🔍 Customer Churn Prediction with ML Ever wondered which customers might leave before they actually do? I built a machine learning pipeline to predict churn and help businesses take action early. From data preprocessing to model evaluation — it's all in here! 📊 Techniques: Logistic Regression, Decision Trees, Feature Engineering 📁 Tools: Python, Scikit-learn, Pandas, Matplotlib 🚀 Check it out on GitHub: https://lnkd.in/gg5vQ2Y6 done this task as AIML intern at OutriX #DataScience #MachineLearning #CustomerChurn #GitHub #Python #AIProjects #RetentionStrategy

Hey everyone 👋 Today's learning the very most Important Machine learning Concept, 1. Cross-Validation (https://lnkd.in/gM_mhpEf) 2. Hyperparameter Tuning(https://lnkd.in/gcH9WpQp) and put the ideas into practice in Python on Google Colab. I’ve uploaded the notebook to my GitHub so you can see the code and results. What I learned: Cross-Validation (k-fold): split data into k parts to train and test the model multiple times so the model’s performance is more reliable and not just lucky on one split. Why it matters: it helps avoid overfitting and gives a better estimate of how the model will work on new data. Hyperparameter Tuning: tried methods like Grid Search and Random Search to find the best combination of parameters (for example: regularization strength, number of trees, max depth). Why it matters: tuning hyperparameters can improve model accuracy and generalization without changing the model itself. What I did: implemented k-fold cross-validation and hyperparameter search in Python (scikit-learn) on Colab, compared results, and logged the best model and metrics in the notebook. Check out the notebook here 👇  🔗 [https://lnkd.in/gfURYdt9] I’m still learning and will keep sharing what I build. Feedback and suggestions are welcome ,they help me improve. 🫡 💪 #MachineLearning #DataScience #CrossValidation #HyperparameterTuning #Python #ScikitLearn #GitHub #LearningJourney

🚀 New Project Update! I just wrapped up a Movie Recommendation System and created a clean, well-documented Jupyter Notebook PDF that walks through the entire workflow — from data exploration to building a recommendation engine. 🎬 What’s Inside the Notebook? Data preprocessing & cleaning Exploratory data analysis Content-based recommendation system Similarity metrics Final recommendation results Clear explanations + code comments This project helped me deepen my understanding of machine learning, Python, pandas, and recommendation algorithms — and I’m excited to share it with everyone! 🔗 GitHub Repository: https://lnkd.in/dheF6rbP If you're interested in machine learning or want to explore how recommender systems work, feel free to check it out. Feedback and suggestions are always welcome! 😊 #MachineLearning #DataScience #Python #RecommenderSystem #Projects #LearningJourney #AI

🚀 Exploring the Titanic Dataset with Python, Seaborn & Google Colab ⚓📊 Just completed a hands on data preparation and visualization project that analyzing passenger survival patterns from the historic Titanic dataset.🧠 🔍 Key insights uncovered: 🛳️ Survival by Passenger Class: First class passengers had the highest survival rates. 👩🦰 Survival by Gender: Females had a much higher chance of survival than males. 👶 Age Distribution by Survival: Younger passengers were more likely to survive. 💰 Correlation Matrix: Clear link between Pclass and Fare, and insights into how Age relates to Survival. All of this was done in Google Colab, using Pandas, Matplotlib, and Seaborn to clean, explore, and visualize data effectively. A great exercise in turning raw data into clear, visual stories! 💡 📂 GitHub Repository: 🔗 https://lnkd.in/gZuCxxJF #DataScience #MachineLearning #Python #Seaborn #Matplotlib #GoogleColab #DataVisualization #TitanicDataset #LearningJourney

Just built a Movie Recommendation System! Excited to share my latest project — a Content-Based Movie Recommender built using the TMDB 5000 Movies Dataset. The app suggests top 5 similar movies based on user-selected titles using cosine similarity on processed metadata. 🔧 Tech Stack Python Pandas, NumPy Scikit-learn (similarity matrix) Streamlit (UI) Pickle (model + metadata storage) 🎯 What it does Reads and processes the TMDB dataset Extracts key features from movie metadata Builds a similarity matrix Uses it to recommend the 5 closest matches Provides a simple, clean UI for the user to choose any movie 🎬 Features Instant recommendations Fast lookup through a precomputed similarity matrix User-friendly web interface built with Streamlit Easily deployable 📂 GitHub Repository: https://lnkd.in/dB2nHSzW Feedback is always welcome 😊 #MachineLearning #Python #AI #RecommendationSystem #Streamlit #DataScience #Project

🚢 PROJECT COMPLETE: Titanic Survival Prediction Model Thrilled to share my latest machine learning project: a model built to predict the survival of passengers on the Titanic! This project allowed me to dive deep into crucial data science practices: ✅ **Model:** Trained using a Random Forest Classifier. ✅ **Performance:** Achieved an **Accuracy of 0.76** on the test set. ✅ **Key Techniques:** Data Preprocessing, Feature Engineering (handling 'Sex', 'Age', and 'Fare'), Training/Testing Split, and comprehensive Model Evaluation. ✅ **Results:** As shown in the video, I successfully generated the **Confusion Matrix** (0: Not Survived, 1: Survived) and a detailed **Evaluation Report** showing precision, recall, and f1-scores. ✅ **Tools:** Python (Scikit-learn, Pandas, Matplotlib/Seaborn). Check out the short video demo below to see the code execution and the key results generated by the model in VS Code! 🔗 **Code & Documentation:** https://lnkd.in/geKKVmev #DataScience #MachineLearning #Python #Titanic #RandomForest #ModelEvaluation #PortfolioProject #DataAnalytics

ICYMI: InfluxDB 3.6 is here with Ask AI, a beta feature that lets you query time series data in plain English. No SQL required — just describe what you want, and get charts, insights, or tasks instantly. Plus: shareable dashboards, a simpler quick start for local dev, and a major Processing Engine upgrade with multifile Python plugins and better observability: Dive in: https://bit.ly/4oFo35X

Turn your raw data into stunning, interactive charts — without writing a single line of code!  This Streamlit app built by Saptarshi Bandyopadhyay takes any CSV or Excel file and instantly creates professional-looking charts using Python libraries like Pandas and Plotly.  → Upload your dataset   → Choose X and Y axes   → Generate bar, line, scatter, or pie charts in seconds  No coding. No Excel formatting. Just clean, insightful visuals — fast.  Explore how Ivy Professional School’s AI & Data programs help you build such real-world Python projects at ivyproschool.com  #datascience #pythonprojects #datavisualization #artificialintelligence #careerupgrade #aiupskilling #ivyproschool #learnwithivy

🧪 Exploring Logistic Regression in Action with Real-World Healthcare Data 💡📊 Just completed an impactful practical on Logistic Regression using Python and Scikit-learn, where I explored a real-world dataset to predict the presence of heart disease. 💻🧠 📥 Loaded and cleaned data using pandas 🔍 Checked for missing values, datatypes, and summary stats 📊 Visualized class distribution with seaborn 📦 Split the dataset into training and testing sets 🤖 Trained a Logistic Regression model with sklearn 🧾 Evaluated the model with a Confusion Matrix 🎨 Plotted the heatmap for better interpretation This experiment helped me understand how classification models work in practice — especially for solving binary outcomes like "disease or no disease". 💡 Special thanks to Prof. Ashish Sawant Sir for constant guidance and support! 🙏 GitHub: https://lnkd.in/eu875cP5 LinkedIn: https://lnkd.in/epsdwKQu Google drive: https://lnkd.in/es63Cp9p #LogisticRegression #Python #MachineLearning #ClassificationModel #HeartDiseasePrediction #DataScience #StudentProject #GitHub #HandsOnLearning #EngineeringLife #DSS #MLWorkflow #LinkedInLearning #ScikitLearn