This repository contains a linear regression model that predicts airbnb prices in Seattle using Python. The model is built using the airbnb Seattle data available on Kaggle. The goal of this project is to provide insights into the pricing of airbnb properties in Seattle by analyzing various factors such as the neighborhood, number of rooms, type of property, etc.
The model uses a combination of categorical and numerical features to make predictions. The categorical features are encoded using one-hot encoding, and numerical features are standardized to have a mean of 0 and a standard deviation of 1.
The data is manipulated and cleaned to handle missing values, and outliers are identified and treated. Exploratory data analysis is performed to get a better understanding of the data and identify any patterns or relationships between the features and the target variable.
The model uses Dash to create interactive choropleth maps to visualize the relationship between the target variable and the different features. These maps provide a quick and easy way to see the distribution of airbnb prices across different neighborhoods in Seattle.
The project beautifully displays an interactive Choropleth Map.
The linear regression model provides a good fit to the data, and the feature engineering and visualization techniques used in this project provide valuable insights into the pricing of airbnb properties in Seattle. This project can be used as a starting point for further analysis of the airbnb data, and the insights can be used to inform decision-making for airbnb property owners and managers in Seattle. Initial setps of Machine Learning were conducted towards the end of the project by creating dummy variables, one-hot encoding & train and test split.
If you would like to contribute to this project, please fork the repository and make your changes. Once you have made your changes, you can submit a pull request for review.