Python Sentiment Analysis Tool with Real-Time Prediction

🎬 Movie Recommendation System | Machine Learning Project Built a content-based movie recommendation system using Python, Scikit-learn, and Streamlit. 🔹 Engineered text-based features from genres, keywords, cast & plot   🔹 Used CountVectorizer + Cosine Similarity for content-based recommendations   🔹 Preprocessed and merged 5,000 movies from the TMDB dataset   🔹 Deployed an interactive recommendation web app using Streamlit  This project strengthened my understanding of feature engineering, similarity metrics, and end-to-end ML system design. 🔗 GitHub: https://lnkd.in/gBx-maWD #MachineLearning #Python #RecommendationSystem #Streamlit #ScikitLearn #Projects

Customer Churn Prediction Built an end-to-end ML pipeline with preprocessing, SMOTE, and GridSearchCV. Trained Logistic Regression and Random Forest, selected the best model, and exported it for deployment-ready use. 🔄🤖DevelopersHub Corporation© #MachineLearning #ChurnPrediction #DataScience #ModelDeployment #Python #ScikitLearn #NED

Auto Dataset Cleaning & ML Visualization App (StreamLit) 📊 Open-Source ML Visualization & Data Cleaning Tool (Python) I built a web application that allows users to upload any dataset, automatically clean the data, train machine learning models, and explore insights using interactive 2D and 3D visualizations. This project focuses on exploratory data analysis (EDA) and model understanding, rather than prediction generation, making it ideal for learning and rapid dataset analysis. 🔗 StreamLit Link: https://lnkd.in/dJzAE367 🛠 Built with Python, Streamlit, Scikit-learn #OpenSource #Python #DataScience #MachineLearning #DataVisualization #EDA #Streamlit

Completed an exploratory project on building a Machine Learning web app using Streamlit and Python. This project was part of the UCS654 Predictive Analytics coursework as a guided exercise and served as an introduction to deploying classical machine learning models through a simple interactive interface. I experimented with models such as SVM, Logistic Regression, and Random Forest, and explored basic performance evaluation using Confusion Matrix, ROC Curve, and Precision Recall Curve. Overall, it was a useful hands on exercise to better understand how ML models can be packaged and exposed through a web application. GitHub Repository: https://lnkd.in/gVttjdZH Live Web App: https://lnkd.in/gpuUY48D #TIET #ThaparUniversity #ThaparOutcomeBasedLearning #ThaparCoursera #Coursera #UCS654_Predictive_Analytics

⌛ This was 8 years ago, and if you try Python in Excel it feels like a feature they are still "considering." The real way to integrate Python and Excel is to move your Excel work to Python environments -- NOT jam python functions into your workbook. Python environments can handle larger datasets, faster processing, and more sophisticated AI. This is what we are building at Mito AI. The Excel-user front end for Python/AI workflows 🚀 #AI #Excel #Python #Data #DataScience

🐍 Day 6 — Working with Numbers in Python Day 6 of #python365ai ➕➖ Python handles numbers very naturally. Example: x = 10 y = 3 print(x + y) print(x * y) print(x / y) Python supports: Addition + Subtraction - Multiplication * Division / Power ** 📌 Why this matters: From financial models to machine learning algorithms, Python’s numerical operations power everything. 📘 Practice task: Calculate the area of a rectangle using variables. Tomorrow: strings and text manipulation. #python365ai #PythonMath #NumbersInPython #CodingBasics #AI #LearnToCode

Day 02: From static code to interactive logic. 🤖 Today’s focus for #30DaysOfPython was Variables and Built-in Functions. To stay aligned with my goal of building AI models, I moved beyond the basics and built a small interactive CLI script. It uses Python’s input() and type conversion to "communicate" with the user. Key takeaways: 🔹 Understanding how Python handles data types (Strings vs Integers). 🔹 The importance of naming variables for readability (crucial for complex ML models). 🔹 Getting comfortable with the Git workflow. One step closer to the AI goal. 🚀 📂 View today's code: https://lnkd.in/gNEUAqPS #AI #MachineLearning #Python #BuildInPublic #CareerTransition #30DaysPythonChallenge

Polynomial Regression Model for Salary Prediction Implemented both Linear and Polynomial Regression (degree 4) using Python and scikit-learn to model non-linear salary trends based on position levels. Performed data preprocessing, model training, visualization, and prediction analysis to compare model performance. Demonstrated how polynomial features improve prediction accuracy for complex real-world relationships. github: https://lnkd.in/ggffdT2s #MachineLearning #PolynomialRegression #LinearRegression #DataScience #Python #ScikitLearn #AI #MLProjects #DataAnalytics #ModelTraining #RegressionAnalysis #LearningByDoing

🚀 Day 38 – Machine Learning Journey Cross Validation (Python Implementation) Today I implemented Cross Validation in Python to evaluate models more reliably instead of trusting a single train–test split. 🔹 Why in code Measures consistency, not luck Reduces overfitting bias Gives a stable performance estimate 🐍 Minimal Python example (scikit-learn) from sklearn.model_selection import KFold, cross_val_score from sklearn.linear_model import LogisticRegression model = LogisticRegression() kf = KFold(n_splits=5, shuffle=True, random_state=42) scores = cross_val_score(model, X, y, cv=kf) print(scores.mean()) 🔹 What this does: Splits data into 5 folds Trains on 4, validates on 1 Repeats for all folds Reports the average score 🔑 Key takeaway: > Cross validation doesn’t change the model — it validates whether the model can be trusted. #Day38 #MachineLearning #CrossValidation #Python #ScikitLearn #LearningJourney #ModelEvaluation #DataScience #AI #LearningJourney

🚀 Just pushed a new machine learning implementation to GitHub! I built **Multiple Linear Regression from scratch** using **vectorized gradient descent in Python/NumPy** to compare performance with traditional loops. Vectorization makes ML code *dramatically faster* by leveraging optimized C/Fortran kernels and SIMD instructions! 🧠💡 :contentReference[oaicite:1]{index=1} 💻 Repository: https://lnkd.in/gppzrgrn 📌 Highlights: ✅ Fully vectorized linear regression training ✅ Gradient descent implemented from first principles ✅ Demonstrated performance improvement over loop‑based code ✅ Clear explanation and concepts inside README If you're learning ML fundamentals or want to see how vectorization boosts efficiency in numerical code, check it out! #MachineLearning #Python #NumPy #GradientDescent #Vectorization #DataScience #MLfromScratch #ANDREWNG