Código feito nos encontros aos sábado em que está fazendo análise
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Updated
May 11, 2024 - Jupyter Notebook
Código feito nos encontros aos sábado em que está fazendo análise
Using Python to visualize various datasets relating to COVID-19 in New York City. Analyses include: plotting rates by age and sex, geographic maps by zip code, and time-series plots with logarithmic axes.
SAMSUNG SIC Finish Project Course - Python
Analyze large datasets of simulated binary star evolution to classify black hole mergers
This project analyzes McDonald's menu nutritional data using Python, Pandas, and Matplotlib. It provides insights into menu category distribution, nutritional correlations, and macronutrient balance, offering actionable information for consumers.
Heatmap showing the number of deaths month to month since the beginning of the pandemic.
A compilation of programs written in R able to visualize, estimate, predict or find patterns in our data. This project is meant for beginners.
KPMG virtual internship on Data Analytics with Sprocet dataset
House price prediction using multivariable regression model
Python analysis of sample school district math and reading test scores.
Most frequent food items consumed during Covid
Study for Financial analysis & Machine Learning with Python
Python code files written to execute a quick profit-loss financial analysis, and tabulate the results of a local election to declare a winner.
Analiza lotu rakiety z uwzględnieniem różnych parametrów.
Complete statistical analysis of cavy lifetime dataset using Python, Pandas, NumPy, Matplotlib, and SciPy to explore, visualize, and infer the impact of bacilli infection on cavy lifetimes 🦫
A Python analysis of sample pharmaceutical clinical trial testing data for potential cancer treatment drugs. Tables and graphs generated using Pandas and Matplotlib libraries
This project provides a comprehensive set of data visualizations for COVID-19 statistics in New York City. It includes time series analyses, geographic plots, and demographic breakdowns of case rates, death rates, and hospitalization rates.
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