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-london-boroughs-and-crimes

In this project, I explore the different boroughs in London and the total crimes in the areas. I explore the neighbourhoods in the safest borough and cluster the neighbourhoods using K-means clustering. used Foursquare API to get the most common venues of the safest borough of London.

comparing-profitable-apps

In this project, profitable apps from App Store and Google Play markets are compared. This would help the developers to make data-driven decisions with respect to the kind of apps they build.

credit-card-fraud-detection

In this project, we identify fraudulent credit-card transactions using a highly unbalanced dataset. Model prediction was done using Isolation Forest Algorithm, Local Outlier Factor and Support Vector Machine.

eda1

interview-question-data-science-

jupyter-notebook-introduction-data-science-intro-000

learn-co-sandbox

py-lists-with-maps-data-science-intro-000

python-lists-lab-data-science-intro-000

python-lists-readme-data-science-intro-000

python-practice-with-datatypes-data-science-intro-000

python-variables-lab-data-science-intro-000

python-variables-readme-data-science-intro-000

segmentation-and-clustering-of-neighborhoods-in-the-city-of-toronto-canada

In this project, we will explore, segment, and cluster the neighborhoods in the city of Toronto. The data was scraped from the Wikipedia page using Beautiful Soup and wrangled, cleaned, and then read into a pandas dataframe so that it is in a structured format.

spamclassifier