Machine Learning Pipeline for House Price Prediction with Scikit-learn

Excited to share my latest Machine Learning project – House Price Prediction System I built a machine learning model using Python and Scikit-learn that predicts house prices based on features like area, number of bedrooms, and bathrooms. The model was trained on real estate data and deployed using a Streamlit web application for real-time prediction. Technologies: Python, Pandas, NumPy, Scikit-learn, Streamlit Concepts: Supervised Learning, Linear Regression, Data Preprocessing, Model Training This project helped me understand how data flows from raw dataset → ML model → real-world application. #MachineLearning #Python #AI #DataScience #BTech #StudentProject

🚀 Machine Learning Project: Liver Cirrhosis Stage Prediction Developed a multi-class classification model using Random Forest to predict liver cirrhosis stages from clinical data. 🛠 Tech Stack: Python, Pandas, NumPy, Scikit-learn, Streamlit 📊 Performed pre-processing, feature selection & model evaluation (Accuracy, Precision, Recall, F1-score) Built an interactive UI for real-time prediction. Excited to build more impactful AI solutions! 🙌 #MachineLearning #DataScience #RandomForest #Python #HealthcareAI #AIProjects

Building a Diamond Price Predictor 💎 I just wrapped up a Machine Learning project using Linear Regression to predict diamond prices based on physical attributes. Key steps involved: Data preprocessing & Feature selection. Feature scaling using StandardScaler. Model evaluation with R^2 and MSE. Happy to see a strong correlation in the results! 🚀 💻 GitHub Repository: https://lnkd.in/dzeFdHRn #MachineLearning #Python #DataScience #AI #ScikitLearn

🚀 From Non-ML Background to Machine Learning No ML degree. No shortcuts. Just learning Machine Learning from scratch — understanding how models work, not just how to use them. Building Linear Regression manually, working with NumPy & Pandas, and visualizing learning step-by-step. Choosing fundamentals over hype and consistency over speed. This transition is intentional — and it’s just getting started. 💪 #CareerTransition #NonMLtoML #MachineLearning #SelfGrowth #Python #DataScience #BuildInPublic

Sharpening my NumPy skills 🔢 This intermediate NumPy cheat sheet is a great reminder of how powerful array operations, broadcasting, indexing, and linear algebra can be when working with data at scale. Mastering these fundamentals makes everything—from data analysis to machine learning—faster and more efficient. Small steps every day lead to big progress 📈 #NumPy #Python #DataScience #MachineLearning #AI #DataAnalytics #LearningInPublic #DeveloperJourney #Consistency

🎬 Movie Recommendation System using Machine Learning In this project, I built a content-based recommendation system that suggests movies based on similarity metrics. 🔍 Key Highlights: • Data preprocessing and feature engineering • Text vectorization for movie features • Cosine similarity for recommendation logic • Interactive interface for user input 🛠 Tech Stack: Python | Pandas | NumPy | Scikit-learn | Streamlit This project helped me understand how platforms like Netflix recommend personalized content using similarity-based approaches. I’m continuously strengthening my skills in AI & Machine Learning by building real-world projects. Feedback and suggestions are welcome! #MachineLearning #AI #Python #DataScience #RecommendationSystem

🚀 Movie Recommendation System using Machine Learning Excited to share my latest project — a Movie Recommendation System built using Python and real-world movie rating data. This system combines Collaborative Filtering (User–Movie Matrix + Cosine Similarity) and Content-Based Filtering (TF-IDF on genres) to recommend movies intelligently. It can: 🎬 Suggest high-rated movies 🎬 Identify most-rated movies 🎬 Recommend similar movies based on user ratings 🎬 Recommend similar movies based on genre 🛠️ Built using: Python, Pandas, NumPy, Scikit-learn 🔗 GitHub Repository: https://lnkd.in/dNmjc2Y9 This project strengthened my understanding of how recommendation engines work in real-world platforms like Netflix and Amazon. More ML projects coming soon 🚀 #MachineLearning #DataScience #RecommendationSystem #Python #AI #ScikitLearn

I’ve developed a full-stack AI application that automates clustering. from uploading CSV files, auto-detecting the best clusters with KMeans, to predicting and exporting results seamlessly. Built with Python, FastAPI, Pandas, and Scikit_learn. Features and stack: -Data preprocessing & scaling -Machine learning model optimization (Silhouette score for K selection) -API development & deployment -End-to-end automation Always looking for opportunities to turn data into actionable insights with AI. Repo: https://lnkd.in/d3FMuTxw #AI #MachineLearning #Python #FastAPI #DataScience #Automation #KMeans

𝗧𝗵𝗶𝘀 𝗦𝗶𝗺𝗽𝗹𝗲 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗛𝗮𝗯𝗶𝘁 𝗔𝘃𝗼𝗶𝗱𝘀 𝗙𝗮𝗹𝘀𝗲 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 Before trusting model accuracy, always check the data split. If similar or duplicate data exists in both train and test sets, results can look unrealistically good. The model is not learning. It is memorizing. A quick data check can save you from misleading conclusions later. #DataScience #MachineLearning #DataAnalytics #Python #AI #LearningInPublic

🔹 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 – 𝐓𝐡𝐞 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐨𝐫 They make data intelligent. • Build machine learning models • Perform statistical analysis • Predict trends and future outcomes using Python, Scikit-Learn, TensorFlow, Keras 👉 They answer: “What will happen next?”