README_space.md

metadata

sdk: streamlit
sdk_version: 1.50.0

🧪 Advanced ML Sentiment Lab

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📌 Overview

Interactive Streamlit + Plotly app for binary sentiment analysis.

Upload any CSV with a text column and a binary label, then:

• Run quick EDA on text lengths, tokens, and class balance
• Build TF-IDF word + optional char features
• Train multiple classical models (LogReg / RF / GB / Naive Bayes)
• Tune the decision threshold with FP/FN business costs
• Inspect misclassified samples and test arbitrary texts live

Works well with the classic IMDB 50K Reviews dataset, but is generic enough for product reviews, tickets, surveys, etc.

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📊 Dashboard Preview

EDA & KPIs

Train & Validation

Error Analysis

Deploy & Interactive Prediction

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🚀 How to use (in this Space)

1. Load data

   • Upload a CSV file
   • Or place IMDB Dataset.csv / imdb.csv in the Space and reload

2. Map columns

   • Choose the text column
   • Choose the label column and map which values are positive vs negative

3. Train models

   • Go to “Train & Validation”
   • Set TF-IDF options, pick models, click Train models

4. Analyse & deploy

   • Use “Threshold & Cost” to pick a business-aware threshold
   • Check “Compare Models” + “Error Analysis”
   • In “Deploy”, try any text and see the predicted sentiment + confidence bar

No data is stored server-side beyond the current session.

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🧠 Under the hood

• Features

  • Word TF-IDF (1–3 n-grams)
  • Optional char TF-IDF (3–6 n-grams)

• Models

  • Logistic Regression (balanced)
  • Random Forest
  • Gradient Boosting
  • Multinomial Naive Bayes

• Artifacts

  • Saved under models_sentiment_lab/:
    • vectorizers.joblib, models.joblib, results.joblib, metadata.joblib
  • Reused by Threshold, Compare, Error Analysis, and Deploy tabs

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🖥 Run locally

git clone https://github.com/tarekmasryo/advanced-ml-sentiment-lab.git
cd advanced-ml-sentiment-lab

python -m venv .venv
# Windows: .venv\Scripts\activate
source .venv/bin/activate

pip install -r requirements.txt
streamlit run app.py

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📄 License & credit

Code: Apache 2.0
Space & dashboard by Tarek Masryo 🚀