Update README.md

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README.md

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 metrics:
 - accuracy
 pipeline_tag: tabular-classification
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+---
+
+# Random Forest Model for Wine-Quality Prediction
+
+This repository contains a Random Forest model trained on wine-quality data for wine quality prediction. The model has been trained to classify wine quality into six classes. During training, it achieved a 100% accuracy on the training dataset and a 66% accuracy on the validation dataset.
+
+## Model Details
+- **Algorithm**: Random Forest
+- **Dataset**: Wine-Quality Data
+- **Objective**: Wine quality prediction (Six classes) - (3,4,5,6,7,8,9) and prediction above 5 is good quality wine
+- **Dataset Size**: 1599 samples with 11 features
+- **Target Variable**: Wine Quality
+- **Data Split**: 80% for training, 20% for validation
+- **Training Accuracy**: 100%
+- **Validation Accuracy**: 66%
+
+## Usage
+You can use this model to predict wine quality based on the provided features. Below are some code snippets to help you get started:
+
+```python
+# Load the model and perform predictions
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+import joblib
+
+# Load the trained Random Forest model (assuming 'model.pkl' is your model file)
+model = joblib.load('model/random_forest_model.pkl')
+
+# Prepare your data for prediction (assuming 'data' is your input data)
+# Ensure that your input data has the same features as the training data
+
+# Perform predictions
+predictions = model.predict(data)
+
+# Get the predicted wine quality class
+# The predicted class will be an integer between 0 and 5