This project aims to predict the risk of Dengue fever in a region based on various environmental variables, including temperature, humidity, precipitation, and other environemental condition.
The project uses a dataset from Kaggle containing information about Dengue outbreaks in various cities in Latin America and the Caribbean. The dataset includes information about climate, demographics, and location for each city.
The machine learning model used in this project is a Random Forest algorithm, which is known for its ability to handle complex datasets with many variables. The model was trained on a subset of the data and evaluated using cross-validation techniques to ensure its accuracy.
The project includes a Jupyter Notebook with the code for data preprocessing, feature engineering, model training, and evaluation. The notebook also includes visualizations of the data and model performance.
To use the Dengue Fever Predictor, users can input the environmental variables for their region and receive a prediction of the risk of Dengue fever outbreak.
- Python 3.x
- Jupyter Notebook
- Pandas
- NumPy
- Scikit-learn
- Matplotlib
- Seaborn
- Clone the repository:
git clone https://github.com/RENJITHVS/Dengue-Predictor.git
- Install the required packages:
pip install pandas numpy scikit-learn matplotlib seaborn
-
Launch Jupyter Notebook and open the
Dengu Predictionfile. -
Follow the instructions in the notebook to preprocess the data, train the model, and make predictions.
This project was created by Renjith V S. The dataset used in this project was obtained from Kaggle is added as final.csv
This project is licensed under the MIT License.