Link to deployed model: http:https://rossmannsalesprediction-env-1.eba-3wi97vqp.ap-south-1.elasticbeanstalk.com/
In this project, I have attempted to analyze the retail sales dataset of Rossmann stores and build a predictive model to forecast the sales of any Rossmann store for any date. No personal information of customer is provided in this 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. Two datasets are given: one with store data and the other with historical sales data of 1115 stores from January 2013 to July 2015. The main objective is to understand existing data and after identifying the key factors that will predict future sales, a predictive model will be built for making forecasts about future sales.
- Understanding the business task.
- Import relevant libraries and define useful functions.
- Reading data from files given.
- Data pre-processing, which involves inspection of both datasets and data cleaning.
- Exploratory data analysis, to find which factors affect sales and how they affect it.
- Feature engineering, to prepare data for modelling.
- Modelling data and comparing the models to find out most suitable one for forecasting.
- Conclusion and recommendations to boost sales.
The following insights were gained from EDA:
Link to deployed model: http:https://rossmannsalesprediction-env-1.eba-3wi97vqp.ap-south-1.elasticbeanstalk.com/
An interactive dashboard was also created with Power BI to display charts associated with the analysis. It includes features like drill-through and customized tooltip.
Click here to download the data visualization.
The following conclusions were drawn from Modelling:
Midhun R | Avid Learner | Data Analyst | Data Scientist | Machine Learning Enthusiast
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GeeksforGeeks, 'Feature Encoding Techniques – Machine Learning'. [Online].
Available: https://www.geeksforgeeks.org/feature-encoding-techniques-machine-learning/
Medium, 'Which evaluation metric should you choose for evaluating your Classification and Regression models?'. [Online].
Analytics Vidhya, 'Decision Tree Algorithm – A Complete Guide'. [Online].
Available: https://www.analyticsvidhya.com/blog/2021/08/decision-tree-algorithm/