Update README.md

README.md

@@ -12,4 +12,78 @@ tags:
 - hotel-reviews
 - tripadvisor
 - nlp
----
+---
+
+# Logistic Regression Sentiment Analysis Model
+
+This model is a **Logistic Regression** classifier trained on the **TripAdvisor sentiment analysis dataset**. It predicts the sentiment of hotel reviews on a 1-5 star scale. The model takes text input (hotel reviews) and outputs a sentiment rating from 1 to 5 stars.
+
+## Model Details
+
+- **Model Type**: Logistic Regression
+- **Task**: Sentiment Analysis
+- **Input**: A hotel review (text)
+- **Output**: Sentiment rating (1-5 stars)
+- **Dataset Used**: TripAdvisor sentiment dataset (balanced labels)
+
+## Intended Use
+
+This model is designed to classify hotel reviews based on their sentiment. It assigns a star rating between 1 and 5 to a review, indicating the sentiment expressed in the review.
+
+## How to Use the Model
+
+1. **Install the required dependencies**:
+    ```bash
+    pip install joblib
+    ```
+
+2. **Download and load the model**:
+    You can download the model from Hugging Face and use it to predict sentiment.
+
+    Example code to download and use the model:
+    ```python
+    from huggingface_hub import hf_hub_download
+    import joblib
+
+    # Download model from Hugging Face
+    model_path = hf_hub_download(repo_id="your-username/logistic-regression-model", filename="logistic_regression_model.joblib")
+
+    # Load the model
+    model = joblib.load(model_path)
+
+    # Predict sentiment of a review
+    def predict_sentiment(review):
+        return model.predict([review])[0]
+
+    review = "This hotel was fantastic. The service was great and the room was clean."
+    print(f"Predicted sentiment: {predict_sentiment(review)}")
+    ```
+
+3. **The model will return a sentiment rating** between 1 and 5 stars, where:
+   - 1: Very bad
+   - 2: Bad
+   - 3: Neutral
+   - 4: Good
+   - 5: Very good
+
+## Model Evaluation
+
+- **Test Accuracy**: 61.05% on the test set.
+  
+- **Classification Report** (Test Set):
+
+| Label | Precision | Recall | F1-score | Support |
+|-------|-----------|--------|----------|---------|
+| 1.0   | 0.70      | 0.73   | 0.71     | 1600    |
+| 2.0   | 0.52      | 0.50   | 0.51     | 1600    |
+| 3.0   | 0.57      | 0.54   | 0.55     | 1600    |
+| 4.0   | 0.55      | 0.54   | 0.55     | 1600    |
+| 5.0   | 0.71      | 0.74   | 0.72     | 1600    |
+| **Accuracy** | -   | -      | **0.61**  | 8000    |
+| **Macro avg** | 0.61 | 0.61   | 0.61     | 8000    |
+| **Weighted avg** | 0.61 | 0.61 | 0.61     | 8000    |
+ 
+- **Cross-validation Scores**:
+
+* Logistic Regression Cross-validation scores: [0.61463816, 0.609375, 0.62072368, 0.59703947, 0.59835526]
+* Logistic Regression Mean Cross-validation score: 0.6080