HNG 13 Final Project | Slack Engagement Prediction Dashboard скачать в хорошем качестве

HNG 13 Final Project | Slack Engagement Prediction Dashboard

#DataAnalytics #MachineLearning #Streamlit #Python #DataScience Slack Engagement Prediction App Demo | Streamlit + Machine Learning In this video, I demonstrate a Slack message engagement prediction app built during the final stage of the HNG i13 Data Analytics internship. The focus here is on showing the application in action. You will see how a Slack message is entered, how metadata is adjusted, and how the model predicts the likelihood of engagement in real time. 🔍What you’ll see in this demo: a. Live Slack message input b. Engagement probability prediction c. Switching between metadata-only and SBERT-based models d. Exploratory data analysis snapshots from the Slack workspace e. Channel activity distributions f. Hourly and weekday engagement patterns g. Model explainability using SHAP feature importance This video does not cover the full data pipeline or model training process. It is strictly a product demo showing how the final deployed system behaves from a user’s perspective. 🧠 Behind the scenes (brief) The app is powered by: Machine learning models trained on Slack workspace data Text embeddings using Sentence-BERT Metadata-based features such as posting time, message length, and author activity XGBoost classifiers A Streamlit frontend for interactive exploration 🛠️ Tech stack Python Streamlit XGBoost Sentence-BERT SHAP Pandas and NumPy 📌 Live App: https://hng-engagement-predictor-h3re... 📂 GitHub Repo: https://github.com/Boateng-Yaw-Edmund... #MachineLearning #Streamlit #DataAnalytics #Python #HNGi13