Update README.md

README.md

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-## Model Training
-
-The sentiment analysis model is trained using a Support Vector Machine (SVM) classifier with a linear kernel. The cleaned text data is transformed into a bag-of-words representation using the CountVectorizer. The trained model is saved as `Sentiment_classifier_model.joblib`, and the corresponding TF-IDF vectorizer is saved as `vectorizer_model.joblib`.
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+# Sentiment Analysis Model
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+## Overview
+This repository contains a sentiment analysis model trained using scikit-learn for predicting sentiment from text inputs. The model leverages TF-IDF vectorization for text representation and a machine learning classifier for sentiment classification.
+
+## Model Details
+- **Model Name:** Sentiment Analysis Model
+- **Framework:** scikit-learn
+- **Model Type:** TF-IDF Vectorization + Machine Learning Classifier
+- **Architecture:** Linear SVM Classifier
+- **Input:** Text
+- **Output:** Sentiment Label (Positive/Negative)
+- **Performance:** Achieves 93% accuracy on test dataset
 
 
 # Download the Vectorizer model first and load the model :