Prepping tables for machine learning

Client interface for all things Cleanlab Studio

The open-source tool for building high-quality datasets and computer vision models

Data analysis

This assignment contain information on the contributions to the campaigns of the US politicians at the state and the federal level. The contribution data has been collected from various sources and covers the 1989-2017 period

A light-weight, flexible, and expressive statistical data testing library

This repository showcases my data science skills, including EDA, Python, data cleaning/wrangling, and visualization. It demonstrates my problem-solving abilities through interactive insights. Explore the notebooks and provide feedback.

Easy to use Python library of customized functions for cleaning and analyzing data.

Comparing random forest, support vector regression and xgboost in predicting housing prices.

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This repository features a comprehensive ML pipeline for supervised learning tasks, covering data collection, cleaning, EDA, feature engineering, model training, and evaluation.

Данный репозиторий содержит проекты (преимущественно Data Science / Data Analysis), созданные в процессе обучения на потоке Skill Factory.

Atliq mart sales analysis dashboard

EDA of Market Campaign

This repository contains the code, documentation, and datasets for a comprehensive exploration of machine learning techniques to address class imbalance. The project investigates the impact of various methods, like ADASYN, KMeansSMOTE, and Deep Learning Generator, on classification performance while effectively demonstrating benefits of pipelining.

To analyze the provided cancer.csv data and predict whether or not a patient has breast cancer using ensemble techniques

Those with a strong interest in machine learning and data science should use this repository. I have coded step-by-step with machine learning and data science learners.

Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.

The project utilizes python and its various libraries like pandas, matplotlib and seaborn to evaluate credit card data that influence customer spending pattern and repayment behavior. The aim is to enhance the effectiveness of revenue generation processes and provide insightful business suggestions for improvement.