This project aims to perform an exploratory data analysis on housing data obtained from a Kaggle competition. The goal is to gather insights and create visualizations using Tableau to better understand the housing market. The project also has the possibility of adding a logistic regression machine learning model to predict house prices.
The housing data used in this project comes from a Kaggle competition and includes features such as the location, number of rooms, square footage, and more.
- Data Cleaning: Perform cleaning operations on the data to ensure its quality and integrity for analysis.
- Exploratory Data Analysis: Use Python's Pandas library to perform an in-depth analysis of the data, including descriptive statistics and data visualizations.
- Data Visualization: Use Tableau to create interactive dashboards and visualizations to better understand and communicate the insights gained from the EDA.
- Optional: Logistic Regression Model: Consider building a logistic regression machine learning model to predict house prices.
The results of this project will be a comprehensive understanding of the housing market and its various features, as well as visual representations of the insights gained from the EDA. If the logistic regression model is implemented, there will also be predictions for house prices.
This project will provide valuable insights into the housing market and its various features, allowing for a better understanding of the industry and its trends. Additionally, the visual representations created using Tableau will effectively communicate the findings to stakeholders.