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

Decision Tree Classifier model implemented in a python program.

Simple linear regression model implementation in Python and R for to showing the relationship between salary and years of experience.

kaggle 比赛 使用sklearn进行kaggle数据竞赛基础及实践

企业信用分析平台（EEAP）

Python Data Analytics, Machine Learning & Natural Language Processing

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

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.

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

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

Machine Learning algorithms implementation using Python

Data Science - low level to high level

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.

This repo contains step by step procedure to predict whether an employee is promoted or not. Based on the Practice competition on AnalyticsVidhya

Machine learning project for predicting the number of deaths on Titanic dataset using linear regression,scikit learn.

QuickCNN is high-level library written in Python, and backed by the Keras, TensorFlow, and Scikit-learn libraries. It was developed to exercise faster experimentation with Convolutional Neural Networks(CNN). Majorly, it is intended to use the Google-Colaboratory to quickly play with the ConvNet architectures. It also allow to train on your local…

Implementation of Machine Learning Algorithms

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

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