https://github.com/riteshghorse/data-science-r-python

Basic data science approaches to deal with the data from data cleaning to model building. It also contains the text mining with R to build a word cloud. Moreover, the data-visualisation in Python to analyse the data.
https://github.com/riteshghorse/data-science-r-python

data-science text-mining visualize-data wordcloud

Last synced: 10 months ago
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Basic data science approaches to deal with the data from data cleaning to model building. It also contains the text mining with R to build a word cloud. Moreover, the data-visualisation in Python to analyse the data.

• Host: GitHub
• URL: https://github.com/riteshghorse/data-science-r-python
• Owner: riteshghorse
• License: apache-2.0
• Created: 2018-04-19T05:33:19.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2018-04-19T05:43:57.000Z (almost 8 years ago)
• Last Synced: 2025-02-08T16:37:28.595Z (12 months ago)
• Topics: data-science, text-mining, visualize-data, wordcloud
• Language: Jupyter Notebook
• Homepage:
• Size: 1.25 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.txt
  • License: LICENSE

Awesome Lists containing this project

README

          
Basic Data Science in R/Python

1. Data-Science-1-Air Quality & Data-Science-1-Facebook Metrics 

This module contains the basic data science approaches to deal with the data.

It contains the steps for-

  -Creating subsets of dataframe

  -Merging datasets

  -Sorting the dataframe based on a column

  -Transpose of a dataframe

  -Melting the dataframe (wide to long)

  -Casting of dataframe (long to wide)

2. Data-Science-2-Breast-Cancer

This module contains the further data science steps listed below-

  -Data cleaning(Remove NA, ?, Negative values)

  -Error Correcting(Outlier detection and removal)

  -Data transformation

  -Build data models using regression and naive bayes classifier

 

3. Text-mining-R-1 & Text-mining-R-2

This module contains the text mining approcahes with R.

It contains the following implementation-

  -Text mining operations

  -Calculating the tf count(text frequency count)

  -Generating a word cloud

4. Visualizations-in-Python-1 & Visualizations-in-Python-2

This module explores the data visualization option in Python with matplotlib and seaborn

Following visualizations are implemented-

  -Histograms

  -Dot Plots

  -Bar Plots

  -Line Charts

  -Pie Charts

  -Box Plots

  -Scatter Plots

  -Point Plot

Note: All Python notebooks are created on Python3 environment. 

    : It is better to install anaconda for Python3 and run these codes.