Abhayparashar31

from selenium import webdriver
from selenium.webdriver.common.keys import Keys
import pandas as pd

driver = webdriver.Chrome('chromedriver.exe')
driver.get('https://www.boxofficemojo.com/chart/top_lifetime_gross/?area=XWW')

## Scraping Movie Names
movies_names = driver.find_elements_by_xpath('//td[@class="a-text-left mojo-field-type-title"]/a[@class="a-link-normal"]')  ## targets all the elements on the web that has same Xpath
movie_name_list = []                                                                                                        ## an empty list for storing movie names

Abhayparashar31

import scrapy
from ..items import BookscraperItem                                                ## Importing The Class Inside Items.py for storing data

class Bookscraper(scrapy.Spider):
    name='books'                                                                    ## Name of Spider
    counter = 1                                                                     ## Counter Variable To Check The Status of Scraped Pages

    def start_requests(self):
       

Abhayparashar31

import scrapy
from ..items import QuotescraperItem

class Quotescraper(scrapy.Spider):
    name = 'quotes'
    counter = 1   ## Counter Variable To Check The Status of Scraped Pages
    start_urls = [
        'https://quotes.toscrape.com/page/1/'
    ]

Abhayparashar31

from os import link
from matplotlib.pyplot import title
import scrapy
from ..items import AmazonItem  

## https://pypi.org/project/scrapy-user-agents/

class Amazonbookscraper(scrapy.Spider):
    name = "amazonbooks"

Abhayparashar31

import streamlit as st
import qrcode
from PIL import Image, ImageOps
st.write(''' # QR Code Generator ''')
st.write(' A Web App That Generates QR Code')
text = st.text_input("Enter Some Text...")
if text!="":
    qr = qrcode.make(text)
    qr.save(f"{text}.png")
    st.image(f"{text}.png", use_column_width=True)

Abhayparashar31

left =  pd.DataFrame({'Courses': ["CSS","JS","JAVA"],
                      'Fee':[2499,4599,5799],
                      'Ratings':[3.5,4.2,3.9],
                      'ID':[1,2,3]
                        })
right = pd.DataFrame({'Trn_ID':[1,1,1,2,2,3],
                     'Company':['Amazon','Dell','HP','Lenovo','Dell','HP']})
                     
left.merge(right,left_on='ID',right_on='Trn_ID')

Abhayparashar31

base_learners = [
                 ('l1', KNeighborsClassifier(n_neighbors=3)),
                 ('l2', SVC(gamma=2, C=1)),
                 ('l3',DecisionTreeClassifier()),
                ]


model = StackingClassifier(estimators=base_learners, final_estimator=LogisticRegression())

Abhayparashar31

layer_1 = [
  ('l1', KNeighborsClassifier(n_neighbors=3)),
  ('l2', SVC()),
  ('l3',DecisionTreeClassifier())
]

layer_2 = [
  ('l4',RandomForestClassifier()),
  ('l5',DecisionTreeClassifier())
]

Abhayparashar31

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.2,random_state=42)

dt = DecisionTreeClassifier()
dt.fit(X_train,y_train)

Abhayparashar31

from sklearn.datasets import load_iris
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import StackingClassifier
from sklearn.model_selection import train_test_split

X, y = load_iris(return_X_y=True)