Hunter.io scraper

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This Python code extracts email addresses from a list of company websites by: 1) Reading a spreadsheet containing company names and website domains 2) Cleaning the domain names and using them to search for emails on Hunter.io 3) Appending any emails found to a list 4) Exporting the list of emails to a new Excel file

$#| emails.append(temp_emails["emails"]) for j in range (0,len(temp_emails["emails"])): print(temp_emails["emails"][j]["value"]) emails.append(temp_emails["emails"][j]["value"]) else: #print("Non existant domain name") emails.append("N/A") print(emails) import pandas as pd from pandas import ExcelWriter #from pandas #from pandas import ExcelFile import numpy as np df = pd.DataFrame({'Email': emails}) writer = ExcelWriter('ContactList_.xlsx') df.to_excel(writer,'SHEET_NAME',index=False) writer.save() #emails$

Output Sample view
Python code written by:
Mamoon Ismail Khalid
VC Associate @ Quake Capital Partners
Mik279@nyu.edu

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Image recognition, which comes under Artificial Intelligence (AI) is a critical aspect of computer vision, enabling computers or other computing devices to identify and categorize objects within images. Among numerous fields of life, food processing is an important area, in which image processing plays a vital role, both for producers and consumers. This study focuses on the binary classification of strawberries, where images are sorted into one of two categories. We Utilized a dataset of strawberry images for this study; we aim to determine the effectiveness of different models in identifying whether an image contains strawberries. This research has practical applications in fields such as agriculture and quality control. We compared various popular deep learning models, including MobileNetV2, Convolutional Neural Networks (CNN), and DenseNet121, for binary classification of strawberry images. The accuracy achieved by MobileNetV2 is 96.7%, CNN is 99.8%, and DenseNet121 is 93.6%. Through rigorous testing and analysis, our results demonstrate that CNN outperforms the other models in this task. In the future, the deep learning models can be evaluated on a richer and larger number of images (datasets) for better/improved results.

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Introduction to Project Management: Introduction, Project and Importance of Project Management, Contract Management, Activities Covered by Software Project Management, Plans, Methods and Methodologies, some ways of categorizing Software Projects, Stakeholders, Setting Objectives, Business Case, Project Success and Failure, Management and Management Control, Project Management life cycle, Traditional versus Modern Project Management Practices.

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Hunter.io scraper

1. from pyhunter import PyHunter hunter = PyHunter(‘API KEY from Hunter.io/API’) import pandas as pd Place the Excel file here: table = pd.read_excel('VC Fund Master List.xlsx', sheetname = 'Wisconsin', convert_float = True) linkedIn_ProfileHandles = table['LinkedIn Profile'] webDomains = table['Website'] ################################# emails = [] for i in range(0,len(webDomains)): #len(webDomains)): #print(a[i]) print(webDomains[i]) if (str(webDomains[i]) != "nan"): temp_domain = webDomains[i].replace("www.","") #print(temp_domain) temp_domain = temp_domain.replace("://","") #print(temp_domain) temp_domain = temp_domain.replace("https","") #print(temp_domain) temp_domain = temp_domain.replace("http","") temp_domain = temp_domain.replace(".com/",".com") temp_domain = str(temp_domain) print(temp_domain) temp_emails = hunter.domain_search(domain = temp_domain, limit=5, offset=2) #print(temp_emails) #print(temp_emails["emails"]) if (temp_emails["emails"] == []): # emails.append("N/A") print ("!! NO EMAIL ADDRESS FOUND FOR THIS DOMAIN!! ") if (temp_emails["emails"] != []): #print("Emails found for this organization ")

2. #| emails.append(temp_emails["emails"]) for j in range (0,len(temp_emails["emails"])): print(temp_emails["emails"][j]["value"]) emails.append(temp_emails["emails"][j]["value"]) else: #print("Non existant domain name") emails.append("N/A") print(emails) import pandas as pd from pandas import ExcelWriter #from pandas #from pandas import ExcelFile import numpy as np df = pd.DataFrame({'Email': emails}) writer = ExcelWriter('ContactList_.xlsx') df.to_excel(writer,'SHEET_NAME',index=False) writer.save() #emails

3. Output Sample view Python code written by: Mamoon Ismail Khalid VC Associate @ Quake Capital Partners Mik279@nyu.edu

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