Walmart Sales Forecasting Data Science Project

Data Science Project in R-Predict the sales for each department using historical markdown data from the Walmart dataset containing data of 45 Walmart stores.

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What will you learn

  • Understanding the problem statement and importing the file

  • Performing basic EDA

  • Merging multi datasets on basis of unique columns

  • How to study a merged dataset?

  • Using groupby function to analyze the effect of multiple columns

  • Plotting a time-series plot

  • How to analyze a time series graph

  • Seasonality and Trend analysis

  • How to decompose a time-series dataset to remove any trends

  • ARIMA model and its insights

  • How to fit dataset into an ARIMA model for training

  • Selecting the most important features for increasing prediction accuracy

  • Making final predictions using the most important selected features

  • Saving the made predictions into CSV format

Project Description

We have been provided with historical sales Data of 45 Walmart stores located in different regions. Each store contains many departments and we have to project the sales for each department in each store.

To add to the challenge, selected holiday markdown events are included in the dataset. These markdowns are known to affect sales, but it is challenging to predict which departments are affected and the extent of the impact.

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Curriculum For This Mini Project

 
  Introduction
00m
  Import Data Files
11m
  Explore Data Set
02m
  Calculate Average sales by Store
05m
  Calculate Average sales by Department
02m
  Average Sales by Store and Department
07m
  Test Data Set
04m
  Sample Submission Data
05m
  Check null values
07m
  Submitting to Kaggle
03m
  Problem with current solution
01m
  Recap of Code
07m
  Avg Sales by Store, Department, Holiday & week
18m
  Forecast Methods - Overview
06m
  Arima Model
19m
  Holt-Winters Forecasting
03m
  How Machine Learning works
01m