https://github.com/jigyasag18/data-analysis-using-ms-excel

This project is on analyzing real-time data from Ambuvians Healthcare, a health products startup. It included data cleaning, such as removing duplicates and addressing missing values, followed by analyses to reveal insights into sales trends, customer demographics, and purchasing behaviors. Visualizations in MS-Excel including bar and pie charts.
https://github.com/jigyasag18/data-analysis-using-ms-excel

analysis data data-visualization dataanalysis datacleaning datapreprocessing dataset msexcel visualization

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This project is on analyzing real-time data from Ambuvians Healthcare, a health products startup. It included data cleaning, such as removing duplicates and addressing missing values, followed by analyses to reveal insights into sales trends, customer demographics, and purchasing behaviors. Visualizations in MS-Excel including bar and pie charts.

• Host: GitHub
• URL: https://github.com/jigyasag18/data-analysis-using-ms-excel
• Owner: jigyasaG18
• Created: 2024-11-12T14:02:00.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-11-12T14:14:14.000Z (over 1 year ago)
• Last Synced: 2025-03-03T02:17:48.024Z (about 1 year ago)
• Topics: analysis, data, data-visualization, dataanalysis, datacleaning, datapreprocessing, dataset, msexcel, visualization
• Homepage:
• Size: 3.11 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Minor Project 1 - Data Analysis of Ambuvians Healthcare

This presents the minor project conducted using real-time data from Ambuvians Healthcare, a startup focused on health-related products and services. The project involved cleaning the dataset, performing various analyses, and visualizing the findings using graphs and charts. Below, I address the specific analytical questions posed by the project and summarize the results derived from the dataset.

## 6.1 Data Cleaning and Preparation

The first step in this project was cleaning the data. The dataset included entries related to sales, orders, customer demographics, and product categories. Important data cleaning tasks included:

- Removing Duplicates: Identified and eliminated any duplicate order entries to ensure accuracy.

- Handling Missing Values: Checked for and addressed any missing data points to maintain the integrity of the analysis.

- Standardizing Formats: Ensured consistency in date formats, categorical data, and numerical representations.

Following the data cleaning process, the dataset was ready for analysis.

## 6.2 Analysis and Findings

1. Comparison of Sales and Orders

Using a single chart, sales and order comparisons were visualized. The data depicted a growth trend in sales corresponding with the number of orders over the months. This analysis highlights the correlation between sales volume and order count, affirming the efficiency and efficacy of customer engagement strategies throughout the examined period.

2. Highest Sales and Orders by Month

The month with the highest sales and orders was March.

![image](https://github.com/user-attachments/assets/80dd0a44-035a-402a-be5d-4e7d59a2e226)

3. Gender Purchasers in 2022

In 2022, Women accounted for more purchases compared to men.

![image](https://github.com/user-attachments/assets/0026d344-59c0-46d5-bbe9-121b0270d6b5)

4. Order Statuses in 2022

The following order statuses emerged from the analysis, indicating various outcomes of customer transactions:

![image](https://github.com/user-attachments/assets/95b647d8-49f4-41d5-bfa2-5917d62bfc5e)

5. Top States Contributing to Sales

The states contributing most significantly to sales in 2022 are as follows:

![image](https://github.com/user-attachments/assets/9e63e1f7-ca86-46be-a4ad-f1a4f3f392d7)

6. Relation Between Age and Gender

The analysis indicated a varied breakdown of age groups across genders, reflecting purchasing behavior:

![image](https://github.com/user-attachments/assets/20a61aa1-6637-4de2-b16c-20168a2bb6ac)

7. Sales Channel Contribution

Amazon emerged as the channel contributing the maximum sales for Ambuvians Healthcare:

![image](https://github.com/user-attachments/assets/0ba32b5f-047f-416c-a55f-6651f7a1cba3)

8. Highest Selling Category

The highest-selling product category was Set:

![image](https://github.com/user-attachments/assets/3171a708-08cb-4aaf-8f6c-54bf969fed0d)

## 6.3 Visualizations

Graphs were created using MS-Excel software tool to efficiently present the findings. Specific visualizations included:

- Bar Charts to show sales and orders by month.

- Pie Charts illustrating the proportion of purchases by gender.

- Stacked Bar Charts to visualize order statuses.

- State contributions represented as horizontal bar graphs.

These visual aids succinctly summarize the analytical findings, making it easier for stakeholders to grasp key insights at a glance.

## 6.4 Conclusion

The analysis of Ambuvians Healthcare's data has provided significant insights into sales trends, customer demographics, purchasing channels, and product categories. The findings suggest that women significantly outpurchased men, with Amazon being the leading sales channel. Moreover, March stood out as the peak month for sales and orders, illustrating a notable period for the company. 

The project underscored the importance of data cleaning in providing accurate analysis results, as well as the valuable role of graphical representations in conveying complex data in an understandable format. These insights will help inform strategic decisions for the future growth of Ambuvians Healthcare.