Sohail Zia (@sohailziahh)
github

sohailziahh

Twitter logo Share LinkedIn logo Share Facebook logo Share
  • Stars
    star
    4
  • Global Rank 1,604,650 (Top 56 %)
  • Followers 1
  • Following 10
  • Registered about 6 years ago
  • Most used languages
    Jupyter Notebook
         66.7 %
    HTML
         33.3 %
  • Location 🇵🇰 Pakistan
  • Country Total Rank 4,444
  • Country Ranking
    Jupyter Notebook
         722
    HTML
         1,084

Top repositories

1

Escrow-esque-DAPP-on-ethereum-

Built a very simple escrow-esque smart contract using Solidity that allows user to deposit ether into the contract only to be released by a third party to its eventual destination. Keep in mind, of course, that it's exactly these kind of third parties that the blockchain's going to allow us to circumvent, but I am doing it this way for simplicity. This could easily be modified to have the funds released based on some kind of off-chain event that won't require any user interaction. A simple user interface is built that will allow users to send ether from one address to another, check the balance that's being stored, see who the approver is, and finally enable the approver to approve the transaction.
HTML
2
star
2

Minimizing-Customer-Churn-Rate

Subscription products often are the main source of revenue for companies across all industries. These products can come in the form of a 'one size fits all' overcompassing subscription, or in multi-level memberships. Regardless of how they structure their membership, or what industry they are in, companies almost always try to minimize customer churn (in other words ; Subscription Cancellation). To retain their customers, these companies first need to identify behavioral patterns that act as catalyst in disengagement with the product.
Jupyter Notebook
1
star
3

Careem-Dataset

Careem provided this dataset in a recent hackathon where the problem was to predict the destination of the user when he opens the app and the app would then book a ride in a blink of an eye. One-click booking makes things faster and easier for the customer, and knowing where a customer would like to go next – even before they tell us – allows us to improve our service by ensuring the busier areas have a greater supply of transport options. The Careem data science team asked the question, “if we build a supervised machine-learning model to solve this problem, what are the most important and informative features needed to predict where a user goes?”
Jupyter Notebook
1
star