一些常用的机器学习算法实现
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Updated
Apr 19, 2018 - Python
一些常用的机器学习算法实现
Karma of Humans is AI
Diabetes mellitus, commonly known as diabetes is a metabolic disease that causes high blood sugar. The hormone insulin moves sugar from the blood into your cells to be stored or used for energy. With diabetes, your body either doesn’t make enough insulin or can’t effectively use its insulin.
Natural language processing on tweets
Multi class and Binary Classification through Logistic Regression and SVM
SVM, Logistic Regression, K-Nearest Neighbors Classifier, GaussianNB, Random Forest, XGBoost, DecisionTree Classifier, Ensembled Classifier, ExtraTrees Classifier, Voting Classifier
Machine learning model Visualizer in web using streamlit
The Water Quality Checker uses machine learning to analyze water quality parameters such as pH, solids, and conductivity, to determine if water is safe to drink. By inputting the values into the form, the model can predict if the water is fit for consumption or not.
Machine Learning Lecture Notes
In this project I intend to predict customer churn on bank data.
Legal Taxonomy (https://taxonomy.legal/) Classifier on Reddit /r/legaladvice
AI 小项目代码、笔记
Context: Customer behavior prediction to retain customers
This repository contains some machine learning projects as a practise on machine learning course on Coursera for Prof. Andrew Ng from Stanford University.
It is a full stack ml app , compared multiple ml models(KNeighborsClassifier, LogisticRegression, RandomForestClassifier ) , later deploy the best model using flask , and the frontend is created with react.js
Predicting the churn of telecommunication custumers
Leverage external data and non-traditional methods to accurately assess and shortlist candidates with the relevant skillsets, experience and psycho-emotional traits, and match them with relevant job openings to drive operational efficiency and improve accuracy in the matching process
This project involves the implementation of efficient and effective Logistic Regression (FROM SCRATCH) classifiers on MNIST data set. The MNIST data comprises of digital images of several digits ranging from 0 to 9. Each image is 28 x 28 pixels. Thus, the data set has 10 levels of classes.
Unlock the potential of agricultural production with innovative optimization techniques. Explore strategies, technologies, and practices to enhance crop yields, improve efficiency, and sustainably increase output. Revolutionize farming practices and cultivate a thriving agricultural ecosystem
Machine Learning Algorithms Scratch Implementations
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