Implementing End-to-End ML Pipeline with Titanic Dataset

🚀 Day-70 of #100DaysOfCode 📊 NumPy Practice – Finding Top K Elements Today I worked on finding the top 3 largest elements in a NumPy array. 🔹 Concepts Practiced ✔ Array sorting using np.sort() ✔ Array slicing ✔ Extracting top values from datasets 🔹 Key Learning Finding top-K elements is a common task in data analysis, ranking systems, and machine learning, where identifying the most significant values is important. Step by step improving my NumPy and data manipulation skills 🚀 #Python #NumPy #DataScience #PythonProgramming #100DaysOfCode #LearningJourney

🚀 Excited to share my latest project: House Price Prediction an end-to-end Machine Learning pipeline for residential house price prediction. By implementing Scikit-Learn Pipelines and comparing Random Forest vs. Gradient Boosting, I achieved a Mean Absolute Error of ~$16.7k. This project highlights my focus on building clean, reproducible code and robust data preprocessing. Check out the full code and technical breakdown on GitHub: 🔗 [https://lnkd.in/dR-3adKr] #MachineLearning #Python #DataScience #ScikitLearn #Kaggle"

Day 6 of Solving ML Problems From Scratch: Adam Optimizer Today I worked on implementing the Adam Optimizer from scratch. What I like about Adam is that it combines the benefits of momentum and adaptive learning rates in a very practical way. Instead of taking the same type of step every time, it adjusts based on both past gradients and gradient magnitude, which makes optimization more stable and efficient. While solving this, I got a better understanding of: how momentum helps smooth the update direction how the velocity term adapts the step size why bias correction is important, especially in the early steps how Adam can converge faster than plain SGD in many cases Building these concepts from scratch is helping me understand what is really happening behind the libraries we use every day. It is one thing to call an optimizer in code, but it is very different to actually implement and reason through each update step yourself. Small daily practice like this is making machine learning feel much more intuitive. #MachineLearning #DeepLearning #ArtificialIntelligence #Python #DataScience

✅ Numpy arrays.... Today in our Python class at FIT – Future Innovative Technology, we explored NumPy arrays and learned some really interesting concepts. We covered: • Arrays in NumPy • 2D Arrays • Array Dimensions • Array Shapes It was exciting to understand how NumPy helps in handling data efficiently and how multidimensional arrays work. Learning these concepts is making programming feel more practical and powerful, especially for data science and AI. Every day I’m discovering something new, and this journey of learning Python and AI is becoming more interesting and enjoyable. #Python #NumPy #AI #MachineLearning #LearningJourney #FutureInnovativeTechnology

🚀 Day 1: NumPy? Today I started learning NumPy, one of the most important libraries in Python for numerical computing. NumPy allows us to work with large datasets using arrays instead of traditional lists. It is faster, more efficient, and widely used in data science, machine learning, and AI. 💡 Key takeaway: NumPy improves performance and makes complex calculations simple. #Python #NumPy #DataScience #LearningJourney

DSA Tip: Trees If your data feels hard to organize… it might be the structure. Use Trees. They arrange data in levels and relationships, not just lines. From file systems to AI models, trees power how complex systems are built. Insight: Better structure doesn’t just store data, it makes it easier to understand and use. Quick Challenge: How many children can a node have in a Binary Tree? Drop your answer, I’ll review the best ones. FOLLOW FOR MORE DSA TIPS & INSIGHTS #DSA #Trees #Python #CodingTips #LearnToCode

Machine Learning Project: Book Recommender System I built a Book Recommendation System using Collaborative Filtering. The system suggests similar books based on user ratings. 🔹Built using: Python Pandas Scikit-learn Streamlit 🔹 Features: • User-Book Rating Matrix • Cosine Similarity • KNN Model • Interactive Streamlit UI 🌐 Live Demo: https://lnkd.in/ghuZ7PMH 💻 GitHub Repository: https://lnkd.in/g-Y_stfp #MachineLearning #DataScience #Python #Streamlit #AIProjects

𝗠𝗔𝗖𝗛𝗜𝗡𝗘 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚 𝗙𝗢𝗥 𝗕𝗘𝗚𝗜𝗡𝗡𝗘𝗥𝗦 𝗡𝘂𝗺𝗣𝘆: 𝗧𝗵𝗲 𝗡𝘂𝗺𝗲𝗿𝗶𝗰𝗮𝗹 𝗘𝗻𝗴𝗶𝗻𝗲 𝗕𝗲𝗵𝗶𝗻𝗱 𝗠𝗼𝗱𝗲𝗿𝗻 𝗔𝗜 Behind every Machine Learning model lies something simpler but incredibly powerful — NumPy. It’s the library that turns Python into a high-performance numerical computing engine. Understanding arrays, vectorization, and broadcasting completely changes how you think about data and computation. I put together a structured deep dive covering these fundamentals — sharing the notebook as a PDF below. #NumPy #MachineLearning #DataScience #Python #ArtificialIntelligence #LearningJourney #AIEngineering #GenerativeAI

🚀 Exploring Machine Learning with Linear Regression Today I practiced a simple Machine Learning model using Python and Scikit-learn. I implemented Linear Regression to predict prices based on area values. Using Pandas for data handling and Scikit-learn’s LinearRegression, I trained a model with historical data and predicted the price for a new area value (10,000 sq.ft). This small exercise helped me understand: • Data loading using Pandas • Feature selection (dropping target column) • Training a Linear Regression model • Making predictions on new data Step by step, improving my understanding of Machine Learning fundamentals and predictive modeling. #MachineLearning #Python #LinearRegression #DataScience #ScikitLearn #DataAnalytics #LearningJourney

Most models train once and that's it. AdaBoost works differently — each new tree focuses on what the previous one got wrong, gradually turning weak predictions into a strong, accurate model. In this video I walk through the full picture: why decision trees overfit, where random forests fall short, and how AdaBoost's sequential learning addresses both. I also cover my implementation on the California Housing dataset, including hyperparameter tuning, Permutation Importance, and Partial Dependence Plots. This project was completed as part of my Applied Machine Learning for Business coursework at IBA, DU. You can find the full implementation here: https://lnkd.in/gpN9cuzq Feedback is always welcome. #MachineLearning #AdaBoost #EnsembleLearning #DataScience #Python #CaliforniaHousing #RandomForest #DecisionTrees