Update Gadh
Hotel Price Prediction Machine Learning
Hotel Price Prediction This project aims to predict hotel room prices on Booking.com for major cities in Saudi Arabia, with prices given in
📊 Hotel Price Prediction – Python Project
Predict hotel room prices using machine learning and historical data. Ideal for developers exploring regression, time-series models, and pricing strategy tools.
Key Features
• Uses historical hotel data to train prediction models
• Applies regression techniques (e.g., Linear Regression, Random Forest, XGBoost)
• Visual comparison of actual vs. predicted price trends
• Built with Python data libraries like pandas, scikit-learn, or statsmodels
🔗 Explore the full project here:
Hotel Price Prediction – View Project
📢 Discover more practical Python & ML projects:
https://t.me/Projectwithsourcecodes
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Predict hotel room prices using machine learning and historical data. Ideal for developers exploring regression, time-series models, and pricing strategy tools.
Key Features
• Uses historical hotel data to train prediction models
• Applies regression techniques (e.g., Linear Regression, Random Forest, XGBoost)
• Visual comparison of actual vs. predicted price trends
• Built with Python data libraries like pandas, scikit-learn, or statsmodels
🔗 Explore the full project here:
Hotel Price Prediction – View Project
📢 Discover more practical Python & ML projects:
https://t.me/Projectwithsourcecodes
#HotelPricePrediction #PythonProject #MachineLearning #DataScience #RegressionModels #TimeSeriesForecasting #OpenSourceCode #FinalYearProject #projectwithsourcecodes
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Update Gadh
Best Bike Sharing Demand Prediction Using Python Machine Learning
Bike Sharing Demand Prediction is one of the most innovative approaches to sustainable urban transportation. Unlike traditional
🚲 Bike Sharing Demand Prediction – Python Project
Forecast how many bikes will be rented using historical data and predictive analytics. Ideal for those working on urban mobility, time-series forecasting, or ML in smart cities.
Key Features
• Uses historical bike-sharing data with features like weather, hour, day, season, and holidays
• Applies regression models such as Linear Regression, Random Forest, Gradient Boosting, or XGBoost
• Visualizes actual vs. predicted demand trends
• Built with Python data science libraries like pandas, scikit-learn, and possibly Streamlit or Flask for interface
🔗 Explore the full project here:
Bike Sharing Demand Prediction – View Project
📢 Discover more practical Python & ML projects:
https://t.me/Projectwithsourcecodes
#BikeSharingDemandPrediction #PythonProject #MachineLearning #DataScience #TimeSeriesForecasting #RegressionModels #OpenSourceCode #FinalYearProject #projectwithsourcecodes
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Forecast how many bikes will be rented using historical data and predictive analytics. Ideal for those working on urban mobility, time-series forecasting, or ML in smart cities.
Key Features
• Uses historical bike-sharing data with features like weather, hour, day, season, and holidays
• Applies regression models such as Linear Regression, Random Forest, Gradient Boosting, or XGBoost
• Visualizes actual vs. predicted demand trends
• Built with Python data science libraries like pandas, scikit-learn, and possibly Streamlit or Flask for interface
🔗 Explore the full project here:
Bike Sharing Demand Prediction – View Project
📢 Discover more practical Python & ML projects:
https://t.me/Projectwithsourcecodes
#BikeSharingDemandPrediction #PythonProject #MachineLearning #DataScience #TimeSeriesForecasting #RegressionModels #OpenSourceCode #FinalYearProject #projectwithsourcecodes
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seoul bike sharing demand prediction
hands on lab build a bike-sharing demand prediction app using coursera labs
bike-sharing dataset github
bike sharing dataset kaggle