Business intelligence as code: build fast, interactive data visualizations in pure SQL and markdown

My Streamlit Web App in which I show the findings of analyzing all my chess.com games 😮 Analyze your own games by just providing the username 😍

Always know what to expect from your data.

DATA: 606 | Capstone Project

Descriptive And Inferential Data Analysis Using Python

This project aims to leverage predictive analytics to forecast the outcomes of rocket launches for Space Y, a new player in the commercial space industry.

Project 5 of AI Engineer Program with OpenClassrooms.

Developer-first embedded analytics

Project 4 of AI Engineer Program with OpenClassrooms.

Análisis exploratorio de datos (EDA) de los alquileres Airbnb de Madrid. Se construyen varios modelos para predecir el precio, registrandolos con MLflow, y se crean dos imágenes de Docker (una con FastAPI y otra con Streamlit). Al final, se despliegan las dos imágenes con dos contenedores en un cluster de Kubernetes, exponiéndolos como un servicio.

This project explores and visualizes supermarket sales data to gain insights into customer behavior, product trends, and sales performance. It includes data cleaning, feature engineering, exploratory data analysis (EDA), and visualization techniques using Python libraries such as Pandas, Matplotlib, and Seaborn.

Project 3 of AI Engineer Program with OpenClassrooms.

Project 2 of AI Engineer Program with OpenClassrooms.

I followed a guided exploratory data analysis project using MySQL.

In this section we will be predicting body fat using regression machine learning algorithms

This repository contains the code and analysis for predicting flight cancellations for American Airlines Inc. using logistic regression, as well as exploratory data analysis (EDA) of the dataset.

Objective : We will try to build a machine learning model to accurately predict whether or not the patients in the dataset have diabetes or not?

we aim to predict the finishing positions and pit stop lap in Formula 1 races based on a set of features derived from driver statistics, circuit characteristics, and race conditions

Built a predictive machine learning model using a Streamlit application to predict weekly sales. Model achieved 97.4% accuracy and analyzed trends, patterns, and data insights using EDA. Compared various features and identified key contributors with a significant impact on prices.