Tanmay jhalani’s Post

🚀 I’m excited to share my latest project in AI & Data Science! I have developed and deployed a House Price Prediction System using Machine Learning techniques. This project leverages real-world data and a Random Forest model to estimate property prices based on key features. 🌐 Live Application: https://lnkd.in/gZrHhe74 🔍 Key Highlights: • End-to-end ML pipeline (data preprocessing → model training → evaluation) • Interactive web application built with Streamlit • Real-time predictions based on user input • Model performance evaluated using R² score This project helped me strengthen my understanding of building and deploying scalable ML solutions. I would love to hear your feedback and suggestions! #ArtificialIntelligence #MachineLearning #DataScience #Python #Streamlit #StudentProject #AIProjects

Machine Learning for Classification: From Data to Intelligent Decisions This Sunday, we had an insightful session in collaboration with Alliance4ai where we explored how machine learning can turn raw data into intelligent decisions. We covered the full classification workflow: ✔️ Data preparation & cleaning ✔️ Exploratory Data Analysis (EDA) ✔️ Model training (Logistic Regression, Decision Trees, Random Forest) ✔️ Model evaluation (Accuracy, Precision, Recall, F1-score, AUC) ✔️ Model improvement through tuning and feature selection We also emphasized the importance of Python libraries like NumPy, Pandas, Matplotlib, and Seaborn in building an effective and continuous data analysis pipeline. From raw data to meaningful predictions — this session highlighted how structured approaches in machine learning can solve real-world problems. #MachineLearning #DataScience #Python #AI #Classification #AllianceForAI #LearningJourney

Excited to share my Machine Learning project: Customer Churn Prediction This project focuses on predicting customers who are likely to leave a service or business by analyzing customer behavior, usage patterns, and account details. Using Machine Learning algorithms, I built a predictive model that helps businesses identify at-risk customers early and take proactive retention strategies. 1. Performed Data Cleaning & Preprocessing 2. Applied Exploratory Data Analysis (EDA) 3. Built and evaluated ML models for prediction 4. Improved decision-making through data-driven insights This project enhanced my skills in Python, Pandas, Scikit-learn, Data Visualization, and Machine Learning. #MachineLearning #DataScience #Python #CustomerChurn #PredictiveAnalytics #LinkedInProjects #AI GitHub link : https://lnkd.in/ghYsGRsd

Day 10 of my AI & Data Science journey Today felt like going back to the basics… but in a really good way. I started learning NumPy and Pandas from scratch, and it honestly changed how I see data. 💡 What I learned: – NumPy makes calculations way simpler – Pandas helps organize messy data into something I can actually understand – How to filter and explore data instead of just looking at it The best part? For the first time, I wasn’t just staring at data… I was actually working with it and understanding what it’s saying. That moment just clicked 🔥 📌 Realization: Before building AI models, you need to understand your data first. That’s the real foundation. #MachineLearning #AI #DataScience #Python #LearningJourney #NumPy #Pandas

Labeled data is a luxury. In the real world, most data is messy, unlabeled, and silent. Unsupervised learning is how you make it speak. I just wrapped up DataCamp's Unsupervised Learning in Python course, and it shifted how I think about data entirely. No labels. No predefined answers. Just raw data and the challenge of letting patterns reveal themselves. A few things really stuck with me: → K-Means and hierarchical clustering: grouping data points by similarity to uncover hidden segments → Dimensionality reduction with PCA and t-SNE : making sense of high-dimensional data without losing the story it's telling → Non-negative Matrix Factorization (NMF) : an elegant way to discover interpretable topics in text and features in images What I appreciate most is how unsupervised learning mirrors real-world problems. In practice, data rarely comes neatly labeled. The ability to find structure where none is obvious is a skill that pays off across domains from customer segmentation to anomaly detection to recommendation systems. On to the next one. 🚀 #MachineLearning #Python #DataScience #UnsupervisedLearning

🚢 Titanic Survival Prediction — End-to-End Machine Learning Project I recently completed a full machine learning project where I predicted passenger survival on the Titanic dataset. 🔍 What I did: • Performed Exploratory Data Analysis (EDA) to uncover patterns • Handled missing values using imputation techniques • Encoded categorical features using One-Hot Encoding • Built a preprocessing pipeline using ColumnTransformer & Pipeline • Trained models: Logistic Regression and Random Forest • Evaluated performance using Accuracy, F1-score, ROC-AUC, and Confusion Matrix 📊 Key Insights: • Female passengers had significantly higher survival rates • First-class passengers were more likely to survive • Age had missing values and required proper imputation 🛠️ Tools & Libraries: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn This project helped me understand how real-world ML pipelines are built. Looking forward to learning more and building stronger projects 🚀 #MachineLearning #DataScience #Python #BeginnerToIntermediate #PortfolioProject #AI #LearningJourney

🧠 25 Scikit-Learn Commands Every ML Engineer Should Know Whether you're preprocessing data, training models, or tuning hyperparameters — these 25 sklearn commands cover 80% of your daily ML workflow. Here's what's inside 👇 📦 Data Prep → train_test_split, StandardScaler, OneHotEncoder 🤖 Models → RandomForest, SVC, LogisticRegression, KNN ⚙️ Fit & Predict → .fit(), .predict(), .predict_proba() 📊 Evaluation → confusion_matrix, cross_val_score, classification_report 🔧 Pipeline & Tuning → Pipeline, GridSearchCV, PCA, joblib Save this for your next ML project. 🔖 What's your most-used sklearn function? Drop it in the comments 👇 #MachineLearning #Python #ScikitLearn #DataScience #MLEngineering #AI #PythonProgramming #DataScientist #100DaysOfML

45 Days ML Journey — Day 11: Naive Bayes Continuing my Machine Learning journey, today I explored one of the simplest yet powerful classification algorithms — *Naive Bayes*. Tools Used: Scikit-learn, NumPy, Pandas What is Naive Bayes? Naive Bayes is a probabilistic algorithm based on Bayes Theorem, used mainly for classification tasks like spam detection, sentiment analysis, and text categorization. Why is it called "Naive"? It assumes that all features are independent of each other, which is rarely true in real-world data — but surprisingly, it still performs very well! Types of Naive Bayes: Gaussian Naive Bayes → Used for continuous data Multinomial Naive Bayes → Common in text classification Bernoulli Naive Bayes → Works with binary/boolean features How it works (intuitively): It calculates the probability of a data point belonging to a class and chooses the class with the highest probability. Key advantages: Fast and efficient Works well with small datasets Performs great in text-based problems Code notebook: https://lnkd.in/gCs2ceM8 Key takeaway: Even with strong assumptions, Naive Bayes proves that simple models can deliver powerful results — especially when working with high-dimensional data. #MachineLearning #DataScience #NaiveBayes #Python #ScikitLearn #LearningInPublic #MLJourney

🚀 Data Science Interview Question of the Day! 💡 What is the AUC–ROC Curve? 📊 The ROC Curve (Receiver Operating Characteristic) shows the trade-off between: ✔️ True Positive Rate (Recall) ✔️ False Positive Rate 👉 It helps evaluate how well a classification model distinguishes between classes at different thresholds. 📌 AUC (Area Under the Curve): 🔹 Measures overall model performance 🔹 Higher AUC = Better model 🎯 Quick insights: ✔️ AUC = 1 → Perfect model ✔️ AUC = 0.5 → Random guessing ✔️ AUC < 0.5 → Poor model 🔥 In short: 👉 ROC = Performance across thresholds 👉 AUC = Single score summary 💬 Where have you used ROC-AUC in your projects? Let’s discuss! 👉For Data Science Course Details  Visit : https://lnkd.in/gKcJEjgt . #DataScience #MachineLearning #AI #Python #CodingInterview #TechLearning #AshokIT

Practical Machine Learning Insights Regression Tips While working on my recent regression projects, I collected some practical insights that helped me better understand how regression models actually behave beyond theory. Here are a few key takeaways: 1- Regression vs Classification Regression is used to predict continuous values (e.g., salary), while classification predicts categories or classes. 2- Feature Scaling in Multiple Linear Regression In many cases, feature scaling is not strictly required for Multiple Linear Regression because coefficients adjust to feature magnitudes. However, scaling can still be useful depending on the context and when comparing models. 3- Importance of Data Preprocessing Handling categorical variables correctly (e.g., One Hot Encoding) and avoiding issues like the dummy variable trap can significantly impact model performance. 4- Model Interpretation Matters Understanding how features influence the output is just as important as building the model itself. 📄 I’ve summarized these insights in a clean PDF for easier reading. 🔗 You can also check my Regression Projects repository here: https://lnkd.in/eCaJVYSh More insights and projects coming soon 🚀 #MachineLearning #DataScience #AI #Python #Regression #Debugging #DataPreprocessing