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ctr-predition

This project contain the tiny data set of KDD CUP 2012 Track2.

cubedbscan

A gird-based improved DBSCAN which can recognize multi-density clusters.

cubic_reg

Implementation of Nesterov and Polyak's (2006) cubic regularization algorithm and Cartis et al's (2011) adaptive cubic regularization algorithm

cuda-chainer-rbm

cuda-ffm

Field-aware Factorization Machines on CUDA

cudf

cuDF - GPU DataFrame Library

cuhk_sz_dl

CUHK_SZ course for deep learning

cumf_als

CUDA Matrix Factorization Library with Alternating Least Square (ALS)

cuml

cuML - RAPIDS Machine Learning Library

cupy

NumPy-like API accelerated with CUDA

cupy-windows-wheel

cupy prebuilt wheel for windows

currency-portfolio-optimization

Currency Portfolio Optimization - IPython notebook and data

customer-behavior-analysis-using-rfm-model-and-churn-prediction-on-e-retailing

After Covid-19, with the help of advances in the technology online shopping have become a part of daily life and it is expected to grow more all around the world. Accordingly Customer behavior are becoming more and more complex with the passage of time. With increasing competitor in the market, Retailers tries their best to hold their customer because attracting new customers cost several times then retaining the existing customer. For this purpose, Retailer analysis their customers purchase so that they can provide better service and maximize their profit margins. In this work, EDA of e-Retail data has been performed, using RFM analysis to identify the categorical segmentation of customers and Time Series Analysis with ARIMA Model to identify trends and clustering and classification models are implemented to identify the customers who are likely to churn. Furthermore, will also analyze top factors that influence user retention.

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 (£). 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

customer_geolocation_data_clustering

We use our customer geolocation data to perform a clustering algorithm to get several clusters in which the member data of each cluster are closest to each other using KMeans and Constrained-KMeans Algorithms.

cuttsum

Columbia University - Trec 2015 - Temporal Summarization

cvrminer

Data mining of CVR

cvscripts

Computer Vision scripts

cvst

Fast Cross-Validation via Sequential Testing

cvxflow

cvxopt

CVXOPT -- Python Software for Convex Optimization

cyberchain

BlockChain written by Nodejs

cyclegan

Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more (from UC Berkeley)

cyclegan-tensorflow

Tensorflow implementation for learning an image-to-image translation without input-output pairs. https://arxiv.org/pdf/1703.10593.pdf

cyclegan_tensorflow

cylouvain

Python module providing a Cython implementation of the classic Louvain algorithm for graph clustering

cython-example

Converting Python code to C for fast performance

cython_lstm

:snake: LSTM Python library using Cython

cythonconvert

Example of how to convert a python script into a C file to improve performance.

cythonizer

Cythonizer is a script that will attempt to automatically convert one or more .py and .pyx files into the corresponding compiled .pyd or .so binary modules files.