Data Science Feature Engineering and Selection Tutorials

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

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

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 …

Customer Churn Prediction

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).

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

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

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

Machine Learning project used to predict that loan is approved or not 🌟🌟

Data Science Bootcamp @wbs Coding School

Automated Libraries like : DataPrep, AutoViz, SweetViz, Klib, Dtale, Pandas Profiling are used here to help succeed in data analysis endeavors. Happy automating!

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.

Performing EDA on Heart Disease Dataset from Kaggle using sweetviz

sales_analysis

Churn Analysis

Scripts for Data Visualization

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

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