Implementation of some classification and clustering methods
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Updated
Aug 2, 2020 - Jupyter Notebook
Implementation of some classification and clustering methods
The examined group comprised kernels belonging to three different varieties of wheat: Kama, Rosa and Canadian.
Unsupervised learning with different types clustering algorithms..
The objective of this project is to facilitate the use of clustering algorithms by engineering students who are not specialized in AI.
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.
Perform Principal component analysis and perform clustering using first 3 principal component scores both Heirarchial and K Means Clustering and obtain optimum number of clusters and check whether we have obtained same number of clusters with the original data.
Clustering Analysis is used to analyze the travel behaviors, preferences, and attitudes of New York City citizens.
This dataset consists of tv shows and movies available on Netflix as of 2019. Clustering Movies and TV shows
Customer Segmentation Using Unsupervised Machine Learning Algorithms
Land slide prediction :- the classification of individual rocks into three categories: big, medium, and small. Additionally, the project aims to predict the falling pattern of these rocks by analyzing the provided images of the rock dump.
This project explores Netflix's content evolution, analyzes TV shows and movies, and builds a recommendation system. Discover insights from a dataset of 7,787 titles as of 2019 and learn how we clustered content based on textual features.
Unsupervised Machine Learning Models
Machine Learning Algorithms Practicals in Python with Datasets
The project involved natural language processing (NLP) techniques, tokenization, stemming, and the application of the TF-IDF vectorization method.
Modern Information Retrieval Project
Analyzing US crime statistics using hierarchical clustering to uncover patterns in state-level arrest data and Advanced analytics to delineate market segments in retail, optimizing targeted marketing strategies through customer behavior and demographic profiling.
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