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

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

f1 race winner predictor

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

Churn modelling for bank customers using Artificial Neural Network

NHL-Game Analysis 🥅 🏒

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.

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:

Predicts calories burned during physical activity using publicly available workout and physiological data from Kaggle.

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 ⚽⚽⚽

AI agent simulator based upon Snowglobe with adaptations for grounding and engine-scenario separation

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

A Streamlit web application for breast cancer prediction using logistic regression. Users can input key tumor features to receive real-time prediction and confidence scores based on the Breast Cancer Wisconsin Diagnostic Dataset.

End‑to‑end breast cancer prediction workflow with reproducible notebooks and Streamlit dashboard.

Predictive modelling for Zomato restaurant data

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 …

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

Prédiction des prix immobiliers par ML (Random Forest) — MAE ~34k€, précision 90.4% — sur 10 000 transactions. Pipeline complet : EDA, preprocessing, modélisation, évaluation. Python · Scikit-learn · Pandas · Seaborn

Delivery Risk Classifier explored predictive classification to identify tasks likely to complete late, focusing on understanding the limits of snapshot‑based delivery data and how modelling could still provide early warning signals. The team combined exploratory modelling with clear recommendations on how delivery datasets need to evolve to su...