kosi-so Goto Github PK

-data-science-nigeria-2019-challenge-1-insurance-prediction-axamansard-insurance-hackathon

Recently, there has been an increase in the number of building collapse in Lagos and major cities in Nigeria. Olusola Insurance Company offers a building insurance policy that protects buildings against damages that could be caused by a fire or vandalism, by a flood or storm. You have been appointed as the Lead Data Analyst to build a predictive model to determine if a building will have an insurance claim during a certain period or not. You will have to predict the probability of having at least one claim over the insured period of the building. The model will be based on the building characteristics. The target variable, Claim, is a: 1 if the building has at least a claim over the insured period. 0 if the building doesn’t have a claim over the insured period.

automated-feature-engineering

Automated feature engineering in Python with Featuretools

awesome-computer-vision

A curated list of awesome computer vision resources

basic_arithmetic

A Python Scrpt that consists of basic mathematical functions such as addition, subtraction, division, etc.

cohort4

AI Saturdays Lagos Cohort 4 Repository.

cohort_5

The repository contains new format for the load data and the implementation on Jupyter notebook

computer-vision

Computer Vision Projects

covid-19-nlp-text-classification

Classifying tweets about Covid-19 into 5 Sentiment groups; Extremely Negative, Negative, Neutral, Positive, Extremely Positive

cs329s-ml-deployment-tutorial

Code and files to go along with CS329s machine learning model deployment tutorial.

dot-501

A simple Command Line Program that grants clients privileges based on their level of clearance

face-recogition-with-svm-and-pca

facenet-pytorch

Pretrained Pytorch face detection (MTCNN) and recognition (InceptionResnet) models

facial-recognition-with-pytorch

Train a facial recognition model that can recognize my face

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.

house-prices

Predicting house prices with a few features

insurance-prediction-dsn

Using certain characteristics of a building to predict if they have a claim to the insurance or not

kosi

masakhane-mt

Machine Translation for Africa

mlproject

mvnwebapp

Simple Web app for Jenkins Tutorial

nigerians-and-their-salaryy-ai-saturday-data-science-project-

object-detection-model

used a pretrained Mask R CNN to create my model

pipeline_script

Jenkins Tutoral for pipelines

sentiment_analysis_with_keras

simple-chatbot-with-pytorch

Creating a chatbot trained with "cornell movie dialog corpus"

simple-line-plots

soccer-analyses

The code here is pretty much everything I use to create my datasets (from FBRef) or visuals. Most my scatter plots are made in Tableau, though, using the dataset created from the FBRef dataset download

spot-the-mask-challenge-2nd-place-solution-

2nd Place Solution using Pytorch and pretrained models

winequality_simple