The retail store (WALMART) is facing issues in managing inventory to match demand with supply across multiple outlets in the country.

Problem Statement: The retail store is facing issues in managing inventory to match demand with supply across multiple outlets in the country.

Project Objective: The objective is to analyze historical sales data and build a prediction model to forecast sales for the next X number of months/years.

The dataset contains the following columns:

Store: Store number Date: Date of sales Weekly_Sales: Sales for the week Holiday_Flag: Binary indicator if the week contains a holiday (1) or not (0) Temperature: Temperature of the week Fuel_Price: Fuel price of the week CPI: Consumer Price Index of the week Unemployment: Unemployment rate of the week Data Pre-processing Steps and Inspiration: Checked for null values: No null values found. Checked statistics of columns: Provides an overview of the numerical columns. Checked data types: Date column needs to be converted to datetime format. Outlier Detection: Visualized boxplots for numerical columns, decided not to remove outliers as they might contain valuable information. Analyzed sales by stores: Identified stores with higher and lower sales. Converted date to datetime format and set it as the index.

Choosing the Algorithm for the Project: ARIMA (AutoRegressive Integrated Moving Average) model is chosen for time series forecasting. Motivation and Reasons For Choosing the Algorithm: The initial implementation using ARIMA shows promising results in forecasting sales for the retail store. Further optimization and integration with additional factors can improve the accuracy of the forecasts. References: Documentation of Python libraries used (pandas, numpy, matplotlib, seaborn, statsmodels, sklearn) Time series forecasting literature for understanding ARIMA and other techniques.