Rust for data analysis encyclopedia (WIP).

DataCamp Jupyter Notebooks for Machine Learning

Repository for my practice files and notes following CampusX "100 Days of ML" YouTube playlist. This repo includes Jupyter notebooks and datasets organized by day, documenting my learning journey in machine learning.

The goal of this project is to perform Exploratory Data Analysis (EDA) on a dataset using Python tools and libraries within a Jupyter Notebook environment.

A simple widget for interactive EDA / QA. Works on top of Pandas [in Jupyter Notebook] using IPyWidgets with a sprinkle of Regex.

Learning Data Science, So saving notebook and stuff for future help.

This repository contains a Jupyter Notebook exploring the adult income dataset. The notebook performs Exploratory Data Analysis (EDA), including visualizations with charts and graphs. Additionally, it implements various classification models to predict income and analyzes their accuracy.

This project involves the Exploratory Data Analysis (EDA) of an airline dataset to uncover insights about passenger demographics, travel patterns, and airport usage. The analysis includes data cleaning, visualization, and interpretation of key metrics such as gender distribution, age distribution, nationality distribution, and airport analysis.

In this notebook, I have done Data Cleaning, Data Wrangling, EDA and Feature Engineering. After that I trained the dataset using Machine Learning Algorithm Random Forest Regressor.

The Forbes Billionaires Analysis project provides a comprehensive exploration of the world's billionaires using data from Forbes. The accompanying Jupyter Notebook (forbes-Billionaires-Analysis.ipynb) contains detailed analysis, visualizations, and insights derived from the Forbes billionaires dataset.

Análisis de la fuga de empleados de una empresa con implementación de un modelo de Machine Learning para acciones de fidelización.

Notebooks with some EDA and Machine Learning Models

This repository is a hub for data science enthusiasts, offering a diverse collection of projects, notebooks, and resources covering topics such as data analysis, machine learning, deep learning, and generative AI. Explore innovative ideas, contribute to cutting-edge research, and enhance your skills in the dynamic field of data science

LABS Proyecto Individual: Siniestros Viales - Rol: Data Analyst | Bootcamp Henry: Carrera Data Science | Cohorte DataFT 20

Aquí compartiré y documentaré mi aprendizaje en el análisis predictivo utilizando ML y prácticas de Python. Variando desde simples ejercicios hasta proyectos prácticos. Cada proyecto incluye archivos de soporte y notebooks con código y análisis y las prácticas sus enunciados comentado al inicio.

This repository contains my Exploratory Data Analysis (EDA) project using Python Pandas. The project focuses on analyzing marathon race data sourced from Kaggle, utilizing Jupyter Notebook for coding.

In this repository, I have saved my Python_Amazon_sales_analysis Notebook. To do this Amazon_sales_analysis, I have done end to end process. cleaned the dataset, Did EDA, ploted graph and reached to the conclusion.

This repository contains notebooks on various datasets as a practice on data analysis, all notebooks include: Data Cleaning. Data Visualization. Exploratory Data Analysis.

This repository contains code for analyzing and predicting outcomes in the Indian Premier League (IPL) cricket matches from 2008 to 2022. It includes data analysis notebooks, a prediction model, and a Flask-based web application for interactive predictions. Explore historical match data, gain insights, and make predictions on upcoming matches .

Explore diverse datasets through statistical analysis. Jupyter notebooks and scripts uncover trends and insights. Contribute to the world of data exploration!