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1.Automation Program Using schedule Library

import schedule
import time
def job():
 print(" Reminder: Take a short break and stretch!")
schedule.every(5).seconds.do(job)
print("Scheduler started. Press Ctrl+C to stop.")
while True:
 schedule.run_pending()
 time.sleep(1)

2.Robotic Process Automation

import requests
from bs4 import BeautifulSoup
import pandas as pd

all_books = []
for page in range(1, 51):
    url = f"https://books.toscrape.com/catalogue/page-{page}.html"
    headers = {"User-Agent": "Mozilla/5.0"}
    response = requests.get(url, headers=headers)
    if response.status_code != 200:
        print(f"⚠️ Failed to fetch page {page}. Status code: {response.status_code}")
        break
    soup = BeautifulSoup(response.text, "html.parser")
    books = soup.find_all("h3")
    prices = soup.find_all("p", class_="price_color")
    for book, price in zip(books, prices):
        all_books.append({
            "Title": book.a['title'],
            "Price": price.text
        })

df = pd.DataFrame(all_books)
df.to_csv("books_data.csv", index=False, encoding='utf-8')
print("Scraping complete!")
print(f"Total books collected: {len(df)}")
print("\nSample output:")
print(df.head())

3.Ex.No: 4 Process Automation

import pandas as pd
import smtplib
from email.mime.text import MIMEText
# STEP 1: Read data from Excel
data = pd.read_excel("/content/drive/MyDrive/employee.xlsx") # sample file with 
columns: Name, Email, Status
# STEP 2: Process data (filter rows where Status = 'Pending')
pending = data[data['Status'] == 'Absent']
# STEP 3: Send automated email
sender_email = "integratedaidnnn@gmail.com"
password = "hhprrnmnugkrihcf"
# Setup SMTP
server = smtplib.SMTP("smtp.gmail.com", 587)
server.starttls()
server.login(sender_email, password)
for _, row in Absent.iterrows():
 name = row['Name']
 recipient = row['Email']
 message = f"Hello {name},\n\nYour are Absent Today for Intelligent Automation 
Subject." 
 msg = MIMEText(“ABSENT”)
 msg['Subject'] = "Absent Message"
 msg['From'] = sender_email
 msg['To'] = recipient
 server.sendmail(sender_email, recipient, msg.as_string())
 print(f"Email sent to {name}")
server.quit()
print("Automation completed!")

4.COGNITIVE AUTOMATION USING NLP: EMAIL CLASSIFICATION

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
# Sample dataset of emails (text + label)
data = {
 'email': [
 'I want a refund, the product is defective',
 'Your service is excellent, thank you!',
 'Where is my order? It’s late again!',
 'Please cancel my subscription immediately',
 'This is spam. Stop sending these emails!',
 'I need help with my account login issue',
 'Thanks for the quick response, great support',
 'You guys are scammers, I lost my money!',
 'My package was damaged during delivery',
 'Congratulations! You won a free iPhone!'
 ],
 'label': [
 'complaint',
 'praise',
 'complaint',
 'support_request',
 'spam',
 'support_request',
 'praise',
'complaint',
 'complaint',
 'spam'
 ]
}
# Convert to DataFrame
df = pd.DataFrame(data)
# Split into training and test sets
X_train, X_test, y_train, y_test = train_test_split(df['email'], df['label'], test_size=0.3, 
random_state=42)
# Build a simple NLP pipeline with TF-IDF and Naive Bayes
model = make_pipeline(TfidfVectorizer(), MultinomialNB())
# Train the model
model.fit(X_train, y_train)
# Predict on test set
y_pred = model.predict(X_test)
# Print evaluation
print("Classification Report:")
print(classification_report(y_test, y_pred))
# Test on a new email (Cognitive Task)
new_email = "Why did you charge me twice for the same item?"
predicted_label = model.predict([new_email])[0]
print(f"\nNew Email: \"{new_email}\"\nPredicted Category: {predicted_label}")

5.Ex.No:5 Web Scraping 
import requests
from bs4 import BeautifulSoup
url = "https://news.ycombinator.com/"
headers = {"User-Agent": "Mozilla/5.0"} # Trick: Makes it look like Chrome browser
response = requests.get(url, headers=headers)
if response.status_code == 200:
 soup = BeautifulSoup(response.text, "html.parser")
 title_spans = soup.find_all("span", class_="titleline")
 print("Top Headlines:")
 for i, span in enumerate(title_spans[:5], start=1):
 link = span.find("a")
 print(f"{i}. {link.text}")
else:
 print("Failed to fetch page. Status code:", response.status_code) EX.NO: 6 Web Scraping -2
import requests
from bs4 import BeautifulSoup
url = "https://books.toscrape.com/catalogue/category/books_1/index.html"
headers = {"User-Agent": "Mozilla/5.0"}
response = requests.get(url, headers=headers)
if response.status_code == 200:
 soup = BeautifulSoup(response.text, "html.parser")
 books = soup.find_all("h3")
 prices = soup.find_all("p", class_="price_color")
 for i, (book, price) in enumerate(zip(books, prices), start=1):
 print(f"{i}. {book.a['title']} – {price.text}")
else:
 print(f"Failed to fetch page. Status code: {response.status_code}")

6.2. WEATHER DATA COLLECTION
import requests
def get_weather(city):
 url = f"https://wttr.in/{city}?format=3"
 try:
 response = requests.get(url)
 if response.status_code == 200:
 print(f"Current weather in {city}: {response.text}")
 else:
 print("Failed to get weather info")
 except Exception as e:
 print("Error:", e)
if __name__ == "__main__":
city = "Chennai" # Change city here
 print(f"--- Weather Information for {city} ---")
 get_weather(city)

7.Automated data preprocessing

import pandas as pd

df = pd.read_excel("C:/Users/User/Desktop/bike.xlxs")
print("Original Data:")
print(df.head())

df = df.drop_duplicates()

for col in df.select_dtypes(include=['object']).columns:
    df[col] = df[col].str.strip()

for col in df.select_dtypes(include=['object']).columns:
    df[col] = df[col].str.lower()

df['age'] = df['age'].fillna(df['age'].mean())

df['date'] = pd.to_datetime(df['date'], errors='coerce')

df = df.dropna(subset=['date'])

print("\nCleaned Data:")
print(df.head())

df.to_csv("data_cleaned.csv", index=False)

8.Predictive analytics

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
# Sample data
data = {
 'age': [22, 35, 58, 45, 25, 33, 52, 40, 60, 48],
 'browsing_time': [5, 10, 2, 8, 7, 12, 1, 4, 3, 9],
 'previous_purchases': [0, 3, 1, 5, 0, 2, 0, 1, 0, 4],
 'made_purchase': [0, 1, 0, 1, 0, 1, 1, 1, 1, 1]
}
df = pd.DataFrame(data)
# Features and target
X = df[['age', 'browsing_time', 'previous_purchases']]
y = df['made_purchase']
# Train/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Train model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# New customer data to predict
new_customer = {'age': 35, 'browsing_time': 10, 'previous_purchases': 3, 'email': 
'integratedaidnnn@gmail.com'}
new_df = pd.DataFrame([new_customer])
# Predict
prediction = model.predict(new_df[['age', 'browsing_time', 'previous_purchases']]) if prediction[0] == 1:
 # Prepare email
 sender_email = "integratedaidnnn@gmail.com" # Your Gmail address
 sender_password = "hhprrnmnugkrihcf" # Your app password
 receiver_email = new_customer['email']
 message = MIMEMultipart()
 message["From"] = sender_email
 message["To"] = receiver_email
 message["Subject"] = "Special Offer Just for You!"
 body = f"""
 Hi there,
 We noticed you're interested in our products. Here's a special offer just for you!
 Thanks for visiting us.
 Best regards,
 Your Company
 """
 message.attach(MIMEText(body, "plain"))
 try:
 server = smtplib.SMTP("smtp.gmail.com", 587)
 server.starttls()
 server.login(sender_email, sender_password)
 server.sendmail(sender_email, receiver_email, message.as_string())
 print("Email sent successfully to", receiver_email)
 except Exception as e:
 print("Failed to send email:", e)
 finally:
 server.quit()
else:
 print("Customer unlikely to purchase — no email sent.")