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-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.

advance-machine-learning

building-deep-neutral-net-from-scratch-using-numpy

coursera_how-to-win-a-data-science-competition-learn-from-top-kagglers

Labs and Project from the course "How to Win a Data Science Competition: Learn from Top Kagglers"

data-cleaning-and-feature-engineering-loan-data-

SuperLender is a local digital lending company, which prides itself in its effective use of credit risk models to deliver profitable and high-impact loan alternative. Its assessment approach is based on two main risk drivers of loan default prediction:. 1) willingness to pay and 2) ability to pay. Since not all customers pay back, the company invests in experienced data scientist to build robust models to effectively predict the odds of repayment. These two fundamental drivers need to be determined at the point of each application to allow the credit grantor to make a calculated decision based on repayment odds, which in turn determines if an applicant should get a loan, and if so - what the size, price and tenure of the offer will be. There are two types of risk models in general: New business risk, which would be used to assess the risk of application(s) associated with the first loan that he/she applies. The second is a repeat or behaviour risk model, in which case the customer has been a client and applies for a repeat loan. In the latter case - we will have additional performance on how he/she repaid their prior loans, which we can incorporate into our risk model. It is your job to predict if a loan was good or bad, i.e. accurately predict binary outcome variable, where Good is 1 and Bad is 0.

flood-prediction-in-malawi--zindi-competition

Simple starter code

geospatial_data_with_python

Introduction to Geospatial Data with Python

house-price-prediction

indegobikestations

Analyzing Indego's bike stations in Philly.

kaggle_learn_with_other_kaggle_users

Kaggle Competition

staff-promotion-prediction

suicide-rate-prediction-

taitanic-dataset

twitter-sentiment-analysis

web-scrapping

Basic Web Scrapping

womxn-in-big-data-south-africa-female-headed-households-in-south-africa

Female household headship has been on the rise in South Africa in recent years. Compared to male-headed households, female-headed households tend to face greater social and economic challenges. Female-headed households, in general, are more vulnerable to lower household incomes and higher rates of poverty. The South African census collects data on female headship and income levels of every household across the country every 10 years. However, it is important for policymakers and other actors to have accurate estimates of these statistics even in between census years. This challenge explores how machine learning can help improve monitoring key indicators at a ward level in between census years.