Covid19 Prediction based on Symptoms
Midori R
ML Engineer
Data Analyst
Software Engineer
pandas
Python
scikit-learn
In this machine learning project, I utilized the powerful Random Forest algorithm to predict COVID-19 based on symptoms reported by individuals. With the aim of providing an efficient and accurate prediction tool, I developed and trained a robust Random Forest model using Python.
The first step involved collecting a comprehensive dataset consisting of symptoms reported by COVID-19 patients, as well as individuals without the disease. This dataset served as the foundation for training and testing the machine learning model.
Next, I preprocessed the data to ensure its quality and suitability for the algorithm. This included handling missing values, normalizing or scaling features, and encoding categorical variables. Proper data preprocessing ensured optimal performance of the Random Forest model.
To train the model, I divided the dataset into training and testing subsets. The training subset was used to teach the Random Forest algorithm to recognize patterns in the data, specifically the relationship between reported symptoms and the presence or absence of COVID-19. The testing subset was used to evaluate the model's performance and determine its accuracy.
Using the Random Forest algorithm, the model learned to make predictions based on the symptoms reported by individuals. The algorithm leverages an ensemble of decision trees, each providing a prediction, and then combines these predictions to arrive at a final prediction. This ensemble approach allows for robust and accurate predictions, as the model learns from the collective wisdom of multiple decision trees.
Throughout the project, I conducted rigorous testing and validation to ensure the reliability and effectiveness of the model. I employed various evaluation metrics, such as accuracy, precision, recall, and F1-score, to assess the model's performance and determine its predictive capabilities.
The final Random Forest model proved to be highly accurate and efficient in predicting COVID-19 based on reported symptoms. Its ability to generalize from the training data to unseen testing data was a testament to the model's effectiveness. The model holds great potential as a valuable tool for early detection and screening of COVID-19 cases based on readily available symptoms.
By developing and implementing this machine learning model, I contributed to the growing field of data-driven healthcare solutions. This project showcases my proficiency in utilizing Python and the Random Forest algorithm for predictive modeling, as well as my ability to preprocess data, train models, and evaluate their performance.
