[ECCV 2020 Spotlight] A Simple and Versatile Framework for Image-to-Image Translation

Converting night into day is one of the most interesting applications in generative models, due to the great difficulty in recreating the scene during the day, especially in cases of extreme darkness, and thus the difficulty lies in imagining the scene during the day when the lighting is very weak.

Open source machine learning library with various machine learning tools

This repository contains source code to the article: Piotr Szwed: Classification and feature transformation with Fuzzy Cognitive Maps, Applied Soft Computing, Elsevier 2021

Creating Customer Segments - 4th project for Udacity's Machine Learning Nanodegree

Feature engineering in machine learning

Code for <Traceable Automatic Feature Transformation via Cascading Actor-Critic Agents>

Implementation of the stacked autoencoder in Tensorflow

TSIT implementation in TensorFlow; TSIT: A Simple and Versatile Framework for Image-to-Image Translation

A practical repository covering real-world feature engineering techniques to transform raw data into machine-learning-ready features using Python and Scikit-Learn.

Machine Learning Nano-degree Project : To identify customer segments hidden in product spending data collected for customers of a wholesale distributor

Apply unsupervised learning techniques to identify customers segments.

A collection of working snippets used for machine learning related tasks.

Extracting, transforming and selecting features using Spark MLlib

Airbnb price prediction with machine learning models using Amsterdam dataset.

Machine Learning Engineer Nanodegree, Unsupervised Learning, Creating Customer Segments

This project aims to predict Prices of House. It involves several key stages, including data preprocessing, feature engineering, model selection, and evaluation. The goal is to develop a model that provides accurate and reliable price predictions based on the given features.

Using the dataset compiled by Dean De Cock. Applying Feature Transformation, Feature Selection and K-fold Cross Validation

This project aims to predict property prices using advanced regression techniques, providing accurate estimations based on input features. By leveraging machine learning algorithms, this project enables data-driven insights into real estate pricing trends, helping stakeholders make informed decisions.

Implemented Xgboost model with optimum hyperparameters to predict sales in a BigMart mall.

Customer Segments - Machine Learning Nanodegree from Udacity

In this project we have performed all types of feature transfromation on the titanic dataset and we have seen the usage of qqplot to check whether a feature is normal/gaussian distributed or not.

No description provided.

Scikit-klearn compatible BinaryEncoder class capable of handling unseen categories in an automated fashion

In-depth EDA to uncover patterns, trends, and insights using visualizations and statistical summaries. Includes data cleaning, feature understanding, and correlation analysis.

Apply unsupervised machine learning techniques on product spending data collected for customers of a wholesale distributor in Lisbon, Portugal to identify customer segments hidden in the data

Identifying Customer Segments using unsupervised learning techniques

No description provided.

Here I have Demonstrated Some of my Machine Learning works

Data preprocessing is a data mining technique that is used to transform the raw data into a useful and efficient format.