The Airbnb Hotel Booking Price Prediction Analysis project is a Machine Learning-based system designed to predict the price of Airbnb listings using historical booking data. The model analyzes various factors such as number of bedrooms, bathrooms, guest ratings, and property features to estimate the most appropriate listing price.
This system helps property owners make informed pricing decisions and allows travelers to identify fairly priced accommodations.
Determining the correct price for an Airbnb listing is challenging because prices vary depending on multiple factors like property size, amenities, location, and customer reviews. Incorrect pricing can lead to fewer bookings or reduced revenue.
This project addresses this problem by building a predictive model that estimates listing prices based on historical data and important features.
- Analyze Airbnb listing data
- Perform data cleaning and preprocessing
- Explore relationships between features and price
- Build a machine learning model for price prediction
- Evaluate model performance using standard metrics
- Generate predictions for new listings
- Data preprocessing and cleaning
- Exploratory Data Analysis (EDA)
- Feature engineering
- Machine learning model training
- Price prediction
- Data visualization
- Model evaluation
- Python
- Pandas
- NumPy
- Scikit-learn
- Matplotlib
- Seaborn
- openpyxl
- Google Colab
- Jupyter Notebook
The dataset contains Airbnb listing information such as:
- Number of bedrooms
- Number of bathrooms
- Location
- Guest ratings
- Property type
- Cleanliness score
- Communication score
- Price
The dataset is used to train and evaluate the regression model.
- Data Collection
- Data Cleaning
- Data Preprocessing
- Exploratory Data Analysis
- Feature Engineering
- Model Training
- Model Evaluation
- Prediction
Regression Models:
- Linear Regression
- Decision Tree Regressor
- Random Forest Regressor
The model learns the relationship between input features and listing price to generate accurate predictions.
The model performance is evaluated using:
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- R-squared (R²) Score
Clone the repository:
git clone https://github.com/git-tarik/git-tarik-VOIS_AICTE_Oct2025_Md_Tarik_Anvar