COVID-19 Case Forecasting with Python by Anastasiya KotelnikovaCOVID-19 Case Forecasting with Python by Anastasiya Kotelnikova

COVID-19 Case Forecasting with Python

Portfolio project
Anastasiya Kotelnikova
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COVID-19 Case Forecasting (Python)

This project forecasts COVID-19 case surges across various regions using traditional machine learning and deep learning (LSTM) models in Python. The goal is to identify potential outbreaks and support public health planning.

Dataset

Format: CSV (~1MB)
Fields: Region, Date, Confirmed Cases, Deaths, Recoveries, etc.

Techniques Used

Data preprocessing and feature selection
Time series visualization and analysis
Logistic regression & Random Forest models
LSTM model with Keras & TensorFlow
Model tuning and evaluation (RMSE, accuracy)

Model Performance

Achieved accurate case trend forecasts across multiple regions
LSTM model outperformed traditional models in long-term trend prediction

Project Structure

covid-case-forecasting/
├── covid_case_trends.ipynb         # Main notebook for ML & LSTM forecasting
├── data/                           # (Optional) Raw or cleaned COVID-19 datasets
├── outputs/                        # Forecast plots, metrics, and visualizations
└── README.md                       # Project overview and documentation

Key Takeaways

Practiced real-world data cleaning and forecasting
Compared regression vs. LSTM performance on time series
Applied ML to global health prediction challenges

Author

Anastasiya Kotelnikova MS Data Science Candidate | NJIT Email: anastasiyakotelnikova21@gmail.com GitHub Profile • Portfolio Website • LinkedIn

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COVID-19 Case Forecasting (Python)

This project forecasts COVID-19 case surges across various regions using traditional machine learning and deep learning (LSTM) models in Python. The goal is to identify potential outbreaks and support public health planning.

Dataset

Format: CSV (~1MB)
Fields: Region, Date, Confirmed Cases, Deaths, Recoveries, etc.

Techniques Used

Data preprocessing and feature selection
Time series visualization and analysis
Logistic regression & Random Forest models
LSTM model with Keras & TensorFlow
Model tuning and evaluation (RMSE, accuracy)

Model Performance

Achieved accurate case trend forecasts across multiple regions
LSTM model outperformed traditional models in long-term trend prediction

Project Structure

covid-case-forecasting/
├── covid_case_trends.ipynb         # Main notebook for ML & LSTM forecasting
├── data/                           # (Optional) Raw or cleaned COVID-19 datasets
├── outputs/                        # Forecast plots, metrics, and visualizations
└── README.md                       # Project overview and documentation

Key Takeaways

Practiced real-world data cleaning and forecasting
Compared regression vs. LSTM performance on time series
Applied ML to global health prediction challenges

Author

Anastasiya Kotelnikova MS Data Science Candidate | NJIT Email: anastasiyakotelnikova21@gmail.com GitHub Profile • Portfolio Website • LinkedIn

0 likes · 23 views · Jun 24, 2025

Forecasted COVID-19 case surges using ML and DL models in Python.

Timeline

Oct 2, 2024 - Nov 7, 2024

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