Feature Engineering with Python
Encoding: converting categorical data into a numerical data
This repository is totally focused on Feature Engineering Concepts in detail, I hope you'll find it helpful.
CyberSoft Machine Learning 03 - Overview
Showcasing data science skills for a dataset provided by State Farm for a coding interview.
Machine Learning Project
Machine Learning Models
Book price dataset analysis and modeling
Data preprocessing is a data mining technique that is used to transform the raw data into a useful and efficient format.
Focus on selecting datasets suitable for a machine learning experiment, with an emphasis on data cleaning, encoding, and transformation steps necessary to prepare the data.
A machine learning model to accurately predict house prices based on various features such as quality, size, and location, utilizing Random Forest and XGBoost algorithms (Python) (Done alongside Kaggle ML courses)
Data Cleaning and Data Visualization with python libraries like numpy , pandas, sklean,seaborn, matplotlib-pyplot
Kaggle Project
Прогнозирование рыночной стоимости автомобилей
Supervised Learning project from TripleTen
Classification of an imbalanced dataset using SMOTE oversampling technique and ML Algorithms - KNN , XGBoost and Naive Bayes classifier
Students Placement based on some characteristics.
Job-A-thon ML challenge
To associate your repository with the ordinal-encoding topic, visit your repo's landing page and select "manage topics."