🔢 Experiment 8: Logistic Regression Continuing my Data Science & Statistics practical journey — I’ve completed Experiment 8, which focuses on Logistic Regression, a key classification algorithm in machine learning. This experiment includes: 📊 Understanding binary classification concepts ⚙ Implementing logistic regression using Python 📈 Evaluating model accuracy with metrics like confusion matrix and accuracy score Logistic Regression forms the backbone of many classification problems, from spam detection to medical predictions. 🔗 View the complete notebook and repository on GitHub: 👉 https://lnkd.in/eB8drAJj #DataScience #MachineLearning #LogisticRegression #Python #Statistics #Classification #Analytics #GitHub #StudentProject #LearningJourney

🧠 Support Vector Machine (SVM) in Action – Heart Disease Classification 📊 Experiment No. 10 | DSS Lab | Python | scikit-learn As a part of my Data Science & Statistical Lab, I implemented the Support Vector Machine (SVM) algorithm to classify heart disease using real patient data. 🔍 What’s inside: 📂 Loaded & explored the dataset using pandas 🧹 Cleaned & prepared the data for modeling ⚙️ Trained an SVM classifier using scikit-learn 📈 Evaluated the model using accuracy score 🎥 Screen Recording + Full Code Walkthrough Included 🔗 GitHub Repository: https://lnkd.in/e9MmsHNp 📁 Google Drive: https://lnkd.in/eNBnV47d 📌 This practical helped me understand how SVMs can find the optimal boundary between classes in high-dimensional space — small steps, real learning. 👨🏫 Guided by: Prof. Ashish Sawant #SVM #MachineLearning #HeartDisease #Python #DataScience #DSS #StudentProjects #MLPracticals #GitHub #LinkedInLearning #OpenSource #SupportVectorMachine #AI #scikitLearn #GuidedLearning #AcademicProjects

🧠 Support Vector Machine (SVM) in Action – Heart Disease Classification 📊 Experiment No. 10 | DSS Lab | Python | scikit-learn As a part of my Data Science & Statistical Lab, I implemented the Support Vector Machine (SVM) algorithm to classify heart disease using real patient data. 🔍 What’s inside: 📂 Loaded & explored the dataset using pandas 🧹 Cleaned & prepared the data for modeling ⚙️ Trained an SVM classifier using scikit-learn 📈 Evaluated the model using accuracy score 🎥 Screen Recording + Full Code Walkthrough Included GitHub: https://lnkd.in/eu875cP5 LinkedIn: https://lnkd.in/epsdwKQu Google drive: https://lnkd.in/es63Cp9p 📌 This practical helped me understand how SVMs can find the optimal boundary between classes in high-dimensional space — small steps, real learning. 👨🏫 Guided by: Prof. Ashish Sawant #SVM #MachineLearning #HeartDisease #Python #DataScience #DSS #StudentProjects #MLPracticals #GitHub #LinkedInLearning #OpenSource #SupportVectorMachine #AI #scikitLearn #GuidedLearning #AcademicProjects

🧠 Support Vector Machine (SVM) in Action – Heart Disease Classification 📊 Experiment No. 10 | DSS Lab | Python | scikit-learn As a part of my Data Science & Statistical Lab, I implemented the Support Vector Machine (SVM) algorithm to classify heart disease using real patient data. 🔍 What’s inside: 📂 Loaded & explored the dataset using pandas 🧹 Cleaned & prepared the data for modeling ⚙️ Trained an SVM classifier using scikit-learn 📈 Evaluated the model using accuracy score 🎥 Screen Recording + Full Code Walkthrough Included 🔗 GitHub Repository: https://lnkd.in/g-YT3aCd ▶ Google Drive : https://lnkd.in/gYgqFVvd 📌 This practical helped me understand how SVMs can find the optimal boundary between classes in high-dimensional space — small steps, real learning. 👨🏫 Guided by: Prof. Ashish Sawant #SVM #MachineLearning #HeartDisease #Python #DataScience #DSS #StudentProjects #MLPracticals #GitHub #LinkedInLearning #OpenSource #SupportVectorMachine #AI #scikitLearn #GuidedLearning #AcademicProjects

🚀 Project: Advanced House Price Prediction using XGBoost and Stacked Ensemble Learning # 1 of 384,400 I recently built a machine learning model to predict housing prices using structured tabular data for Kaggle Competition. The project focuses on end-to-end data preprocessing, model training, and performance optimization. 🔹 Techniques Used Data preprocessing using ColumnTransformer for numeric and categorical features Feature scaling, encoding, and missing value handling Model training with XGBoost and a Stacked Ensemble (XGB + KRR + Linear Regression as meta-model) Hyperparameter tuning using GridSearchCV Model evaluation with mae metric 🔹 Tools & Libraries Python | scikit-learn | xgboost | pandas | numpy 🔗 Project Notebook : https://lnkd.in/gAPTkd3Z - any comments on improvement, solution highly appreciated #MachineLearning #DataScience #XGBoost #Stacking #Regression #Python #FeaturePreProcessing

🚀 Diving Deep into Python Libraries! 🚀 I’ve been exploring NumPy & Pandas, and wow — the possibilities for data manipulation and analysis are incredible! 💻📊 What I’ve learned: NumPy: Fast numerical computations, arrays, matrices, and linear algebra. ⚡ Pandas: Clean, analyze, and manipulate data effortlessly with Series & DataFrames. Handles missing data seamlessly and integrates perfectly with visualization tools. 📈 Key Takeaway: Mastering these libraries builds a strong foundation for Data Analysis, Machine Learning, and Scientific Computing Excited to keep growing my data skills and apply them in real-world projects! 🌟 Here’s a quick comparison I made for clarity: #Python #DataScience #NumPy #Pandas #MachineLearning #Analytics #ProfessionalGrowth

Our team conducted Descriptive, Predictive, and Prescriptive Analytics on a Car Crashes dataset using Pandas, Seaborn, and Scikit-learn. We developed a Multiple Linear Regression model to identify and visualize significant predictors such as speeding and alcohol involvement. This project strengthened our expertise in data visualization, model evaluation, and collaborative analytics under the guidance of Dr. Pritpal Singh. 🔗 [Link to the main worksheet] (https://lnkd.in/gwyF_tdq) #DataScience #MachineLearning #Python #TeamWork #AnalyticsProject #RoadSafety #PredictiveAnalytics #Visualization

🎥 Screen Recording: K-Nearest Neighbors (KNN) in Data Science & Statistics Lab In this practical, I’ve implemented the K-Nearest Neighbors (KNN) algorithm — a simple yet powerful supervised learning method used for both classification and regression tasks. 📊 Key highlights: Understanding the concept of distance metrics Implementing KNN using Python Evaluating model performance with accuracy scores Visualizing decision boundaries 🤖 KNN helps us understand how proximity in feature space can influence predictions — a great foundation for machine learning! GitHub Link : https://lnkd.in/eM9vBrBf Guidance by : Ashish Sawant #DataScience #Statistics #MachineLearning #KNN #Python #AI #StudentProjects #DataScienceLab #DataAnalytics

🎩 One week later… and the Bayesian model still teaches lessons! This piece made me reflect on how evidence-based decisions reshape our way of thinking — especially when uncertainty tries to take over. With Python and Bayes, we turn guesses into probabilities, intuition into parameters, and raw data into logical management. 🔗 Full article and charts in the comments 👇 If you believe great management begins with reasoning, this one’s for you. 🎯 Hashtags #Python #Bayes #DecisionScience #BayesianStatistics #DataDriven #EvidenceBased #ZVPython #RetroTech #MachineLearning #Analytics #DecisionMaking #LinkedInCreator

🚀 Day 14: Exploratory Data Analysis (EDA) in Action Today was all about applying EDA on real datasets to uncover insights. 📊 Lesson 1: Hands-on with Cars Dataset Cleaned and explored data using Pandas Looked at distributions, correlations, and key statistics 📊 Lesson 2: EDA Assignment Practiced identifying trends Detected missing values, duplicates, and outliers Learned how EDA guides the next steps in analysis or modeling EDA feels like being a detective of data — asking the right questions and letting the data reveal its story. #Day14 #Python #EDA #Pandas #DataScience #DataCleaning #WomenInTech #MachineLearning