Iris Dataset Machine Learning Project

Day 12 – Saving & Reusing Machine Learning Models Today I learned how to save and reload a trained machine learning model using joblib. After building and tuning my churn prediction model, I exported it as a .pkl file so it can be reused without retraining. Key Learning: Training a model is one step — deploying and reusing it is what makes it practical in real-world applications. This step gave me insight into how ML models move from development to production. #MachineLearning #DataScience #ModelDeployment #Python #LearningJourney

🚀 Day 45/100 – Python, Data Analytics & Machine Learning Journey 🤖 Module 3: Machine Learning 📚 Today’s Learning: Supervised Learning – Classification Algorithm 2: Logistic Regression Today I explored Logistic Regression, one of the fundamental algorithms used for classification problems in machine learning. It helps predict the probability of an outcome, such as whether a patient has a disease based on medical data. Understanding these core algorithms is helping me build a strong foundation in machine learning and prepare for solving real-world problems using data. Machine Learning continues to be an exciting field, and I’m looking forward to exploring more algorithms and practical implementations in the coming days. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #LogisticRegression #AIML #Python #LearningInPublic #DataScience

Machine learning models lack explainability, making it difficult to understand their predictions. This is a significant obstacle in various cases, including regulated industries where black box models are unacceptable. Shap is a Python library utilizing shapley additive explanations, a game theoretic approach that explains the output of machine learning models. The library generates plots visualizing the effect of each variable, hence being a significantly useful tool! Check the lins below for more information, and make sure to follow us for regular data science content. 𝗦𝗵𝗮𝗽 𝗹𝗶𝗯𝗿𝗮𝗿𝘆 𝘄𝗲𝗯𝘀𝗶𝘁𝗲: https://lnkd.in/dE2cxKN8 #datascience #python #machinelearning #deeplearning

🚀 Day 42/100 – Python, Data Analytics & Machine Learning Journey 🤖 Started Module 3: Machine Learning 📚 Today I learned: 5. Encoding • Label Encoding • One Hot Encoding 6. Feature Scaling • Standardization(Standardization()) Machine Learning is the core of AI systems, and I’m excited to explore algorithms, models, and real-world applications in the coming days. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience

I’ve created simple and clear notes on Logistic Regression, one of the most important classification algorithms in Machine Learning. 📌 In this, I covered: What Logistic Regression is Why not use Linear Regression Sigmoid function (with intuition) Step-by-step example calculation Types of Logistic Regression Python implementation using scikit-learn Evaluation metrics (Accuracy, Precision, Recall, F1-score) Advantages & limitations Real-world applications 🎯 This is useful for: Beginners in Data Science Students preparing for exams Anyone who wants strong fundamentals I explained everything in a simple and practical way with examples. 📄 Feel free to check it out and share your feedback! #MachineLearning #LogisticRegression #DataScience #Python #AI #Learning #Beginners #DataAnalytics

I’ve been diving deep into how models actually "learn" by implementing Gradient Descent from scratch in Python. While libraries like PyTorch and TensorFlow handle this under the hood, building it manually helped me grasp the importance of: - The Cost Function: Quantifying error to guide the model. - Learning Rate Selection: Balancing the risk of "overshooting" vs. the inefficiency of slow convergence. - Partial Derivatives: Using the chain rule to calculate gradients and update weights. Understanding these fundamentals is crucial for debugging complex Deep Learning architectures. Next stop: Stochastic Gradient Descent (SGD) and Momentum! #MachineLearning #DeepLearning #Python #Mathematics #Optimization

🚀 Day 46/100 – Python, Data Analytics & Machine Learning Journey 🤖 Module 3: Machine Learning 📚 Today’s Learning: Supervised Learning – Classification Algorithm 3: K-Nearest Neighbors (KNN) Today I explored K-Nearest Neighbors (KNN), a simple yet powerful classification algorithm in Machine Learning. KNN works by identifying the k closest data points (neighbors) to a new data point and classifying it based on the majority class among those neighbors. This algorithm is widely used in pattern recognition, recommendation systems, and classification problems because of its simplicity and effectiveness. Learning these core algorithms step by step is helping me strengthen my Machine Learning fundamentals and understand how models make predictions using data. The journey continues as I explore more algorithms and their real-world applications in the coming days. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #KNN #AIML #Python #LearningInPublic #DataScience

📘 Learning Update – Linear Regression As part of my Machine Learning journey, I studied and documented my understanding of Linear Regression. In this learning note, I covered: • The concept of supervised learning • How regression works mathematically • Actual vs Predicted values • Model training using Scikit-learn Sharing my notes as part of my continuous learning process. Always open to feedback and suggestions! #MachineLearning #DataScience #LearningJourney #Python

I’ve been building a machine learning–based approach to extract data from engineering graphs. 📊 The goal is to take graph images (like pressure vs depth) and convert them into structured, usable data instead of relying on manual digitization. I developed a Python pipeline using OpenCV and explored ML-based approaches to improve how curves are detected and separated — including experimenting with U-Net for segmentation and a CNN-based model for prediction.🤖🧠 One of the more challenging parts was getting consistent curve detection and accurately mapping pixel values to real-world units. It took quite a bit of iteration to get the extracted output to closely match the original graph behavior. On the left is the Original Graph, and on the right is the extracted output. I’m really happy with how it’s coming together so far, especially working on something that connects machine learning with a practical, real-world use case.🚀 Tools used: Python, OpenCV, NumPy, Pandas, CNN, U-Net 💻 Sharing a snapshot of the output below 👇 #MachineLearning #DataAnalytics #ComputerVision #Python

🚀 Day 51/100 – Python, Data Analytics & Machine Learning Journey 🤖 Module 3: Machine Learning 📚 Today’s Learning: Supervised Learning – Regression Algorithm 3: Support Vector Regression (SVR) Today, I explored Support Vector Regression (SVR), a powerful supervised machine learning algorithm used for predicting continuous values. SVR works by finding the best-fit line (or hyperplane) that not only fits the data but also keeps the prediction error within a defined margin (epsilon). It focuses on maintaining a balance between model complexity and prediction accuracy. SVR is widely used in applications like stock price prediction, demand forecasting, and time-series analysis. The learning journey continues as I explore more regression algorithms and their real-world applications. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience