clustering with crypto!

This is project 1 of the Udacity Data Scientist Nanodegree.

Machine Learning (Pyspark-MLlib and Pyspark-Sql)

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

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.

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

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)

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.

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

In this i have performed complete feature engineering that is from handling null values, Categorical features upto performing feature scaling on our test_data and train_data.

In this code handling of the missing values for the categorical features from any dataset is shown.

Using supervised machine learning models to determine credit worthiness

Data Science Session: TabNet

benchmarking various categorical encoding techniques for tabular data across 6 classification tasks and using 5 different downstream classifiers.

Exploratory Data Analysis standard templated in markdown and txt format