Create README.md

Author: SamIeer

README.md

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+# 💬 Twitter Sentiment Analysis App
+
+A complete **end-to-end NLP project** that classifies tweets as **Positive** or **Negative** using the [Sentiment140 Dataset](https://www.kaggle.com/datasets/kazanova/sentiment140).  
+This project combines **Natural Language Processing**, **Machine Learning**, and **MLOps** — from data cleaning to model deployment.
+
+---
+
+## 🚀 Features
+
+✅ Text Preprocessing (cleaning, tokenization, lemmatization)  
+✅ TF-IDF Vectorization for feature extraction  
+✅ Multiple ML models with cross-validation & hyperparameter tuning  
+✅ Streamlit web app for real-time sentiment prediction  
+✅ Model saving & reusability with `joblib`  
+✅ Fully Dockerized for consistent deployment  
+✅ GitHub Actions CI Workflow for automated testing & build  
+✅ Kubernetes/Manifest ready for cloud deployment *(optional)*  
+
+---
+
+## 🧩 Project Structure
+```
+├── .github
+    └── workflows
+    │   └── sentimentlsis.yml
+├── Docker-compose.yml
+├── Dockerfile
+├── dashboard.py
+├── manifest.yml
+├── requirements.txt
+└── src
+    ├── preprep.ipynb
+    ├── sentiment_model.pkl
+    └── tfidf_vectorizer.pkl
+```
+
+---
+
+## 🧠 Tech Stack
+
+| Category | Tools / Libraries |
+|-----------|------------------|
+| **Language** | Python |
+| **Data Handling** | Pandas, NumPy |
+| **NLP** | NLTK, Regex, Emoji |
+| **Feature Extraction** | TF-IDF (sklearn) |
+| **Modeling** | Logistic Regression, SVM, Random Forest |
+| **App Framework** | Streamlit |
+| **Model Persistence** | Joblib |
+| **Containerization** | Docker |
+| **Automation** | GitHub Actions |
+| **Deployment** | Streamlit Cloud / Render / Kubernetes |
+
+---
+
+## 🧹 Data Preprocessing
+
+- Lowercasing text  
+- Removing URLs, mentions, hashtags, and punctuation  
+- Tokenization using **nltk**  
+- Stopword removal  
+- Lemmatization (`WordNetLemmatizer`)  
+- Emoji handling (`emoji.demojize`)  
+
+This ensures the model sees only meaningful words.
+
+---
+
+## 🧮 Feature Engineering — TF-IDF
+
+**Why TF-IDF?**  
+It represents each tweet as a numerical vector based on **word importance**.
+
+\[
+TFIDF(w) = TF(w) \times \log\left(\frac{N}{df(w)}\right)
+\]
+
+Used `TfidfVectorizer(max_features=5000, ngram_range=(1,2))` for best balance between accuracy and speed.
+
+---
+
+## 🤖 Model Training
+
+| Model | Description | Accuracy (CV) |
+|--------|--------------|---------------|
+| Logistic Regression | Simple & effective for text data | ✅ Best |
+| SVM | Handles high-dimensional data | Good |
+| Random Forest | Captures non-linear patterns | Moderate |
+
+Performed:
+- **5-Fold Cross-Validation**
+- **GridSearchCV** for hyperparameter tuning  
+- **Evaluation Metrics:** Accuracy, Precision, Recall, F1-score
+
+---
+
+## 💾 Model Saving
+
+Used `joblib` to persist model and TF-IDF vectorizer:
+```python
+joblib.dump(model, 'sentiment_model.pkl')
+joblib.dump(tfidf, 'tfidf_vectorizer.pkl')
+```
+## Streamlit Web App
+
+<h3>Simple, interactive web app for real-time predictions.</h3>
+<h3>Run locally:</h3>
+``` streamlit run app.py ```
+
+
+<ol> <h3>App Flow:</h3>
+<li>Input tweet text 📝</li>
+<li>Clean & preprocess</li>
+<li>Convert text → TF-IDF vector</li>
+<li>Predict sentiment using model</li>
+<li>Display result (😊 Positive / 😠 Negative)</li>  </ol>
+
+---
+## 🐳 Docker Integration
+<pre>
+docker build -t sentiment-app .
+docker run -p 8501:8501 sentiment-app
+</pre>
+---
+<ul>
+## 📊 Results
+<li>Logistic Regression achieved ~85% accuracy on validation data</li>
+<li>Clean UI for sentiment prediction</li>
+<li>Fully automated CI/CD pipeline with Docker integration</li>
+</ul>
+---
+<ul>
+  ##  Key Takeaways
+  <li>Built a complete ML workflow: from preprocessing → training → deployment</li>
+  <li>Learned to ensure preprocessing consistency between training & inference</li>
+  <li>Containerized the app for reproducibility</li>
+  <li>Automated CI/CD with GitHub Actions</li>
+  <li>Gained experience with MLOps fundamentals</li>
+</ul>
+---
+## Setup Instructions
+<pre>
+## Clone repo
+git clone https://github.com/<your-username>/sentiment-analysis.git
+cd sentiment-analysis
+# Install dependencies
+pip install -r requirements.txt
+# Run Streamlit app
+streamlit run app.py
+</pre>
+<h3>or run in Docker:</h3>
+<pre>docker-compose up --build</pre>
+---
+## Author
+<h2>Sameer Chauhan</h2>
+<h3>MLOps & Machine Learning Engineer</h3>
+<h3>💼 Passionate about bridging ML with real-world deployment through Docker, CI/CD, and automation.</h3>