Poisson Distribution Problem Solved in Python with Solution

🌳 Today I Learned & Implemented: Random Forest Today I worked on the Random Forest algorithm and implemented it in Python as part of my machine learning journey. 🔍 Random Forest is an ensemble learning technique that builds multiple decision trees and combines their outputs to improve prediction accuracy and reduce overfitting. 💡 Key Learnings: • How multiple decision trees work together (bagging) • Difference between single decision tree vs Random Forest • Model training, prediction, and evaluation • Importance of reducing overfitting in ML models 🧠 What I Did: ✔️ Built a Random Forest model using Python ✔️ Trained and tested it on dataset ✔️ Evaluated performance using accuracy metrics 📂 Project Link: https://lnkd.in/gjFfNV5H Excited to explore more advanced ML algorithms and improve model performance 🚀 #MachineLearning #RandomForest #Python #DataScience #AI #LearningJourney

Day 26 of My AI & Data Science Journey Today I learned about Lists in Python and explored various list methods that make data handling easier. 🔹 append() – Add elements to a list 🔹 insert() – Insert element at a specific position 🔹 remove() – Remove an element 🔹 pop() – Remove element using index 🔹 sort() – Sort the list 🔹 reverse() – Reverse the list 💡 Key takeaway: Lists are powerful for storing and manipulating data, and understanding their methods helps in writing efficient and clean code. Practiced small exercises to strengthen my understanding. #Python #DataScience #CodingJourney #LearningEveryday #AI

Day 27 of My AI & Data Science Journey Today I learned about Tuples in Python and their methods 🔹 What is a Tuple? A tuple is an ordered, immutable collection of elements. ✔ Allows duplicate values ✔ Faster than lists ✔ Cannot be modified after creation 🔧 Tuple Methods: • count() → Returns how many times a value appears • index() → Returns the position of a value Tuples are useful when data should remain constant and secure. Consistency is the key to growth #Python #AI #DataScience #CodingJourney

Ridge Regression is like adding a speed limiter to your model: * No limit → it goes fast, but risks crashing (overfitting) * Too strict → it barely moves (underfitting) * Just right → smooth, stable, reliable The hyperparameter Alpha is the secret sauce. A small tweak in this parameter can completely change how your model behaves. In this post, I break it down with: ✔ Simple intuition (no heavy math) ✔ A simple Python example ✔ Visual comparison of different alpha values 👉 Read it here: https://lnkd.in/eqyYMMBC #DataScience #MachineLearning #AI #Python #Analytics

Wrapped a session of the Harvard AI / Python course today and it sharpened a few things for me. What stood out: • Python is less about syntax and more about thinking clearly. Break problems down properly and the code follows. • AI models are only as good as the data and assumptions behind them. That responsibility sits with us. • The real power is in building small working pieces fast, then stacking them into something useful. • It’s practical, buildable, and ready to deploy into real workflows. I’m already thinking about how this feeds directly into Mana Review AI — tighter models, cleaner data pipelines, better decision support. This is the level-up phase. #AI #Python #GovTech #IndigenousTech #Harvard

🚀 Day 18 of My Generative & Agentic AI Journey! Today’s focus was on understanding the return statement in Python functions and how it controls the output of a function. Here’s what I learned: 🔙 Return in Functions: • return is used to send a value back from a function 👉 We can return strings, numbers, or any data type • If we use print instead of return 👉 The function outputs None when we try to store its result • If nothing is returned explicitly 👉 Python automatically returns None 🔢 Types of Returns: • Single value → Function returns one value • Multiple values → Function can return multiple values together • Early return → Function can exit before completing all steps 👉 Useful when a condition is met early 💡 Key takeaway: return makes functions more useful and reusable by allowing them to send results back instead of just displaying output. Understanding this helps in writing cleaner and more functional code 🚀 #Day18 #Python #GenerativeAI #AgenticAI #LearningJourney #BuildInPublic

𝗧𝗶𝗺𝗲 𝘀𝗲𝗿𝗶𝗲𝘀 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 𝗶𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗼𝘀𝗲 𝘁𝗼𝗽𝗶𝗰𝘀 𝘁𝗵𝗮𝘁 𝗹𝗼𝗼𝗸𝘀 𝘀𝗶𝗺𝗽𝗹𝗲... 𝘂𝗻𝘁𝗶𝗹 𝘆𝗼𝘂 𝘁𝗿𝘆 𝘁𝗼 𝗱𝗼 𝗶𝘁 𝗽𝗿𝗼𝗽𝗲𝗿𝗹𝘆. Most tutorials stop at fitting ARIMA or Prophet on a clean dataset. In practice, it’s messier: – missing data – leakage – feature engineering – evaluation pitfalls – deployment constraints That’s where most real-world projects fail. There’s an upcoming workshop that focuses exactly on this gap — end-to-end forecasting in Python, not just models in isolation. What I like about it: – covers the full pipeline (data → features → models → evaluation) – hands-on, not just theory – focused on practical decisions you actually face If you're working with time series (or planning to), this is a useful one to check. 👉 https://lnkd.in/dnergVrT Use code 𝗔𝗡𝗗𝗥𝗘𝗬𝟰𝟬 for 40% off. Event date: May 2 #MachineLearning #TimeSeries #DataScience #Forecasting #MLOps

To make a decision tree, all data has to be numerical. We have to convert the non numerical columns 'Nationality' and 'Go' into numerical values. Pandas has a map() method that takes a dictionary with information on how to convert the values. {'UK': 0, 'USA': 1, 'N': 2} Means convert the values 'UK' to 0, 'USA' to 1, and 'N' to 2. #MachineLearning #DataScience #Python #ArtificialIntelligence #AI #ScikitLearn #DataAnalysis #ML

Starting my journey in Machine Learning! Today, I worked on a simple Linear Regression model using Python and Scikit-learn. 🔹 Created a dataset with input (X) and output (y) 🔹 Trained the model using Linear Regression 🔹 Predicted the output for a new input value This small step helped me understand how machines can learn patterns from data and make predictions. Key takeaway: Even a simple model can give powerful insights when the relationship between data is clear. Looking forward to exploring more concepts like classification, model evaluation, and real-world datasets! #MachineLearning #Python #DataScience #LearningJourney #AI #StudentLife

🚀 Day 21 of My Generative & Agentic AI Journey! Today’s focus was on understanding how to import functions and modules in Python — an important step towards organizing code in real-world projects. Here’s what I learned: 📦 Importing Modules: • We can import an entire module and access its functions using dot notation 👉 Example: import math Using functions like math.sqrt(), math.floor() 📥 Importing Specific Functions: • Instead of importing everything, we can import only required functions 👉 Example: from math import sqrt, ceil 👉 Makes code cleaner and avoids unnecessary imports ⚠️ import * (Not Recommended): • Using import * brings all functions and variables into the current namespace • Can cause confusion and naming conflicts 👉 Better to explicitly import only what is needed 💡 Key takeaway: Proper use of imports helps in writing modular, clean, and maintainable code — especially in large projects. Taking one more step towards writing structured and scalable applications 🚀 #Day21 #Python #GenerativeAI #AgenticAI #LearningJourney #BuildInPublic