marianaborgal / P002_Sales_Predictions_Drugstore

P002 Sales Predictions for a Drugstore Chain

Sales prediction for a drugstore chain.

This repository contains codes for the sales predictions for Rossman drugstores.
The data used was available on Kaggle. All additional information below are fictional

Project 002 - Sales Predictions:

The objetives of this project are:

• Perform exploratory data analysis on sales available on dataset.
• Predict the sales for the next 6 weeks from each store of the pharmacy chain.
• Develop a telegram bot that can be acessed by the CEO from a mobile or computer.

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1. Business Problem

Rossmann is a pharmacy chain that operates over 3,000 stores in 7 European countries. The stores are going to be renovated and the CFO needs to know how much can be invested in each one of them.
The Data Scientist was requested to develop a sales prediction model that forecast the sales for the next 6 weeks for each store. Therefore, the telegram bot must return this sales prediction for the given store.

2. Business Results

The model developed predicts a gross income of $286.69 MM in the next 6 weeks for the stores available, where the best and worst case scenarios results on $313.65 MM and $259.73 MM, respectively. These scenarios were calculated based on mean absolute percentage error for each store.

3. Business Assumptions

• The data available is only from 2013-01-01 to 2015-07-31.
• Stores without information on distance from competitors are considered without competition nearby.
• Seasons of the year:
  
  • Spring starts on March 1st
    
  • Summer starts on June 1st
    
  • Fall starts on September 1st
    
  • Winter starts on December 1st
    

The variables on original dataset goes as follows:

Variable	Definition
store	unique ID for each store
days_of_week	weekday, starting 1 as Monday.
date	date that the sales occurred
sales	amount of products or services sold in one day
customers	number of customers
open	whether the store was open (1) or closed (0)
promo	whether the store was participating on a promotion (1) or not (0)
sate_holiday	whether it was a state holiday (a=public holiday, b=easter holiday, c=christmas) or not (0)
store_type	designates the store model as a, b, c or d.
assortment	indicates the store assorment as: a=basic, b=extra, c=extended
competition_distance	distance in meters to the nearest competitor store
competition_open_since_month	the approximate month competitor was opened
competition_open_since_year	the approximate year competitor was opened
promo2	wheter the store was participating on a consecutive promotion (1) or not (0)
promo2_since_week	indicates the calendar week the store was participating in promo2
promo2_since_year	indicates the year the store was participating in promo2
promo2_interval	indicates the intervals in which promo2 started

Variables created during the project development goes as follow:

Variable	Definition
year	year from date that the sales occurred
month	month from date that the sales occurred
day	day from date that the sales occurred
week_of_year	week of the year from date that the sales occurred, considering the first week of a year a thursday and begins at 1. (int type)
year_week	week of the year from date that the sales occurred, considering the first week of a year with a monday and begins at 0. (object type, %Y-%W)
season	season from date that the sales occurred
competition_open_since	concatenation of 'competition_open_since_year' and 'competition_open_since_month'
competition_open_timeinmonths	calculates the time in months that competitor has been open based on the purchased date
promo2_since	concatenation of 'promo2_since_year' and 'promo2_since_week'
promo2_since_timeinweeks	calculates the time in weeks that promotion began based on the purchased date
month_map	month from date that the sales occurred as auxiliar feature
is_promo2	whether the purchase occurred during an active promo2 (1) or not (0)

4. Solution Strategy

1. Data Description
2. Feature Engineering
3. Data Filtering
4. Exploratory Data Analysis
5. Data Preparation
6. Feature Selection
7. Machine Learning Modeling
8. Hyper Parameter Fine Tuning
9. Model-to-Business Interpretation
10. Model Deploy

5. Top 3 Data Insights

1. Distance from competitors does not seem to correlate with store sales.

2. Stores sold more in the seconde semester in 2013, but not in 2014.

3. Sales during the sring correspond to 41.41% of total.

6. Machile Learning Model

Machine learning models used:

• Linear Regression
• Regularized Linear Regression
• Random Forest Regressor
• Xgboost Regressor
  
  

Results after cross-validation, where: MAE = mean absolute error; MAPE = mean absolut percentage error; RSME = root mean squared error.

Final xgboost result after fine tunning:

Error rate:

7. Telegram Bot

Access telgram bot here.

8. Conclusion

The objective of this project was develop a prediction model for Rossmann stores. Developing the telegram bot as the data deliverable product successfully satisfies the CFO demands.

9. Next Steps

• Address missing values in a better way.
• Test other machine learning models.
• Improve messages on telegram bot.

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References:

• Blog Seja um Data Scientist
• Dataset Rossmann Store Sales from Kaggle
• Variables meaning on Kaggle

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