• Application uses time-series based data for prediction of future trend of storage consumption
• Forecast trends for the specified time interval in the future on the trained model
• Storage consumption prediction in real-time : Model trains in real time while giving future prediction
• Built a web application using React as frontend and Flask as backend.
• Generated a dataset using python scripts for different usage patterns.
• Implemented a variety of forecasting models like ARIMA, Au- toArima, LR, XGB, RNN-LSTM and Prophet.
• Analyzed the ML models based on their MAE, MSE, R2 and RMSE scores. • Got an accuracy of around 85% on increasing-decreasing data and 95% on seasonal data.
- ReactJS
- Axios
- React-google-charts
- Material UI
- Flask_Cors==3.0.10
- flask_restful==0.3.9
- matplotlib==3.5.1
- numpy==1.21.2
- pandas==1.3.2
- pmdarima==1.8.5
- scikit_learn==1.1.1
- tensorflow==2.9.1
- xgboost==1.6.1
cd client
npm install
npm start
Create new environment(preferable, For conda: conda create --name myenv, conda activate myenv)
pip install -r requirements.txt
flask run