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IDENTIFYING INDUSTRY GROUPINGS WITH FOOD AND BEVERAGE ECOSYSTEM USING ML -…
IDENTIFYING INDUSTRY GROUPINGS WITH FOOD AND BEVERAGE ECOSYSTEM USING ML
Implementation Aspects
Tools
Programming Language
Python
Database
Mongo Db Compass
Server
Hosting locally
Hosting on Cloud
Development Editors
Anaconda Jupyter
Visual Studio Code
Spyder
Code Maintenance Repositories
Git
Challenges
Data Cleaning and Preprocessing
Data Collection
Authorization to collect data from web
Huge time taken to scrap the data
Computational power needed to scrap over 50 GB of data
Server Connectivity issues
Machine Learning Models
Neural Network Model
K-Means Clustering
Data Scrapping
Web Site Scrapping
Twitter Scraping
Named Entity Recognition
SpaCy Library
Fast Processing
More research articles point towards using SpaCy
NLTK Library
Higher Accuracy
Theoretical and management aspects
Type of companies
Food Companies in canada
Beverage Companies in Canada
Companies that deals both in food and beverage
Authorization to users to get server accounts created
Get onboard and understand the work done by previous students
Understand the ontology created already
Learn how to use the existing framework developed to use DB and connect to server
Create a timeline
Break down the project deliverables
Web Scrapper
Entities created by NER
Results from Neural Network Model
Results from K-means Clustering
Project Report
Project Proposal
Get the NAICC Codes for companies
List the companies without NAICC Codes
Feedback meetings with Supervisor