Built InsightSort: A TensorFlow model for image report categorization with 95% accuracy

🎬 Movie Recommender System — Built with Streamlit & Machine Learning! Excited to share my latest project — a Movie Recommendation System that helps users find movies similar to their favorites! 🍿 🔍 How it works: The system takes a selected movie as input and recommends similar movies using a content-based filtering algorithm. It leverages machine learning to analyze movie features and find the best matches. 🧠 Tech Stack: Python 🐍 Streamlit (for the interactive web interface) Pandas, NumPy, Scikit-learn (for ML logic) Pickle (for model storage) TMDB API (for fetching movie posters 🎥) Try it live:https://lnkd.in/dQKu6XUq #MachineLearning #DataScience #Python #Streamlit #MovieRecommendation

Can a computer recognize your mood? Yes! I just built a Binary Image Classifier that predicts whether you're Happy or Not Happy using a custom-trained CNN. With Gradio integration, it works like a mini web-app—just upload your image and get instant results. Using TensorFlow, Keras, OpenCV, and CNN architecture, the model was trained on custom datasets with multiple convolution + max-pooling layers. I also integrated the model with Gradio to deploy an interactive web UI, allowing users to upload images and instantly get mood predictions. Tech Stack: ✔ TensorFlow, Keras ✔ CNN (Conv2D, MaxPooling, Flatten, Dense) ✔ OpenCV ✔ ImageDataGenerator (Data Preprocessing) ✔ Python ✔ Gradio for UI Deployment #AIProjects #ComputerVision #Python #DeepLearningJourney

🎬 Day 1 of GUVI x HCL Netflix-Style Movie Recommender Workshop! Here’s what I learned in 7 simple steps: 1️⃣ Downloaded the movies and ratings datasets. 2️⃣ Loaded them into Google Colab using Pandas. 3️⃣ Explored the datasets using .head() and .info(). 4️⃣ Checked for missing and duplicate values. 5️⃣ Merged both datasets on movieId. 6️⃣ Performed data cleaning and preprocessing. 7️⃣ Visualized ratings and top-rated movies using Matplotlib and Seaborn. A great start toward building a smart movie recommender system! 🍿💻 #HCLGUVI #Day1OfGUVI #MovieRecommenderSystem #Python #MachineLearning #DataScience #AIWorkshop #GUVIWorkshop #CodingCommunity #NetflixRecommendation

🧠 Hands-on Practical on Missing Value Treatment | Titanic Dataset 🚢 Today, I explored one of the most important preprocessing steps in Machine Learning — Missing Value Treatment — using the Titanic dataset. Handled missing data using various techniques like mean/median imputation, mode replacement, and row/column removal to ensure the dataset is clean and ready for analysis. This exercise helped me understand how data quality directly impacts model performance and reliability. It was a great experience working on real-world data and applying practical data cleaning techniques using Python (Pandas, NumPy). 📘 GitHub Repository: https://lnkd.in/gsPj_hxs 🎓 Under the guidance of: Ashish Sawant #DataScience #MachineLearning #Python #Pandas #DataCleaning #TitanicDataset #DataPreprocessing #LearningEveryday #MLJourney #AI

In our previous post, we explored the basics of Gradient Descent. Now, it's time to take things further! 🚀 This post dives into the key variants of Gradient Descent – Batch, Stochastic, and Mini-Batch – explaining how they work, their advantages, disadvantages, and when to use each. Whether you're working with small datasets or large-scale machine learning models, understanding these variants is essential for faster and smarter optimization. 📄 Page highlights: Page 1 to 2: Batch Gradient Descent – working, formula, Python code, pros & cons Page 3 to 4: Stochastic Gradient Descent – working, formula, Python code, pros & cons Page 5 to 7: Mini-Batch Gradient Descent – working, formula, Python code, pros & cons Page 5: Key takeaway & teaser for advanced variants coming next 💡 Why read this? Gain clarity on when to use each variant and improve your ML model performance efficiently. #MachineLearning #DataScience #GradientDescent #MLAlgorithms #AI #DeepLearning #Optimization #Python #MLTips #LearningPath

📈 Exploring Simple Linear Regression using Python This Jupyter Notebook demonstrates the implementation of Simple Linear Regression, a fundamental concept in Machine Learning used to model and predict the relationship between two variables. In this practical, I learned to: 🔹 Build a regression model using NumPy 🔹 Visualize data points and the best-fit regression line using Matplotlib 🔹 Understand concepts like slope, intercept, and error minimization This experiment helped me gain hands-on experience in understanding data patterns, trend prediction, and model evaluation, guided by Ashish Sawant Sir. 📊 Linear regression is the first step toward mastering predictive analytics and data-driven decision-making! 🔗 GitHub: https://lnkd.in/ez_NstrZ 📁 Google Drive: https://lnkd.in/ezXFx_py #LinearRegression #MachineLearning #Python #Matplotlib #NumPy #DataScience #PredictiveModeling #AI #DataVisualization #JupyterNotebook #DSSPractical #LearningByDoing #CodingJourney #DataAnalytics

💻 Capstone Project: Housing Price Prediction using Machine Learning & Flask 🏠 Developed an end-to-end regression project using Python and Flask to accurately predict house prices. Trained and compared multiple machine learning models — Linear Regression, Ridge Regression, Random Forest, XGBoost, and LightGBM (LGBM) — and deployed the best-performing model through a Flask web application for real-time predictions. This project strengthened my skills in: 📊 Data cleaning and feature engineering 🤖 Model training, hyperparameter tuning, and evaluation 🌐 Model deployment using Flask for interactive user predictions Grateful to Kodi Prakash Senapati Sir for his continuous guidance and mentorship throughout this learning journey. 🙏 #CapstoneProject #MachineLearning #Flask #DataScience #Python #Regression #XGBoost #LightGBM #AI #EndToEndProject #LearningJourney

🎯 Decision Trees & Random Forests — From Concept to Implementation Today’s session with Monal S. Sir helped me deeply understand how Decision Trees make predictions by splitting data based on the feature that gives the best variance reduction or information gain. 🌳 I learned how overfitting can be controlled using parameters like min_samples_leaf and min_samples_split, and how Ensemble Methods like Bagging and Boosting combine multiple models for stronger performance. We also explored the Random Forest algorithm, which builds several decision trees using bootstrap datasets and random subsets of features — making it more accurate and less prone to overfitting. Finally, I implemented everything in Python using the Iris dataset, visualized the tree, checked feature importance, and even saved the model using joblib. It was a great blend of theory and hands-on learning! 💻 #MachineLearning #DataScience #DecisionTree #RandomForest #Python #AI #LearningJourney

📖 Day 80: SVM & Train-Test Split 1️⃣ SVM (Support Vector Machine) A machine learning algorithm for classification. Draws the best boundary (hyperplane) between classes. Works for binary & multi-class problems. Can handle non-linear data using kernels (RBF, polynomial). 2️⃣ Train-Test Split Splits dataset into training set and test set. Training set → teaches the model. Test set → checks model performance on new data. Prevents overfitting and ensures generalization. 3️⃣ Random State Makes splitting reproducible. Same random_state → same train-test split every time. Python Example: from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) ✅ Learned how SVM works and why splitting data correctly is important!

Week 5 of my AI & Data Science journey 🚀 This week, I explored Python Memory Management — a crucial concept for writing efficient and scalable programs. Key learnings: Understanding how Python allocates and manages memory Exploring the heap, stack, and reference counting mechanism Working with the garbage collector (gc module) Analyzing memory leaks and optimization techniques for data-heavy applications Efficient memory handling is key to ensuring ML models and data pipelines run smoothly — especially when working with large datasets. 📂 Notes & Assignments: https://lnkd.in/gPnQkhGY #Python #DataScience #AI #MachineLearning #MemoryManagement #LearningJourney #CodeOptimization