Data analytics for air passengers using ML

• 

  The home page where you can select years for prediction, change the historical data with your .csv file for prediction and view the raw data

• 

  The main graph where you can view the plotted value of the prediction

• 

  More components to help users understand the trend better

Inspiration

Data analysis is a common practice among large institutes but very less users have access to an user friendly app where they can have a look at ML prediction for setting their goals like orders for fuel, etc. Or even anomaly detection.

What it does

This is an ARIMA (Auto Regressive Integrated Moving Average) model integrated with a web-app to predict the changes in number of air-passengers. for eg: with 11 years of data from 2011 to 2022, the model will be able to predict till 2024 with an user friendly graph plot.

How I built it

Tech stack used:

• Python
• Streamlit
• FBProphet
• Pandas

Challenges I ran into

The most challenging part was to integrate ARIMA model with the web app as both had different set of rules

Accomplishments that I'm proud of

Developing and training the model

What I learned

This project surely exposed me to new opportunities like expanding the web app. And helped me in learning new techs

What's next for Data analytics for air passengers using ML

Future plans:

• Deploy a full fletched web app with a friendly UI
• Integrate more ML models for prediction in different time frames
• integrate comparision analysis
• add more editable parameters

Built With

• fbprophet
• pandas
• python
• streamlit