Liquor Store Sales Forecast
The "Liquor Store Sales Analysis and Forecast" project focuses on analyzing and predicting sales for liquor stores in the state of Iowa, USA. Leveraging Python, Apache Airflow, Google BigQuery, Google APIs, and containers, this project performs exploratory data analysis, builds regression models, and provides valuable insights and forecasts for liquor store sales.
Key Requirements:
Public Dataset on Google BigQuery: The project utilizes a public dataset stored on Google BigQuery, containing sales data for all liquor stores in Iowa. This dataset serves as the foundation for data analysis and forecasting.
Exploratory Data Analysis: The project performs exploratory data analysis to gain insights into the liquor store sales dataset. This includes examining data distributions, identifying trends, exploring correlations, and uncovering patterns that can guide further analysis and model development.
Sales Prediction using Regression: The project employs linear and polynomial regression models to predict liquor store sales. By utilizing historical sales data and relevant features, the project aims to create accurate and reliable sales forecasts for future periods.
Tools Used:
Python: The primary programming language for data manipulation, analysis, and model development.
Apache Airflow: A workflow management platform used for task scheduling, automation, and monitoring of data processing tasks.
Google BigQuery: A cloud-based data warehouse used to store and query large datasets efficiently.
Google APIs: Google APIs can be utilized to access additional data sources or services, such as geolocation data or weather data, to enhance the analysis and forecasting.
Containers: Containerization technology, such as Docker, ensures portability and reproducibility of the project's components and dependencies.
By leveraging these tools and technologies, the "Liquor Store Sales Analysis and Forecast" project enables data-driven insights and accurate sales predictions for liquor stores in Iowa. This empowers store owners, analysts, and stakeholders to make informed decisions, optimize inventory management, and plan future strategies effectively.
