ikonthomas Goto Github PK

amazon-sagemaker-fraud-detection

amazon-sagemaker-keras-text-classification

A step-by-step guide that shows how to do text classification by run training/inference for a custom model in Amazon SageMaker

assets_rental_projects

In this repo I will deal with price modelling in Assets Rentals. The application is varied as it can be in hotels, airbnb, plant rentals, outdoor billboards and immovable equipment hire

banking_products_recommendation

cape_town_airbnb_pricing_model

I will give my go at modelling the cape town AirBnB data to come up with a pricing model and help us price and predict Prices in Cape Town

coindrop

The easiest way to accept donations & tips anywhere

decision-tree

demand-side-platform

An open source implementation of a Demand-Side Platform (DSP) to serve for testing and educational purpose

digitize-handwritten-texts

I will tackle the issue of handwritten scribes usually written by Doctors and Nurses. In my project I will digitize handwritten scripts to digital records which can then be stored in non relational databases. The data used to build the model was collected from IAM Handwriting database. This model is built on handwriting from 657 different writers. Each writer has written multiple paragraphs and sentences have been extracted from those paragraphs.

docker

Useful docker codes

docker_tutorial

Code and helper scripts for article on Medium "How Docker Can Help You Become A More Effective Data Scientist"

flutter_blue

Bluetooth plugin for Flutter

genetic-meyer-packard-algorithm

Predicting a Stock Price Using a Genetic Algorithm

json_to_dart

Library that generates dart classes from json strings

lambda-refarch-fileprocessing

Serverless Reference Architecture for Real-time File Processing

liberapay.com

Source code of the recurrent donations platform Liberapay

live_stream_client

loan_repayment

Description of the challenge: SuperLender is a local digital lending company, which prides itself in its effective use of credit risk models to deliver profitable and high-impact loan alternative. Its assessment approach is based on two main risk drivers of loan default prediction:. 1) willingness to pay and 2) ability to pay. Since not all customers pay back, the company invests in experienced data scientist to build robust models to effectively predict the odds of repayment. These two fundamental drivers need to be determined at the point of each application to allow the credit grantor to make a calculated decision based on repayment odds, which in turn determines if an applicant should get a loan, and if so - what the size, price and tenure of the offer will be. There are two types of risk models in general: New business risk, which would be used to assess the risk of application(s) associated with the first loan that he/she applies. The second is a repeat or behaviour risk model, in which case the customer has been a client and applies for a repeat loan. In the latter case - we will have additional performance on how he/she repaid their prior loans, which we can incorporate into our risk model. It is your job to predict if a loan was good or bad, i.e. accurately predict binary outcome variable, where Good is 1 and Bad is 0.

mason

Tools which allow developers to create and consume reusable templates called bricks.

meyer-packard-genetic-algorithm

Meyer Packard Genetic Algorithm in Python

mushroomsproject

Data Science project: Poisonous/Edible Mushrooms Classifier

mytensorflow

My Tensorflow Codes for future reference

openssp

openssp

power-bi-assistant

This project is meant to help Power BI desktop developers providing functions which are not included natively or giving useful information and links

pythondataanalysis

Python data repo, jupyter notebook, python scripts and data.

pythondatasciencehandbook

Python Data Science Handbook: full text in Jupyter Notebooks

redshift-immersionday-labs

This GitHub project provides a series of lab exercises which help users get started using the Redshift platform.

schoolplay

My personal notebooks

self-hosted

Create an entire Flagsmith environment locally

shoes-project

A client would like to open a shoe store and we are in the process of helping with this start-up and for this purpose we have obtained the following dataset (shoes_data.csv). This is a list of women's shoes and their associated information. Each shoe will have an entry for each price found for it, so a single shoe may have multiple entries. Data includes shoe name, brand, price, and more. We are unsure of what this data could mean but we would like you to investigate the following: This data provides a lot of information, which means you can pull out a lot of different trends.