--> PROBLEM STATEMENT : The "Big Mart Sale Prediction" project involves creating a predictive model that can accurately forecast the sales of products in a Big Mart store. This involves analyzing historical sales data, as well as various attributes such as product characteristics, store location, and more, to develop a model that can predict future sales. The goal is to help the store optimize inventory management and marketing strategies based on these predictions.

--> WORK FLOW:

--> ALGORITHM USED :

1. XGBoost for Regression -a Forecast for Big Mart Sales Based on Random Forests and Multiple Linear Regression used Random Forest and Linear Regression for prediction analysis which gives less accuracy. To overcome this, we can use XGboost. Algorithm which will give more accuracy and will be more efficient.

2. R-Squared (R² or the coefficient of determination) is a statistical measure in a regression model that determines the proportion of variance in the dependent variable that can be explained by the independent variable. In other words, r-squared shows how well the data fit the regression model (the goodness of fit). A R-squared between 0.50 to 0.99 is acceptable in social science research especially when most of the explanatory variables are statistically significant.