stock-data-analysis

📊 Stock Data Analysis This repository contains a comprehensive Exploratory Data Analysis (EDA) of stock inventory data over two consecutive years (2022 and 2023). The analysis includes data cleaning, summary statistics, correlation analysis, and insightful visualizations to understand trends and patterns in stock management.

🔍 Overview The project aims to:

Clean and preprocess raw Excel data.

Compare opening stock levels between 2022 and 2023.

Analyze issue quantity distributions.

Identify the most frequently issued materials.

Visualize changes in stock over time.

📁 Dataset The dataset (sheettt1.xlsx) is assumed to contain columns such as:

MAIN_STORE_ITEMS

STOCK_QTY_AS_ON__01.04.2022

OPENING_STOCK_01.04.2022

STOCK_QTY_AS_ON__01.04.2023

OPENING_STOCK_01.04.2023

ISSUE_QTY

MATERIAL_DESCRIPTION, etc.

Note: The dataset is not included in this repo for privacy reasons. Please update the file path in the code to match your local setup.

📦 Libraries Used pandas – Data manipulation

matplotlib – Static data visualization

seaborn – Statistical graphics

plotly.express – Interactive plots (optional, included for future expansion)

📈 Key Visualizations Correlation Heatmap – Understand relationships between numerical columns

Opening Stock Distribution (2022 vs 2023)

Issue Quantity Distribution

Top 10 Most Issued Items

Year-over-Year Stock Change Histogram

📌 Future Enhancements Add interactive dashboards with Plotly or Streamlit

Include anomaly detection or forecasting

Export cleaned data or insights to Excel/CSV

🧠 Author Anshika Rao