There are 1 repository under heirarchical-clustering topic.
In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).
A group of small robots capable of organizing themselves in any given structure using OpenCV and Clustering.
A Repository Maintaining My Summer Internship Work At Datalogy As A Data Science Intern Working On Customer Segmentation Models Using Heirarchical Clustering, K-Means Clustering And Identifying Loyal Customers Based On Creation Of Recency, Frequence, Monetary (RFM) Matrix.
A Shape Finder using Corner-Harris and Heirarchical Clustering
5 Projects based on Unsupervised learning
CSE601 Course Projects - Fall 2017
A hub that contains notebooks that implement Regression models, illustrates LR via Gradient Descent, compares K-means vs Spectral vs Hierarchical, compares PCA vs t-SNE
Complete package for all Data Science models using R. Starting form Preprocessing, Data Manipulation, Feature Engineering, Model Building, and Model Validation.
Unlock personalized content recommendations on Netflix with my cutting-edge ML project. Say goodbye to aimless scrolling and elevate your binge-watching experience with our user-centric content-based recommender system.
Performed PCA on wine dataset and applied clustering algorithms
Customer Segmentation using kmeans, hc, dbscan and gmm
Perform Principal component analysis and perform clustering using first 3 principal component scores
This repository contains all the projects carried out to understand and experiment on Machine Learning using Python. Projects scripts are created to build model on classification , clustering and regression machine learning models for future predictions.
Repository for Customer Segment Analysis using Python & Shiny App Dashboard
Machine Learning with Python, Numpy & SciKitLearn
To sanitize and analyze the data to profile the sellers present and develop selection criteria to identify the best or most promising sellers in this dataset, that the Acquisitions team at any company should reach out to, and acquire. The model used Kmeans clustering and Heirarchical clustering to identify sellers targets for acquisition.
Which countries can be grouped together and have the highest need of help from the charity?
Streamlit App for The Analysis of team tactics and player performances
This is my 6th semester Essentials of Data Analytics project.
Performed clustering analysis on OnSports player data for the English Premier League. The clustering analysis successfully identified 4 unique player clusters and uncovered valuable business recommendations by identifying trends and patterns in the EDA, meeting the objective of determining player pricing next season.
Problem Statement Perform clustering (Hierarchical,K means clustering and DBSCAN) for the airlines data to obtain optimum number of clusters. Content This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas
Application of Machine learning to show acts of conspiracy, insider trading, and securities fraud in Enron dataset
SUPERVISED LEARNING: REGRESSION: Linear - Polynomial - Ridge/Lasso CLASSIFICATION: K-NN - Naïve Bayes - Decision Tree - Logistic Regression - Confusion Matrix - SVM TIME SERIES ANALYSIS: Linear & Logistic Regr. - Autoregressive Model - ARIMA - Naïve - Smoothing Technique UNSUPERVISED LEARNING: CLUSTERING: K-Means - Agglomerative - Mean-Shift - Fuzzy C-Mean - DBSCAN - Hierarchical - Canopy DIMENSION REDUCTION: PCA - LSA - SVD - LDA - t-SNE PATTERN SEARCH: Apriori - FP-Growth - Euclat RECOMMENDATION ENGINE: Association Rules - Market Basket Analysis - Apriori Algorithm - Real Rating Matrix - IBCF - (Item) - User-Based Collaborative Filtering UBCF - Method & Model ENSEMBLE METHODS: BOOSTING: AdaBoost - XG Boost - LightGBM - CatBoost. BAGGING: Random Forest STACKING
Big Data Project- Catch the pink flamingo
In this repository i have performed Hierarchical clustering on car clus dataset, DBSCAN on penguins datset, K-means clustering on mall customer dataset.
HELP International is an international humanitarian NGO that is committed to fighting poverty and providing the people of backward countries with basic amenities and relief during the time of disasters and natural calamities. It runs a lot of operational projects from time to time along with advocacy drives to raise awareness as well as for funding
This clustering analysis aims to provide valuable insights into the viability of introducing an original language cinema in Milan, Italy.
To study the customer profile coming to a mall. We try to solve this problem using Clustering technique.
The Stanford Open Policing Project (SOPP) aims to bring transparency to police interactions by collecting and analyzing data on traffic stops across the United States. It accumulates a vast dataset on traffic stops, encompassing details such as demographics, location, and outcomes.
Advanced analytics in R to delineate market segments in retail, optimizing targeted marketing strategies through customer behavior and demographic profiling
This Project is a Simple Content-Based Song Recommendation System. It suggest similar item to the user based on the content the user provide.
Analyzing US crime statistics using hierarchical clustering to uncover patterns in state-level arrest data