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https://github.com/ginga1402/store_sales_analysis_and_profit_prediction

Exploratory Data Analysis on Store Sales Data
https://github.com/ginga1402/store_sales_analysis_and_profit_prediction

exploratory-data-analysis knn-regression lasso-regression linear-regression machine-learning random-forest-regression ridge-regression

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Exploratory Data Analysis on Store Sales Data

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README 

        
# Store_sales_Analysis_and_Profit_Prediction



## Introduction:



The Store Data Analysis and Profit Prediction project is a Python-based data analysis and machine learning project aimed at gaining insights into the sales data of a store and predicting future profits. In today's competitive business landscape, understanding sales trends, customer behavior, and predicting future profitability are crucial for making informed business decisions and maximizing profits.



By leveraging advanced data analysis techniques and machine learning algorithms, this project enables businesses to analyze their sales data, uncover hidden patterns and trends, and make accurate predictions about future profits. It provides a comprehensive framework for exploring the dataset, visualizing key metrics, and training predictive models.



This project offers a user-friendly and efficient solution for analyzing sales data, regardless of the size of the dataset. It empowers businesses with the ability to harness the power of data and transform it into actionable insights that drive growth and profitability.



Whether you are a business owner, a sales manager, or a data analyst, the Store Data Analysis and Profit Prediction project provides a valuable tool for understanding the dynamics of sales data and making informed decisions to maximize profitability.



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## Objective



The objective of the Superstore Data Analysis and Profit Prediction project is to provide businesses with a comprehensive toolkit to analyze sales data from a superstore and make accurate predictions about future profits. By achieving this objective, the project aims to:



1) Gain Insights: Uncover valuable insights into sales patterns, trends, and customer behavior to understand the factors influencing profitability. By analyzing the dataset, businesses can identify key drivers of sales performance and make data-driven decisions to optimize their operations.



2) Visualize Data: Provide visual representations of sales data through charts, graphs, and interactive visualizations. Visualizations aid in understanding complex information, identifying outliers, and communicating findings effectively, enabling stakeholders to grasp sales performance at a glance.



3) Predict Future Profits: Develop and train machine learning models using historical sales data to forecast future profits. Accurate profit predictions empower businesses to make proactive decisions, such as adjusting pricing strategies, optimizing inventory management, or identifying potential growth opportunities.



4) Evaluate Model Performance: Assess the performance of the predictive models by measuring key metrics such as accuracy, precision and Adjusted R2-score. Evaluating model performance ensures that predictions are reliable and can be used with confidence to support business decisions.



5) Enable Data-Driven Decision Making: Provide businesses with the tools and insights necessary to make data-driven decisions. By leveraging the project's capabilities, businesses can align their strategies, allocate resources effectively, and adapt to changing market conditions, ultimately maximizing profitability.



## Data Description:



Row ID         - Unique identifier for each row



Order ID       - Unique identifier for each order



Order Date     - Date of the order



Ship Date      - Date of shipment



Ship Mode      - Shipping mode (e.g., Standard Class, Second Class)



Customer ID    - Unique identifier for each customer



Customer Name  - Name of the customer



Segment        - Customer segment (e.g., Consumer, Corporate)



Country        - Country of the customer



City           - City of the customer



State          - State of the customer



Postal Code    - Postal code of the customer



Region         - Region of the customer



Product ID     - Unique identifier for each product



Category       - Product category (e.g., Furniture, Office Supplies)



Sub-Category   - Product sub-category (e.g., Chairs, Tables)



Product Name   - Name of the product



Sales          - Sales amount



Quantity       - Quantity ordered



Discount       - Discount applied



Profit         - Profit amount



## Observations:



The store sales analysis provides valuable insights into the sales performance of different categories and sub-categories within the store. Here are the key findings:



1) Category Sales:



 * The Technology category has the highest sales with $836,154.0330, followed by Furniture with $741,999.7953, and Office Supplies with $719,047.0320.



2) Sub-Category Sales:



  * Among the sub-categories, the top three in terms of sales are Chairs ($328,449.1030), Phones ($330,007.0540), and Storage ($223,843.6080). The sub-categories with the lowest sales are Fasteners ($3,024.2800), Labels ($12,486.3120), and Envelopes ($16,476.4020).



3) Monthly Sales:



  * December has the highest sales with $43,369.1919, followed by September ($36,857.4753) and November ($35,468.4265). January has the lowest sales with $9,134.4461, followed by February ($10,294.6107) and April ($11,587.4363).



4) Monthly Sales:



  * November has the highest sales with $352,461.0710, followed by December ($325,293.5035) and September ($307,649.9457). The months with the lowest sales are January ($94,924.8356) and February ($59,751.2514). 



5) Sales and Profit by Segment:



  * The Consumer segment has the highest total sales of $1,161,401.00 and the highest total profit of $134,119.2092.

  * The Corporate segment follows with total sales of $706,146.40 and total profit of $91,979.1340.

  * The Home Office segment has total sales of $429,653.10 and total profit of $60,298.6785.



These insights allow businesses to understand the sales performance across different months and segments. By identifying the highest and lowest performing months, businesses can plan marketing campaigns and promotions accordingly. The analysis of sales and profit by segment helps in understanding the revenue and profitability contribution from different customer segments, enabling businesses to focus on target segments for growth and optimization.



Overall, the store sales analysis provides valuable information for businesses to make data-driven decisions, optimize sales strategies, and maximize profitability.