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FMCG Sales Exploratory Data Analysis (EDA)

Introduction:

Welcome to the FMCG Sales Exploratory Data Analysis (EDA) repository! This project aims to analyze transactional data from multiple stores across different months in the Fast-Moving Consumer Goods (FMCG) industry. The dataset contains information such as the day of the transaction, bill ID, bill amount, quantity, value, price, product group, sub-group, sub-sub-group, company, main brand, and brand.

Objective:

The primary objective of this analysis is to gain insights into the sales trends, patterns, and behaviors within the FMCG sector. By exploring the dataset, we aim to answer various questions related to sales performance, product popularity, seasonal variation.

Dataset:

The dataset consists of transactional records from multiple stores, each representing a single transaction. Here's a brief overview of the columns present in the dataset:

  • STORECODE: Unique identifier for each store
  • MONTH: Month of the transaction
  • DAY: Day of the transaction
  • BILL_ID: Unique identifier for each bill
  • BILL_AMT: Bill amount
  • QTY: Quantity of products sold
  • VALUE: Value of the transaction
  • PRICE: Price of the product
  • GRP: Product group
  • SGRP: Sub-group of the product
  • SSGRP: Sub-sub-group of the product
  • CMP: Company associated with the product
  • MBRD: Main brand
  • BRD: Brand

Exploratory Data Analysis (EDA)

  • Numeric Variables:

1.Descriptive Statistics: Compute basic statistics such as mean, median, mode, range, variance, and standard deviation to understand the central tendency and variability of numerical variables like BILL_AMT, QTY, VALUE, and PRICE histograms plots to visualize the distribution of numeric variables and identify outliers or skewness.

  • Categorical Variables:

2.Frequency Distribution: Count the frequency of each category in categorical variables like STORECODE, MONTH, GRP, MBRD, and BRD. Bar Charts: Create bar charts or count plots to visualize the distribution of categories and identify the most common or rare categories.

3.Time Series Analysis: For temporal variables like MONTH and DAY, analyze trends over time by plotting line charts or time series plots.

Contributing

Contributions to this project are welcome! If you encounter any issues or have suggestions for improvements, please feel free to submit a pull request.

Contact

📧 Email: thangamani1128@gmail.com

🌐 LinkedIn:linkedin.com/in/thangarasu-m-

For any further questions or inquiries, feel free to reach out. We are happy to assist you with any queries.

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