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hi, i'm Khaing Htoo Ko, a passionate self-taught data scientist from Myanmar. my passion for data sciencee lies with dreaming up ideas and making them come true with elegant interfaces. i take great care in the experience, architecture, and code quality of the things I build.

i am also an open-source enthusiast and maintainer. i learned a lot from the open-source community and i love how collaboration and knowledge sharing happened through open-source.

GIF

  • 💼 any freelance work? do reach, email :)
  • 💬 ask me about anything, i am happy to help;

✅languages and tools:

📊 this week i spent my time on:

Machine Learning   3 hrs 36 mins   ████████████░░░░░░░░░░░░░   47.76 %
Tensorflow   2 hrs 35 mins   ████████▓░░░░░░░░░░░░░░░░   34.26 %
GCP         1 hr 1 min      ███▒░░░░░░░░░░░░░░░░░░░░░   13.58 %
Other        14 mins         ▓░░░░░░░░░░░░░░░░░░░░░░░░   03.22 %

🚧 where to find me:

Khainghk's Instagram Khainghk's LinkedIN

📈 my github stats:

Khainghk's GitHub stats

Khaing Htoo Ko's Projects

awesome-notebooks icon awesome-notebooks

Ready to use data science templates, organized by tools to jumpstart your projects in minutes. 😎 published by the Naas community.

ml-basics icon ml-basics

Exercise notebooks for Machine Learning modules on Microsoft Learn

pandas icon pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

rfm-analysis icon rfm-analysis

Python script (and IPython notebook) to perform RFM analysis from customer purchase history data

time-series-eda-and-forecast icon time-series-eda-and-forecast

In this section, I begin with the excel file of sales data, which I obtained from the Tableau Community Forum. As a recall, the data contains mostly categorical variables and components of the vectors from the description column. The index column is a timeseries format. The major objective of this section is to understand the general trends in the data, and gain some quick insights, and then predict and forcast the Sales of the category "Technology" of the given sales data.The statistical significance of these observations will be also tested in 'Exploratory Data Analysis'.

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