olMailItem = 0x0
obj = win32com.client.Dispatch("Outlook.Application")
newMail = obj.CreateItem(olMailItem)
newMail.Subject = "I AM SUBJECT!!"
# newMail.Body = "I AM\nTHE BODY MESSAGE!"
newMail.BodyFormat = 2 # olFormatHTML https://msdn.microsoft.com/en-us/library/office/aa219371(v=office.11).aspx
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attachment1 = r"C:\Temp\example.pdf"
newMail.Attachments.Add(Source=attachment1)
newMail.display(True)
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##python ##pandas ##format

f string format

number = 1_000_000.50
f"${number:,.0f}"
'$1,000,000'

f"${number:,.2f}"
'$1,000,000.50'

##python ##for-loop ##if-statements

A prime Checker Code

## Your code should check if each number in the list is a prime number
check_prime = [26, 39, 51, 53, 57, 79, 85]

## write your code here
## HINT: You can use the modulo operator to find a facto
prime_numbers = []
counter_prime = 0
for prime in check_prime:
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       print("{} is not a prime number".format(prime))
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        print("{} is a prime number".format(prime))
    counter_prime += 1

print("These are the prime numbers : {}".format(prime_numbers))



## Altenatively,

check_prime = [26, 39, 51, 53, 57, 79, 85]

# iterate through the check_prime list
for num in check_prime:

# search for factors, iterating through numbers ranging from 2 to the number itself
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# number is not prime if modulo is 0
        if (num % i) == 0:
            print("{} is NOT a prime number, because {} is a factor of {}".format(num, i, num))
            break

# otherwise keep checking until we've searched all possible factors, and then declare it prime
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##python ##imageprocessing

Box Blur | MLPro

import numpy as np

# Please do not change the below function name and parameters
def box_blur(image):
    # print(sum(image))
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    image =image.astype(np.float64)
    # print(type(image))
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##python ##for-loop ##if-statements

Installing Virtualenv on Windows, MacOX, Linux

STEP 1: UNIVERSAL INSTALL THE VIRTUAL ENV PACKAGE ON YOUR TERMINA
$ pip install virtualenv // this installs the virtualenv

STEP 2: MOVE TO THE DIRECTORY OF YOUR CHOICE 
$ cd Documents/chisomloius/Data-labelling //for example


STEP 3: CREATE THE VIRTUAL ENV NAME OF YOUR CHOICE //
$ virtualenv [name of virtual environment] or python -m virtualenv [name of virtual environment] //this create a directory with the name e.g virtual_env
$ virtualenv env or python -m virtualenv virtual_env //this create a directory with the name 'virtual_env'


STEP 4: ACTIVATE/DEACTIVATE VIRTUAL ENV
$ .\/virtual_env/scripts/activate // use this to activate for command prompt
$ .\/virtual_env/Scripts/activate.ps1 // use this to activate for Powershell
$ source bin/activate // use this to MacOSX and Linux
---------------------------------
$ deactivate // deactiate virtaul env

STEP 5: ADDING OTHER PACKAGES ON THE VIRTUAL ENVIRONMENT OR USING THE REQUIREMENT FILE
pip install -r requirements.txt //import more than one package
pip install pageName

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Box-Muller Transformation

#!/usr/bin/python3

import numpy as np

# Please do not change the below function name and parameters
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    z2 = np.sqrt(-2* np.log(u1)) * np.sin(2*np.pi*u2)
    return (z1,z2)

##python ##machinelearning

Bubble Sort | MLPro

def bubbleSort(values):
    for i in range(len(values)):
        for j in range(len(values)-1):
            if values[j] > values[j+1]:
                values[j] ,values[j+1] = values[j+1], values[j]
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##pandas ##python

find the percentage of missing values in pandas

percent_missing = df.isnull().sum() * 100 / len(df)
missing_value_df = pd.DataFrame({'column_name': df.columns,
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##flask ##python

Flask Basic Configuration

from flask import Flask, redirect, url_for, request, render_template, flash

app = Flask(__name__)
  
@app.route('/', methods = ['GET'])
def index():
    return render_template('index.html')
  
if __name__ == '__main__':
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Week -7 program: Implementation of KNN USING SKlinear

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