Repositories of this project

In this project, the objective is to analyze from the dataset sample FROM THE OLIST E-COMMERCE SITE

# DIGITAL MEDIA SPENDS DATA

## RESOURCES OF THE DATASETS USED IN THIS PROJECT

The given dataset was taken from the dataset in Kaggle Datasets.

### <li><strong> (contains all information about the digital media data)</strong> -  <a href="https\://www\.kaggle.com/datasets/olistbr/brazilian-ecommerce">Source</a><br>

### <li> <strong>olist_ecommerce_dataset.csv-  </strong> -  <a href="https\://www\.kaggle.com/datasets/olistbr/brazilian-ecommerce/download?datasetVersionNumber=2">Download</a>

## DATA MODELING

<iframe title="Report Section" width="800" height="486" src="https://app.powerbi.com/view?r=eyJrIjoiMDhmMDFmOWYtNDljMi00NDBmLTlmOWMtYzU2Y2QyMjk2NzAxIiwidCI6ImU5OTQwNzJiLTUyM2UtNGJmZS04NmUyLTQ0MmM1ZTEwYjI0NCIsImMiOjR9" frameborder="0" allowFullScreen="true"></iframe>

## INFERENCES AND CONCLUSION

*   The Olist platform sells many products and the best sellers include beds, bathroom tables, sports leisure, furniture decoration, beauty, health, home, and computer accessories, among others.
*   Most sellers and consumers are located in the state of Sao Paulo
*   Most sales are made through credit cards and the second is a method called boleto, which is widely used in Brazil.


In this project, the objective is to analyze from the dataset sample HR ANALYTICS

# HR ANALYTICS

## RESOURCES OF THE DATASETS USED IN THIS PROJECT

The given dataset was taken from the dataset in Kaggle Datasets.

### <li><strong> (contains all information about the digital media data)</strong> -  <a href="https\://www\.kaggle.com/datasets/vjchoudhary7/hr-analytics-case-study?select=general_data.csv">Source</a><br>

### <li> <strong>hr_analytics.csv-  </strong> -  <a href="https\://www\.kaggle.com/datasets/vjchoudhary7/hr-analytics-case-study/download?datasetVersionNumber=1">Download</a>

## DATA MODELING

<iframe title="Report Section" width="800" height="486" src="https://app.powerbi.com/view?r=eyJrIjoiZjJhMDNlNGMtNmZlOS00ZTVlLWI4MGMtYThjNzU4YWQ1OTA4IiwidCI6ImU5OTQwNzJiLTUyM2UtNGJmZS04NmUyLTQ0MmM1ZTEwYjI0NCIsImMiOjR9" frameborder="0" allowFullScreen="true"></iframe>

## INFERENCES AND CONCLUSION

*   The company has 4410 employees and they cost around 287 M per month, they range in age from 18 to 60 years old, and with a monthly payment range from 10,000 to 200,000 dollars.

*   60% of the employees are men, 85% of employees have a performance rating of 3 out of 4, and 15% of 4 out of 4, despite this the job involvement is low at 30% of employees.

*   65% of employees belong to research and development, 30% to sales, and 5%to hr, despite this, the role with most employees are sales executives.

*   The work balance is good for around 70% of employees, job satisfaction is good for 60% of the employees, and environment satisfaction is 60% as well, the last 2 metrics could improve


HR ANALYTICS

In this project, the objective is to analyze from the dataset sample the spending of a digital media  campaign 

# DIGITAL MEDIA SPENDS DATA

## RESOURCES OF THE DATASETS USED IN THIS PROJECT

The given dataset was taken from the dataset in Kaggle Datasets.

### <li><strong> (contains all information about the digital media data)</strong> -  <a href="https\://www\.kaggle.com/datasets/yugagrawal95/sample-media-spends-data">Source</a><br>

### <li> <strong>media_data_spends.csv-  </strong> -  <a href="https\://www\.kaggle.com/datasets/yugagrawal95/sample-media-spends-data/download?datasetVersionNumber=2">Download</a>

## DATA MODELING

<iframe title="Media impressions & Sales" width="800" height="486" src="https://app.powerbi.com/view?r=eyJrIjoiMzgyNWY4MTAtNzg5OS00ODBmLWExZWItZDNjOWFjYTQxOWE3IiwidCI6ImU5OTQwNzJiLTUyM2UtNGJmZS04NmUyLTQ0MmM1ZTEwYjI0NCIsImMiOjR9" frameborder="0" allowFullScreen="true"></iframe>

## INFERENCES AND CONCLUSION

*   Sales in the span of 2 years have been around 570 M
*   There have been two sales peaks in November 2018 and 2019
*   Sales seem to be related to impressions from google and meta campaigns, in fact, google seems to be more incidental

## RECOMMENDATIONS

*   Detail in depth the correlation of affiliate and email campaigns, since it does not seem to see any correlation with sales


OLIST E-COMMERCE

DIGITAL MEDIA SPENDS DATA

RESOURCES OF THE DATASETS USED IN THIS PROJECT

(contains all information about the digital media data) - Source

olist_ecommerce_dataset.csv-

Download

DATA MODELING

INFERENCES AND CONCLUSION

Repositories of this project

DIGITAL MEDIA SPENDS DATA

RESOURCES OF THE DATASETS USED IN THIS PROJECT

(contains all information about the digital media data) - Source

olist_ecommerce_dataset.csv- Download

DATA MODELING

INFERENCES AND CONCLUSION

Repositories of this project

olist_ecommerce_dataset.csv-

Download