https://github.com/sayamalt/amazon-sales-data-analysis

In this project, I have analyzed Amazon sales records, defined KPIs(Key Performance Indicators) and established meaningful relationships between them for deriving useful statistical insights.
https://github.com/sayamalt/amazon-sales-data-analysis

Last synced: 8 months ago
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In this project, I have analyzed Amazon sales records, defined KPIs(Key Performance Indicators) and established meaningful relationships between them for deriving useful statistical insights.

• Host: GitHub
• URL: https://github.com/sayamalt/amazon-sales-data-analysis
• Owner: SayamAlt
• Created: 2021-08-16T20:06:37.000Z (almost 5 years ago)
• Default Branch: main
• Last Pushed: 2021-09-03T08:25:31.000Z (almost 5 years ago)
• Last Synced: 2024-12-28T08:09:58.743Z (over 1 year ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 9.75 MB
• Stars: 2
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Amazon Sales Data Analysis Project

## Problem Statement:

Sales management has gained importance to meet increasing competition and the need for improved methods of distribution to reduce cost and to increase profits. Sales management today is the most important function in a commercial and business enterprise. We need to extract all the Amazon sales datasets, transform them using data cleaning and data preprocessing and then finally loading it for analysis. We need to visualize sales trend month-wise, year-wise and yearly-month wise. Moreover, we need to find key metrics and factors and show meaningful relationships between attributes.


## Approach

The main goal of the project is to find key metrics and factors and then show meaningful relationships between them based on different features available in the dataset.

 

 Data Collection      : Imported data from various datasets available in the project using Pandas library. 

 Data Cleaning        : Removed missing values and created new features as per insights. 

 Data Preprocessing   : Modified the structure of data in order to make it more understandable and suitable and convenient for statistical analysis. 

 Data Analysis        : I started analyzing dataset using Pandas,Numpy,Matplotlib and Seaborn. 

 Data Visualization   : Plotted graphs to get insights about dependent and independent variables. Also used Tableau and PowerBI for data visulization. 


## Tableau Dashboard Links

Wine Reviews Dataset Dashboard Link: https://public.tableau.com/app/profile/sayam.kumar3450/viz/WineReviewsDashboard/WineReviewsDashboard

## PowerBI Dashboard Links

Link: https://drive.google.com/drive/folders/1VN4Jc4Nm1xwl8-dGgt_RHH-81HY2yhkZ?usp=sharing

## Technologies Used

 

1. Python 

2. Sklearn

3. Pandas

4. Scipy

5. Numpy

6. Seaborn

7. Matplotlib

8. Tableau 

9. PowerBI 



## HLD, LLD, WireFrame, Architecture and Detailed Project Report

Link : https://drive.google.com/drive/folders/1di__hyAQvxpkILnb9Lak3DfIxtg2g2pg?usp=sharing

## Help Me Improve

 Hello readers, if you find any bugs, please consider raising issue so that I can address them asap.