An interactive approach to understanding Machine Learning using scikit-learn

Capstone Project for the IBM Professional Certificate on Coursera

The project involves performing clustering analysis (K-Means, Hierarchical clustering, visualization post PCA) to segregate stocks based on similar characteristics or with minimum correlation. Having a diversified portfolio tends to yield higher returns and face lower risk by tempering potential losses when the market is down.

Face Recognition Algorithm using Unsupervised and Semi-supervised techniques

This case requires to develop a customer segmentation to understand customer's behaviour and separate them in different groups according to their preferences, and once the division is done, this information can be given to marketing team so they can plan the strategy accordingly.

In this project, we apply different Unsupervised and Semi-supervised techniques on a Supervised Dataset which is Breast Cancer Dataset and evaluate the model accuracy

All-in-1 notebook which applies different clustering (K-means, hierarchical, fuzzy, optics) and classification (AdaBoost, RandomForest, XGBoost, Custom) techniques for the best model.

Perform Clustering (Hierarchical, K Means Clustering and DBSCAN) for the airlines and crime data to obtain optimum number of clusters. Draw the inferences from the clusters obtained.

Face Recognition Algorithm using Unsupervised and Semi-supervised techniques using Olivetti faces dataset

This repository contains clustering techniques applied to minute weather data. It contains K-Means, Heirarchical Agglomerative clustering. I have applied various feature scaling techniques and explored the best one for our dataset

Unsupervised learning algorithms to cluster students of a public school

Given the e-commerce data, k-means clustering algorithm is used to cluster customers with similar interest. The data was collected from a well known e-commerce website over a period of time based on the customer’s search profile.

This course teaches you how to calculate distance metrics, form and identify clusters in a dataset, implement k-means clustering from scratch and analyze clustering performance by calculating the silhouette score

Clustream, Streamkm++ and metrics utilities C/C++ bindings for python

It's the HAC algorithm that Im using to sort newspaper articles by news. You can adapt it to pretty much any type of text.

This repository contains introductory notebook for clustering techniques like k-means, hierarchical and DB SCAN

Leverage unsupervised machine-learning techniques (K-means) to segment mall customers

Data Science - PCA (Principal Component Analysis)

Analyze past orders and create innovative features to build a customer's segmentation.

Clustering usuarios de cartão de crédito usando KMeans.