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Plotly-Dash NLP project. Document similarity measure using Latent Dirichlet Allocation, principal component analysis and finally follow with KMeans clustering. Project is completed with dynamic visual interaction.
Clustering Analysis Performed on the Customers of a Mall based on some common attributes such as salary, buying habits, age and purchasing power etc, using Machine Learning Algorithms.
Implementation of hierarchical clustering on small n-sample dataset with very high dimension. Together with the visualization results implemented in R and python
全球新冠肺炎的数据分析,包括基础知识有:kmeans算法设计,SSE算法设计,分级聚类算法设计,cophenetic distance 算法设计。
Project on hyperspectral-image clustering for the Μ402 - Clustering Algorithms course, NKUA, Fall 2022.
This repository contains introductory notebooks for principal component analysis.
Segment airline customers, analyze the characteristics of different customer categories, compare the value of customers from different customer categories, provide personalized services for categories of customers with different values, and formulate the right marketing strategy.
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.
Problem Statement: This data set is created only for the learning purpose of the customer segmentation concepts , also known as market basket analysis . I will demonstrate this by using unsupervised ML technique (KMeans Clustering Algorithm) in the simplest form.You are owing a supermarket mall and through membership cards , you have some basic data about your customers like Customer ID, age, gender, annual income and spending score. Problem Statement You own the mall and want to understand the customers like who can be easily converge [Target Customers] so that the sense can be given to marketing team and plan the strategy accordingly.
🗽🚕 Performance of data analysis in taxi trips in NYC and creation of a Random Forest Regressor in order to predict the duration of taxi trips.
This Repo Consists of some of the Tasks for The Sparks Foundation-Machine Learning and Data Science Internship, containing Supervised and Unsupervised Machine Learning Techniques to solve A ML Problem in a Systematic Way.
Use unsupervised machine learning, PCA algorithm, and K-Means clustering to analyze and classify a database of cryptocurrencies.
This project uses the CGPA.csv file as the dataset (provides CGPA of the students) and uses the K-means algorithm to cluster the points using the elbow point method.
A customer profiling project based on RFM (Recency, Frequency, Monetary) analysis using a dataset from an online retail company in the United Kingdom. The aim is to identify customer habits and create personalized marketing strategies for targeted advertising.
Comparing the Elbow Method and Silhouette Method for choosing the optimal number of clusters in K-Means algorithm
EIGEN FREQUENCY CLUSTERING USING [KMEANS] [KMEANS & PCA ] [DBSCAN] [HDBSCAN]
Customers RFM Clustering (Market Segmentation based on Behavioral Approach)
:chart_with_downwards_trend: Clustering of HTTP responses using k-means++ and the elbow method
Apply KNN algorithm to classify whether cancer is present or not. Implemented Pipeline, GridSearchCV, Elbow method to fit the best model.
Analysing practical examples by using principal component analysis (PCA) and Clustring
Explore my solo Customer Segmentation Project, diving into data analysis, clustering, and visualization. Uncover distinct customer segments for tailored marketing strategies and enhanced engagement. Discover the power of data-driven insights in this independent project.
Iris dataset
I aim to automate playlist creation for Moosic, a startup known for manual curation, using Machine Learning, while addressing skepticism about the ability of audio features to capture playlist "mood."
It's the continuation of my kleanee_ClusteringAnalysis project, in which I include the Silhouette Method for KMeans Clustering.
Machine Learning Course [ECE 501] - Spring 2023 - University of Tehran - Dr. A. Dehaqani, Dr. Tavassolipour
K-Means Project For Coffe Plant
Data Science Content from DNC School
Using the Elbow Method and Silhouette Analysis to find the optimal K in K-Means Clustering.
Customers RFM Clustering (Market Segmentation based on Behavioral Approach)
Enhancing the Performance of PSO Algorithm for Clustering High dimensional data using Autoencoders
The goal of this project is to use clustering techniques to segment employees based on their absenteeism patterns and provide insights that can help organizations to reduce absenteeism and improve employee productivity.
Leverage unsupervised machine-learning techniques (K-means) to segment mall customers