kashaziz

'''
Example of web scraping using Python and BeautifulSoup. 

Sraping ESPN College Football data 
http://www.espn.com/college-sports/football/recruiting/databaseresults/_/sportid/24/class/2006/sort/school/starsfilter/GT/ratingfilter/GT/statuscommit/Commitments/statusuncommit/Uncommited

The script will loop through a defined number of pages to extract footballer data. 
'''

from bs4 import BeautifulSoup

kashaziz

# input name of image folder
image_folder = str(input("Enter folder name to fetch images: ")).lower()
 
# instantiate Dropbox connection
dbx = dropbox.Dropbox('dropbox access token')
 
# proceed if image folder exists in Dropbox
if dbx.files_get_metadata(image_folder).path_lower == image_folder:
      
    # iterate through the files inside Dropbox folder

kashaziz

""" Perform sentiment analysis on product reviews of Amazon product """
import requests
from bs4 import BeautifulSoup
from textblob import TextBlob

def analyze_product_reviews(url):
    response = requests.get(url)
    soup = BeautifulSoup(response.content, 'html.parser')

    reviews = soup.find_all('div', {'data-hook': 'review-collapsed'})

kashaziz

"""
This script utilizes machine learning techniques to predict house prices based on various input features.
It performs data preprocessing, splits the data into training and testing sets, trains a linear regression model,
evaluates its performance using  mean squared error (MSE) and R-squared.
"""

import pandas as pd
import numpy as np
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import train_test_split

kashaziz

"""
This Python script demonstrates the usage of logistic regression to predict whether customers will make the next purchase on an e-commerce site. 
The code performs the following steps:

1. Load and Preprocess Data:
    - Loads an e-commerce dataset containing customer features such as 'time_on_site', 'total_spent', 'is_returning_customer', and 'will_make_next_purchase'.
    - Splits the data into training and testing sets.

2. Model Training:
    - Creates a logistic regression model using scikit-learn.

kashaziz

"""
This Python script showcases the application of decision trees in the context of e-commerce data analysis. 
"""

# Import necessary libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.tree import export_text

kashaziz

TransactionAmount	TransactionTime	MerchantInfo	LocationInfo	UserInfo	DeviceInfo
68.2	01/03/2023 14:30	LocalShop3	CityA	JohnDoe123	Mobile
95.4	02/03/2023 9:45	LocalShop2	CityB	AliceSmith456	Desktop
110.8	03/03/2023 16:15	OnlineStore1	CityC	BobJohnson789	Tablet
45.6	04/03/2023 12:00	OnlineStore1	CityA	EveWilliams123	Mobile
75.3	05/03/2023 10:30	OnlineStore2	CityB	CharlieBrown456	Desktop