Airbnb Price Prediction Using Machine Learning and Sentiment Analysis

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We describe the procedural steps to attain this project:

1. Objective Description:

            By considering property factors, location information and sentiments acquired from feedbacks, our project create ML based framework to forecast Airbnb listing prices.

2. Data Gathering:

• Airbnb Data: Here, we make use of environments such as Inside Airbnb to obtain information of listings including location, count of rooms, and resources and feedbacks.

3. Sentiment analysis on Reviews:

We follow the below discussed steps before combining sentiment scores into the pricing framework:

• Text Preprocessing: In preprocessing step, we carry out tokenize process, eliminate stop words, manage missing values and lemmatize or stem the feedback text.
• Vectorization: Texts are transformed into numerical patterns by us utilizing methods such as IF-IDF and word embedding (for instance: Word2Vec).
• Training of Model: By considering the feedback text, we forecast the sentiments as positive, negative and neutral by employing techniques including Logistic Regression, neural networks or Naïve Bayes.
• Sentiment Scoring: We calculate an average sentiment score or the measure of positive or negative feedbacks for every listing.

4. Preprocessing of data for Price Forecasting:

• Feature Engineering: In this step, we include sentiment scores from the old step and some other features also involved related to famous landmarks or transport centre.
• Managing Missing values: Our work carry out imputation or elimination process to manage missing values.
• Encoding: Through the utilization of one-hot encoding or label encoding, we change the categorical attributes like neighborhood or kind of property into numerical pattern.
• Standardization or Normalization: To maintain the same scale among numerical characteristics, normalization or standardization process is performed.

5. Exploratory Data Analysis (EDA):

• We examine the dispersion of listing prices and also examine the interconnection of prices with various features like sentiment scores.
• Patterns and outliers are detect that may impact the efficiency of our framework.

6. Model Chosen:

Select methods such as:

• We employ Linear Regression or its regular types like Lasso or Ridge.
• Neural Networks
• Decision trees and random Forest
• Gradient Boosting Machines (for example: LightGBM & XGBoost)

7. Training and Validation:

• We divide the dataset into various sets like training set, validation set and test set.
• Our framework is undergone training process through the use of training set and we verify its efficiency by utilizing validation data.

8. Model Evaluation:

• Regression Metrics: In terms of various metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and R^2, the framework is evaluate.
• Residual Plots: In residuals, we check the format to ensure whether the framework skip any systematic data or not.

9. Optimization and Hyperparameter Tuning:

            To optimize the efficiency of framework, we utilize methods such as random search or grid search and our approach considers that, regularization method assist to avoid overfitting issue.

10. Deployment:

            We create a web application or model to obtain a recommended price when the Airbnb user feed property information and feedbacks.

11. Reviews and Continuous Learning:

• Our project gathers the customer reviews to interpret the possible demerits and the enhancement areas.
• By the use of new data, we often reconstruct the framework for the latest forecasting.

12. Conclusion and Future work:

            We comprise the following factors in this phase such as documenting results, Limitations and future works. The below specified steps are involved in future improvement task:

• Temporal Dynamics: To investigate seasonal pricing modifications, we include time-series analysis.
• More Granular Sentiment Analysis: Consider only relevant sentiments about particular factors like location and clearness without analyzing all the sentiments.

Tips:

• Feature Importance: After the framework is done, we will detect the features that have the more contribution on prices.
• Domain Knowledge: Our research will integrate with Airbnb users or customers to know about the unclear attributes that has the impact on pricing.
• Bias & Fairness: We make sure that whether the framework accidently provides biased prices or not in terms of sensitive factors.

Through the integration of conventional property factors with the sentiments obtained from feedbacks, our framework offers an efficient interpretation for Airbnb listing price directions and also provide a special benefit in the market.

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