Update README.md

README.md

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----
-license: mit
-language:
-- en
-metrics:
-- accuracy
-library_name: keras
-tags:
-- medical
-- biology
----
+This model, MalariaGuard, is designed to predict malaria cases in Africa using data from the World Health Organization.
+
+### Model Description
+
+MalariaGuard is a machine learning model developed to predict malaria cases in Africa. It uses historical data from the World Health Organization to make accurate predictions, potentially aiding in resource allocation and prevention strategies.
+
+- **Developed by:** Alok Pandey
+- **Model type:** Neural Network
+- **Language(s) (NLP):** Not applicable (uses numerical data)
+- **License:** MIT
+- **Finetuned from model :** Developed from scratch
+
+
+### Direct Use
+
+This model can be used by health organizations, governments, and researchers to predict malaria cases in African countries. It can assist in planning prevention strategies, allocating resources, and preparing healthcare systems for potential outbreaks.
+
+### Out-of-Scope Use
+
+This model should not be used as the sole basis for medical decisions or to replace professional medical advice. It is a predictive tool and should be used in conjunction with other data sources and expert knowledge.
+
+## Bias, Risks, and Limitations
+
+- The model's predictions are based on historical data and may not account for sudden changes in environmental factors, healthcare policies, or unforeseen events.
+- The accuracy of predictions may vary across different regions of Africa due to potential differences in data quality or coverage.
+- Users should be aware that the model's high accuracy (98%) on the test set may not necessarily translate to real-world performance with the same level of accuracy.
+
+### Recommendations
+
+Users should:
+- Regularly update the model with the most recent data available.
+- Use the model's predictions as part of a broader decision-making process, not as the sole input.
+- Be aware of the model's limitations and potential biases when interpreting results.
+
+### Training Data
+
+The model was trained on malaria case data from the World Health Organization, focusing on African countries. 
+
+
+#### Training Hyperparameters
+
+- **Training regime:** Full precision (fp32)
+- **Framework:** Keras
+- **Language:** Python
+
+#### Testing Data
+
+The model was tested on a held-out portion of the World Health Organization's malaria case data for Africa.
+
+#### Metrics
+
+- **Accuracy:** 98%
+
+### Model Architecture and Objective
+
+The model uses a neural network architecture implemented in Keras. Its objective is to predict malaria cases based on historical data.
+
+#### Software
+
+- Python
+- Keras