Update README.md

README.md

@@ -9,41 +9,30 @@ app_file: app.py
 pinned: false
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-Air Quality Prediction Model
-This repository contains a machine learning model trained to predict air quality categories based on air quality index (AQI) values. The model takes AQI values of individual pollutants as input and predicts the overall air quality category, such as 'good', 'moderate', 'unhealthy', etc.
-
-Dataset
-The model was trained using the Global Air Pollution Dataset available on Kaggle. The dataset contains air quality data from various cities and countries, including AQI values for different pollutants.
-
-Dataset Shape: (23035, 12)
-Model Performance
-Accuracy: 99.91%
-Classification Report:
-yaml
-Copy code
-               precision    recall  f1-score   support
-
-           0       1.00      1.00      1.00      1926
-           1       1.00      0.91      0.95        45
-           2       1.00      1.00      1.00      1841
-           3       1.00      1.00      1.00       405
-           4       1.00      1.00      1.00       333
-           5       0.93      1.00      0.97        57
-
-    accuracy                           1.00      4607
-   macro avg       0.99      0.99      0.99      4607
-weighted avg       1.00      1.00      1.00      4607
-Usage
-To use the model:
-
-Install the required dependencies.
-Load the trained model (hackathonrf.joblib).
-Provide AQI values of individual pollutants as input.
-Obtain the predicted air quality category.
-Dependencies
-scikit-learn
-pandas
-numpy
-requests
-Author
-This model was developed by [Your Name].
+# Air Quality Prediction Model
+
+This repository contains a machine learning model for predicting air quality based on AQI (Air Quality Index) values of various pollutants. The model is trained on a dataset sourced from Kaggle's Global Air Pollution Dataset.
+
+## Dataset
+
+- **Dataset Name**: Global Air Pollution Dataset
+- **Dataset Source**: [Kaggle](https://www.kaggle.com/datasets/hasibalmuzdadid/global-air-pollution-dataset/)
+- **Dataset Size**: 23,463 rows, 12 columns
+
+## Model Performance
+
+- **Model Name**: hackathonrf.joblib
+- **Accuracy**: 99.91%
+- **Classification Report**:
+
+          precision    recall  f1-score   support
+
+       0       1.00      1.00      1.00      1926
+       1       1.00      0.91      0.95        45
+       2       1.00      1.00      1.00      1841
+       3       1.00      1.00      1.00       405
+       4       1.00      1.00      1.00       333
+       5       0.93      1.00      0.97        57
+
+accuracy                           1.00      4607
+