oayodeji Goto Github PK

ai_sustainability

sentiment tracking during floods

aima-data

Data files to accompany the algorithms from Norvig And Russell's "Artificial Intelligence - A Modern Approach"

aima-python

Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"

bert

TensorFlow code and pre-trained models for BERT

cartonize

Convert photo into cartonize

complete-python-bootcamp

Lectures for Udemy - Complete Python Bootcamp Course

cracking-the-data-science-interview

A Collection of Cheatsheets, Books, Questions, and Portfolio For DS/ML Interview Prep

creative-profile-readme

A Collection of GitHub Profiles with awesome readme

d2l-en

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 300 universities from 55 countries including Stanford, MIT, Harvard, and Cambridge.

data-mining

UK Road Safety: Traffic Accidents and Vehicles

first-contributions

🚀✨ Help beginners to contribute to open source projects

heart_disease

identify_customer_segments

Data Scientist Nanodegree Unsupervised Learning Challenge: Udacity Data Scientist Nanodegree project for unsupervised learning module titled as 'Identify Customer Segments' brings Bertelsmann partners AZ Direct and Arvato Financial Solutions whose two datasets one with demographic information about the people of Germany, and one with that same information for customers of a mail-order sales company are provided for this challenge. The objective is to look at relationships between demographics features, organize the population into clusters, and see how prevalent customers are in each of the segments obtained. Prior to applying the machine learning methods, we also require to assess and clean the data in order to convert the data into a usable form. Solution: Preprocessed the data which includes identifying missing or unknown values encoded in the data and checking if certain features (columns) that should be removed from the analysis because of missing data. Feature transformation which includes using dimensionality reduction techniques to identify relationships between variables in the dataset, resulting in the creation of a new set of variables that account for those correlations. Lastly clustered, using the k-means method to cluster the demographic data into groups. Result: Using seaborn package created a visual comparison representation of customer data vs demographic data and concluded which segments/clusters of customers can then be used to direct marketing campaigns towards audiences that will have the highest expected rate of returns. Software and Libraries This project uses the following software and Python libraries: NumPy, pandas, Sklearn / scikit-learn, Matplotlib (for data visualization), Seaborn (for data visualization) Code File Open file jupyter notebook Identify_Customer_Segment.ipynb

image-captioning

ktrain

ktrain is a Python library that makes deep learning and AI more accessible and easier to apply

mediumposts

oayodeji

Config files for my GitHub profile.

password-generator

pierian-data-complete-python-3-bootcamp

predict-yoruba-hymn

This project was inspired by Wuraola Oyewusi by using a transformer.

reservinglifeinsurance

Reserving Model for Life Insurance Products. Details and Code will be uploaded soon.

ritik2629

road-traffic-accident

speech-recognition

This is a simple speech recognition

thinkful-supervised-learning-capstone

I built and evaluated several machine learning models to predict fatal accidents in UK’s public roads using ​2016 Road Safety Data​ from UK's Department for Transport.

trikit

A Pythonic Approach to Actuarial Reserving

tutorials

Includes data, Python files and Notebooks of tutorials published on Omdena blog

utiva-sql

water-quality-project