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DAVID OTOOSAKYI's Projects

scrapingdata icon scrapingdata

Code for getting data from social media for your data science projects!

sktime icon sktime

A unified toolbox for machine learning with time series

stat4ml icon stat4ml

Statistics and Mathematics for Machine Learning, Deep Learning , Deep NLP

stats-maths-with-python icon stats-maths-with-python

General statistics, mathematical programming, and numerical/scientific computing scripts and notebooks in Python

sympy icon sympy

A computer algebra system written in pure Python

tweets-sentiment-analysis icon tweets-sentiment-analysis

Tweets Sentiment Analysis Project to classify the the polarity of tweets either as ‘Positive’, ‘Negative’, and ‘Neutral' with high accuracy

tweetsclassification icon tweetsclassification

Exploring Jaccard and Cosine similarities performances then visualising their output using k means and kmeans with pca. Additional input on time series analysis, web scrapping and twitter scrapping.

twitteranalysis icon twitteranalysis

Python Notebooks for Collecting Tweets and Analyze their text using various text classification and clustering techniques

twittersa icon twittersa

This is a script for analysis the sentiments of a tweet stream in json format.

umoja-hack icon umoja-hack

This repo contains team solo's best solutions for the umoja hackathon, it is the combined effort of Fajamila Oluwadunsin, Okewunmi Paul and Olufemi Victor

umojahack-hackathon-nigeria-2020 icon umojahack-hackathon-nigeria-2020

The objective of UmojaHack Nigeria 2020 hackathon is to Build a Classification model that classifies a customer if that customer will submit a vehicle insurance claim within the first three months.This hackathon was hosted by AXA insurance Nigeria and will help it make better decision regatding customers and their tendencies of submitting a claim within the first three months of issuance. This Hackathon was hosted on Zindi Africa. This solution was ranked 11th on the final leaderboard

umojahack-nigeria icon umojahack-nigeria

The objective of this hackathon is to develop a predictive model that determines if a customer will submit a claim within 3 months from their first transaction.

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