A collection of heterogeneous distance functions handling missing values.
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Updated
Jan 24, 2022 - MATLAB
A collection of heterogeneous distance functions handling missing values.
A repository for various Data Science projects I've worked on, both university-related and in my spare time.
Data fetched by wafers is to be passed through the machine learning pipeline and it is to be determined whether the wafer at hand is faulty or not apparently obliterating the need and thus cost of hiring manual labour.
This repository is a collection of basic code templates for Data Preparation. All codes I am sharing are from the practical exercises I did from the Data Science Infinity Program.
This repository is totally focused on Feature Engineering Concepts in detail, I hope you'll find it helpful.
Modelling the relationship between a player’s first-time eligible arbitration salary and multiple variables.
Data imputation is used when there are missing values in a dataset. It helps fill in these gaps with estimated values, enabling analysis and modeling. Imputation is crucial for maintaining dataset integrity and ensuring accurate insights from incomplete data.
Streamlit app developed for bank customer deposit prediction, using a fine-tuned XGBClassifier model.
Kaggle UK Used Car challenge
My Capstone for the HarvardX Course "Introduction to Data Science with Python"
Machine learning models for enhanced fraud detection in e-commerce transactions, exploring feature engineering, distance prediction, and clustering analysis.
This flask web app is used to detect if a wafer(sensor chip) is default or not based on sensor readings.
[Kaggle Submission] -Using XGBRegressor with shap, grid search and hyperopt to predict house prices
we perpuse a method to fill nan values using clustering
Filling missed data-points with the most common values among nearest neighbors
This project focuses on predicting customer churn in an e-commerce setting using machine learning techniques.
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