Pharmaceutical Sales Data Analysis

Sales Data Analysis Project 📊 Project Overview This project performs a comprehensive analysis of six years of pharmaceutical sales data. Using Python, it involves cleaning the raw dataset, exploring the data to uncover trends and patterns, and visualizing insights to support data-driven decision-making.

🎯 Objectives Clean and preprocess the sales dataset for analysis

Perform Exploratory Data Analysis (EDA) to understand sales trends over time

Identify correlations and seasonal patterns in pharmaceutical product sales

Visualize key findings using charts and graphs for better interpretation

📁 Dataset The dataset contains sales data spanning six years, including:

Product category-level sales volumes

Time-based columns (Year, Month, Weekday, Hour)

🛠 Tools & Technologies Python: pandas, matplotlib, seaborn

Editor: Thonny (Python IDE)

Excel: for quick initial inspection

🗂 Project Structure 02_data_clean/: Cleaned version of the original dataset

03_notebooks/: EDA notebooks with visualizations

04_outputs/: Final outputs, charts, and summary insights

analysis.py: Core script for analysis

README_draft.txt: Draft notes and planning

sales_data.csv: Main dataset

📌 Key Findings Seasonal spikes in sales during specific months and weekdays

Strong correlation between product categories and time-based features

Patterns that can inform inventory and marketing strategies

▶️ How to Use Clone the repository

Install required packages:

bash Copy Edit pip install pandas matplotlib seaborn Run the notebook or script files to reproduce the analysis and visualizations