Intro-of-Probability-in-Python

Intro-of-Probability-in-Python

This repository provides an introductory crash course on Probability and Statistics using Python, leveraging Jupyter notebooks for hands-on learning. It includes foundational concepts in probability, statistical reasoning, and data analysis using real-world datasets such as NHANES (National Health and Nutrition Examination Survey).

📁 Repository Structure

Notebook	Description
00-basic-python-crash-course.ipynb	Quick Python refresher covering variables, loops, functions, and NumPy basics.
00-nhanes_data_basics.ipynb	Introduces the NHANES dataset. Includes basic data exploration, cleaning, and variable understanding.
01-data-types.ipynb	Overview of categorical and numerical data types; includes examples from NHANES.
02-studying-variables.ipynb	Summarizing single variables using measures like mean, median, mode, and visualizations.
03-dispersion.ipynb	Discusses measures of spread: variance, standard deviation, IQR, and range.
04-coin-flips.ipynb	Simulates coin flips to explain basic probability concepts like outcomes and distributions.
05-spam-detection.ipynb	Applies basic probability to a real-world classification problem: spam email detection.
06-distributions.ipynb	Introduction to probability distributions (Bernoulli, Binomial, Normal, etc.).
06-joints-and-marginals.ipynb	Explains joint and marginal probability distributions using NHANES.
07-decision-tree.ipynb	A simple decision tree classifier to illustrate probabilistic decision-making.
08-bayesian-inference.ipynb	Introduction to Bayesian inference and belief updating with prior and likelihood.

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📊 NHANES Dataset

We use a subset of the NHANES dataset which includes the following variables:

Key Variables Explained

Variable	Description
SEQN	Respondent sequence number
RIAGENDR	Gender (1 = Male, 2 = Female)
RIDAGEYR	Age in years
RIDRETH1	Race/ethnicity
SMQ020	Smoking status
ALQ101	Ever had at least 12 drinks in 1 year
BPXSY1, BPXDI1	Systolic and Diastolic BP readings
BMXWT, BMXHT, BMXBMI	Body weight, height, and BMI
DMDEDUC2	Education level
INDFMPIR	Family poverty income ratio
HIQ210	Health insurance coverage

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🧠 Learning Outcomes

By working through this repository, you will:

• Understand basic and intermediate probability concepts
• Learn to apply statistics using real-world health survey data
• Gain practical experience with Python tools like Pandas, NumPy, and Matplotlib
• Apply Bayesian reasoning and explore predictive modeling techniques

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📌 Requirements

Install the required libraries using:

pip install -r requirements.txt

Minimal libraries used:

1.pandas

2.numpy

3.matplotlib

4.seaborn

5.scikit-learn (for ML examples)

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🚀 Getting Started

Clone this repository

git clone https://github.com/yourusername/Intro-of-Probability-in-Python.git
cd Intro-of-Probability-in-Python

Launch Jupyter Notebook

jupyter notebook