amirsoahil101/New_York_City_Airbnb_ML_Analytics_Model

🏙️ New York City Airbnb ML & Analytics Web App

An end-to-end Machine Learning web application designed to evaluate and predict New York City Airbnb listing attributes. The repository combines exploratory data analysis, a robust Scikit-Learn preprocessing pipeline (RandomizedSearchCV), and a custom Web Frontend interface deployed on Render.

---

Next-Generation Vocal Acoustic Analytics for Emergency Triage Scoring

---

🖼️ Application Preview

---

🖼️ Application Preview & UI

---

🔗 Live Application

The application is hosted live on Render. You can test it directly here:
👉 Click Here to Launch Render Live Demo

---

📊 Model Benchmarks & Metrics

Multiple baseline models were trained and tuned using hyperparameter optimization. Random Forest yielded the highest performance metrics across the board:

Model Algorithm	Accuracy Score	F1-Score	Status
Random Forest Classifier 🏆	0.851 (85.1%)	0.713	Selected Model
Gradient Boosting Classifier	0.850 (85.0%)	0.706	High Precision
Decision Tree Classifier	0.786 (78.6%)	0.655	Baseline
Logistic Regression	0.726 (72.6%)	0.575	Baseline

---

⚙️ Model Pipeline & Architecture

The machine learning core uses a unified ColumnTransformer integrated into a Scikit-Learn Pipeline tuned with RandomizedSearchCV:

• Numerical Preprocessing: SimpleImputer ➔ PowerTransformer ➔ StandardScaler
• Categorical Preprocessing: SimpleImputer ➔ OneHotEncoder
• Final Estimator: RandomForestClassifier

---

📁 Repository Structure

├── AB_NYC_2019.csv            # NYC Airbnb Dataset
├── .gitignore                 # Git Ignore File
├── .gitattributes             # Git Attributes File
├── requirements.txt           # Python Dependencies
├── runtime.txt                # Python Runtime Version
├── notebook.ipynb             # Analysis, Pipeline Design & Model Training
├── main.py                    # Backend Server Script (API/Inference)
├── Model_Pipeline.pkl         # Serialized Scikit-Learn Pipeline
├── index.html                 # Frontend Web Interface UI
├── style.css                  # Custom Application Stylesheet
├── script.js                  # Frontend Interactivity & API Fetching
├── the_build_line_guide.html  # Pipeline & Application Guide Documentation
├── UI.png                     # Application UI Preview
├── readme.md                  # Project Documentation
└── New_York_City_.png         # Application Banner / Asset

git clone https://github.com/amirsohail100/New-York-City-Airbnb-ML-Analytics-Web-App.git

cd New-York-City-Airbnb-ML-Analytics-Web-App

streamlit run app.py

pip install -r requirements.txt

📄 License

This project is licensed under the MIT License.

📝 Author

👤 Amir Sohail

---

NYC Airbnb Machine Learning pipeline evaluating listings using Random Forest (85.1% accuracy) with ColumnTransformer preprocessing. Features a web UI (HTML/CSS/JS + FastAPI/Flask main server) & serialized pipeline inference.