search

Recode-Hive / machine-learning-repos

A curated list of awesome machine learning frameworks, libraries and software (by language). I
MIT License
52 stars 126 forks source link

Covid-19 prediction using ML #133

Open jeet-Abhi123 opened 1 month ago

jeet-Abhi123 commented 1 month ago

:red_circle: Aim : This dataset was collected in the initial phases of covid-19 during march and april 2020. The aim of the project is to predict a person is covid-19 +ve or not with minimum possible symtoms like fever, cough, age_above_60, sore_throat etc
:red_circle: Dataset : Taken from research paper and provided by Israeli Ministry of Health https://www.nature.com/articles/s41746-020-00372-6
:red_circle: Approach : First will perform EDA on the dataset, and then perform feature engineering on the columns. Will experiment with multiple models like ANN, Gradient Boosting , Decision trees etc. After will perform hyperparameter tuning through KerasTuner and will plot the results graph.

Kindly assign me the issue under the label of GSSoC'24.

github-actions[bot] commented 1 month ago

Thank you for creating this issue! 🎉 We'll look into it as soon as possible. In the meantime, please make sure to provide all the necessary details and context. If you have any questions reach out to LinkedIn. Your contributions are highly appreciated! 😊

You can also check our CONTRIBUTING.md for guidelines on contributing to this project.

yashuu02 commented 1 month ago

Aim : The aim of the project is to predict a person is covid-19 +ve or not with minimum possible symtoms like fever, cough, age_above_60, sore_throat etc

Approach :I will perform following steps Data Collection: Gather COVID-19 case data, mobility data, and vaccination data from reliable sources. Preprocessing: Clean the data, handle missing values, and normalize it. Feature Engineering: Create features like moving averages and infection rates.Model Selection: Choose a suitable model, such as ARIMA for time series or Random Forest for regression. Training and Validation: Train the model on historical data and validate it using metrics like RMSE. Prediction: Use the model to make future predictions under different scenarios. Visualization: Create clear visualizations and reports to present the predictions.Continuous Update: Regularly update the model with new data for improved accuracy.

Kindly assign me the issue under the label of GSSoC'24.