Customer Churn Prediction with ML Pipeline Improvements

Excited to share my latest Machine Learning Project! Food Delivery Time Prediction Using XGBoost Regressor with Hyperparameter Tuning Built an end-to-end ML Regression model to predict food delivery time (in minutes) based on real-world factors like distance, traffic, weather, ratings and vehicle type — using 1000 records! What I did: - Performed Feature Engineering — created 4 new meaningful features - Applied One-Hot Encoding (no data leakage) - Built baseline XGBoost Regressor (gradient boosting) - Tuned n_estimators, max_depth, learning_rate, subsample & colsample_bytree - Evaluated using MAE, MSE, RMSE and R² Score - Built Prediction Report showing Actual vs Predicted values Results: Before Tuning → R²: 93.89% | MAE: 3.770 | RMSE: 4.751 After Tuning → R²: 95.39% | MAE: 3.482 | RMSE: 4.129 Best Parameters Found: N Estimators: 200 | Max Depth: 3 | Learning Rate: 0.1 Subsample: 0.8 | Colsample Bytree: 0.8 Regression Model Comparison: Random Forest → R²: 94.08% AdaBoost   → R²: 94.64% XGBoost    → R²: 95.39% Best! Key Learnings: → XGBoost Gradient Boosting outperforms all regression models → colsample_bytree=0.8 & subsample=0.8 reduce overfitting → R² improved by +1.50% — strong improvement! → All 4 metrics improved after hyperparameter tuning → Average prediction error = only 3.48 minutes — production ready! → Feature Engineering significantly boosted model performance Tools: Python | XGBoost | Pandas | Scikit-learn | NumPy - Grateful for the guidance from Abhishek Jivrakh Sir during this project. GitHub Repository : https://lnkd.in/g-eRVMaR #MachineLearning #DataScience #Python #XGBoost #GradientBoosting #Regression #FeatureEngineering #FoodDelivery #MLProject #ScikitLearn #AI #HyperparameterTuning

𝐎𝐮𝐭𝐥𝐢𝐞𝐫𝐬 -- 𝐎𝐧𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐈 𝐤𝐞𝐞𝐩 𝐟𝐚𝐜𝐢𝐧𝐠 𝐰𝐡𝐢𝐥𝐞 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐦𝐨𝐝𝐞𝐥𝐬… While working on a recent dataset before model building, I ran into a common issue ---- outliers. We all know: "Outliers are unusual data points that behave very differently from the rest of the data." But what I realized practically is: Outliers are not always “bad”. 𝐖𝐡𝐞𝐫𝐞 𝐨𝐮𝐭𝐥𝐢𝐞𝐫𝐬 𝐜𝐫𝐞𝐚𝐭𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬 Some ML algorithms are sensitive to outliers: 1. Linear Regression 2. Logistic Regression 3. AdaBoost 4. Deep Learning models These models can get biased because a few extreme values pull the learning in the wrong direction. 𝐁𝐮𝐭 𝐬𝐨𝐦𝐞𝐭𝐢𝐦𝐞𝐬 𝐰𝐞 𝐍𝐄𝐄𝐃 𝐨𝐮𝐭𝐥𝐢𝐞𝐫𝐬 Example: Fraud Detection Fraud transactions = outliers Removing them = removing the actual problem So decision depends on business context, not just data. 𝐇𝐨𝐰 𝐈 𝐡𝐚𝐧𝐝𝐥𝐞𝐝 𝐨𝐮𝐭𝐥𝐢𝐞𝐫𝐬 𝐢𝐧 𝐦𝐲 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰 There are mainly two approaches: 1. Trimming (Removing Outliers) --> Completely removing extreme values 2. Capping (Winsorization) --> Limiting values to a threshold instead of removing Method depends on distribution 1. 𝐍𝐨𝐫𝐦𝐚𝐥 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 --> 𝐙-𝐒𝐜𝐨𝐫𝐞 Rule: Mean ± 3 * Standard Deviation 2. 𝐒𝐤𝐞𝐰𝐞𝐝 𝐃𝐚𝐭𝐚 --> 𝐈𝐐𝐑 𝐌𝐞𝐭𝐡𝐨𝐝 Outliers are not just noise They can be signal depending on the problem #datascience #machinelearning #modelbuilding #outlier #python #Statistics #dataanalyst

Excited to share my latest Machine Learning Project! Real Estate Price Prediction Using Decision Tree Regressor with Hyperparameter Tuning Built an end-to-end ML Regression model to predict house prices (in USD) based on features like size, bedrooms, age, location, type, condition and furnishing using a real estate dataset of 750 records. What I did: - Performed One-Hot Encoding before Train-Test Split (no data leakage) - Built a baseline Decision Tree Regressor - Applied GridSearchCV with expanded parameter grid - Used max_features tuning for better generalization - Evaluated using MAE, MSE, RMSE and R² Score Results: Before Tuning → R²: 97.04% | MAE: 28,319 | RMSE: 35,556 After Tuning → R²: 97.45% | MAE: 26,279 | RMSE: 32,988 Best Parameters Found: Criterion: friedman_mse | Max Depth: None | Max Features: None Min Samples Leaf: 3 | Min Samples Split: 2 Key Learnings: → Correct ML pipeline prevents data leakage → Expanding param grid improves tuning results → R² above 97% = outstanding regression model → All 4 metrics improved after hyperparameter tuning → Decision Tree Regressor can match complex models on clean data This project gave me deep hands-on experience in regression modeling, feature encoding and hyperparameter optimization! Tools Used: Python | Pandas | Scikit-learn | NumPy - Grateful for the guidance from Abhishek Jivrakh Sir during this project. Github repository : https://lnkd.in/gsAPDMrW #MachineLearning #DataScience #Python #DecisionTree #Regression #RealEstate #GridSearchCV #HyperparameterTuning #MLProject #ScikitLearn #AI #DataAnalysis

#Hello_Connection..... 🚀 Just completed my Heart Stroke Prediction Web App using Machine Learning! I built this project to understand the complete ML pipeline — from data analysis to deployment. 🔍 What I did in this project: Performed EDA (Exploratory Data Analysis) Cleaned and preprocessed the dataset Trained multiple models: Logistic Regression, KNN, Decision Tree, SVM, Naive Bayes Selected Logistic Regression as the final model based on best accuracy Deployed the model using Streamlit 💡 The app takes user health inputs like age, cholesterol, blood pressure, etc., and predicts whether the person is at: ✔️ Low Risk ❌ High Risk of Heart Disease 🛠️ Tech Used: Python | Pandas | Scikit-learn | Streamlit | Joblib This project really helped me understand how ML models are used in real-world applications. 📌 GitHub Project: https://lnkd.in/g5ZfpgYA I’d love to hear your feedback and suggestions! You can check: 👇 https://lnkd.in/gwZW_AT4 #MachineLearning #DataScience #Python #Streamlit #AI #Projects #LearningJourney

MODEL TUNING — PRACTICAL CHEAT SHEET (STEP-BY-STEP) 1. Start with Baseline Model Train with default settings Goal: Get a number to beat model = RandomForestClassifier() model.fit(X_train, y_train) 2. Evaluate Performance Check your current level accuracy_score(y_test, model.predict(X_test)) 3. Focus on Important Parameters Do not tune everything For RandomForest: - n_estimators - max_depth - min_samples_split Rule: Tune only 2–3 parameters 4. Tune One Parameter at a Time for n in [50, 100, 200]: model = RandomForestClassifier(n_estimators=n) model.fit(X_train, y_train) Pick the value that gives best result 5. Use Grid Search (Thorough) GridSearchCV(model, params, cv=5) Use when dataset is small or medium 6. Use Random Search (Faster) RandomizedSearchCV(model, params, n_iter=10) Use when dataset is large 7. Check Overfitting If train score >> test score Fix: - Reduce max_depth - Increase min_samples_split - Use cross-validation Goal: Train score ≈ Test score 8. Use Cross Validation cross_val_score(model, X, y, cv=5) Use 5-fold or 10-fold 9. Check Feature Importance model.feature_importances_ Remove low importance features 10. Save Best Model joblib.dump(model, "best_model.pkl") QUICK CHECKLIST - Start simple - Tune 2–3 parameters only - Use cross validation - Compare before vs after tuning - Stop if improvement is less than 1 percent - Data quality is more important than tuning COMMON METRICS Classification: - Accuracy - Precision - Recall - F1 Score Regression: - MAE - RMSE - R2 GOLDEN RULE Try -> Measure -> Adjust -> Repeat Small changes lead to better results #MachineLearning #ModelTuning #HyperparameterTuning #DataScience #Python #ScikitLearn #MLOps #AI #DataEngineering #Analytics #LearnML #MLBasics #TechCareers #Coding #AIProjects

Excited to share my latest Machine Learning Project! Income Prediction Using XGBoost Classifier with Hyperparameter Tuning Built an end-to-end ML Classification model to predict whether an individual earns more or less than $50K/year using the Adult Census Dataset (32,561 records). What I did: - Handled missing values encoded as ' ?' in the dataset - Applied One-Hot Encoding with drop_first=True (no multicollinearity) - Built baseline XGBoost Classifier (gradient boosting) - Tuned n_estimators, max_depth, learning_rate, subsample & colsample_bytree - Evaluated using Accuracy, Precision, Recall, F1 & Classification Report Results: Before Tuning → Accuracy: 87.25% | Precision: 0.777 | F1: 0.705 After Tuning → Accuracy: 87.67% | Precision: 0.796 | F1: 0.711 Best Parameters Found: N Estimators: 200 | Max Depth: 5 | Learning Rate: 0.1 Subsample: 1.0 | Colsample Bytree: 0.8 Model Comparison across my portfolio: Decision Tree → ~85.00% Random Forest → ~86.17% AdaBoost    → ~86.40% XGBoost    → 87.67% Best! Key Learnings: → XGBoost uses Gradient Boosting — most powerful boosting algorithm → colsample_bytree=0.8 reduces overfitting by using 80% features per tree → XGBoost outperformed all 4 ensemble models in my portfolio → Weighted avg F1 Score of 0.87 — highest in portfolio → GridSearchCV with 5-fold CV found the best combination Tools: Python | XGBoost | Pandas | Scikit-learn | NumPy - Grateful for the guidance from Abhishek Jivrakh Sir during this project. GitHub Repository : https://lnkd.in/gj6EpCnc #MachineLearning #DataScience #Python #XGBoost #GradientBoosting #EnsembleLearning #MLProject #ScikitLearn #AI #HyperparameterTuning #Classification #DataAnalysis

Logistic Regression: From Lines to Logic! 📊 Have you ever wondered how machines make "Yes" or "No" decisions? Whether it's spotting spam emails or predicting if a customer will subscribe, Logistic Regression is the go-to tool! 🛠️ Here is a simple 3-step breakdown of how it works: 1️⃣ Linear Prediction: We start with a basic line (y = mx + b). But since a line can go to infinity, it doesn't give us a clear "yes/no" answer. 2️⃣ The Sigmoid "Magic": We pass that line through the Sigmoid Function. This acts like a "squasher," taking any number and squeezing it between 0 and 1. 🔄 3️⃣ Binary Output: Now we have a probability! 📈 Above 0.5? It's a 1 (Yes!). Below 0.5? It's a 0 (No!). It’s simple, powerful, and the foundation of many classification tasks in Data Science. 💡 What’s your favorite classification algorithm? Let’s discuss below! 👇 #DataScience #MachineLearning #Python #LogisticRegression #AI #LearningJourney #DataAnalytics

🚀 Car Price prediction project using ML – Part 2: Model Building & Evaluation Continuing from yesterday’s update on Data Cleaning & ETL, today I focused on the next critical phase of the ML pipeline 👇 🔹 Data Splitting Divided the dataset into training and testing sets to ensure unbiased model evaluation. 🔹 Model Training Experimented with multiple algorithms: • Linear Regression • Random Forest Regressor • XGBoost Regressor 🔹 Hyperparameter Tuning Applied **GridSearchCV** to optimize model performance and find the best parameters. 🔹 Results & Insights After comparing all models, Linear Regression performed the best for this dataset—simple, interpretable, and effective. 💡 Key takeaway: Sometimes simpler models outperform complex ones depending on the data. Looking forward to taking this further with model evaluation metrics and deployment 🚀 #MachineLearning #DataScience #MLProjects #Python #AI #LearningInPublic

Stock Price Prediction Using SVM | Machine Learning Project 📈 I’m excited to share my latest project where I built a Stock Price Prediction model using Python and Scikit-Learn! Stock markets are notoriously volatile, making them a perfect challenge for Data Science. In this project, I leveraged Support Vector Regression (SVR) to analyze and predict price movements. Key Technical Highlights: Feature Engineering: Used Pandas for date-indexing and created lagged price values to capture time-series trends. Model Optimization: Implemented GridSearchCV to fine-tune hyperparameters ($C$, $\gamma$, and kernels), significantly boosting the model's accuracy. Data Scaling: Applied StandardScaler to normalize input features for better SVR performance. Visualization: Used Matplotlib to plot "Actual vs. Predicted" prices, making the results easy to interpret. Results: The tuned SVR model successfully captured the market trends with a very low Error Rate (RMSE), demonstrating the effectiveness of SVMs in financial forecasting. Check out the video below to see the full workflow and results! 🎥👇 #MachineLearning #DataScience #Python #SVM #StockMarket #AI #PredictiveAnalytics #ScikitLearn

Exciting Update: My First MLflow Experiment on Credit Card Fraud Detection! I’ve just completed my first end-to-end machine learning experiment using MLflow for a Credit Card Fraud Detection project. Here’s what I did: I compared 4 different models: - Decision Tree - Random Forest - XGBoost - LightGBM I used metrics like accuracy, precision, recall, F1 score, and Cohen’s Kappa to evaluate their performance on a real-world dataset. Key Takeaways: - MLflow helped me track experiments, log parameters and metrics, and easily compare models. - The models had varied performances, and the choice of evaluation metric (especially recall for fraud detection) made a big difference in model selection. - I used techniques like class weighting and scale_pos_weight to handle the imbalanced dataset effectively. Tech Stack: - Python - MLflow for experiment tracking - Scikit-learn, XGBoost, and LightGBM for model building - Pandas, NumPy for data manipulation - Matplotlib, Seaborn for data visualization If you’re working on any ML project, I highly recommend giving MLflow a try—it’s a great tool for managing experiments and improving reproducibility. #MachineLearning #MLflow #DataScience #CreditCardFraudDetection #AI #Python #ScikitLearn #XGBoost #LightGBM #ModelEvaluation #MLOps #DataScienceCommunity #MachineLearningExperiment