Repository for capstone project for IBM professional certificate in data science.
coursera course title: 'Machine Learning with Python'
https://www.coursera.org/learn/machine-learning-with-python/home/info
Original code created by Saeed Aghabozorgi: https://www.linkedin.com/in/saeedaghabozorgi/
- download training data ('Loan.csv') and complete exploratory data analysis
- conduct data transformation and scaling in preparation for model development. -- split training & validation data for model development and initial testing
- develop 4 models using SciKit Learn Machine Learning: KNN, D_tree, SVM & Log_reg
- download test data and perform data transformations -> then test ML models
- recommendation for 'best fit'
coursera course title: 'Applied Data Science Capstone' This is the peer reviewed assignment for week 3:
https://www.coursera.org/learn/applied-data-science-capstone/home/info
Original code created by Alex Aklson: https://www.linkedin.com/in/aklson/
- System & Data Setup
- Part 1 - Create initial table with 103 postal codes ('Postcode', 'Borough','Neighborhood')
- Part 2 - Concatinate table from part 1 with Geospacial Coordinates ('Latititude', 'Longitude')
- Part 3 - Generate maps to visual neighborhoods and how they cluster using geopy & folium 3a - general map of toronto by Postcode 3b - review of venues in 'Studio District' 3c - cluster analysis of district by venue