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data-mining

Data mining notebooks and scripts

https://github.com/nakulcr7/data-mining

Science Score: 18.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (2.1%) to scientific vocabulary

Keywords

data-mining data-science python
Last synced: 6 months ago · JSON representation ·

Repository

Data mining notebooks and scripts

Basic Info
  • Host: GitHub
  • Owner: nakulcr7
  • Language: Jupyter Notebook
  • Default Branch: master
  • Homepage:
  • Size: 52.6 MB
Statistics
  • Stars: 4
  • Watchers: 2
  • Forks: 1
  • Open Issues: 3
  • Releases: 0
Topics
data-mining data-science python
Created over 8 years ago · Last pushed about 3 years ago
Metadata Files
Readme Citation

README.md

Data Mining Notebooks and Scripts

This repository contains a few of my notebooks and scripts I wrote as a part of an Unsupervised Learning class I took at Northeastern University.

Owner

  • Name: Nakul Camasamudram
  • Login: nakulcr7
  • Kind: user
  • Location: SF Bay Area
  • Company: Meta Inc

Citation (citation_analysis/README.md) 

# Extraction and Mining of Academic Social Networks - A Data Analysis

Data analysis of a public citation [dataset](https://www.aminer.cn/aminernetwork) from [Aminer](https://www.aminer.cn).

Download dataset from [here](http://arnetminer.org/lab-datasets/aminerdataset/AMiner-Paper.zip) and copy the file `AP_train.txt` into this directory, before running the Jupyter notebook.

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Dependencies

requirements.txt pypi
  • beautifulsoup4 ==4.6.0
  • certifi ==2017.7.27.1
  • chardet ==3.0.4
  • idna ==2.6
  • mpmath ==1.0.0
  • numpy ==1.13.3
  • pandas ==0.21.0
  • python-dateutil ==2.6.1
  • pytz ==2017.3
  • requests ==2.20.0
  • scikit-learn ==0.19.1
  • scikit-surprise ==1.0.4
  • scipy ==1.0.0
  • six ==1.11.0
  • surprise ==0.1
  • sympy ==1.1.1
  • urllib3 ==1.24.2