clustering with crypto!

Kaggle Competition (Encoding categorical variables)

This is project 1 of the Udacity Data Scientist Nanodegree.

Machine Learning (Pyspark-MLlib and Pyspark-Sql)

Multimodal deep learning package that uses both categorical and text-based features in a single deep architecture for regression and binary classification use cases.

It predicts the right group of new customers by Segmentation among A, B, C, and D segments using LightGBM Classifier.

This study creates machine learning models to predict the seriousness of car crashes using 2019 and 2020 crash reports from the publicly accessable database maintained by the Chicago Police Department. A car crash is considered serious if the crash results in an injury or the car is towed due to the crash. Models use categorical features that de…

Data Science in the Banking Industry [Volume 1]

Develop a predictive model to understand the LTV of each customer for a DTC meal-kit business.

This repository contains notebooks on different topics across - linear algebra, image classification, language models etc.

A lightweight library for encoding categorical features in your dataset with robust k-fold target statistics in training with credibility filtering, and custom statistics.

Medium Post: some techniques useful to deal with missing values of Categorical Features

Why data analysis? , How to understand the problem, what to do for data analysis, and how clean the data for building Machine Learning models

Project of a coursework - Categorical Data Analysis (M.Stat Semester 2) under the supervision of Prof. Arindam Chatterjee.,ISID

Data Analysis with Python project from freeCodeCamp (3 of 5)

Supervised Learning Problem. In this categorizing the customers in four groups, as follows: 1- Basic Service 2- E-Service 3- Plus Service 4- Total Service.

Load monitoring/ load detection is one big breakthrough in tackling the problem of increasing carbon footprint. It helps to provide detailed electricity consumption information in residential households. This project is dedicated to providing a perfect estimate of the usage of the most common appliances in residential buildings.

Category transformation

Mostl oftenly used Encoding techniques for categorical Varibales are performed here.