skrobot is a Python module for designing, running and tracking Machine Learning experiments / tasks. It is built on top of scikit-learn framework.

This repository contains the data analytics lessons I took from the clarusway bootcamp between 5 Jan - 4 Aug 2022 and includes 48 sessions, 10 labs, 12 assignments, 12 weekly agendas, and 5 projects.

Churn modelling for bank customers using Artificial Neural Network

Regression models for "epigenetic clock" estimation of canine chronological age

Given the dataset, can we predict the Co2 emission of a car using another field such as engine size?

Show case for modelStudio based on ⚽⚽⚽FIFA 20 ⚽⚽⚽

A data-driven approach to assess property prices in a Midwestern state, using regression and decision tree models to evaluate housing data from 2006-2010.

NHL-Game Analysis 🥅 🏒

Predictive Modelling of Pathological Complete Response Classification and Relapse-Free Survival Regression in Cancer Patients

This repo contains the code (data analysis, models, results) of my diploma thesis with title "A recommender system to predict the behaviour of an e-commerce page visitor". The official university's listing of this thesis is on the link bellow:

Perform exploratory data analysis techniques, such as predictive models and advanced visualization, on the Boston Housing Dataset.

Profiles of healthy people and diabetic people were analyzed and used to build a predictive model to gauge the diabetes risk index of an untested person.

Predictive Modelling – Exercises (in R)

Here I provide a sample of one of my consultations. I was reached out to consult on how to approach a predictive model for customer churn for an investment banking company.

📈 Train yourself to make better predictions.

This is a group project in the Data Science for Business I course where we took a data-driven approach to foster employee retention and enhance operational efficiency by building predictive models on Python.

topsis package created which can be directly used through command line or terminal for ranking of information provided

Customer Churn is a burning problem for Telecom companies. In this project, we simulate one such case of customer churn where we work on a data of postpaid customers with a contract. The data has information about the customer usage behavior, contract details and the payment details. The data also indicates which were the customers who canceled …

Develop classification strategies and preprocess data with pandas to prepare for predicative modeling.