Walmart Sales Insights

Welcome to the Walmart Sales Insights repository! This project involves analyzing and visualizing Walmart sales data to derive meaningful insights and answer key business questions. The analysis is performed using Python with libraries such as pandas, matplotlib, and seaborn.

Introduction
Features
Data
Installation
Usage
Visualizations
Contributing
License

Introduction

Walmart Sales Insights is a project aimed at exploring Walmart sales data to identify trends, patterns, and insights that can help in decision-making processes. The project involves data preprocessing, analysis, and visualization to provide a comprehensive overview of sales performance across various dimensions such as time, store locations, and external factors.

Features

Data preprocessing and cleaning
Trend analysis of total weekly sales over time
Comparison of sales during holiday and non-holiday weeks
Identification of top-performing and underperforming stores
Analysis of the impact of external factors (temperature, fuel price, CPI, unemployment) on sales
Visualization of sales distribution and correlation between variables
Heatmap of sales by store and month
Monthly and yearly sales trends
Interactive and static visualizations

Data

The dataset used in this project includes the following columns:

Store: The store number
Date: The week end date
Weekly_Sales: Sales for the given store and week
Holiday_Flag: Indicates whether the week is a special holiday week (1) or not (0)
Temperature: Temperature in the region
Fuel_Price: Cost of fuel in the region
CPI: Consumer Price Index
Unemployment: Unemployment rate

Installation

To run this project, you need to have Python installed along with the necessary libraries. You can install the required:

pip install pandas matplotlib seaborn psycopg2

Usage

Load Data: Load the Walmart sales data from the CSV file.

import pandas as pd

# Load data from CSV file into a DataFrame
csv_file_path = 'path_to_your_csv_file.csv'
df = pd.read_csv(csv_file_path)

# Convert 'Date' column to datetime format
df['Date'] = pd.to_datetime(df['Date'], format='%d-%m-%Y')

Data Preprocessing: Perform necessary data preprocessing steps such as converting date formats and extracting year and month.
```
# Extract Year and Month from Date
df['Year'] = df['Date'].dt.year
df['Month'] = df['Date'].dt.month
```

Visualization: Create various visualizations to analyze the data.

import matplotlib.pyplot as plt
import seaborn as sns

# Overall Sales Trends
plt.figure(figsize=(12, 6))
sns.lineplot(data=df, x='Date', y='Weekly_Sales', estimator='sum')
plt.title('Total Weekly Sales Over Time')
plt.xlabel('Date')
plt.ylabel('Total Weekly Sales')
plt.show()

# Add more visualizations as needed...

Run Analysis: Execute the Python script to generate insights and visualizations.

Visualizations

The project includes the following visualizations:

Total Weekly Sales Over Time
Weekly Sales: Holiday vs Non-Holiday Weeks
Top Performing Stores
Sales vs. Temperature
Sales vs. Fuel Price
Sales vs. CPI
Sales vs. Unemployment
Total Monthly Sales
Heatmap of Sales by Store and Month
Distribution of Weekly Sales
Sales Trends by Store
Total Yearly Sales
Correlation Matrix Heatmap
Sales Trends on Holidays vs. Non-Holidays

Contributing

Contributions are welcome! If you have any suggestions or improvements, feel free to open an issue or submit a pull request.

Fork the repository.
Create a new branch: git checkout -b my-feature-branch.
Make your changes and commit them: git commit -m 'Add some feature'.
Push to the branch: git push origin my-feature-branch.
Open a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

PPathole/Walmart-Sales-Insights