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Latest users' studies


Jupyter study nationality players

jamesle / fifa18

A brief statistical analysis of FIFA 18 data to determine the strongest contenders for this year's World Cup in Russia. The author looks first at interesting stats on individual players, before diving into a more complex analysis of the strongest national squads.


Introspect the reachable memory notebook

sachinprabhu / mm_heap_dump_analysis

Optimising analysis to reduce memory footprint.


iteration hit rate notebook

prithag / mnl

Mutli-class multinomial model.


KyleOS / bike-weather

Introduction to Linear Regression - Predicting the total number of daily riders in Madrid’s bike-sharing system, depending on certain weather conditions.

126Fork

share / machine-learning-101

Simple introduction to different machine learning techniques & models, such as regression, classification, regularisation, SVM, and logistic regression.

106Fork

Active portfolio - costumer breakdown

briainodonnell / risk-dashboard

Comprehensive risk & pricing dashboard for a loan portfolio.


tensoflow logic regression training phase notebook

adithyaksk / tf-logistic-regression

Tensorflow logistic regression study, using a dataset from one of Kaggle's product classification challenges.


Jupyter notebook analyse the sentiment of any twitter

waldohiding / twitter

Application to analyse the sentiment of any Twitter user's input & to carry out a correlation analysis with the price of Bitcoin. The goal is to find users whose actions on Twitter can relatively predict the short-term price of Bitcoin.


Jollker / interview_qualogy

Analysis of Greece’s economic recovery from the euro crisis, looking at the relationship between the employment rate and government debt & GDP.


Predicting the heart disease prediction using data mining and machine learning notebook

mohamed / heart-disease-prediction-using-data-mining

Predicting the heart disease using data mining, machine learning and Framingham Heart Study dataset. Multiple demographic, behavioural, medical history and physical risk factors are taken into consideration.

124Fork

In-depth statistical analysis bike share notebook

justin / capstone

In-depth statistical analysis of Chicago's Bike Share Program, plotting customers against subscribers, as well as looking at originations and destinations.


Beyond machine learning equations

romankazinnik / adjoint-state-formulas-markdown

A quick demonstration of how to add implicit equations to an optimisation problem, including an example of fitting a model to data, where the model and data are constrained by an implicit equation.


Jupyter Visualising crimes committed in city of Chicago

nathancooperjones / did-someone-get-robbed-at-iit-today

Visualising a statistical analysis of crime committed in the city of Chicago, broken down by crime type, location, day of the week and time.


jan_erish / unsupervised-learning-hacker-statistics

Unsupervised machine learning model to create a re-election campaign strategy in Washington, with emphasis on the contrast between East and West.


Jupyter Visualising Employees with deferred income

pranav_suri / PyData-EDA

This study uses machine learning to build a POI (Person of Interest) identifier based on financial and email data made public in the aftermath of the Enron scandal of the early noughties. The goal of the project is to identify Enron employees who may have committed fraud (POIs) using a Gaussian Naïve Bayes algorithm.