NLP, Recommendation Sys
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Updated
Aug 27, 2024 - Jupyter Notebook
NLP, Recommendation Sys
RAYAN AI international competition training course : Homeworks and Projects
The main goal of this project is to use various Clustering Methods for Bank Customer Segmentation.
In this project, unsupervised methods were employed to form clusters of similar vehicles based on sales data from Italy between 2003 and 2005. Through detailed analysis of monthly sales volumes, vehicles were grouped to reveal competitive relationships. This approach aids in understanding market dynamics and identifying key competitors.
Analyzed the Silhouette score to determine the optimal number of clusters for K-Means clustering using the IRIS dataset. The notebook includes data preprocessing, clustering, and Silhouette score evaluation, providing insights into cluster quality and optimal cluster count for effective data segmentation.
Conducted a comprehensive clustering analysis to categorize beers based on features such as Astringency, Alcohol content, Bitterness, Sourness, and more. Utilized k-medoids and hierarchical agglomerative clustering algorithms to achieve this classification. Tech: Python (numpy, pandas, seaborn, matplotlib, sklearn, scipy)
1.Digital Marketing Advertisement Data Segmentation using clustering techniques. 2. Identify Optimum Principal Components that explains the most variance in the Primary Census data.
Classification Model of Potential Credit Card Customers
An interactive approach to understanding Machine Learning using scikit-learn
This method suggests a technique for removing outliers that takes standard deviation into account.
This repository contains code for creating ml model for clustering
Customer-Segmentation---Purchasing-Behavior
Agglomerative Clustering from scratch without using built-in library with different hyper-parameters using Python and evaluated the cluster quality using intrinsic and extrinsic scores
Based on a user's preferred movie or TV show, Unsupervised Machine Learning-Netflix Recommender suggests Netflix movies and TV shows. These suggestions are based on a K-Means Clustering model. These algorithms base their recommendations on details about movies and tv shows, such as their genres and description.
This repository is a machine learning project entailing clustering of regions/districts based on crime types features. Application of k-means simplifies this clustering as you can easily tell districts with similar crime patterns, know regions of high risk due to the diversity of crimes committed.
This repository contains a practical exercise focused on clustering techniques, designed to train and enhance skills in data analysis and machine learning.
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.
This repository contains my solutions and implementations for assignments assigned during the Machine Learning course.
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