expression sequence machine learning application
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Updated
May 2, 2024 - Python
expression sequence machine learning application
Assignment & notes from CS677-Data Science with Python. Use stock prices and features (average daily return and volatility) as the required dataset and apply many basic classifier models and algorithms of data science in analysis.
This program makes personalized shopping recommendations based on user input criteria. It uses a machine learning model trained on shopping trends data to predict what product a user is most likely to purchase along with a recommended color.
Aim was to classify the provided data with different classifiers and compare their performance. 17 different classifiers were employed and their results are documented in a Jupyter notebook
Classificação de Distúrbios de Sono usando Machine-Learning Python
Beer Wine Classification by Machine Learning based on alcoholic content and color
Some machine learning practices examples. From course: https://cursos.alura.com.br/course/machine-learning-introducao-a-classificacao-com-sklearn/
Predict the region of origin of an english-speaking tweet author by analyzing tweet content using machine learning classifier
Práctica de la asignatura Inteligencia de Negocio de cuarto curso de Ingeniería Informática.
Conducting geodemographic classification for ethnic groups in NYC using K-means algorithm available in sklearn.cluster module.
This repo is about Machine Learning and Classification
This Iris classification webapp is made using sklearn and streamlit library in Python. Random Forest Classifier model is used to classify.
KMeans Clustering of data using Sklearn library, numpy and Pickle data
Language processing for better query answering
This web app provide 3 different Machine Learning (Classification) algorithms on 3 different SK_LEARN built in Data sets.
A project that applies machine learning to solve a real-world challenge: what cryptocurrencies are available on the trading market and how they can be grouped using classification.
Some nlp stuff.
Use positive and negative sentiment words dictionaries to predict sentiment
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