>_ William Mburu (@SirWilliam254)
  • Stars
    star
    23
  • Global Rank 586,799 (Top 21 %)
  • Followers 4
  • Following 8
  • Registered about 4 years ago
  • Most used languages
    Python
    36.4 %
    R
    27.3 %
    HTML
    27.3 %
    Ruby
    9.1 %
  • Location πŸ‡°πŸ‡ͺ Kenya
  • Country Total Rank 1,114
  • Country Ranking
    R
    17
    Ruby
    280
    Python
    385
    HTML
    489

Top repositories

1

Streamlit-math-app

This is a streamlit webapp that contains mathematical python models.
Python
4
star
2

Geospatial-analysis

This is a geospatial analytic visualization of social amenities in Kenya
HTML
2
star
3

WordCloud_function

A function for generating a word cloud for text analysis visualization
1
star
4

Time_series----Python-R----

time series analysis in both python and R.
HTML
1
star
5

CLICK-TO-EXPAND-md-content

1
star
6

Decision-Trees_python

1
star
7

PIPELINES_Python

1
star
8

Main_Plots_Python

some codes for producing major plots in python.
Python
1
star
9

probability-statistics-R

This repo consists of a semi-comprehensive overview of probability and statistics. Coding is done in the R- language
R
1
star
10

marketting-analytics-in-R

some code to perform marketting analytics in R.
R
1
star
11

SirWilliam254

Config files for my GitHub profile.
1
star
12

array-to-series

some simple code to convert an array to a series
Python
1
star
13

Random-Forests

Python
1
star
14

scrapping-twitter-R

scrapping twitter in R. This is a basic way to get data from twitter using a certain keyword of interest. And saving the data in a dataframe for later use.
R
1
star
15

rail-contact

This is a simple webapp built on Ruby, Javascript and html. It stores contacts of interest in a database. One can create, read, update or delete contacts from the list in their logged in instance.
Ruby
1
star
16

Feature-Importance

This repo is all about feature importance. Whereby we look at the ways one can identify if a feature is worth having in the model or rather if it has a significant influence in the prediction. The methods are model-agnostic.
HTML
1
star