Code Samples from my blog posts

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
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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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
```

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