Project Proposal for COVID19 prediction

COVID-19 Prediction Project

Overview

This project aims to predict COVID-19 test results based on various features such as symptoms, age, and contact history. The dataset includes information about individuals who underwent the RT-PCR test from 11th March 2020 to 30th April 2020.

Dataset

• The dataset contains 11 columns, including features suspected to play a role in predicting COVID-19 outcomes.
• The target variable is 'Corona,' indicating test results as 'positive,' 'negative,' or 'other.'
• There are 2,78,848 individuals in the dataset.

Project Structure

• covid.ipynb: Jupyter notebook for data preprocessing, handling missing values, and encoding.
• Covid.ipynb: Jupyter notebook for training and evaluating machine learning models.
• README.md: Project documentation.

Data Preprocessing

• Features such as symptoms and contact history are encoded.
• Target variable 'Corona' is encoded as 'positive,' 'negative,' and 'other.'

Model Training

• Five machine learning models are trained: Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting, SVM.
• Model performance is evaluated on the validation set.
• The best-performing model is selected based on accuracy.

Requirements

• Python 3.7
• Jupyter Notebook
• Required Python packages are specified in Requirements.txt. Install them using: # COVID-19 Prediction Project

Overview

This project aims to predict COVID-19 test results based on various features such as symptoms, age, and contact history. The dataset includes information about individuals who underwent the RT-PCR test from 11th March 2020 to 30th April 2020.

Dataset

• The dataset contains 11 columns, including features suspected to play a role in predicting COVID-19 outcomes.
• The target variable is 'Corona,' indicating test results as 'positive,' 'negative,' or 'other.'
• There are 2,78,848 individuals in the dataset.

Project Structure

• data_processing.ipynb: Jupyter notebook for data preprocessing, handling missing values, and encoding.
• model_training.ipynb: Jupyter notebook for training and evaluating machine learning models.
• Covid EDA Report.html: EDA html file using ydata-profiling(pandas-profiling)
• README.md: Project documentation.

Data Preprocessing

• Features such as symptoms and contact history are encoded.
• Target variable 'Corona' is encoded as 'positive,' 'negative,' and 'other.'

Model Training

• Four machine learning models are trained: Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting.
• Model performance is evaluated on the validation set.
• The best-performing model is selected based on accuracy.

Requirements

• Python 3.x
• Jupyter Notebook
• Required Python packages are specified in requirements.txt. Install them using pip install -r requirements.txt

Results

• The best model achieved an accuracy of [0.9837] on the test set.

Feel free to customize this based on your specific project details and requirements.