MhdHabboub / Bayes-theorem

What if Sherlock was a Data Scientist: Solve Crimes Using Bayes' Theorem - Visual Guide

Introduction

Welcome to our captivating YouTube video where we blend the genius of Sherlock Holmes with the cutting-edge world of data science. In this unique exploration, we delve into the intriguing question: What if the legendary detective were a data scientist? Join us on a thrilling journey as we uncover the power of Bayes' Theorem and witness its application in solving crimes, all through the lens of probabilities, deductions, and modern analytics.

Table of Contents

• Prerequisites
• Installation
• Usage
• Code Explanation
• Scenario and Probabilities
• Results
• Further Explorations
• Contributing
• License

Prerequisites

To follow along with this guide, you'll need a basic understanding of Python programming and probability concepts.

Installation

Clone this repository to your local machine and ensure you have Python (version x.x) installed.

git clone https://github.com/MhdHabboub/Bayes-theorem.git

Usage

1. Open a terminal or command prompt.
2. Navigate to the project directory.
3. Run the Python script using the command:

python sherlock_data_scientist.py

Code Explanation

The sherlock_data_scientist.py script calculates the probability of a hypothesis being true given a specific evidence using Bayes' Theorem. The script takes into account the prior probability, the likelihood of the evidence given the hypothesis is true, and the likelihood of the evidence given the hypothesis is false.

Scenario and Probabilities

In this section, we outline the scenario used in the script and provide details about the probabilities involved:

• Hypothesis (H): There is someone behind the curtains.
• Prior Probability (P(H)): 0.1
• Likelihood of Evidence (P(E|H)): 0.6
• Likelihood of Evidence if Hypothesis is False (P(E|¬H)): 0.2

Results

Upon running the script, you'll see the calculated probabilities and the resulting probability of the hypothesis being true given the evidence.

Further Explorations

This visual guide provides just a glimpse into the potential of applying data science principles to detective work. Consider exploring:

• More complex scenarios and hypotheses.
• Different Bayesian techniques and applications.
• Real-world crime-solving examples using data science methods.

Contributing

Contributions are welcome! If you have ideas for improvements, new scenarios, or code optimizations, feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License.

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Disclaimer: This is a fictional scenario for educational purposes only. Sherlock Holmes and related characters are the creation of Sir Arthur Conan Doyle.