andreacorvi Goto Github PK
Type: User
Company: UCSC
Bio: Student of Data Analytics for Business and Economics at UCSC.
Location: Milano
Type: User
Company: UCSC
Bio: Student of Data Analytics for Business and Economics at UCSC.
Location: Milano
University team project regarding the class of data mining. In this work our team analyzed a dataset from a telecommunication company in order to predict the churn of its current costumers.
Statistical Learning project. I, with a classmate, developed different alghortims in order to build a model for direct marketing.
Classification on the Kobe Bryant Shot Selection dataset (https://www.kaggle.com/c/kobe-bryant-shot-selection/data) using Decision Trees
The core of this work is represented by networks, specifically from a dynamical point of view. Longitudinal networks can be statistically modelled in several ways, most of them developed in the social sciences field. In this essay it is attempted an extension of the use of one of those models, namely the Temporal Exponential Random Graph model,to a different science field: finance. The TERGM is here applied to a stock correlation dataset built on the monthly correlations of the daily returns of thirteen listed tech companies. The model has been tested both from a predictive and a inferential perspective. It is presented that it is possible to model such networks through the use of a TERGM. The results though can vary depending on the selected period of time. Specifically the main issue, exposed through a rolling origin cross validation methodology, is the consistency of the predictive performance over time. Still, the model has shown satisfactory results over small period of time, such as one or two years, both in terms of prediction and inference and tested on an out-of-sample prediction.
My project for the neural networks class. I employed transfer learning in order to perform a multilabel classification of cloud images. The dataset can be found on kaggle (https://www.kaggle.com/c/understanding_cloud_organization).
This was my first project using python. I performed a sentiment analysis on a dataset made of tweets. In particular I wanted to determine whether a tweet was sexist/racist/violent or not. For this purpose I used a Naive Bayes classifier and I built some wordclouds in order to improve the data visualization.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.