amirsoahil101/Flights_Prediction_System

✈️ 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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🚀 Live Demo & Deployment

You can interact with the live dashboard application here: 🔗 Live Streamlit Application Link

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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:

(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

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

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

├── 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