📊 CREATING AIR FLIGHT PASSENGER BUSINESS INTELLIGENCE DASHBOARD

✏️ PROJECT EXPLANATION

---

This project used Airport passenger of San Fransisco Airport from 2005 to 2016

Click here to get the Dataset

This Project was created to aim these following objectives :

• Create Time Series Model for Forecasting Purpose.
• Create Business Intelligence Dashboard .
• Create API for forecasting number of passenger of given period

✏️ PROJECT WORKFLOW

---

1. Dataset Exploration
2. Chart Exploration
3. Dashboard Layouting
4. Creating Dashboard
5. Deploy both on heroku

🔧 Package / Tech Stack Used

---

• Dashboard Development :

  1. Dash
  2. Dash Bootstrap Component
  3. Dash Extention -> for lottie sticker
  4. Plotly -> creating figure

• Deployment :

  1. Heroku

⌛ FUTURE PLANS

---

• Time Series Model Development

  1. Use Deep Learning, Supervised ML,
  2. Pandas
  3. Numpy

• API Development :

  1. Generate API Key
  2. Create DB (Using PostgreSQL) for API Key and Request

• Deployment :

  1. Deploy on GCP / AWS

• Other Devs :

  1. Create workflow to fetch API Data with Apache Airflow
  2. Create Package using Pip
  3. Use cookiecutter
  4. Implement Testing

💎 FINAL PRODUCT

You can access the webapp in : https://dashboardpassenger.herokuapp.com/

Snapshot of the app :

🔨 INSTALLATION

---

#clone the repository first 
    git clone https://github.com/fakhrirobi/forecast_passenger_BI.git

#change directory 
    cd forecast_passenger_BI

#run index.py 
    python index.py 

📗 Modelling Result

	RMSE Score	MAE Score	MAPE Score	model_name
1	2.9332e+10	120793	0.028163	RandomForestRegressor
2	3.94132e+10	160227	0.0383572	XGBRegressor
3	8.33301e+11	823894	0.195214	SVRegressor
4	1.98349e+11	364297	0.0889501	KNeighborsRegressor
5	1.51227e+11	323093	0.0806871	LinearRegression
6	3.45687e+11	484491	0.11898	PassiveAggressiveRegressor
7	4.29552e+09	52562.4	0.0160444	NeuralProphet_hidden_layer3_epoch_3_weekly_seasonality12
8	0.00879809	0.0750631	0.00494308	SARIMA_1,0,1_1,0,1,12_ts_log
9	0.000148183	0.00870418	0.0005716	SARIMA_1,0,1_1,0,1,12_ts_log_moving_avg
10	3.51627e+10	127422	0.0310854	SARIMA_1,0,1_1,0,1,12_ts_moving_avg
11	0.000148428	0.00895553	0.000589953	SARIMA_1,0,1_1,0,1,12_ts_log_ewma
12	0.00758013	0.0672904	0.977108	SARIMA_1,0,1_1,0,1,12_ts_log_ewma_diff
13	7724.52	69.9086	0.0352462	SARIMA_1,0,1_1,0,1,12_sqrt_ts
14	308.334	10.7121	0.00528588	SARIMA_1,0,1_1,0,1,12_moving_avg_sqrt

..

📧 Connect With Me

---

Dashboard Development :

1. Dash Syntax is challenging, since its require callback compared to streamlit which only needs like storing as variable to update value. However dash create more stunning dashboard.

..

Connect With Me

---

1. Linkedin
2. Medium
3. Kaggle