Survival Analysis for Data Scientists: Time-to-Event Modeling in Python

🚀 Day 15 – Data Science Learning Journey Today I explored Classification, a Machine Learning technique used to predict categorical or discrete outcomes (for example: yes/no, spam/not spam, survive/not survive). I learned how classification models are evaluated using a Confusion Matrix, which compares actual values with predicted values and includes: - True Positive (TP) - True Negative (TN) - False Positive (FP) - False Negative (FN) Based on this, we calculated important evaluation metrics such as: 📊 Accuracy 📊 Misclassification Rate (Error Rate) 📊 Precision 📊 Recall 📊 Specificity 📊 F1 Score We also implemented Logistic Regression, one of the fundamental algorithms used for classification problems. What I found most interesting is how these complex statistical calculations can now be performed efficiently using Python libraries with just a few lines of code. Step by step, gaining a deeper understanding of Machine Learning concepts and their practical implementation. 🚀📊 #DataScience #MachineLearning #Classification #LogisticRegression #Python #LearningJourney Lakshminarayana Bobbili

I recently performed Exploratory Data Analysis (EDA) and Feature Engineering on two datasets using Python. Projects: 1. Google Play Store Dataset: a.Cleaned the dataset, handled missing values and duplicates. b.Visualized insights using Matplotlib and Seaborn. 2. Flight Price Dataset: a.Performed data preprocessing and feature extraction. b.Handled categorical variables using encoding techniques. c.Prepared the dataset for machine learning modeling. These projects helped me improve my understanding of data cleaning, visualization, and preparing datasets for predictive analysis. #Python #EDA #FeatureEngineering #DataAnalytics #DataScience #MachineLearning #Pandas #NumPy #Matplotlib #Seaborn

🚀 Day 54 of My 90-Day Data Science Challenge Today I worked on Loss Functions in Machine Learning. 📊 Business Question: How do we measure how wrong a model’s predictions are? Loss functions calculate the difference between actual and predicted values. Using Python concepts: • Learned Mean Squared Error (MSE) • Understood Mean Absolute Error (MAE) • Explored Log Loss (Binary Cross-Entropy) • Compared regression vs classification loss • Understood impact on model training 📈 Key Understanding: Loss functions guide the model to improve by minimizing error. 💡 Insight: Choosing the right loss function is crucial for correct model learning. 🎯 Takeaway: Better loss function → better learning → better predictions. Day 54 complete ✅ Understanding model errors 🚀 #DataScience #MachineLearning #DeepLearning #LossFunction #Python #LearningInPublic #90DaysChallenge

Starting to understand why Pandas is the first tool every data scientist learns. I built a simple Student Marks Analyzer — nothing fancy, but it clicked something for me. With just a few lines I could: → Build a table from scratch → Explore rows, columns, specific values → Get average, highest and lowest marks instantly 📊 Average: 84.0 | Highest: 95 | Lowest: 70 The interesting part? I didn't write a single formula. No Excel. No manual counting. Just Python doing the heavy lifting in milliseconds. This is exactly what data analysis feels like at the start — small project, but you can already see the power behind it. Still a lot to learn. But this one felt good. #Python #Pandas #DataScience #MachineLearning #AI #100DaysOfCode #PakistanTech

🚀 Built a Stock Price Prediction Pipeline using Python & Machine Learning I recently developed a configurable time-series forecasting pipeline that predicts next-day stock returns using engineered financial features and regression models. 🔧 Key highlights: • Feature engineering with lag variables, rolling statistics, momentum, and volatility signals • Random Forest regression for return prediction • CLI-based training and prediction workflow • YAML-driven configuration system for reproducible experiments • Baseline comparison against persistence forecasting • Automated dataset generation, evaluation metrics, and visualization outputs 📊 Example training run: python main.py --mode train --ticker NFLX Model performance (NFLX): MAE: 1.36 RMSE: 1.99 R²: 0.992 📊 Example prediction: python main.py --mode predict --ticker NFLX Predicted next-day return: -0.8589% Predicted next closing price: 106.86 The chart below shows actual vs predicted closing prices generated automatically by the pipeline. This project strengthened my understanding of financial time-series modeling and building reproducible ML pipelines. 🔗 GitHub repository: https://lnkd.in/dCqeH5vr Next, I’m exploring walk-forward validation and gradient boosting models to further improve forecasting performance. #MachineLearning #DataScience #TimeSeries #Python #Finance #ScikitLearn #RandomForest #FeatureEngineering #Forecasting

🚀 Top 5 Skills Needed for Data Science 1️⃣ Python 2️⃣ Statistics 3️⃣ Machine Learning 4️⃣ Data Visualization 5️⃣ Problem-solving 🎯 But most important? 👉 Ability to apply skills in real-world projects --- That’s where most students struggle. --- We focus on practical training, not theory overload. 📩 Let’s connect for training programs #DataScience #AI #Skills #CareerGrowth #Training #Innovat

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

📘 New Release from Deepsim Press We are pleased to announce the publication of: Practical Data Modeling and Machine Learning with Python From Data Preparation to Model Evaluation and Optimization This book presents a structured and practical approach to data modeling, emphasizing the complete workflow—from feature engineering and statistical modeling to machine learning, evaluation, and optimization. Rather than focusing on isolated techniques, it highlights how to build models that are reliable, interpretable, and applicable in real-world scenarios. Key topics include: • Data preparation and feature engineering • Regression and classification models • Ensemble methods and model improvement • Validation strategies and evaluation metrics • Hyperparameter tuning and model optimization • Model interpretation and explainability This title is part of the Practical Data Science with Python series, designed to guide readers from foundational analysis to advanced modeling and real-world applications. 📖 Available now: https://lnkd.in/gFFnegZH #DataScience #MachineLearning #Python #AI #Analytics #DataModeling

🚀 Excited to Share My Machine Learning Project! 🏠 House Price Prediction System I recently worked on a Machine Learning project that predicts house prices based on various features like location, area, and other key factors. 💡 Key Highlights: 📊 Data preprocessing & visualization 🤖 Model building using Machine Learning algorithms 📈 Accurate price prediction 🧠 Improved understanding of regression techniques 🛠️ Tech Stack: Python | Scikit-learn | Pandas | NumPy | Matplotlib This project helped me strengthen my skills in Machine Learning and data analysis. Looking forward to building more AI-based solutions! 💡 #MachineLearning #Python #DataScience #AI #Projects #Learning #Student 🔗 Project Link: https://lnkd.in/g6K7qVSv

Hey fam 👋 🚨 Your ML model improved… but it might be FAKE You increased accuracy from 85% → 90% 🎉 But here’s the truth 👇 👉 That improvement could just be random Most beginners stop at accuracy ❌ Real data analysts go deeper ✅ 🧠 Hypothesis Testing helps us validate this ✔ H₀ → No real improvement ✔ H₁ → Actual improvement 🔥 Step-by-step approach (practical) 1️⃣ Define hypothesis 2️⃣ Choose test (T-test / ANOVA) 3️⃣ Calculate p-value 4️⃣ Make a decision 🎯 Interview tip: “I validate model improvements using statistical testing to ensure they are significant.” 👉 Don’t just build models… 👉 Prove they actually work #DataScience #MachineLearning #Python #DataAnalytics #LearningInPublic