Predicting the sales of a store
"The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and find out the sales of each product at a particular store. Using this model, BigMart will try to understand the properties of products and stores which play a key role in increasing sales." The dataset is available at https://datahack.analyticsvidhya.com/contest/practice-problem-big-mart-sales-iii/. The data contains missing values. After preprocessing the data and performing feature engineering, I have implemented three regression models namely Logistic Regression, Random Forest and XGBoost. Random Forest and XGBoost gives comparable performance. Both the models give better results than logistic regression.