StreamLit App for Auto Data Cleaning & ML Visualization

🎬 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

🚀 Project Showcase: Movie Recommendation System using Machine Learning I built a machine learning–based movie recommendation pdf that suggests similar movies based on user selection. 🔹 Tech Stack: Python, Streamlit, Scikit-learn   🔹 Dataset: TMDB   🔹 Deployed on: Hugging Face Spaces  Project Link 👇   [https://lnkd.in/gKg9qp-p] #MachineLearning #DataScience #Python #Projects #HuggingFace #StudentProject

Why data preprocessing matters more than the model itself How feature scaling impacts model performance Reading confusion matrices & classification reports the right way Building models is important, but understanding the data and evaluation metrics is what actually makes predictions reliable. If you missed the live, the recording is available — and more data analysis sessions coming soon 🚀 #DataScience #MachineLearning #HealthcareAI #Python #EDA #YouTubeLive #LearningInPublic https://lnkd.in/gpPRhcRA https://lnkd.in/gjxb-Z5E

From raw data to real insights. 💡 This visual breaks down a complete Python data analysis workflow—environment setup, cleaning, exploration, modeling, and visualization—step by step. Practical. Reproducible. Scalable. ♻️ #DataAnalytics #Python #DataScience #Pandas #LearningByDoing #AnalyticsWorkflow

⌛ 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

🚢 Titanic Survival Prediction – Machine Learning & Streamlit ✅ Developed and deployed a user-friendly Streamlit web application to predict Titanic survival using Machine Learning, with clear model comparison and performance insights GITHUB LINK : https://lnkd.in/gaptG8kv STREAMLIT.IO LINK : https://lnkd.in/g6R6TwAd #DataScience #MachineLearning #Streamlit #Python #MLProject #LearningJourney #CareerRestart

Built an end-to-end machine learning application to predict whether a person is diabetic using clinical health data. The project focuses on data preprocessing with feature scaling, training a Support Vector Machine (SVM) model, evaluating performance on training and test data, and converting the model into an interactive Streamlit web interface for real-time predictions. Tech stack: Python, Pandas, NumPy, Scikit-learn, Streamlit. #MachineLearning #DataScience #Python #Streamlit #ScikitLearn #MLProjects #LearningByDoing #BuildInPublic #AspiringDataScientist

🚀 House Price Prediction | Machine Learning Project Built a machine learning regression model to predict house prices using Python. Performed data cleaning, EDA, feature encoding, model training, and evaluation. Tech Stack: Python | Pandas | NumPy | Scikit-learn | Matplotlib | Jupyter Notebook GitHub Project: https://lnkd.in/ggrBHjNM #MachineLearning #DataScience #Python #MLProject #LearningJourney

🚀 Top Python libraries for Data + ML (simple list) If you work with data, these tools cover almost everything: cleaning, charts, ML, APIs, and databases. If you’re starting: Pandas + NumPy → Matplotlib/Seaborn → Scikit-learn → PyTorch/TensorFlow ✅ Which library do you use the most? #Python #DataAnalytics #MachineLearning #DataScience #Programming #AI

Sharing Part 2 of my final year project, where I focus on building the dashboard layer of the system using Python. In this video, I explain how the dashboard code is structured to visualize and present the model outputs in a clear and user-friendly way. This step bridges the gap between machine learning models and real-world usability. 🔹 Dashboard logic and structure 🔹 Integration with trained ML models 🔹 Preparing outputs for visualization 🔹 Designing a clear flow for end-user interaction 📌 Results and performance analysis will be shared in the next video, where I’ll walk through the outputs and insights generated from the models. This phase helped me understand the importance of data visualization, interpretability, and application-oriented ML development. Looking forward to sharing the results soon! Feedback and suggestions are always welcome 😊 #FinalYearProject #Python #DashboardDevelopment #MachineLearning #DataVisualization #DataScience #StudentDeveloper #LearningInPublic