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.

Kaggle UK Used Car challenge

Streamlit app developed for bank customer deposit prediction, using a fine-tuned XGBClassifier model.

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.

Modelling the relationship between a player’s first-time eligible arbitration salary and multiple variables.

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

Built a model to determine the risk associated with extending credit to a borrower. Performed Univariate and Bivariate exploration using various methods such as pair-plot and heatmap to detect outliers and to monitor the behaviour and correlation of the features. Imputed the missing values using KNN Imputer and implemented SMOTE to address the i…

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.

we perpuse a method to fill nan values using clustering

the multivariate analysis compares different rows and columns for beat accuracy eg:knn imputer in univariate analysis it only compares with the same columns eg mean or median for numbers

Predicting employee burnout using machine learning algorithms: Random Forest and k-Nearest Neighbors.