The "Random Swap" algorithm with a random dataset, visuals and example notebooks
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Updated
May 12, 2022 - Python
The "Random Swap" algorithm with a random dataset, visuals and example notebooks
Use transfer learning for image classification followed by clustering to create/identify clusters in images
Jupyter Notebook: documentation of the implementation of the ToMATo algorithm to the GUDHI library (Topological Data Analysis), using real datasets.
Notebooks for Global AI Hub ML course in Aug 2022
NBA players clustering and Points prediction
- Notebook making penguin cluster using KMeans algorithm.
Notebook to enrich clustering going a little bit beyond Sklearn
If you liked my analysis, pls upvote my notebook!
Notebook version implementation of unsupervised learning techniques. Analysis and Visualization.
A Jupyter notebook that run PCA and KMeans on population demographic data.
This repository contains notebooks based on kaggle challenge of customers segmentation using ML.
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning
A Jupyter Notebook with a Clustering and PCA Analysis of a Spotify songs dataset.
Machine learning course at IDC. Implemented several amount of ML algorithms in Python using Jupyter notebooks
It contains Google colab notebooks which I have created based on Data analysis and different Machine learning Techniques.
This project contains a Jupyter Notebook project focused on analyzing customer data. The project involves Exploratory Data Analysis (EDA), data preprocessing, and the implementation of clustering algorithms to derive insights from the data.
This repository contains a customer segmentation project implemented in a Jupyter Notebook using Python. Customer segmentation is a crucial strategy for businesses aiming to understand their customer base better, enabling targeted marketing strategies and personalized customer experiences.
This project used a Kmeans after PCA model to segment retail customers to optimize marketing efforts. When the model repeatedly returned a single cluster, the model was used to prove the customers' homogenous characteristics. Influenced the bank's marketing strategies and initiatives. Developed in Jupyter Notebook with Python for FNB.
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