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andreacorvi's Projects

customerchurnprediction icon customerchurnprediction

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.

directmarketing-r icon directmarketing-r

Statistical Learning project. I, with a classmate, developed different alghortims in order to build a model for direct marketing.

kobebryant-decisiontrees icon kobebryant-decisiontrees

Classification on the Kobe Bryant Shot Selection dataset (https://www.kaggle.com/c/kobe-bryant-shot-selection/data) using Decision Trees

master_thesis icon master_thesis

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.

neuralnet-imageclass icon neuralnet-imageclass

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).

sentimenttwitter icon sentimenttwitter

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.

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