praffiah,Raphael Patrick Affiah,praffiah,github

2020-pandas-tutorial_notes

This Notebook contains common pandas trips and tricks for data processing and manipulation.

2020_force_lithology_prediction

3rd-place-solution-on-machinehack-video-game-sales-prediction

The gaming industry is certainly one of the thriving industries of the modern age and one of those that are most influenced by the advancement in technology. With the availability of technologies like AR/VR in consumer products like gaming consoles and even smartphones, the gaming sector shows great potential. In this hackathon, you as a data scientist must use your analytical skills to predict the sales of video games depending on given factors. Given are 8 distinguishing factors that can influence the sales of a video game. Your objective as a data scientist is to build a machine learning model that can accurately predict the sales in millions of units for a given game.

4d-seismic-neural-inversion

Deep Neural Networks for Map-Based 4D Seismic Pressure-Saturation Inversion

4d-seismic-warping-voxelmorph

Unsupervised 3D time-shift extraction from 4D seismic data

52things

52 Things You Should Know About Geocomputing

ai16tech

aj_ml_library

This library is a wrapper for sklearn and works with data stored using Pandas module.

akeed-restaurant-recommendation-challenge_getting_train-test

A brief notebook on getting the train and test set.

analytics_vidhya

Codes related to activities on AV including articles, hackathons and discussions.

applied-machine-learning-in-python

University of Michigan on Coursera

article-fastai_regression

automatic_well_tie

Prototyping an Automatic Seisimic-to-Well tie using Dynamic Time Warping

awesome-cheatsheets

👩‍💻👨‍💻 Awesome cheatsheets for popular programming languages, frameworks and development tools. They include everything you should know in one single file.

awesome-open-geoscience

Curated from repositories that make our lives as geoscientists, hackers and data wranglers easier or just more awesome

axa-mansard-10th-position-solution

This challenge was designed by Data Science Nigeria specifically for the DSN Bootcamp 2018, which takes place 19-24 November 2019. Welcome to the DSN participants! After the Bootcamp, this competition will remain open to allow others in the Zindi community to learn and test their skills. Description of the challenge: Recently, there has been an increase in the number of building collapse in Lagos and major cities in Nigeria. Olusola Insurance Company offers a building insurance policy that protects buildings against damages that could be caused by a fire or vandalism, by a flood or storm. You have been appointed as the Lead Data Analyst to build a predictive model to determine if a building will have an insurance claim during a certain period or not. You will have to predict the probability of having at least one claim over the insured period of the building. The model will be based on the building characteristics. The target variable, Claim, is a: 1 if the building has at least a claim over the insured period. 0 if the building doesn’t have a claim over the insured period.

booster_bootcamp

Intro to Python bootcamp at Booster Total, Paris, June 9, 2017

bruges

Geophysics library with various helpful functions

churn-prediction

this project represents the analysis exploratory and the creation of machine learning model to solve the problem of churn classification and Customer retention in the field of telecommunication.