Exploring Support Vector Regression (SVR) in Machine Learning

🚀 Day 52/100 – Python, Data Analytics & Machine Learning Journey 🤖 Module 3: Machine Learning 📚 Today’s Learning: Supervised Learning – Regression Algorithm 4: KNN Regression Today, I explored K-Nearest Neighbors (KNN) Regression, a simple yet powerful supervised machine learning algorithm used for predicting continuous values. KNN Regression works by identifying the ‘K’ nearest data points to a given input and predicting the output as the average (or weighted average) of those neighbors. KNN is widely used in applications like recommendation systems, pattern recognition, and demand forecasting. 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

🚀 Day 62/100 – Python, Data Analytics & Machine Learning Journey 🤖 Module 3: Machine Learning 📚 Today’s Learning: Unsupervised Learning Algorithm 3: PCA Today, I explored the fundamentals of Unsupervised Learning a type of machine learning where models work with unlabeled data to discover hidden patterns and structures. I learned about PCA (Principal Component Analysis), a powerful dimensionality reduction technique used to reduce the number of features while preserving the most important information in the dataset. It transforms the original variables into a new set of uncorrelated variables called principal components. PCA works by identifying directions (principal components) where the data varies the most. The first principal component captures the maximum variance, followed by the second, and so on. This helps in simplifying complex datasets, improving model performance, and reducing computation time. 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

🚀 Day 53/100 – Python, Data Analytics & Machine Learning Journey 🤖 Module 3: Machine Learning 📚 Today’s Learning: Classification Metrics – Confusion Matrix, Accuracy, Precision, Recall, F1 Score Regression Metrics – Mean Squared Error (MSE), R² Score Today, I focused on understanding how to evaluate machine learning models effectively using different performance metrics. In classification, I learned about metrics like Accuracy, Precision, Recall, and F1 Score along with the Confusion Matrix, which help in analyzing model predictions in detail. For regression, I explored Mean Squared Error (MSE) and R² Score, which are essential to measure how well a model predicts continuous values. Understanding these metrics is important to improve model performance and make better data-driven decisions. The learning journey continues as I dive deeper into machine learning concepts and real-world applications. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience

Day 2 of learning Machine Learning. Today I worked on a simple linear regression model using Python in Jupyter Notebook. The idea was straightforward: - Input (x): house size - Output (y): price Model used: f(x) = wx + b I understood how: - Training data is structured (x_train, y_train) - Parameters (w, b) define the relationship - The model uses this to make predictions on new inputs Also got hands-on with NumPy and basic plotting using Matplotlib. Still very early, but it's becoming clearer how data is converted into predictions. #MachineLearning #AI #Python #LearningInPublic

🚀 Day 54/100 – Python, Data Analytics & Machine Learning Journey 🤖 Module 3: Machine Learning 📚 Today’s Learning: • Cross Validation Today, I focused on understanding how to evaluate machine learning models effectively using different performance metrics and validation techniques. I explored Cross Validation (K-Fold), a powerful technique that helps in evaluating model performance by splitting the dataset into multiple folds and training/testing the model multiple times. This ensures better reliability and reduces the chances of overfitting. In classification, I learned about metrics like Accuracy, Precision, Recall, and F1 Score, along with the Confusion Matrix, which help in analyzing model predictions in detail. For regression, I explored Mean Squared Error (MSE) and R² Score, which are essential to measure how well a model predicts continuous values. Understanding these metrics and validation techniques is important to improve model performance and make better data-driven decisions. The learning journey continues as I dive deeper into machine learning concepts and real-world applications. 🚀 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience

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

𝗖𝗿𝗲𝗮𝘁𝗲 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀! Machine learning models usually have low explainability, hence making it difficult to understand their predictions. This is a serious obstacle in many cases, including industries where black box models are unacceptable. Shapash is a Python library that lets you understand machine learning models by providing an interactive web dashboard. Furthermore, shapash can also be used to generate reports, hence being a significantly useful tool for data scientists and analysts! Visit the link below for more information, and make sure to follow me for regular data science content. 𝗦𝗵𝗮𝗽𝗮𝘀𝗵 𝗹𝗶𝗯𝗿𝗮𝗿𝘆 𝘄𝗲𝗯𝘀𝗶𝘁𝗲: https://lnkd.in/dDiid5Vj 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟 𝗮𝗻𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴: https://lnkd.in/dyByK4F #datascience #python #deeplearning #machinelearning

🚀 Day 57/100 – Python, Data Analytics & Machine Learning Journey 🤖 Module 3: Machine Learning 📚 Today’s Learning: • Bagging and Boosting Today, I explored ensemble learning techniques, specifically Bagging and Boosting, which are powerful methods to improve machine learning model performance. Bagging (Bootstrap Aggregating) works by training multiple models on different subsets of the data and combining their predictions. This helps in reducing variance and improving model stability. Boosting, on the other hand, focuses on building models sequentially, where each new model learns from the mistakes of the previous ones. This approach helps in reducing bias and creating stronger predictive models. I also understood how these techniques enhance accuracy and make models more robust compared to individual models. Learning ensemble methods is essential for building high-performance machine learning solutions used in real-world applications. The learning journey continues as I dive deeper into machine learning. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience 🚀

Built a Car Sales Prediction model using Machine Learning 🚗📊 • Analyzed dataset and visualized trends • Applied regression models for prediction • Evaluated performance using metrics This project improved my understanding of data analysis and business insights. 🔗 GitHub: https://lnkd.in/gBg6zAEp #DataScience #MachineLearning #Python #Analytics

🚀 Excited to share my latest project: AI Log Analyzer I built a web-based application using Python and Streamlit that can: ✔ Upload and analyze log files (.txt / .log) ✔ Classify logs into ERROR, WARNING, INFO, CRITICAL ✔ Visualize log distribution with graphs 📊 ✔ Search logs instantly 🔍 ✔ Generate downloadable reports 📄 ✔ Predict log type using Machine Learning 🤖 🌐 Live Demo: https://lnkd.in/gTNK_NQ5 This project helped me strengthen my skills in Python, data analysis, and basic machine learning using libraries like scikit-learn and matplotlib. Looking forward to exploring more real-world AI applications and improving this project further! #Python #MachineLearning #Streamlit #AI #DataScience #Projects #GitHub #Learning #Developer