The project involved using KMeans clustering to segment customers based on behavioral patterns and preferences providing customer segments for targeted marketing strategies.

This project segments customers based on their purchasing behavior to identify different target groups. It demonstrates skills in data analysis, clustering, and visualization using Power BI.

Parallel & lightning fast implementation of available classic and contemporary variants of the KMeans clustering algorithm

K-means clustering algorithm using MapReduce.

Developed a custom clustering algorithm to analyze wine data without traditional machine learning. The project standardizes features and employs mathematical formulas using NumPy to identify distinct clusters, offering insights into wine sample groupings and their characteristics.

An implementation of a recommender system based on clustering anime user ratings

Implemented the K-Means Clustering Algorithm. This project can cluster pixels of a similar kind together.

K-Means Clustering: Airline-Customer-Value-Analysis

This project focuses on predicting Loan Defaults using Supervised Learning, Segmenting Customers with Unsupervised Learning, and Recommending Bank Products through a Recommendation Engine.

The global objective of this data analysis is to do User Overview analysis as well as User Satisfaction Analysis from User Experience analysis and User Engagement analysis

Credit scoring and segmentation refer to the process of evaluating the creditworthiness of individuals or businesses and dividing them into distinct groups based on their credit profiles.

K-Means Clustering and Gradient Descent Variants in Spark

This Python notebook demonstrates an exploratory data analysis (EDA) and clustering exercise using the pandas, seaborn, and matplotlib libraries. The code works with a dataset called 'College_Data' and explores college-related attributes, including 'Private' status, graduation rates, and enrollment data.

SISTEMA DE AGRUPACION DE CASAS

This project implements a K-means clustering algorithm with data visualization using Matplotlib and SciPy, including an Elbow method for optimal cluster determination and animated visualizations of the clustering process. It generates random data, performs clustering, and visualizes the results with cluster boundaries.

maneuver the shape and distribution of kmeans clusters

A File Type Classifier that predicts whether a file is a document, executable or a script based on a given set of attributes statically extracted from a file using K-means clustering trainer based on the Yinyang Method.

Here are the projects I completed during my semesters at IIT Bombay. These projects involved Python coding and focused on various aspects of machine learning, including linear regression, logistic regression, and clustering.

Codes for Practical experiments of Data Warehousing and Mining (Semester V - Computer Engineering - Mumbai University)

The dataset includes the following columns: Id, SepalLengthCm, SepalWidthCm, PetalLengthCm, PetalWidthCm, and Species. We will use the Sepal and Petal measurements to predict the optimum number of clusters using the KMeans algorithm.