Powerful machine learning library for Node.js – uses Python's scikit-learn under the hood.

A library that unifies the API for most commonly used libraries and modeling techniques for time-series forecasting in the Python ecosystem.

I have a dream: to give HomeAssistant the ability to reason and perform actions logically and comprehensively, using AppDaemon, Sklearn, and Decision Tree.

Application prepares data to learning process. Including preprocessing, cleaning, reformating, feature extraction using PyEEG library and learning using Sklearn tool.

A Medicine Recommendation System in machine learning (ML) is a software application designed to assist healthcare professionals and patients in selecting the most appropriate medication based on various factors such as medical history, symptoms, demographics, and drug interactions

AutoML - Hyper parameters search for scikit-learn pipelines using Microsoft NNI

In this project, we have detected the malicious URLs using lexical features and boosted machine learning algorithms

Leveraging the power of Machine Learning as a tool, we delve into the realm of app permissions to discern the true nature of applications, whether they harbor malicious or benign intent. By analyzing and predicting based on these permissions, we unlock valuable insights to safeguard users in the digital landscape.

Problem Statement: Given the tweets from customers about various tech firms who manufacture and sell mobiles, computers, laptops, etc, the task is to identify if the tweets have a negative sentiment towards such companies or products.

企业信用分析平台（EEAP）

Develop a data science project using historical sales data to build a regression model that accurately predicts future sales. Preprocess the dataset, conduct exploratory analysis, select relevant features, and employ regression algorithms for model development. Evaluate model performance, optimize hyperparameters, and provide actionable insights.

The project involves performing clustering analysis (K-Means, Hierarchical clustering, visualization post PCA) to segregate stocks based on similar characteristics or with minimum correlation. Having a diversified portfolio tends to yield higher returns and face lower risk by tempering potential losses when the market is down.

Machine Learning/Pattern Recognition Models to analyze and predict if a client will subscribe for a term deposit given his/her marketing campaign related data

Data Science - low level to high level

Gender Classification Machine Learning Model using Sk-learn in Python with 97%+ accuracy and deployment

Implementation of Linear regression on Boston House Pricing and Diabetes data sets using python.

Exploring sklearn 🌟

SVR for multidimensional labels

Building a iOS Application using Apple's Core ML Framework, we will builed a Linear SVC model using sklearn library on the SMS Data, users will type a message on their iPhone and our model will then identify whether the message is a Spam or a Ham (Not spam).