Data Science Feature Engineering and Selection Tutorials

Loan Application Prediction through machine learning moldes : Logistic Regression, Random Forest, DecisionTree,

In this repository, we would see different available libraries for Exploratory Data Analysis

Beautiful, high-density visualizations for EDA (Exploratory Data Analysis) in Streamlit

This repository contains an exploratory data analysis of my writer data at Medium. I use it to carry out data analysis once in every 4 months to see audience and fan growth, and topics they love! You can check out the articles here👇

EDA (Exploratory Data Analysis) -1: Loading the Datasets, Data type conversions,Removing duplicate entries, Dropping the column, Renaming the column, Outlier Detection, Missing Values and Imputation (Numerical and Categorical), Scatter plot and Correlation analysis, Transformations, Automatic EDA Methods (Pandas Profiling and Sweetviz).

In statistics, exploratory data analysis is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task

ML.GUIDE is a Multi-LLM Agent System designed to help professionals define, evaluate, and solve machine learning problems. It leverages specialized AI agents for tasks like problem definition, data assessment, model recommendations, research, and code generation. Developed in 17 hours during the AI National Summit (AINS) hackathon on June 9, 2024.

This repo contains basic understand of what is automated EDA is about

Based on aspects of building location and construction, your goal is to predict the level of damage to buildings caused by the 2015 Gorkha earthquake in Nepal.

Use Python to explore data related to blood donors and we want to predict whether or not a donor will give blood the next time when the blood donation will be organised.

This project shows the data analysis of economic data using Fred API (Federal Reserve Economic Data) by cleaning the data, fetching data from the API, exploring and analyzing.

An indepth EDA was carried out on XYZ Atm dataset for insights.

This is a Phishing Classifier project in which model identify the phishing data using various machine learning techniques and help to prevent from malicious virus and cyber attack.

Análise Exploratória de Dados utilizando a biblioteca Sweetviz no Google Colab

Este projeto foi desenvolvido durante 7DaysOfCode da Alura, onde são propostos desafios durante sete dias. Este se trata do desafio do primeiro dia onde foi proposto a visualização e o storytelling dos dados disponíveis do CEAPS

Performing EDA on Heart Disease Dataset from Kaggle using sweetviz

Automatic EDA Report Creator CLI Using SweetViz and inquirerPy to analyze datasets, analyze target and compare datasets

Data Science Bootcamp @wbs Coding School