Walmart Sales Forecasting Data Science Project

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

What you will learn
Understand the Problem Statement
Perform basic EDA to familiarize with the data
Take care of missing values and datatype issues in the data
Understand the unique key in different data and merging the data
Perform Univariate analysis for both numeric and categorical variables
Perform Bi-variate analysis to identify redundant variables
Plot Trend of each predictor with the target variable
Do in-depth analysis on the impact of Date/Week on Sales
Create new features that might add value to the model
Define a function for each set of code that might need to be repeated again
Prepare the data for modelling
Make prediction using statistical techniques
Make model using machine learning techniques
Create time series ARIMA models and learn to give their parameters
Perform Hyper-parameter tuning to get the best parameters
Learn how to make predictions where data is sparse
Compare the performance of different models using multiple metrics

Project Description

Every Departmental store chain like Walmart wants to predict the store sales in the nearby future so that inventory planning can be done. Along with that, sales prediction helps to increase/decrease store staff based on the rush (More sales can mean more customers are coming to the stores). Also, it is always a good idea to do sales and revenue forecasting to better understand the company's cash-flows and overall growth.

For inventory planning, you also need to know what products (or category of products aka department) will be utilised more. Under-stock some products and your sales are hit. Over-stock items like perishables and you run into losses if the product expires. That's why the sales prediction is done at a combination of store and department level (and sometimes even at product level for high-selling products).

In this problem, we have been given the sales data of 45 stores based on store, department and week. The size and type of each store has been provided. Holiday weeks have been marked. Along with these, price markdown data (almost like discount data) has been given. A few macro-indicators like CPI, Unemployment rate, Fuel price etc. are also provided.

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Curriculum For This Mini Project

Problem Statement
Exploratory Data Analysis - Sales Data
Exploratory Data Analysis - Stores Data
Data Pre-processing - Imputing Missing Values
Data Pre-processing - Merging Data
Data Pre-processing - Splitting The Data
Univariate Analysis
Bivariate Analysis
Dependent Variables Trends - 1
Dependent Variables Trends - 2
Date Trends - 1
Date Trends - 2
Feature Creation
Building The Model - 1
Building The Model - 2
Building The Model - 3
Building The Model - 4
Building The Model - 5
Building The Model - 6
Model Comparsion