Olawale Ayodeji's repositories
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"
Predict-Yoruba-Hymn
This project was inspired by Wuraola Oyewusi by using a transformer.
speech-recognition
This is a simple speech recognition
bert
TensorFlow code and pre-trained models for BERT
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
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
ktrain
ktrain is a Python library that makes deep learning and AI more accessible and easier to apply
ReservingLifeInsurance
Reserving Model for Life Insurance Products. Details and Code will be uploaded soon.
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