📚 Text Explorer App

An interactive Streamlit app for learning how text data is processed, transformed, and classified using machine learning.
Students can explore datasets, preprocess text, build models, and visualize results — all with clear explanations and examples.

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🚀 Features

• Dataset Explorer (📂)
  Preview datasets, inspect rows/columns, and understand the structure of text + labels.

• Preprocessing (🔍)

  • Tokenization demo (split sentences into words).
  • Bag of Words vs TF‑IDF vectorization.
  • Worked example showing how TF‑IDF is calculated step‑by‑step.
  • Vocabulary preview to see which words are included.

• Model Builder (🤖)

  • Train Logistic Regression, Naive Bayes, and Support Vector Classifier.
  • Compare accuracy across models.
  • Confusion matrix visualization.
  • Top Features chart showing which words drive spam vs ham predictions.

• Results (📊)

  • Test new messages against the trained model.
  • See predictions (spam/ham) with probability scores.
  • Word clouds for spam vs ham vocabulary.
  • Explanation of confidence levels in predictions.

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🛠️ Tech Stack

• Streamlit for interactive UI
• scikit-learn for ML models
• NLTK for tokenization
• Matplotlib & Seaborn for plots
• WordCloud for text visualization

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📂 Project Structure

├── app.py                  # Main entry point and landing page
├── pages/
│   ├── 1_Data_Explorer.py  # Load and preview text datasets
│   ├── 2_Preprocessing.py  # Clean, tokenize, and vectorize text
│   ├── 3_Model_Builder.py  # Train and evaluate ML models on text data
│   └── 4_Results.py        # Display predictions, metrics, and misclassifications
├── requirements.txt        # Dependencies with pinned versions
└── README.md               # Project guide and documentation

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⚡ How to Run Locally

1. Clone the repo:

   git clone https://github.com/your-username/text-explorer-app.git
   cd text-explorer-app

2. Install dependencies:

   pip install -r requirements.txt

3. Launch the app:

   streamlit run app.py

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🌐 Deployment

• Push your repo to GitHub.
• Go to Streamlit Cloud.
• Connect your repo and select app.py as the entry point.
• Deploy and share the link with students!

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🎓 Learning Outcomes

By using the Text Explorer App, students will:

• Data Explorer (📂)
  Understand how text datasets are structured, preview samples, and recognize the importance of dataset inspection.
• Preprocessing (🧹)
  Learn how to clean text (remove punctuation, stopwords), tokenize words, and convert text into numerical features (e.g., bag‑of‑words, TF‑IDF).
• Model Builder (🤖)
  Train and compare machine learning models (e.g., Logistic Regression, Naive Bayes) for text classification.
  Explore how different algorithms handle sparse text features.
• Results (📊)
  Interpret predictions, evaluate accuracy, and analyze misclassifications.
  Gain experience with confusion matrices and probability scores to understand model confidence.

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📸 Screenshots (optional)

Add screenshots of each page here once deployed.

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🙌 Credits

Built with ❤️ by Arpit to make machine learning hands‑on and approachable for everyone.