WEBSITE BLOCKER:-

There are certain times when we face the need to block different websites on our system. This is where a Website Blocker helps us.

We are going to create a website blocker using python which will give us a window where we can type the url of the websites and then we can block those websites. We will also have an unblock feature added to it so that we can unblock any website in future if we want and Tkinter Module for creating GUI.

PROJECT DETAILS

While creating Python Website Blocker Application, we are going to use only one module – Tkinter Module to make easy GUI using Python. We will be making two functions Block() and Unblock(). As the names suggest these functions will perform blocking and unblocking of websites respectively. These websites are added to the host file of the system.

Project File Structure

Let us have a look at the steps we will be performing to create the Python Website Blocker project

1. Importing the required library
2. Creating GUI window
3. Hostpath and IP address.
4. Creating Block function to block websites
5. Creating Unblock function to unblock websites
6. Creating labels and buttons
7. Main command

Python Website Blocker Output:

Let’s have a look at what happens when we try to block a website. See a blocked label is visible to us showing that the website has been blocked.

pip install tk

pip install tkpip install tk

CREDIT CARD FRAUD DETECTION 

“Fraud detection is a set of activities that are taken to prevent money or property from being obtained through false pretenses.”

• A consumer reveals his credit card number to unfamiliar individuals.
• A card is lost or stolen and someone else uses it.
• Mail is stolen from the intended recipient and used by criminals.
• Business employees copy cards or card numbers of its owner.
• Making a counterfeit credit card.

PREVENTION:

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 CUSTOMER SEGMENTATION:

These days, you can personalize everything. There’s no one-size-fits-all approach. But, for business, this is actually a great thing. It creates a lot of space for healthy competition and opportunities for companies to get creative about how they acquire and retain customers.

One of the fundamental steps towards better personalization is customer segmentation. This is where personalization starts, and proper segmentation will help you make decisions regarding new features, new products, pricing, marketing strategies, even things like in-app recommendations.

But, doing segmentation manually can be exhausting. Why not employ machine learning to do it for us? In this article, I’ll tell you how to do just that.

MACHINE LEARNING

Machine learning methodologies are a great tool for analyzing customer data and finding insights and patterns. Artificially intelligent models are powerful tools for decision-makers. They can precisely identify customer segments, which is much harder to do manually or with conventional analytical methods.

There are many machine learning algorithms, each suitable for a specific type of problem. One very common machine learning algorithm that’s suitable for customer segmentation problems is the k-means clustering algorithm. 

IMPLEMENTATION:-

# Import required libraries

import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
import plotly.express as px
import plotly.graph_objects as go
import matplotlib.pyplot as plt

DATA SET:-


CONCLUSION:-
It’s not wise to serve all customers with the same product model, email, text message campaign, or ad. Customers have different needs. A one-size-for-all approach to business will generally result in less engagement, lower-click through rates, and ultimately fewer sales. Customer segmentation is the cure for this problem.

CERTIFICATIONS

I have completed various certification in my Domain. These certificates are well known to my knowledge.

Now, the real work begins: enticing you to see my certifications.

• HACKER RANK (SQL):-

• HACKER RANK(python):-

• LETS UPGRADE(Python):-