Currency Exchange Forecasting For Asian Countires
Utilized Cloud Technologies: We are using Amazon Ec2 for web app deployment,if we have more time we will enhance the application more by using Heroku or lambda for API development.We may also netlify for the better frontend optimization
Names & Roles: Nagaraju Rathna Kalyan Naga Sai Nayani Mohith Vanka Sindhu Vardhan Madala We have assigned roles for each person such as Development done by Nagaraju & Sindhu Vardhan. Also ML data modelling by Nagaraju & Mohith.Kalyan & Mohith collectively working on data engineering focusing on data that includes testing and completing the missing data on dataset parallelly Sindhu & kalyan taking care of testing the model and application. However, since it is difficult for a single person to work on the tasks, we assigned them to each other, and we work together and help each other in achieving those tasks with in the time.
Things we tried and accomplished: We Collected Dataset from Kaggle and corrected missing data , trained and tested the model.we partially developed frontend using react.js also started working on the flask to export the created model.
Things to do: We need to complete the flask application and frontend application. We also need to deploy the model and the application on Amazon Ec2 Cloud.
Challenges faced: While preprocessing and modelling the dataset we faced little challenge but the Frontend and flask application development became easy. Utilized Cloud Technologies: We are using Amazon Ec2 for web app deployment,if we have more time we will enhance the application more by using Heroku or lambda for API development.We may also netlify for the better frontend optimization
Names & Roles: Nagaraju Rathna Kalyan Naga Sai Nayani Mohith Vanka Sindhu Vardhan Madala We have assigned roles for each person such as Development done by Nagaraju & Sindhu Vardhan. Also ML data modelling by Nagaraju & Mohith.Kalyan & Mohith collectively working on data engineering focusing on data that includes testing and completing the missing data on dataset parallelly Sindhu & kalyan taking care of testing the model and application. However, since it is difficult for a single person to work on the tasks, we assigned them to each other, and we work together and help each other in achieving those tasks with in the time.
Things we tried and accomplished: We Collected Dataset from Kaggle and corrected missing data , trained and tested the model.we partially developed frontend using react.js also started working on the flask to export the created model.
Things to do: We need to complete the flask application and frontend application. We also need to deploy the model and the application on Amazon Ec2 Cloud.
Challenges faced: While preprocessing and modelling the dataset we faced little challenge but the Frontend and flask application development became easy.
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