Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+pipeline_tag: text-classification
+tags:
+- NLP
+- sentiment
+- logistciregression
+---
+# 🧠 Sentiment Analysis with Logistic Regression
+
+This model performs **multi-class sentiment analysis** on tweets, classifying them into the following categories:
+- Positive
+- Negative
+- Neutral
+- Irrelevant
+
+It uses a custom preprocessing pipeline with:
+<!-- - Text cleaning (URL, mention, hashtag, punctuation removal)-->
+- CountVectorizer
+- TF-IDF transformation
+- Logistic Regression classifier (`max_iter=1000`)
+
+---
+
+## 🏗 Model Architecture
+
+<!-- - **TextCleaner**: Custom scikit-learn transformer for consistent text preprocessing.-->
+- **CountVectorizer**: Converts tweets into token count vectors.
+- **TfidfTransformer**: Reweights tokens by importance.
+- **LogisticRegression**: Interpretable and robust classification baseline.
+
+---
+
+## 🧪 Evaluation
+
+Evaluated on a separate validation set of 999 tweets:
+
+| Class       | Precision | Recall | F1-score |
+|-------------|-----------|--------|----------|
+| Irrelevant  | 0.88      | 0.85   | 0.87     |
+| Negative    | 0.87      | 0.94   | 0.91     |
+| Neutral     | 0.97      | 0.86   | 0.91     |
+| Positive    | 0.89      | 0.94   | 0.91     |
+| **Overall Accuracy** |        |        | **0.90**     |
+
+---
+
+## 📦 Usage
+
+```
+python
+import joblib
+
+model = joblib.load("sentiment_model_lr.pkl")
+user_input = "This update is surprisingly good!"
+
+prediction = model.predict([user_input])
+print(prediction[0])  # → Positive, Negative, etc.
+```
+---
+```> ⚠️ Requires scikit-learn 1.6.1+ to avoid version mismatch warnings.```
+
+---
+
+## 📚 Dataset
+```
+Tweets were preprocessed using a clean_text routine and labeled into
+the four sentiment categories. If you’d like to experiment or re-train, contact
+the author or fork this repo.
+```
+
+---
+## 🧑‍💻 Author
+```
+Built by @arshvir Model version: 1.0 License: MIT
+```
+
+---