Accommodating Insights: A Data Exploration of the Transient Landscape

Overview:

Objective

This project delves into a rich dataset containing information about short-term rentals in a geographic location. By analyzing this data, we aim to uncover insights and trends within the rental market. By employing a dataset rich in listing attributes, location data, and user reviews, we aim to develop a robust model capable of accurately predicting listing prices.

Methodology

To achieve our objective , we will employ the following analytical methods :

• Data Preparation: Data Acquisition: Collect the dataset from a dataset folder.

• Exploratory Data Analysis (EDA):

  • Visualization: Explore data distributions and patterns using visualizations.
  • Correlation Analysis: Identify key features correlated with pricing.

• Cleaning & Preprocessing: Handle missing values, outliers, and encode categorical variables.

• Feature Engineering: Create relevant features and transform data for better model performance.

• Model Development:

  • Algorithm Selection: Experiment with regression algorithms (e.g., Linear Regression, Random Forest) to find the best performer.
  • Hyperparameter Tuning: Optimize model parameters for improved accuracy.
  • Validation: Validate model performance using cross-validation techniques.

Expected Outcomes

By the end of this project, we anticipate the following Outcomes:

• Accurate Price Predictions: Achieve accurate predictions of listing prices, enabling hosts to set competitive rates and maximize revenue.
• Insightful Analysis: Provide valuable insights into factors influencing pricing, aiding hosts in optimizing listing attributes.
• User Satisfaction: Enhance user experience for the guests by offering accurate price estimates, leading to increased satisfaction and retention.

Setup Locally

• Fork the repository

Click theFork button at the top right corner of this repository's page on GitHub. This will create a copy of the repository in your GitHub account.

• Clone this project

bash git clone https://github.com/OPCODE-Open-Spring-Fest/Accommodating-Insights

• Enter project directory

bash cd hidden-consumer-patterns

• Install the nodeJS dependecies

bash npm i

• Create a new branch for your feature or bug fix.

• Make your changes and commit them.

• Push to the branch.

• Submit a pull request.