Babaniyi Olaniyi (babaniyi)

babaniyi

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Location:Barcelona, Spain

Home Page:http://babaniyi.com

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Babaniyi Olaniyi's repositories

Deep-contextual-bandits

A benchmark to test decision-making algorithms for contextual-bandits. The library implements a variety of algorithms (many of them based on approximate Bayesian Neural Networks and Thompson sampling), and a number of real and syntethic data problems exhibiting a diverse set of properties.

BCG

Used Random Forest model to predict customers likely to churn and recommended discount and pricing strategies to improve customers retention.

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babaniyi.github.io

Blog template

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BusinessML

Using ML to solve business questions such as customer metrics, journey, growth and segmentation.

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Development-Economics

For Growth and Development Class IDEA, PhD UAB

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analytics-handbook

Getting started with soccer analytics

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babaniyi

Github profile

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bandits

Algorithms for multiarmed bandits such as e-greedy, thompson sampling, etc.

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covid_data_nigeria

A python code that scrapes the NCDC website to track the spread of covid-19 in various Nigeria states.

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Metyis

I provide a recommendation about the type of movies a film production company would have to do if the box takings and profit have to be maximized.

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applied-ml

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

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BirthdayReminder

A function that reminds me of an event at a particular time.

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Data-Engineering-with-Python

Data Engineering with Python, published by Packt

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fix-ComputeModelStatistics.scala

Simple and Distributed Machine Learning

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handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

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ISLR-python

An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code

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LLM_from_scratch

This repo documents my approach to building LLMs from scratch using basic numpy as opposed to PyTorch

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pySpark-learn

Practising PySpark by solving exercises such as email classification, clustering data and pandas equivalent to pySpark.

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Skyline-pareto-optimal

Python code to derive the skyline using block-nested loop

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stock

A web app built using Streamlit to visualise Apple's stock prices across the years.

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