Poisson Distribution in Python: Hands-On Example with Scipy & Numpy скачать в хорошем качестве

Poisson Distribution in Python: Hands-On Example with Scipy & Numpy

🧠 Don’t miss out! Get FREE access to my Skool community — packed with resources, tools, and support to help you with Data, Machine Learning, and AI Automations! 📈 https://www.skool.com/data-and-ai-aut... Need to model the number of events happening over a fixed interval—like web traffic, calls per hour, or errors per day? This beginner-friendly tutorial walks you through the Poisson Distribution using SciPy and NumPy in Python. It’s perfect for data science, statistics, or anyone exploring discrete probability distributions. Code: https://ryanandmattdatascience.com/po... 🚀 Hire me for Data Work: https://ryanandmattdatascience.com/da... 👨‍💻 Mentorships: https://ryanandmattdatascience.com/me... 📧 Email: ryannolandata@gmail.com 🌐 Website & Blog: https://ryanandmattdatascience.com/ 🖥️ Discord:   / discord   📚 *Practice SQL & Python Interview Questions: https://stratascratch.com/?via=ryan 📖 *SQL and Python Courses: https://datacamp.pxf.io/XYD7Qg 🍿 WATCH NEXT Statistics for Data Science Playlist:    • Statistics for Data Science   Uniform Distribution:    • Mastering Uniform Distribution in Python: ...   Normal Distribution:    • Normal Distribution in Python: A Beginner'...   Binomial Distribution:    • Binomial Distribution in Python: A Beginne...   In this video, I break down the Poisson distribution and show you how to implement it in Python through seven practical examples. The Poisson distribution is essential when you need to find the probability of a number of events occurring within a specific time frame, like subscribers joining a YouTube channel per day, website visitors per hour, or chargebacks per order. We start by covering the fundamentals of Poisson distribution, including key terms like Lambda (the average rate of events) and K (the number of events you're calculating probability for). Then we dive into generating random Poisson distributions using both NumPy and SciPy libraries, exploring the differences between each approach. I walk you through calculating Probability Mass Function (PMF) for exact probabilities at specific points, as well as Cumulative Distribution Function (CDF) for probabilities at or below certain values. You'll learn how to work with both single values and multiple values, and I explain when to use each method in real-world scenarios. The video wraps up with two visualization examples using matplotlib and seaborn: creating stem plots to show the probability distribution across different event counts, and building histograms to visualize sample data. By the end, you'll understand how to confidently apply Poisson distribution to solve real problems in data science and statistics, complete with professional visualizations to communicate your findings.' TIMESTAMPS 00:00 Introduction to Poisson Distribution 00:32 Poisson Distribution Definition & Examples 01:12 Lambda and K Values Explained 02:08 PMF vs CDF Overview 02:46 Python Setup & Imports 03:24 Example 1: Generate Random Poisson (NumPy) 04:02 Example 2: Generate Random Poisson (SciPy) 06:02 Mean and Standard Deviation 06:42 Example 3: PMF Single Value 07:42 Example 4: PMF Multiple Values 09:03 Example 5: CDF Single Point 10:00 Example 6: CDF Multiple Points 11:52 Example 7: Creating a Stem Plot 14:22 Example 8: Creating a Histogram 17:32 Recap & Conclusion OTHER SOCIALS: Ryan’s LinkedIn:   / ryan-p-nolan   Matt’s LinkedIn:   / matt-payne-ceo   Twitter/X: https://x.com/RyanMattDS Who is Ryan Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF. Who is Matt Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One. *This is an affiliate program. We receive a small portion of the final sale at no extra cost to you.