sczhai

sczhai's Stars

• dair-ai/ml-visuals

  🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.

  13.8k116491.4k

• jerry-git/learn-python3

  Jupyter notebooks for teaching/learning Python 3

  Language:HTML6.5k267181.8k

• yyhsong/iDataV

  大屏数据可视化 Big screen data visualization demo

  Language:JavaScript5.4k14981.6k

• yangwohenmai/LSTM

  基于LSTM的时间序列预测研究

  Language:Python2.8k2020704

• PacktPublishing/Deep-Learning-with-Keras

  Code repository for Deep Learning with Keras published by Packt

  Language:Jupyter Notebook1.1k10413716

• hustcxl/Rotating-machine-fault-data-set

  Open rotating mechanical fault datasets (开源旋转机械故障数据集整理)

  931256283

• spring-projects/spring-data-neo4j

  Provide support to increase developer productivity in Java when using Neo4j. Uses familiar Spring concepts such as a template classes for core API usage and lightweight repository style data access.

  Language:Java8381022.5k618

• aialgorithm/Blog

  Python机器学习算法技术博客，有原创干货！有code实践！ 【更多内容敬请关注公众号 "算法进阶"】

  Language:Jupyter Notebook8191465239

• Azure/DeepLearningForTimeSeriesForecasting

  A tutorial demonstrating how to implement deep learning models for time series forecasting

  Language:Jupyter Notebook658514281

• jiwidi/time-series-forecasting-with-python

  A use-case focused tutorial for time series forecasting with python

  Language:Jupyter Notebook650129212

• yangwohenmai/DeepLearningForTSF

  深度学习以进行时间序列预测

  Language:Python59060166

• GreedyAIAcademy/Machine-Learning

  讲解常见的机器学习算法

  Language:Jupyter Notebook312250297

• yangwohenmai/TimeSeriesForecasting

  基于统计学的时间序列预测(AR,ARM).

  Language:Python2414177

• BLarzalere/LSTM-Autoencoder-for-Anomaly-Detection

  AI deep learning neural network for anomaly detection using Python, Keras and TensorFlow

  Language:Jupyter Notebook1943475

• FrancescaLazzeri/Machine-Learning-for-Time-Series-Forecasting

  Language:Jupyter Notebook15762105

• aaxwaz/Multivariate-Time-Series-forecast-using-seq2seq-in-TensorFlow

  Language:Jupyter Notebook150191384

• aaxwaz/Fraud-detection-using-deep-learning

  Language:Jupyter Notebook11313159

• liuhuanyong/PoemMining

  Chinese Classic Poem Mining Project including corpus buiding by spyder and content analysis by nlp methods, 基于爬虫与nlp的**古代诗词文本挖掘项目

  Language:Python1059037

• PacktPublishing/Learning-Pandas-Second-Edition

  Learning pandas, Second Edition, published by Packt

  Language:Jupyter Notebook10460168

• PatientEz/lstm-time-series-forecasting_keras

  lstm time series forecasting implemented by keras 2.4 & tensorflow 2.3

  Language:Python28218

• Tekraj15/AnomalyDetectionTimeSeriesData

  Anamoly Detection in Time Series data of S&P 500 Stock Price index (of top 500 US companies) using Keras and Tensorflow

  Language:Jupyter Notebook25309

• cystanford/EM_data

  EM聚类数据-王者荣耀英雄数据

  Language:Python231038

• nikhils10/Time-Series-Forecasting-Apple-Stock-Price-Using-SARIMA-Prophet

  Time Series forecasting using Seasonal ARIMA & Prophet. Applied statistical tests like Augmented Dickey–Fuller test to check stationary of series. Checked ACF ,PACF plots to identify Moving average and Auto-regressive order of series. Transformed series to make it stationary.

  Language:Jupyter Notebook221112

• yijingchen/RNNForTimeSeriesForecastTutorial

  Please visit https://github.com/Azure/RNNForTimeSeriesForecasting for latest version.

  Language:Jupyter Notebook185011

• robertoamansur/rare_event_pred_maintanance

  Code to solve a open dataset of predictive maintanance of sheet brek on a paper mill.

  Language:Jupyter Notebook7107

• HarigovindV10/Credit-Card-Fraud-Detection

  A credit card fraud detection algorithm.

  Language:Jupyter Notebook2200

• 2315597478/BigMartPredModel

  The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. The aim is to build a predictive model and find out the sales of each product at a particular store.Using this predictive model the data scientists at BigMart can understand the properties of products and stores which play a key role in increasing sales. The tasks done for this project are : *Data Exploration *Data QAQC *Outlier detection and missing data replacement strategy *Data ETL *Understanding about sales domain *Feature Engineering *Advanced Data Analysis *Regression modeling Techniques *Hybrid modeling Techniques *Specific measure to Evaluate Model ​ The datasets were acquired from : https://s3.amazonaws.com/hackerday.datascience/49/Test_u94Q5KV.csv https://s3.amazonaws.com/hackerday.datascience/49/Train_UWu5bXk.csv https://s3.amazonaws.com/hackerday.datascience/49/SampleSubmission_TmnO39y.csv Software Requirements : Continuum Analytics, Inc software Python 2.7 version with seaborn package was used.

  1004

• Daniel-Arayala/rare_event_pred_maintanance

  Code to solve a open dataset of predictive maintanance of sheet brek on a paper mill.

  Language:Jupyter Notebook1001

• faisal-irzal/CC_Fraud_Detector

  Implementation of various machine learning techniques to detect credit card frauds based on a given dataset. This repo will guide you through the data analysis, viz and building predictive models

  Language:Jupyter Notebook1101

• faisal-irzal/customer_segmentation

  This repo explains how to use KMeans classifier to create customer segmentation to help the marketing team of local supermarket in launching a campaign of new products

  Language:Jupyter Notebook1101