Este projeto consiste na minha primeira análise de dados feita em Python utilizando as bibliotecas pandas, numpy, seaborn e matplotlib. A análise foi realizada nos dados do setor de Recursos Humanos (RH) da empresa IBM.
Análise dos fatores depressivos - base de dados NHANES 2005-2006
En este repositorio vas a encontrar análisis realizados en Jupyter Notebook utilizando una variedad de poderosas bibliotecas de Python, entre las cuales se incluyen pandas, numpy, matplotlib, seaborn
Discord graphing bot.
Open-source Intrusion Detection System (IDS) designed to monitor and detect security threats on Windows, Linux, and macOS systems. The IDS includes both Host-based Intrusion Detection (HIDS) and Network-based Intrusion Detection (NIDS) components, providing comprehensive coverage for detecting and mitigating various types of cyber threats.
workbook containing pandas and machine learning files
Complete Data Science, NumPy, Pandas, Matplotlib, Seaborn, Statistics, Power Bi, Machine Learning.
Assignments for Machine Learning
Basics of Data Sciences From 0 to Hero
Case study to understand the driving factors (or driver variables) that contribute to a loan default.
My Jupyter notebooks in which I practice data science.
Hands-on tutorials, exercises, and projects to accompany the No Bullshit Guide to Statistics
📘This repository provides a detailed exploration of `Yulu` Bike rentals data using Hypothesis testing, employing statistical techniques, we delve into the nuances of customer behavior, E-bikes rental patterns offering insights & key metrics to enhance understanding and inform strategic decisions
📗 This repository provides an in-depth exploration of the predictive linear regression model tailored for Jamboree Institute students' data, with the goal of assisting their admission to international colleges. The analysis encompasses the application of Ridge, Lasso, and ElasticNet regressions to enhance predictive accuracy and robustness.
Data Analysis With Python Internship at Main Flow Services and Technologies
Data was downloaded through Kaggle
This model predicts the price of a bicycle based on bicycle specifications
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