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PROJECT


Data Visualization with Seaborn for Walmart Sales Projection

In this project, we’ll leverage the seaborn library to analyze Walmart sales data by creating visualizations that can help with sales projections.

Data Visualization with Seaborn for Walmart Sales Projection

You will learn to:

Perform data cleaning to remove outliers and null values.

Transform raw data into a usable format for data visualization.

Visualize the correlation between sales and external factors.

Visualize past sales data to help identify sales trends.

Skills

Data Visualization

Data Analysis

Data Manipulation

Machine Learning

Prerequisites

Hands-on experience with Python

Basic understanding of data visualization

Familiarity with scikit-learn

Technologies

Python

Pandas

seaborn

Matplotlib

Scikit-learn

Project Description

Data visualization is essential for analyzing sales projections because it turns raw data into insights, providing a clear depiction of the trends that drive business success. These visualizations empower business stakeholders to leverage their data effectively, ensuring sales projections are not only informed by past performance but also offer insights into potential future trajectories.

In this project, we’ll utilize the seaborn library to analyze Walmart sales data, creating visualizations that facilitate sales projections. Seaborn, a Python data visualization library built on Matplotlib, offers a high-level interface for crafting aesthetically pleasing and informative statistical charts. Through seaborn, we will generate various graphs, such as bar charts, line charts, and histograms, to visualize past sales data, identify seasonal trends, and highlight areas for growth or attention. Ultimately, we will conclude the project with the development of a predictive model to forecast weekly sales.

Project Tasks

1

1. Introduction

Task 0: Get Started

Task 1: Import Libraries and Modules

Task 2: Load the Datasets

2

2. Data Transformation

Task 3: Handle Missing Values

Task 4: Merge the Datasets

Task 5: Remove Duplicate Column

Task 6: Remove Outliers

Task 7: Normalize Data

3

Data Visualization

Task 8: Visualize Sales Seasonality

Task 9: Visualize Sales Performance by Type

Task 10: Visualize Sales Performance by Store

Task 11: Visualize Sales Performance by Department

Task 12: Visualize the Correlation Between Sales and Temperature

Task 13: Visualize the Correlation between Sales and Holiday

Task 14: Visualize the Correlation between Sales and Economic Factors

Task 15: Visualize the Correlation between Sales and Markdowns

4

Sales Forecast Modelling

Task 16: Perform Feature Extraction

Task 17: Perform Label Encoding

Task 18: Perform Feature Engineering

Task 19: Train a Model

Task 20: Forecast Sales Using Model

Task 21: Visualize the Model’s Predictions

Congratulations!