Implemented ,Analyzed & compared working, functionalities & Insights of different available Auto EDA libraries

Automatically perform exploratory data analysis, and generate a report in Word '.docx' format.

Perform semi automated exploratory data analysis, feature engineering and feature selection on provided dataset by visualizing every possibilities on each step and assisting the user to make a meaningful decision to achieve a low-bias and low-variance model.

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).

A proof-of-concept for the implementation of an early fault detection system in oil wells, designed to enhance operational efficiency and reduce costs.

A reusable codebase for fast data science and machine learning experimentation, integrating various open-source tools to support automatic EDA, ML models experimentation and tracking, model inference, model explainability, bias, and data drift analysis.

Template for machine learning projects, featuring a diverse collection of ML models, AutoML solutions, and simple EDA tools for streamlined project development. Users only need to specify target features and add their data path in the Config to kickstart a wide array of machine learning tasks

Using SweetViz pacakge auto EDA was done on Wine Dataset

Predicting berlin house prices using different regression models

Repository shows the demonstration of team email reports deliverables by python automation and task scheduler. As a part of organizational process, we need to pull data from client source and get reports activities to team by mails on daily basis. This is productive, time reduction and error free process by automating whole things as per the req…