https://github.com/amaravivian/client-project-analysis

"Comprehensive data analysis project for a new client to provide data-driven recommendations."
https://github.com/amaravivian/client-project-analysis

data-science data-structures data-visualization r tableau

Last synced: 11 days ago
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"Comprehensive data analysis project for a new client to provide data-driven recommendations."

• Host: GitHub
• URL: https://github.com/amaravivian/client-project-analysis
• Owner: Amaravivian
• License: mit
• Created: 2024-08-29T00:51:17.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-08-29T01:23:20.000Z (2 months ago)
• Last Synced: 2024-10-07T08:06:16.422Z (about 1 month ago)
• Topics: data-science, data-structures, data-visualization, r, tableau
• Homepage: https://rpubs.com/Amaravivian/Case_Study2
• Size: 10.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# client-project-analysis

 "Comprehensive data analysis project for a new client to provide data-driven recommendations."

# Client Project Analysis

## Project Overview

This repository contains a comprehensive data analysis for a new client, focusing on optimizing marketing strategy to increase sales for underperforming product categories.

### Business Objective

- Understand which products are underperforming and why.

- Identify seasonal trends affecting sales.

- Provide recommendations for more efficient marketing budget allocation.

### Key Questions

1. What are the current sales trends?

2. Which customer segments show the highest potential for growth?

3. What are the peak sales periods?

### Dataset

- Source: [Kaggle Retail Data Analytics](https://www.kaggle.com/competitions/acm-sf-chapter-hackathon-big/overview)

- Description: The dataset includes sales data from the past two years, detailing transactions, customer demographics, and product categories.

  

### Project Structure

- `data/`: Raw and cleaned datasets.

- `scripts/`: Python and R scripts for data analysis.

- `notebooks/`: Jupyter or R Markdown notebooks documenting analysis.

- `visualizations/`: Visual outputs (graphs, charts).

- `reports/`: Final presentation and summaries.

## Steps Undertaken

1. Data collection and preparation.

2. Data cleaning and integrity checks.

3. Exploratory data analysis (EDA).

4. Statistical modeling and analysis.

5. Visualization of key findings.

6. Preparation of presentation and recommendations.