Built a Movie Recommendation System with Streamlit and ML

Tired of manually categorizing thousands of image reports, I built 'InsightSort'! This is a TensorFlow machine learning model that automatically classifies visual content with 95% accuracy. It saves serious time on moderation. Biggest lesson learned: The nightmare of data imbalance led me to master SMOTE techniques. Huge win for model robustness! Excited to open-source this next. Check out the architecture below! 👇 [Link to Project/GitHub/Demo] #MachineLearning #DataScience #Python #AI

💻 Handwritten Digit Recognizer using KNN 🚀 Excited to share my latest ML project — a Handwritten Digit Recognizer built using the K-Nearest Neighbors (KNN) algorithm! 🧠 Tech Stack: Python 🐍 scikit-learn OpenCV Streamlit (for the web app interface) 🎯 About the Project: This app takes your handwritten digit as input (drawn on canvas) and predicts the correct digit using a KNN classifier trained on the Digits dataset from scikit-learn. 🔗 Try it here: 👉 https://lnkd.in/gXpC8RWM GitHub repo: https://lnkd.in/gMc3z3GN A small step in exploring Machine Learning and Model Deployment! ✨ #MachineLearning #KNN #Streamlit #AI #DataScience #Python #MLProjects

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

💡 Association Learning — Finding Hidden Patterns in Data Association Learning is a key concept in unsupervised machine learning used to discover interesting relationships or patterns among items in large datasets — often applied in market basket analysis. #MachineLearning #DataScience #AssociationRules #Apriori #Analytics #Python  Example: “Customers who buy bread often buy butter too.” These insights help businesses optimize recommendations, marketing strategies, and store layouts. Here’s a quick example using Python 👇

Interested in backtesting and trading machine learning strategies? QuantRocket supports rolling and expanding walk-forward optimization of machine learning strategies using MoonshotML. MoonshotML is built on top of Moonshot, an open-source pandas-based backtester. MoonshotML is compatible with multiple Python machine learning packages including scikit-learn, XGBoost, and Keras + TensorFlow. Learn more: https://lnkd.in/eRQh6r3t

📈 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

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

Fake News Detection using Machine Learning  I built a Fake News Detection model that classifies articles as Real or Fake using Python ,Scikit-learn and TF-IDF Vectorizer.   – Data preprocessing & feature extraction using TF-IDF   – Logistic Regression for classification   – Achieved ~95 % accuracy on test data   – Implemented in Google Colab and uploaded on GitHub   Project Link: [https://lnkd.in/gEqUfWfc)   #MachineLearning #AI #Python #DataScience #FakeNewsDetection #MLProjects #GitHub

🎬 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

Simple Linear Regression Project: Predicting House Prices🏠 In this project, I built a simple Linear Regression model using Python and Scikit-learn to predict house prices based on the area (in m²). 🔹 Steps included: * Data visualization using Matplotlib 📊 * Splitting data into training and testing sets * Training a Linear Regression model * Predicting and evaluating results * Visualizing the regression line 📈 The project demonstrates how machine learning can be used to make real-world predictions in a simple and interpretable way. Taghrida Mohamed ♥️♥️ #MachineLearning #DataScience #Python #LinearRegression #AI #LearningJourney