Paul Okafor's Projects
Answers to 120 commonly asked data science interview questions.
500 AI Machine learning Deep learning Computer vision NLP Projects with code
This project employs deep learning techniques for bird species classification, leveraging convolutional neural networks (CNNs) to analyze and identify various bird species from images. The model is trained on a diverse dataset, enabling accurate species recognition and contributing to ecological research and conservation efforts.
This repository contains a credit card transactions fraud detection model built using the Random Forest algorithm. The model uses a large dataset of credit card transactions to train the algorithm and identify patterns and anomalies that may indicate fraud.
Cheat Sheets
Books for Data Science
A collection of popular and reusable data science codes and operations for Data Science and Machine Learning
DataScience projects for learning : Kaggle challenges, Object Recognition, Parsing, etc.
http://DataScienceSpecialization.github.io
A concise machine learning project leveraging Logistic Regression, SVM, and Decision Trees to classify chemical compounds. Incorporates cross-validation, hyperparameter tuning, and feature selection for performance optimization on the high-dimensional, class-imbalanced Dorothea dataset.
The world's simplest facial recognition api for Python and the command line
Schlumberger i-Handbook
Streamlit-powered YOLO license plate detection app, offering image/video processing with interactive user interface. Data from Roboflow.
The resources for our ML Workshop about Image Recognition
Numpy exercises.
Config files for my GitHub profile.
This project applies machine learning techniques to classify ovarian cancer stages, aiding in early detection and personalized treatment strategies. Using patient data and biomarkers, we train predictive models to identify cancer subtypes accurately.
This is a machine learning model that predicts petrol prices using univariate timeseries data and LSTM (Long Short-Term Memory) neural networks. The model is trained on historical crude oil prices from a public database ranging from 2003 to 2015.
This project focuses on the development of a machine learning model for predicting pore pressure in the context of oil and gas exploration and production. Pore pressure, which represents the pressure exerted by fluids within rock formations, is a critical parameter in drilling operations, reservoir characterization, and wellbore stability analysis.
This repository contains Python code and documentation for univariate time series modeling on PJMs Eastern Interconnection Grid, using Prophet. The aim is to forecast energy consumption based on hourly data spanning 10 years. The results achieved good accuracy, with forecasted values available for future hours.
Comprehensive Python Cheatsheet
I have just started learning python for Data Science.
Jupyter notebook for Udemy course: Python data science and machine learning bootcamp
A list of semi to fully remote-friendly companies (jobs) in tech.
A sentiment analysis was conducted on six digital lending platforms in Nigeria to determine the public perception and sentiment towards these platforms. The analysis analyzed customer reviews and comments from various online sources to determine the overall sentiment towards each platform.
This GitHub repository contains code and resources for a time-series analysis of a smart home dataset with weather and energy information. We explore relationships between environmental parameters, energy consumption, and weather conditions, and use various techniques to uncover patterns and build models for predicting energy consumption.
This repository contains code and resources for predicting sonic wave velocity in oil and gas exploration using Automated Machine Learning (AutoML) techniques.
This repository is a data analysis project focused on improving health outcomes in Africa. Through data exploration and modeling, the project aims to provide insights and recommendations to address pressing health issues. The repository includes code, datasets, and findings and welcomes contributions from the open-source community.
Find strings/words in text; convenience and C speed :fireworks: