Zillow Data Analysis Project

This project aims to use Zillow data to gain insights into the housing market. Zillow is a leading real estate marketplace that provides comprehensive data on properties and neighborhoods across the United States.

Project Overview

The project involves collecting, cleaning, and analyzing Zillow data to gain insights into the housing market. The dataset includes information on the property's characteristics, location, price, and more. The primary goal of this project is to use this data to explore and understand the trends in the housing market and identify factors that impact property values.

Dataset

The dataset used in this project is sourced from Zillow's public data API, which provides a rich set of data on real estate properties across the United States. The dataset includes information on properties' characteristics, location, and sale prices, among other variables. You can find the link on how to use Zillow's API here.

Methodology

The project follows the following steps:

1. Data Collection: The project will collect data from Zillow's public data API. The API provides access to a wide range of real estate data that can be used to analyze the housing market.
2. Data Cleaning: Once the data is collected, it will be cleaned and preprocessed to remove any inconsistencies or missing values. This step will ensure that the data is accurate and ready for analysis.
3. Exploratory Data Analysis (EDA): The cleaned data will be analyzed using various statistical and visualization techniques to identify trends and patterns in the housing market.
4. Feature Engineering: Additional features will be created to enrich the dataset and extract more insights from the data.
5. Model Training: Machine learning models will be trained to predict the housing prices based on the available data. The models will be evaluated using various performance metrics.
6. Interpretation: The results of the analysis will be interpreted to draw meaningful insights and conclusions about the housing market.

Dependencies

The following packages are required to run the project:

• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn

Conclusion

This project aims to use Zillow data to gain insights into the housing market. The project will collect, clean, and analyze the data to identify trends and patterns in the market. The results of the analysis will be interpreted to draw meaningful insights and conclusions about the housing market. The project will be implemented using Python and various machine learning techniques.