This repository is made following the course by Sir Jose Portilla, and focuses on unsupervised Machine Learning algorithms. I studied all these concepts in January 2024
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Updated
Jan 10, 2024 - Jupyter Notebook
This repository is made following the course by Sir Jose Portilla, and focuses on unsupervised Machine Learning algorithms. I studied all these concepts in January 2024
This repository contains a series of notebooks exploring various clustering techniques in machine learning.
In this notebook, i have tried to appy KMeans, Hierarchical and DBSCAN clustering along PCA. The dataset used is Mall_Customers. In DBSCAN, certain type of Heatmaps are used to find the Epsilon and min_samples value which have performed quite well in identifying the correct number of clusters.
Creating an App by training model. There are three unsupervised learning model used for comparing better clustering and finalized herarchical model for our project Customer Segmentation. I have created Detailed notebook,dashboard & streamlit app
This assignment focuses on unsupervised learning techniques. The notebook explores clustering algorithms like K-Means and DBSCAN, applies dimensionality reduction using PCA, and evaluates clustering performance. It includes visualizations and analysis to understand how different methods group data and reduce complexity.
Jupyter Notebooks exploring Machine Learning techniques -- regression, classification (K-nearest neighbour (KNN), Decision Trees, Logistic regression vs Linear regression, Support Vector Machine), clustering (k-means, Hierarchical Clustering, DBSCAN), sci-kit learn and SciPy -- and where it applies to the real world, including cancer detection, …
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