Case study for dataset (Violence in USA, 90`). Combination of socio-economic data with FBI stats. Models: PCA, Clustering, Multiple regression. Methodology CRISP DM

A simple project on the preparation of data using SAS language.

Fully reproducible code for Medium article

Using Deep Learning to create a model that predicts which passengers survived the Titanic shipwreck.

Application Fraud Detection Project.

The project follows a standard industry practice of utilizing CRISP-DM with the data from Stack Overflow Developer Survey Results focusing on Asian and European Developers.

Effect of Regularization of L1 and L2 on Linear Regression with Alpha value selection using GridSearchCV

Менеджмент дата-проектов

Modified CRISP-DM template for the practical researcher

Predicting if a NBA rookie player will last at least 5 years in the league

AXA Data Science Challenge

An analysis of Retailer X's store and creating models that will predict who will participate in their programs.

Exploring Renovation Potentials: Data-Driven Insights for Renovation Service Providers in Chemnitz

CRISP DM implementation for bank marketing data.

A data-based approach to analyse and compare prices and characteristics of Airbnbs listings in Montreal and Toronto and identify the key factors affecting their prices.

Below is a link to the crisp DM methodology and python notebook

Elaborar modelos estatísticos, criar algoritmos, solucionar problemas e expandir a estratégia de negócios.

This project aims to practice the steps of Crisp Data Mining ( CRISP-DM ). The repository includes 3 phases, data understanding, supervised learning, and unsupervised learning.

CRISP-DM Project of Udacity Data Scientist Nanodegree

Using multiple linear regression model to predict customer demand in order to make business decision