Performing EDA on Heart Disease Dataset from Kaggle using sweetviz

Exploratory Data Analysis Part-1

Customer Churn Prediction

Data cleaning and Exploratory data analysis is the challenging task for everyone. Around 80% of time is taken for the data cleaning and EDA and remaining 20% is for model building and all other process. Because of it's time complexity, reasearchers introduced a more Automated libraries for perform Automated EDA and data cleaning operations with …

This repo is about how to use Sweetviz library for Exploratory Data Analyisis using iris dataset for example.

We use the Titanic dataset to implement machine learning and deep learning. Preprocessing data, visualizing, building models, and ensembling are practiced in the ML section; PyTorch basics, PyTorchLightning framework, and RayTune hyperparameter-tuning are in the DL section.

The file contains the sample code for implementation of Sweetviz for EDA (Exploratory Data Analysis) in Python.

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).

Using SweetViz pacakge auto EDA was done on Wine Dataset

Churn Analysis

Google Data Analyst - Capstone Project

Análise Exploratória de Dados utilizando a biblioteca Sweetviz no Google Colab

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👇

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

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

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.

A virtual opportunity to step into the shoes of a Standard Bank team member and complete tasks that replicate the work that the Data Science team does every day.