This is a repository of code samples from my blogposts. I'm writing on Medium at the moment. You can find me here.

Link to the article	Code	Publish Date	Topics
Seizing the Moment: The Hidden Silhouette of Data	Code	September, 2026	statistics, method-of-moments, mle, python
A Case for the T-statistic	Code	January, 2026	statistics, hypothesis-testing, python
Jacobian Adjustment in Probabilistic Models	Code	December, 2025	bayesian, probabilistic-modeling, jacobian
Pandas: apply, map or transform?	Code	January, 2023	jupyter-notebook, pandas, python
Improving the performance of NumPy code	Code	October, 2022	jupyter-notebook, numpy, pandas, matplotlib, numba
Dissecting the Birthday Paradox	Code	April, 2022	jupyter-notebook, statistics, pandas, matplotlib
How do Chatbots Understand?	Code	February, 2022	rasa, python, chatbot, nlu
Handling Chatbot Failure Gracefully	Code	December, 2021	rasa, python, chatbot, nlu
Evaluating Multi-label Classifiers	Code	November, 2021	classification, sklearn, ml, metrics
Rasa Chatbot v2 (not a post)	Code	October, 2021	rasa, python, chatbot, nlu
Building a Chatbot with Rasa	Code	September, 2021	rasa, python, chatbot, nlu
How Imports Work in Python	Code	June, 2021	python, imports
Python: Decorators in OOP	Code	January, 2021	python, oop, decorators
How Neural Networks Solve the XOR Problem	Code	November, 2020	python, jupyter-notebook, matplotlib
Understanding Dynamic Programming	Code	October, 2020	python, algorithms, dynamic programming
Understanding Maximum Likelihood Estimation	TBA	August, 2020	statistics
Visualizing the Defective Chessboard Problem	Code	Jan, 2020	algorithms

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• Fork this repo
• Clone it

https://github.com/Polaris000/BlogCode.git

• Create an environment with the required packages installed. (More info below)
• Navigate to a project
• Check the README inside each project for information specific to it.

• Creating an environment is straightforward. Though there are a few ways to do it, conda is a reliable way to go about it. Install conda from here.

• To create an environment run:

  $ conda create --name <env_name> python=3.8.10
  

• After the setup is complete, activate the env.

  $ conda activate <env_name>
  

• The packages required to run these code samples are mainly of two kinds:

  • Rasa dependencies
  • Python data visualization and machine learning libraries

• If you want to install both, use requirements/requirements.txt in your env

  (env)$ pip install -r requirements/requirements.txt
  

• If you want to install rasa dependencies, use requirements/rasa_requirements.txt in your env

  (env)$ pip install -r requirements/rasa_requirements.txt
  

• If you want to install python machine learning dependencies only, use requirements/non_rasa_requirements.txt in your env

  (env)$ pip install -r requirements/non_rasa_requirements.txt
  

• If you're interested in using Rasa X for a more visual experience while improving and conversing with your bot, you'll require these additional steps:
  • Downgrade pip to fix a circular dependency issue

    $ pip install pip==20.2
    

  • Install rasa x

    $ pip install install rasa-x==0.38.1 --extra-index-url https://pypi.rasa.com/simple