This repository contains the notebook of several mini-projects I did starting from my own personal interest. Topics range from very different domain.
Here is the list with a brief summary of the content for each mini-project (click on the link to jump straightaway into the rendered notebook):
In this notebook I use scikit-learn and Cost Complexity Pruning to build Classification Tree, which uses continuous and categorical data from the UCI ML Repository to predict whether or not a patient has hearth disease based on their sex, age, blood pressure and a variety of other metrics. Specifically, I use the Heart Disease Dataset
In this notebook I implement MC simulation in order to illustrate and get familiar with this method.
Here I try to answer to a simple question which propably we had to answer few times while doing online shopping for example on Amazon: would you rather buy the product with less reviews but higher rating stars, or a similar product with more reviews but lower rating stars? I analyse this issue by means of a Monte Carlo simulation varying sample size and distribution of ratings
I use Support Vector Machine to build and train a model using human cell records, and classify cells to whether the samples are benign (mild state) or malignant (evil state).
in the first part I implement a Monte Carlo simulation and in the second part a Bayesian analysis where based on prior understanding about how likely we are to have any of many 'true' distributions.
- Fractal 🎨
This notebook illustrates how to plot fractals in python. Specifically I plot the Barnsley fern using python, numpy and matplotlib.
This notebook is aimed at locating and plotting the location of the International Space Station.
I want to estimate
, given that having random (0,1)
Did elimination of copayment in Norway increase visits to the physician? This notebook implements the Difference in Difference method for causality.