📊 Superstore Sales Analysis & Profit Prediction 📌 Overview
This project performs Exploratory Data Analysis (EDA) and Profit Prediction on the Sample Superstore dataset. The goal is to uncover sales & profit trends, visualize business insights, and build a simple predictive model using Linear Regression.
🗂 Dataset
The dataset (Sample - Superstore.csv) contains information about customer orders including:
Order details: Order ID, Order Date, Ship Date, Ship Mode
Customer details: Customer ID, Segment, Region, State, City
Transaction details: Sales, Quantity, Discount, Profit
🛠️ Features
✔️ Data Cleaning & Preprocessing (missing values, duplicates, invalid data removal) ✔️ Time-series sales trend analysis ✔️ Visualizations using Matplotlib, Seaborn & Plotly ✔️ Profit vs Discount correlation analysis ✔️ Sales distribution by Region, Segment, and State ✔️ Choropleth Map for sales across USA states ✔️ Linear Regression model for profit prediction
📈 Visualizations
Line Chart: Sales trend over time
Bar Chart: Sales by Segment
Scatter Plot: Discount vs Profit
Pie Chart: Regional sales distribution
Choropleth Map: State-wise sales across the USA
🤖 Machine Learning
A simple Linear Regression model was applied to predict profit from sales:
X (Feature): Sales
y (Target): Profit
The model was trained and predictions were compared against actual profit values.
🖼️ Sample Output Sales Profit Predicted_Profit 0 261.96 41.91 43.12 1 731.94 219.58 207.44 2 14.62 6.87 5.27 3 957.58 -383.03 272.45 4 22.37 2.51 6.75
🧑💻 Tech Stack
Python (Pandas, NumPy, Scikit-learn)
Matplotlib & Seaborn (Data Visualization)
Plotly (Interactive Choropleth Map)
🚀 How to Run
Clone this repository:
git clone https://github.com/HabibFaisal-py/Superstore-Sales-Analysis-Profit-Prediction.git cd superstore-analysis
Install dependencies:
pip install -r requirements.txt
Run the script:
python superstore_analysis.py
📌 Future Improvements
Add advanced ML models (Random Forest, XGBoost) for better predictions
Build a dashboard using Streamlit/Power BI for real-time analysis
Include customer segmentation using clustering
🙌 Acknowledgments
Dataset: Sample Superstore (Tableau Public / Kaggle)