PREMRAJESH/Machine_learning_notes

Machine Learning libraries tutorial and basics notes

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princessnaya1234/ML-House-Prices-Prediction-Model

A trained ML model for prediction of house prices in IOWA using Random Forest Regression technique

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tanvirnwu/ML-Algorithms--Scikit-learn--Python

This folder contains the basic algorithms of ML implemented with Python.

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Didilish/Asana-s-early-career-data-science-take-home-assessment

The project analyzed Asana user data to determine adoption rate and factors influencing adoption. After data cleaning, an adoption rate of 12% was calculated. Predictor variables were extracted and modeled using Random Forest and Decision Tree classifiers. Both models performed well, with Random Forest achieving 87% accuracy.

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cmurga95/oreilly_ml

Learning machine learning with python

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Marksman007577/Machine-Learning-and-Data-Science

Machine Learning and Data Science with Python

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Ghaithkrifa/jobs-analysis

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marinadunn/Foundations-of-Applied-ML

Notebooks for Foundation of Machine Learning (PHYS 243) Fall 2021 course at UCR

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Gowthamchandu307/Personal-Data-Science-Projects

This page contains some of my personal data science model projects. 📊📈📉👨‍💻

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DorisKJ/life_expectancy

The data used in this work contains the life expectancy and probability of survival calculated for a given cohort from 1991 to 2001. This data is made available on the Canadian government website. Each dataset is described by income, education, ancestry, gender and residence type. Data: Life expectancy Survival probability

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stevenhastings/TweetyPy

Full-Stack application that allows client to use a predictive model to determine which user is more likely to have tweeted a given text. This project covers everything from API's to Predictive Modeling, SQLAlchemy database storage, Flask, along with other full-stack components. In the end it is deployed for online usage using Heroku.

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sanchitvaid/Housing-Data-analysis

Exploratory data analysis of housing data using Python

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digvijaytaunk/aps-fault-detection-with-deployment

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npto04/random-forest-with-nlp 📦

Criação de modelo de classificação capaz de identificar documentos como petições iniciais com base de treinamento em dados do CRETA

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ignatius-mbugua/medical-drug-prescription-prediction

A web app that assists patients in diagnosing their illnesses

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SupernovaSatsangi23/Modifying-Biomarker-Gene-Identification-For-Effective-Cancer-Categorization

A project that focuses on implementing a hybrid approach that modifies the identification of biomarker genes for better categorization of cancer. The methodology is a fusion of MRMR filter method for feature selection, steady state genetic algorithm and a MLP classifier.

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RafaBragagd/Python-Code-Testing

Repositório de códigos de teste do Python feito por Rafael da Silva Braga

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Nadia-JSch/scatter-plot-iris-dataset

Permutations of sepal length, sepal width, petal length and petal width from the 3 classes of Iris on a scatter plot

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sotheanithsok/CECS-551-Collection 📦

Explore machine learning models and optimization techniques.

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ShwetaBhattacharjee/Used-Car-Price-Prediction

Collected and classify the used car data from the Dhaka city. Used scikit-learn python toolkit for statistical modeling including classification, regression and predicted new and old car's selling price. This can be a helpful tool for used car selling companies.

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Anjali848/Solve-a-Mystery-in-History-Hamilton-or-Madison-

This code was submitted as part of the IST 707 (Applied Machine Learning) Homework assignment. In this homework assignment, you are going to use clustering methods to solve a mystery in history: who wrote the disputed essays, Hamilton or Madison?

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marama09/Regression-Project-T5

Mobile Price Prediction

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breadrock1/ML-labs

Machine Learning. There is a suite of Machine Learning laboratory tasks.

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benjaminweymouth/machine_learning_Risky_Business

Machine Learning Examples: this repo is to show proficiency in building and evaluating several machine learning models to predict credit risk

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PedroSiqueira1/PrevisaoPrecoImoveis

Projeto realizado para a matéria de Introdução ao Aprendizado de Máquina, onde foi feito um modelo regressor utilizando algoritmos de Machine Learning com a biblioteca Scikit-Learn em Python.

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PedroSiqueira1/Aprovacao-de-Credito-ML

Projeto realizado para a matéria de Introdução ao Aprendizado de Máquina, onde foi feito um modelo classificador utilizando algoritmos de Machine Learning com a biblioteca Scikit-Learn em Python.

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RegaipKURT/ScikitLearnWithFUN

This repository shows the implementation of machine learning algorithms, data pipelines and data visualization with scikit-learn and python.

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soham6125/SMART-Metro

A website powered by Machine Learning to dynamically predict the shortest and safest route from source to destination.

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R1-R0-R0/img-recognition

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KrishnaManmayi/Healthyfy

Analyzing the most affecting factors that deteriorate the health of a person and predicting the risk of developing dreadful diseases using Machine Learning.

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RafeyIqbalRahman/Dimensionality-Reduction-Techniques

This repository demonstrates reducing the dimensions of the dataset using Scikit-learn's Principal Component Analysis (PCA) and T-distributed Stochastic Neighbor Embedding (t-SNE).

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RafeyIqbalRahman/Data-Scaling-Techniques

This repository demonstrates the scaling of the data using Scikit-learn's StandardScaler, MinMaxScaler, and RobustScaler.

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lixcli/simple_classification_of_hanzi

simple classification of hanzi by scikit-learn

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tayyabmughal676/MachineLearningUsingSciKitLearn

Machine learning using sci kit learn

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my-thought-experiments/explore-scikit-learn

A collection of documents and programs to learn about Data Science and ML using Scikit-learn.

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nikhil-chigali/Hands-On_ML

Code samples for the machine learning algorithms that are explained in the book, "Hands-on Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems"

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Edai/MLNetworkEmbedding

Several implementation of ML network embedding and graph embedding

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msb-public/ML-For-Beginners

机器学习课程 机器学习课程为期 12 周、26 节课，在课程中你将了解经典机器学习的相关内容，主要使用 Scikit-learn 框架作为案例演示。 在机器学习型课程中，老师会提供一些数据集和案例。包括翻译、价格预测、情感分类等等，除此之外还会讲解一些基础知识，比如逻辑回归、聚类、序列模型、NLP等。

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