wcss

There are 0 repository under wcss topic.

ArtichaTM / CS2_WCSSkills
WCSSkills plugin for CS2
cs2 wcs wcss wcsskills
Language:C++ 2
vaitybharati / Assignment-08-PCA-Data-Mining-Wine-
Assignment-08-PCA-Data-Mining-Wine data. Perform Principal component analysis and perform clustering using first 3 principal component scores (both heirarchial and k mean clustering(scree plot or elbow curve) and obtain optimum number of clusters and check whether we have obtained same number of clusters with the original data (class column we have ignored at the begining who shows it has 3 clusters)
python pca-analysis data-mining-algorithms decomposition sklearn preprocessing scale covariance-matrix variance-plot scatter-plot hierarchical-clustering scipy dendrogram agglomerative-clustering kmeans-clustering clustering-algorithm elbow-plot wcss
Language:Jupyter Notebook 2
Abhik35 / Assignment-K-Means-Clustering-Airlines-
Perform clustering (K means clustering) for the airlines data to obtain optimum number of clusters. Draw the inferences from the clusters obtained. Data Description: The file EastWestAirlinescontains information on passengers who belong to an airline’s frequent flier program. For each passenger the data include information on their mileage history and on different ways they accrued or spent miles in the last year. The goal is to try to identify clusters of passengers that have similar characteristics for the purpose of targeting different segments for different types of mileage offers ID --Unique ID Balance--Number of miles eligible for award travel Qual_mile--Number of miles counted as qualifying for Topflight status cc1_miles -- Number of miles earned with freq. flyer credit card in the past 12 months: cc2_miles -- Number of miles earned with Rewards credit card in the past 12 months: cc3_miles -- Number of miles earned with Small Business credit card in the past 12 months: 1 = under 5,000 2 = 5,000 - 10,000 3 = 10,001 - 25,000 4 = 25,001 - 50,000 5 = over 50,000 Bonus_miles--Number of miles earned from non-flight bonus transactions in the past 12 months Bonus_trans--Number of non-flight bonus transactions in the past 12 months Flight_miles_12mo--Number of flight miles in the past 12 months Flight_trans_12--Number of flight transactions in the past 12 months Days_since_enrolled--Number of days since enrolled in flier program Award--whether that person had award flight (free flight) or not
python numpy sklearn pandas scatter-plot normalization clustering-algorithm kmeans-clustering sumofasequence elbow-plot matplotlib-pyplot cluster-centers wcss standard-scaler
Language:Jupyter Notebook 1
Abhik35 / Assignment-PCA-Data-Mining-Wine-
Perform Principal component analysis and perform clustering using first 3 principal component scores (both heirarchial and k mean clustering(scree plot or elbow curve) and obtain optimum number of clusters and check whether we have obtained same number of clusters with the original data (class column we have ignored at the begining who shows it has 3 clusters) PCA Implementation Checking with other Clustering Algorithms 1.Hierarchical Clustering 2.K-Means Clustering Build Cluster algorithm using K=3
python scale sklearn pca pca-analysis decomposition scipy scatter-plot preprocessing data-mining-algorithms clustering kmeans-clustering covariance-matrix hierarchical-clustering variance-plot wcss dendrogram elbow-plot agglomerative-clustering
Language:Jupyter Notebook 1
vaitybharati / Assignment-07-K-Means-Clustering-Airlines-
Assignment-07-K-Means-Clustering-Airlines. Perform clustering (K means clustering) for the airlines data to obtain optimum number of clusters. Draw the inferences from the clusters obtained. The file EastWestAirlinescontains information on passengers who belong to an airline’s frequent flier program. For each passenger the data include information on their mileage history and on different ways they accrued or spent miles in the last year. The goal is to try to identify clusters of passengers that have similar characteristics for the purpose of targeting different segments for different types of mileage offers.
python sklearn clustering-algorithm kmeans-clustering standard-scaler normalize sum-of-squares wcss elbow-plot cluster-centers pandas numpy matplotlib-pyplot scatter-plot
Language:Jupyter Notebook 1
vaitybharati / P30.-Unsupervised-ML---K-Means-Clustering-Non-Hierarchical-Clustering-Univ.-
Unsupervised-ML---K-Means-Clustering-Non-Hierarchical-Clustering-Univ. Use Elbow Graph to find optimum number of clusters (K value) from K values range. The K-means algorithm aims to choose centroids that minimise the inertia, or within-cluster sum-of-squares criterion WCSS. Plot K values range vs WCSS to get Elbow graph for choosing K (no. of clusters)
python numpy sklearn pandas scatter-plot normalize clustering-algorithm kmeans-clustering wcss sum-of-squares elbow-plot matplotlib-pyplot cluster-centroids standard-scaler kmeans-algorithm
Language:Jupyter Notebook 1
amangelbhullar / Clustering-Machine-Learning
k-Means Clustering
kmeans clusters elbow wcss
0
alfianhid / Starbucks-EDA-and-Customer-Segmentation-with-K-means-Algorithm
elbow-method exploratory-data-analysis kmeans-clustering minmax-algorithm principal-component-analysis starbucks starbucks-dataset wcss
Language:Jupyter Notebook
DebasmitaBhakat / User_Segmentation_e-commerce_website
Clustering users on the basis of ages, incomes, ets
k-means-clustering wcss elbow-method
Language:Jupyter Notebook
Ninad077 / Machine_Learning-K_means_clustering
Content: Unsupervised ML, Clustering, Customer Segmentation, WCSS, elbow method
clustering elbow-method unsupervised-machine-learning wcss
Language:Jupyter Notebook
PatilSukanya / Assignment-08-PCA
Used libraries and functions as follows:
clustering data-mining-algorithm decomposition dendogram elbow-plot hierarchical kmeans-clustering pca-analysis preprocessing python scale scatter-plot scipy sklearn variance-plot wcss covarience-matrix
Language:Jupyter Notebook
RiteshopShrivastava / Hierarchical_Clustering
Unsupervised-ML---K-Means-Clustering-Non-Hierarchical-Clustering-Univ. Use Elbow Graph to find optimum number of clusters (K value) from K values range. The K-means algorithm aims to choose centroids that minimise the inertia, or within-cluster sum-of-squares criterion WCSS. Plot K values range vs WCSS to get Elbow graph for choosing K (no. of clusters)
python pandas sklearn clustering kmeans-clustering wcss matplotlib elbow-plot standardscaler clustercentroids
Language:Jupyter Notebook
sumitkutty / Sparks-Foundation-Internship
The projects are a part of the internship by The Sparks Foundation
eda wcss internship elbow-method elbow-plot kmeans-clustering
Language:Jupyter Notebook

wcss

ArtichaTM / CS2_WCSSkills

vaitybharati / Assignment-08-PCA-Data-Mining-Wine-

Abhik35 / Assignment-K-Means-Clustering-Airlines-

Abhik35 / Assignment-PCA-Data-Mining-Wine-

vaitybharati / Assignment-07-K-Means-Clustering-Airlines-

vaitybharati / P30.-Unsupervised-ML---K-Means-Clustering-Non-Hierarchical-Clustering-Univ.-

amangelbhullar / Clustering-Machine-Learning

alfianhid / Starbucks-EDA-and-Customer-Segmentation-with-K-means-Algorithm

DebasmitaBhakat / User_Segmentation_e-commerce_website

Ninad077 / Machine_Learning-K_means_clustering

PatilSukanya / Assignment-08-PCA

RiteshopShrivastava / Hierarchical_Clustering

sumitkutty / Sparks-Foundation-Internship