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

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

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.

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

Using SweetViz pacakge auto EDA was done on Wine Dataset

Here I competed in a competition to help a bank be able to find out which banking actions are most relevant to clients. The challenge was to identify the right interaction for different clients through 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 …

Churn Analysis

Titanic: Machine Learning from Disaster - Predict survival on the Titanic - Automation for EDA and Modelling

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

Google Data Analyst - Capstone Project