| --- |
| language: en |
| license: mit |
| tags: |
| - passenger-prediction |
| - machine-learning |
| - scikit-learn |
| - linear-regression |
| - time-series |
| pipeline_tag: tabular-regression |
| metrics: |
| - r2_score |
| - mean_squared_error |
| - mean_absolute_error |
| model-index: |
| - name: Passenger Prediction |
| results: |
| - task: |
| type: tabular-regression |
| name: Passenger Prediction |
| dataset: |
| type: air-passengers-prediction-system |
| name: Air Passengers Prediction System |
| metrics: |
| - type: r2_score |
| value: 0.94 |
| name: R² Score |
| --- |
| |
| # ✈️ Air Passengers Prediction System |
|
|
| An interactive Machine Learning web application that predicts the estimated count of monthly air passengers based on the target **Year** and **Month**. This project utilizes advanced regression algorithms to achieve high-precision forecasting, featuring an intuitive frontend dashboard built with Streamlit. |
|
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| --- |
|
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| ## 🚀 Live Demo & Deployment |
|
|
| You can interact with the live dashboard application here: |
| 🔗 **[Live Streamlit Application Link](https://air-passengers-prediction-system-egp3ynswgkaynwvcbz7bd2.streamlit.app/)** |
|
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| --- |
|
|
| ## 📸 User Interface (UI) Dashboard |
|
|
| Here is a preview of the active web dashboard interface where users can select inputs and get real-time forecasting numbers: |
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|
|  |
| _(Note: Replace `ui.png` in the repository root folder with your actual dashboard screenshot to render it here)_ |
|
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| --- |
|
|
| ## 🛠️ Data Preprocessing & Pipeline |
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|
| To maintain data structure integrity between training and inference phases, the following pipeline operations were carried out: |
|
|
| 1. **Numerical Standardization:** The `year` column is preprocessed using a `StandardScaler` to bring variance to scale. |
| 2. **Categorical Encoding:** The `month` feature is transformed using **One-Hot Encoding** to effectively evaluate seasonal trends. |
| 3. **Artifact Serialization:** Preprocessing pipelines and columns layout structure are safely serialized into `scaler.pkl` and `columns.pkl` respectively for smooth runtime execution. |
|
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| --- |
|
|
| ## 📊 Model Evaluation & Benchmarking |
|
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| Multiple regression variants were trained and thoroughly benchmarked based on $R^2$ and Adjusted $R^2$ matrices. The comparative evaluations are summarized below: |
|
|
| | Model Index | Trained Models | $R^2$ Score | Adjusted $R^2$ Score | Status | |
| | :---------: | :------------------------- | :---------: | :------------------: | :------------------------: | |
| | 0 | Linear Regression (LR) | 0.94 | 0.88 | Evaluated | |
| | 1 | K-Nearest Neighbors (KNN) | 0.92 | 0.86 | Evaluated | |
| | 2 | Decision Tree (DT) | 0.89 | 0.79 | Evaluated | |
| | 3 | Random Forest (RF) | 0.92 | 0.85 | Evaluated | |
| | 4 | AdaBoost (ADA) | 0.86 | 0.74 | Evaluated | |
| | **5** | **Gradient Boosting (GD)** | **0.98** | **0.96** | **🏆 Best Fit (Selected)** | |
| | 6 | XGBoost (XG) | 0.97 | 0.95 | Evaluated | |
|
|
| _Our production system utilizes the **Gradient Boosting (GD)** architecture (`model.pkl`) which delivered an outstanding benchmark performance of **98% accuracy ($R^2 = 0.98$)**._ |
|
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| --- |
|
|
| ## 🗂️ Project Repository Architecture |
|
|
| ```text |
| ├── app.py # Core Streamlit interface script with built-in Exception Handling |
| ├── requirements.txt # Dependencies (streamlit, scikit-learn, joblib, pandas, numpy) |
| ├── model.pkl # Pickled Gradient Boosting model layout |
| ├── scaler.pkl # Serialized StandardScaler instance |
| ├── columns.pkl # Structural column validation checklist array |
| ├── ui.png # Dashboard preview display asset |
| ├── Flights.ipynb # Jupyter Notebook containing Data Analysis & Model Training pipeline |
| └── README.md # Document portfolio profile |
| ``` |
|
|
| An interactive Machine Learning web app built with Streamlit to forecast monthly air passengers. Features a 98% accurate Gradient Boosting Regressor pipeline with robust preprocessing (StandardScaler & One-Hot Encoding) and complete exception handling. Includes the core training Jupyter Notebook |
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