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Understanding the problem statement
Importing the dataset and parsing while importing it
Importing libraries and understanding its significance
Performing basic EDA and checking for null values
Filling the Null values using appropriate techniques
Plotting "lmplot" using seaborn for visualizing relation between target and dependent variables
Using Correlation for visualizing the relationship between dependent variables
Visualizing correlation using "heatmap" plot from Seaborn
Creating new features from existing features(feature engineering)
Converting categorical into numerical vectors
Selecting the most important features
Setting up the train and test data for fitting into model
Defining evaluation metrics
Applying ensemble Random Forecast Regressor model
Applying boosting Gradient Boosting Regressor model
Applying Adaboost regressor along with decision tree
Selecting the best model and making the final predictions on test dataset
Rossmann operates over 3,000 drug stores in 7 European countries. Currently, Rossmann store managers are tasked with predicting their daily sales for up to six weeks in advance. Store sales are influenced by many factors, including promotions, competition, school and state holidays, seasonality, and locality. With thousands of individual managers predicting sales based on their unique circumstances, the accuracy of results can be quite varied.
In this machine learning project, you will work on forecasting 6 weeks of daily sales for 1,115 stores located across Germany. Reliable sales forecasts enable store managers to create effective staff schedules that increase productivity and motivation. By helping Rossmann create a robust prediction model, you will help store managers stay focused on what’s most important to them: their customers and their teams!