mathchi/DS_Novel-Corona-Virus-2019-Data mathchi/DS_Novel-Corona-Virus-2019-Data

  • Stars
    star
    1
  • Language
    Jupyter Notebook
  • Created over 4 years ago
  • Updated about 3 years ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
mathchi/DS_Novel-Corona-Virus-2019-Data
github

mathchi

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

More Repositories

1

Customer-Segmentation-with-RFM-Analysis

Context A real online retail transaction data set of two years. Content This Online Retail II data set contains all the transactions occurring for a UK-based and registered, non-store online retail between 01/12/2009 and 09/12/2011.The company mainly sells unique all-occasion gift-ware. Many customers of the company are wholesalers. Column Descriptors InvoiceNo: Invoice number. Nominal. A 6-digit integral number uniquely assigned to each transaction. If this code starts with the letter 'c', it indicates a cancellation. StockCode: Product (item) code. Nominal. A 5-digit integral number uniquely assigned to each distinct product. Description: Product (item) name. Nominal. Quantity: The quantities of each product (item) per transaction. Numeric. InvoiceDate: Invice date and time. Numeric. The day and time when a transaction was generated. UnitPrice: Unit price. Numeric. Product price per unit in sterling (ร‚ยฃ). CustomerID: Customer number. Nominal. A 5-digit integral number uniquely assigned to each customer. Country: Country name. Nominal. The name of the country where a customer resides. Acknowledgements Here you can find references about data set: https://archive.ics.uci.edu/ml/datasets/Online+Retail+II and Relevant Papers: Chen, D. Sain, S.L., and Guo, K. (2012), Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining, Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3, pp. 197-208. doi: [Web Link]. Chen, D., Guo, K. and Ubakanma, G. (2015), Predicting customer profitability over time based on RFM time series, International Journal of Business Forecasting and Marketing Intelligence, Vol. 2, No. 1, pp.1-18. doi: [Web Link]. Chen, D., Guo, K., and Li, Bo (2019), Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study, 24th Iberoamerican Congress on Pattern Recognition (CIARP 2019), Havana, Cuba, 28-31 Oct, 2019. Laha Ale, Ning Zhang, Huici Wu, Dajiang Chen, and Tao Han, Online Proactive Caching in Mobile Edge Computing Using Bidirectional Deep Recurrent Neural Network, IEEE Internet of Things Journal, Vol. 6, Issue 3, pp. 5520-5530, 2019. Rina Singh, Jeffrey A. Graves, Douglas A. Talbert, William Eberle, Prefix and Suffix Sequential Pattern Mining, Industrial Conference on Data Mining 2018: Advances in Data Mining. Applications and Theoretical Aspects, pp. 309-324. 2018. Inspiration This is Data Set Characteristics: Multivariate, Sequential, Time-Series, Text
Jupyter Notebook
15
star
2

DS_Customer-Relationship-Management_CRM

Jupyter Notebook
5
star
3

DS_Calculation-of-Lead-Generation-with-Rule-Based-Classification

Python
5
star
4

DS_Predict-sales-prices-in-House_Price-Dataset

Jupyter Notebook
3
star
5

DS_Dynamics-Association-Rules-Learning_ARL

Jupyter Notebook
3
star
6

DS_Violent-Crime-Rates-by-US-State-Data

Jupyter Notebook
2
star
7

DS_Titanic-Machine-Learning-from-Disaster

The result of this project repository created by Data Science and Machine Learning Bootcamp with #VBO
Jupyter Notebook
2
star
8

DS_Hitters-Baseball-Data

Major League Baseball Data from the 1986 and 1987 seasons.
Jupyter Notebook
2
star
9

DS_Customer-Lifetime-Value-Prediction-Project_CLTV

Jupyter Notebook
2
star
10

DS_Analyze-and-Present-AB-Test-Results

Jupyter Notebook
2
star
11

DS_Home-Credit-Risk-Project

Jupyter Notebook
2
star
12

DS_Diabetes-Data

Jupyter Notebook
2
star
13

DS_Churn-Problem-for-Bank-Customers

Predict customer churn in a bank
Jupyter Notebook
2
star
14

DS_Upgraded_RFM_Analysis

Jupyter Notebook
2
star
15

DS_Breast-Cancer-Wisconsin-Diagnostic-Data

Jupyter Notebook
2
star
16

DS_the-Life-Expectancy-Data

Statistical Analysis on factors influencing Life Expectancy
Jupyter Notebook
2
star
17

DS_Association-Rules-on-Business-Problem

Context A real online retail transaction data set of two years. Content This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers. Column Descriptors InvoiceNo: Invoice number. Nominal, a 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'c', it indicates a cancellation. StockCode: Product (item) code. Nominal, a 5-digit integral number uniquely assigned to each distinct product. Description: Product (item) name. Nominal. Quantity: The quantities of each product (item) per transaction. Numeric. InvoiceDate: Invice Date and time. Numeric, the day and time when each transaction was generated. UnitPrice: Unit price. Numeric, Product price per unit in sterling. CustomerID: Customer number. Nominal, a 5-digit integral number uniquely assigned to each customer. Country: Country name. Nominal, the name of the country where each customer resides. Acknowledgements Here you can find references about data set: http://archive.ics.uci.edu/ml/datasets/Online+Retail and Relevant Papers: The evolution of direct, data and digital marketing, Richard Webber, Journal of Direct, Data and Digital Marketing Practice (2013) 14, 291รขโ‚ฌโ€œ309. Clustering Experiments on Big Transaction Data for Market Segmentation, Ashishkumar Singh, Grace Rumantir, Annie South, Blair Bethwaite, Proceedings of the 2014 International Conference on Big Data Science and Computing. A decision-making framework for precision marketing, Zhen You, Yain-Whar Si, Defu Zhang, XiangXiang Zeng, Stephen C.H. Leung c, Tao Li, Expert Systems with Applications, 42 (2015) 3357รขโ‚ฌโ€œ3367. Citation Request: Daqing Chen, Sai Liang Sain, and Kun Guo, Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining, Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3, pp. 197รขโ‚ฌโ€œ208, 2012 (Published online before print: 27 August 2012. doi: 10.1057/dbm.2012.17). Inspiration This is Data Set Characteristics: Multivariate, Sequential, Time-Series, Text
Jupyter Notebook
2
star
18

DS_Red-Wine-Quality

Jupyter Notebook
1
star
19

DeepLearning_Sign-Language-MNIST-with-CNN-model

Jupyter Notebook
1
star
20

Machine_Learning_1

Jupyter Notebook
1
star
21

py6-TicTacToe_and_Estimate

Python
1
star
22

Django_Notepad_Project

JavaScript
1
star
23

DS_over-Global-Terrorism-Data

Jupyter Notebook
1
star
24

DS_BasicLevel_Predict-the-type-of-Fish-Data

Jupyter Notebook
1
star
25

Mathchi

1
star
26

Django_Blog_Project

JavaScript
1
star
27

Heroku_Predict-Sales-Production

Python
1
star
28

DS_World-Happiness-Map

Jupyter Notebook
1
star
29

DS_Credit-Risk-Evaluation

Jupyter Notebook
1
star
30

Recommendation_System-for-The-Movies-Data

Jupyter Notebook
1
star
31

Data_Preperation_from_MyNotes

This is my notes of Data Preperation for Data Modelling.
Jupyter Notebook
1
star
32

Django_CV_Project

CV django project
Python
1
star
33

Machine_Learning_2

Jupyter Notebook
1
star
34

NLP_Hillary-Clinton-and-Donald-Trump-Tweets

Jupyter Notebook
1
star