Implementing Random Forest Algorithm in Python

📊 Another step forward in my problem-solving journey! Today, I tackled a Poisson Distribution problem and implemented the solution in Python 🐍 👉 Problem: Find the probability that a random variable ( X = 5 ) given mean ( \lambda = 2.5 ) 💡 What I learned: How to apply the Poisson probability formula in real scenarios Importance of precision (rounding to 3 decimal places) Writing clean, ASCII-only code for platform compatibility ✅ Final Result: 0.067 🧠 Key Insight: Strong fundamentals in probability and statistics are crucial for fields like AI, Machine Learning, and Data Science. Problems like these may seem small, but they build the core intuition needed for advanced concepts. 🚀 Staying consistent and improving every day! #Python #Probability #Statistics #PoissonDistribution #DataScience #MachineLearning #AI #CodingJourney #LearningInPublic link of #Solution :- https://lnkd.in/dKYJeTys

🚀 From raw data to 95% accuracy — my first ML model Built a Random Forest classifier on the Iris dataset 1.Used proper train-test split to avoid misleading results 2.Evaluated performance using accuracy & classification report 3.Achieved ~95% accuracy on unseen data 💡 Key takeaway: A model is useless without proper evaluation Next: Comparing with other models like SVM #MachineLearning #Python #DataScience #AI

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

Today I explored Linear Regression in Machine Learning — from simple to multiple and polynomial models. Understanding how different features shape predictions step by step. 📊 Building a strong foundation, one concept at a time. 🔗 GitHub: https://lnkd.in/g4mDK4fM #MachineLearning #LinearRegression #DataScience #LearningJourney #AI #Python

🚀 Day 17: Matrix Operations in NumPy Today I worked with matrix operations. ✔ matrix multiplication ✔ transpose ✔ inverse These are very important in: 👉 Machine Learning 👉 Data Science 👉 AI 💡 Key takeaway: NumPy makes linear algebra easy and powerful. #NumPy #Python #AI #MachineLearning

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

Most people jump directly into Machine Learning models. I almost did the same. But then I realized something: Without strong fundamentals, everything in ML becomes confusing. So instead of rushing into algorithms, I’m currently focusing on: • Data Structures & Algorithms (for problem-solving) • Probability & Statistics (to actually understand models) • Python fundamentals (clean implementation matters) Because in the long run: Understanding why something works is more powerful than just knowing how to use it. Now I’m building my learning step by step — and documenting it along the way. Curious to know — how did you approach learning ML? #DataScience #MachineLearning #Python #DSA #LearningInPublic

Scikit-Learn Cheat Sheet Every ML Beginner Must Save If you’re learning Machine Learning with Python, mastering Scikit-Learn is non-negotiable. It’s one of the most widely used libraries for building, training, and evaluating ML models. Here’s a quick cheat sheet covering the most commonly used functions 👇 Data Splitting --> Used for splitting your dataset into training and testing sets and performing robust validation. Preprocessing --> Essential for handling missing values, encoding categories, and scaling features. Model Building --> These are the most common baseline models used in interviews and real-world projects. Model Evaluation --> Always evaluate before deployment. Hyperparameter Tuning --> Critical for improving model performance. Pipelines --> A must-know concept for production-ready ML workflows. Dimensionality Reduction --> Used to reduce features and improve efficiency. Tip: If you know preprocessing + model training + evaluation + GridSearchCV + Pipeline, you already know 80% of what’s needed for ML interviews. Save this for your next project. Which library should I create next? Pandas / TensorFlow / PyTorch #ScikitLearn #MachineLearning #Python #DataScience #ArtificialIntelligence #MLInterview #DataAnalytics #AI

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

🤖 Machine Learning is shaping the future. From data to decisions, from code to intelligence. The world is moving towards automation and smart systems. Learning technologies like Python and Machine Learning is no longer optional — it’s the future. 🚀 Start today, stay ahead tomorrow. #MachineLearning #AI #Python #Technology #Future #Learning