OPCODE-Open-Spring-Fest / Accommodating-Insights

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.

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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.

About

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.


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