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agfzb-cloudappdevelopment_capstone

cazgi-ibm-watson-nlu-project

courseraibm_datascience_finals

final-cloud-app-with-database

ibm-applied-ds-projects

Project files submitted for IBM Data Science Professional Certification

ibm-capstone

Using the FourSquare API and the K-Nearest Neighbors clustering algorithm to analyze similarity of different neighborhoods in New York City

peer-graded-assignment-segmenting-and-clustering-neighbourhoods-in-toronto

Peer-graded Assignment: Segmenting and Clustering Neighbourhoods in Toronto - In this assignment, you will be required to explore, segment, and cluster the neighbourhoods in the city of Toronto based on the postal-code and borough information.. However, unlike New York, the neighbourhood data is not readily available on the internet. What is interesting about the field of data science is that each project can be challenging in its unique way, so you need to learn to be agile and refine the skill to learn new libraries and tools quickly depending on the project. For the Toronto neighbourhood data, a Wikipedia page exists that has all the information we need to explore and cluster the neighbourhoods in Toronto. You will be required to scrape the Wikipedia page and wrangle the data, clean it, and then read it into a pandas data-frame so that it is in a structured format like the New York datasets. Once the data is in a structured format, you can replicate the analysis that we did to the New York City datasets to explore and cluster the neighbourhoods in the city of Toronto.

qkfls-movie-reviews-database

serverless-guestbook

vftvk-simple-interest-calculator

xzceb-flask_eng_fr

flask_enf_fr