Car price prediction using Machine learning python

This blog contains the source code for Machine learning project car price prediction using python.

Step 1: Installing libraries

*pip install numpy

*pip install pandas

*pip install sklearn

Step 2: Importing the libraries

import pandas as pd

import numpy as np

from sklearn import linear_model

Step 3: Reading the CSV

df=pd.read_csv('car_data.csv')

df

Step 4: Preprocessing 1:

inputs=df.drop(['Car_Name','Owner','Seller_Type'],axis='columns') 

target=df.Selling_Price

inputs

Step 5: Preprocessing 2

from sklearn.preprocessing import LabelEncoder

Numerics=LabelEncoder()

inputs['Fuel_Type_n']=Numerics.fit_transform(inputs['Fuel_Type'])

inputs['Transmission_n']=Numerics.fit_transform(inputs['Transmission'])

inputs

Step 6: Dropping the string columns

inputs_n=inputs.drop(['Fuel_Type','Transmission','Selling_Price'],axis='columns') 

inputs_n

Step 7: Implemention of Linear regression & Prediction

model=linear_model.LinearRegression()

model.fit(inputs_n,target)

pred=model.predict([[2013,9.54,430000,1,1]])

Fullcode: Github

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OCR using Pytesseract library in python ( 20 lines )

This blog contains code for the text detection using open-cv and Pytesseract libraries in python

STEP1: Installation of libraries 

*pip install OpenCV-python

*pip install pillow

*pip install Pytesseract

STEP2: Import the libraries 

import cv2
from PIL import Image
from pytesseract import pytesseract

STEP3: Accessing webcam

camera=cv2.VideoCapture(0)

STEP4: The Loop

while True:

    _,image=camera.read()

    cv2.imshow('image',image)

    if cv2.waitKey(1)& 0xFF==ord('s'):

        cv2.imwrite('test1.jpg',image)

        break

camera.release()

cv2.destroyAllWindows()

STEP5: Converting Text to speech

def tesseract():

    path_to_tesseract = r"C:\Program Files\Tesseract-OCR\tesseract.exe"

    image_path = "test1.jpg"

    pytesseract.tesseract_cmd = path_to_tesseract

    text = pytesseract.image_to_string(Image.open(image_path))

    print(text[:-1])

tesseract()

Full code :GIthub

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Salary prediction using python

This blog contains the code for salary prediction machine learning project using python.

STEP1:Installing requirements:

*pip install numpy

*pip install pandas

*pip install sklearn

STEP2:Importing modules

import numpy as np

import pandas as pd

STEP3:Reading CSV

data=pd.read_csv('Salary_Data.csv') # Dataset in Github
data

STEP4:Reshaping

x=data.YearsExperience.values.reshape(-1,1)

y=data.Salary.values.reshape(-1,1)

STEP5:Spliting the dataset

from sklearn.model_selection import train_test_split

xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.3,random_state=0)

STEP6: LinearRegression formula

from sklearn.linear_model import LinearRegression

model=LinearRegression()

STEP7: Applying the formula & prediction

model.fit(x,y)

next_salary=model.predict([[6]])

print(int(next_salary))

STEP8: Accuray

model.score(xtrain,ytrain)

Full code:Github

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Weather prediction using python Machine learning project ( Naïve Bayes )

 This blog contains the code for Weather prediction machine learning project using python Programming

STEP1: Installing Dependencies

*pip install pandas

*pip install sklearn

STEP2: Importing the libraries

#NaiveBayes project (Weather Prediction)

#Required Modules

import pandas as pd

from sklearn.preprocessing import LabelEncoder

from sklearn.naive_bayes import GaussianNB

STEP3:Reading the CSV

df = pd.read_csv("new_dataset.csv")

df

STEP4:Encoding the strings into numbers:

Numerics=LabelEncoder()

STEP5:Dropping the target column

inputs=df.drop('Play',axis='columns')

target=df['Play']

target

STEP6:Creating new dataframe

inputs['outlook_n']= outlook_at.fit_transform(inputs['Outlook'])

inputs['Temp_n']= outlook_at.fit_transform(inputs['Temp'])

inputs['Hum_n']= outlook_at.fit_transform(inputs['Humidity'])

inputs['win_n']= outlook_at.fit_transform(inputs['Windy'])

inputs

STEP7: Dropping the string columns

inputs_n=inputs.drop(['Outlook','Temp','Humidity','Windy'],axis='columns')

inputs_n

STEP8: Applying GaussianNB

classifier = GaussianNB()

classifier.fit(inputs_n,target)

STEP9: Prediction

classifier.predict([[0,0,0,1]])

Video explanation:

                                                               

Fullcode:Github

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Voice Translator using python ( 25 lines )

Hello python programmers, This blog contains the code for voice translator using python

STEP 1: Libraries required

*pip install speech Recognition

*pip install google_trans_new

*pip install pyttsx3

STEP 2:Importing the libraries

import speech_recognition as sr

from google_trans_new import google_translator

import pyttsx3

STEP3: Creating Instances 

recognizer=sr.Recognizer()

engine=pyttsx3.init()

STEP4:Recording the audio

with sr.Microphone() as source:

    print('Clearing the background noises..')

    recognizer.adjust_for_ambient_noise(source,duration=1)

    print('Waiting for your message')

    audio=recognizer.listen(source,timeout=1)

    print('Done recording')

try:

    print('Recognizing')

    result=recognizer.recognize_google(audio,language='en')

except Exception as ex:

    print(ex)

STEP5: Translation

def trans():

    langinput=input('Type the language code you want to translate:')

    translator=google_translator()

    translate_text=translator.translate(str(result),lang_tgt=str(langinput))

    print(translate_text)

    engine.say(str(translate_text))

    engine.runAndWait()

trans()

Fullcode: Github

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Instagram Automation ( send messages ) using python

 Hello python programmers, In this video we're going to see about how to create a Instagram Bot using python

With the help of selenium we're going to create this one.

STEP1: Libraries Installation:

*pip install selenium 

STEP2: Importing the Libraries:

from selenium import webdriver

import time

browser = webdriver.Chrome('chromedriver.exe')

browser.get('https://instagram.com/')

time.sleep(4) 

STEP3: Login autoamation:

def login()

    Username=browser.find_element_by_xpath("/html/body/div[1]/section/main/article/div[2]/div[1]/div/form/div/div[1]/div/label/input")

    Username.send_keys("username")

    time.sleep(4)

    password=browser.find_element_by_xpath("/html/body/div[1]/section/main/article/div[2]/div[1]/div/form/div/div[2]/div/label/input")

    password.send_keys("password")

    password.submit()

    time.sleep(7)

STEP4:Blocking the Notifications

def notification():

    Notnow=browser.find_element_by_xpath('/html/body/div[1]/section/main/div/div/div/div/button').click()

    time.sleep(3)

    noti=browser.find_element_by_xpath('/html/body/div[4]/div/div/div/div[3]/button[2]').click()

    time.sleep(7)

STEP5:Message icon:

def message():

    msgclick=browser.find_element_by_class_name('xWeGp')

    msgclick.click()

    time.sleep(6)

STEP6: Final part:

def final():

    chaticon=browser.find_element_by_class_name('QBdPU').click()

    time.sleep(7)

    typename=browser.find_element_by_xpath('/html/body/div[5]/div/div/div[2]/div[1]/div/div[2]/input')

    typename.send_keys('username who you want to send.!')

    time.sleep(7)

    name=browser.find_element_by_xpath('/html/body/div[5]/div/div/div[2]/div[2]/div[2]/div/div[2]/div[2]/div').click()

    time.sleep(7)

    next=browser.find_element_by_class_name('rIacr').click()

    time.sleep(7)

    mbox=browser.find_element_by_tag_name('textarea')

    mbox.send_keys('Hi')

    send=browser.find_element_by_xpath('/html/body/div[1]/section/div/div[2]/div/div/div[2]/div[2]/div/div[2]/div/div/div[3]/button')

    send.click()

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Fullcode: Github

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K means clustering project using python

Hello python programmers, This is AK. In this video we’re going to see about one of the most popular and important algorithms in machine learning that is named as kmeans clustering.

STEP1: Installing Dependencies

* pip install pandas

* pip install matplotlib

* pip install scikit-learn

STEP2: Importing the libraries

from pandas import pandas as pd

from pandas import DataFrame

import matplotlib.pyplot as plt

from sklearn.cluster import KMeans

STEP3: Read the CSV

data=pd.read_csv('Sample scores.csv') data

Dataset link: http://bitly.ws/dIvR

STEP4: Plot the data

plt.scatter(data['Overs'],data['Scores'])

plt.xlabel('x')

plt.ylabel('y')

plt.show()

STEP5: Create dataframe

df=DataFrame(data,columns=['Scores','Overs']) df

STEP6: Create Clusters

kmeans=KMeans(n_clusters=3).fit(df)

STEP7: Place the centroid points

centroids=kmeans.cluster_centers_ print(centroids)

STEP8: Plot the data points

plt.scatter(df['Overs'],df['Scores'],c=kmeans.labels_.astype(float),s=50,alpha=1) plt.scatter(centroids[:,0],centroids[:,1],c='red',s=50) plt.xlabel('Overs') plt.ylabel('Scores') plt.show()

Github repository : https://github.com/akpythonyt/ML-algorithms

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