@Invoker999
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Aug 24, 2025 · 2 min read · Senario 1: You are investigating a money transfer network to detect suspicious behavior. You suspect one account (A6) to be fraudulent due to multiple high-value transfers. CREATE (p1:Person {id:'P1', name:'Alice'}), (p2:Person {id:'P2', name:'B...
Join discussionAug 24, 2025 · 1 min read · Practical 2 CREATE (:Person {name: 'Alice', age: 25}) MATCH (p:Person) RETURN p MATCH (a:Person {name: 'Alice'}), (b:Person {name: 'Bob'}) CREATE (a)-[:FRIENDS_WITH {since:2021}]->(b) MATCH (a:Person {name: 'Alice'}), (b:Person {name: 'Bob'}) MAT...
Join discussionAug 21, 2025 · 1 min read · *POEM* import nltk from nltk import sent_tokenize from nltk import word_tokenize textfile = open('/content/mytext.txt') text = textfile.read() print(text) words = word_tokenize(text) words nltk.download('punkt_tab') #filtereing stop words from nl...
Join discussionApr 7, 2025 · 2 min read · import pandas as pd import matplotlib.pyplot as plt import numpy as np import seaborn as sns from matplotlib import style %matplotlib inline from datetime import datetime import yfinance as yf stock = yf.Ticker("GS") volitality_data = stock.history(...
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