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

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

"""
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 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

""" 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

# 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

'''
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