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Eniacs Discounts Analysis

Welcome to the Eniacs Discounts Analysis project! In this repository, we aim to analyze the effectiveness of offering discounts on products for Eniacs, a fictional company. The project involves assessing various aspects related to discounts, product categorization, and their impact on sales and revenue.

Project Overview

The company is facing a dilemma regarding the implementation of discounts. While the Marketing Team Lead advocates for discounts, emphasizing their potential benefits in customer acquisition and satisfaction, the main investors are concerned about the impact of aggressive discounts on overall revenue and market positioning. Our task is to provide clarity on these issues through data analysis.

Business Questions

To address the concerns and provide insights to the company board, we'll focus on answering the following questions:

  1. How should products be classified into different categories to simplify reports and analysis?
  2. What is the distribution of product prices across different categories?
  3. How many products are being discounted, and what is the magnitude of these discounts as a percentage of product prices?
  4. How do seasonality and special dates (Christmas, Black Friday) affect sales?
  5. How could data collection be improved?

Data Cleaning

Before diving into the business questions, it's crucial to ensure the quality of our dataset. The provided data seems to have inconsistencies, possibly due to issues in data pipelines or encoding. Hence, we'll invest time in data cleaning to make it usable and reliable for analysis.

Repository Structure

This repository contains the following files:

  1. Presentation: - A PDF presentation summarizing the analysis and insights.
  2. Jupyter Notebook: - A Jupyter Notebook containing Python code for data cleaning, analysis, and visualization.

Notebook Overview

The Jupyter Notebook provided in this repository includes the following sections:

  • Data Loading: Loading necessary datasets for analysis.
  • Data Cleaning: Preparing the data for analysis by handling inconsistencies and missing values.
  • Exploratory Data Analysis: Exploring various aspects such as product categories, pricing distribution, discounts, and sales trends over time.
  • Insights and Visualization: Providing visualizations and insights to address the business questions effectively.

Conclusion

Through this analysis, we aim to provide valuable insights to help Eniacs make informed decisions regarding discount strategies and overall business direction. For any questions or further discussions, feel free to reach out.

Let's dive into the data and uncover meaningful insights together!

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