https://github.com/anuj-kshatriya/iphone_sales_data_analysis_project_using_python
This project explores Apple product sales data using Python and Pandas in Jupyter Notebook. It focuses on data cleaning, analysis, and visualization, providing insights into product performance, customer trends, and revenue generation.
https://github.com/anuj-kshatriya/iphone_sales_data_analysis_project_using_python
data-analysis-python dataanalysisusingpython graphical-data pandas python pythonlibrarires pythonproject
Last synced: 3 months ago
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This project explores Apple product sales data using Python and Pandas in Jupyter Notebook. It focuses on data cleaning, analysis, and visualization, providing insights into product performance, customer trends, and revenue generation.
- Host: GitHub
- URL: https://github.com/anuj-kshatriya/iphone_sales_data_analysis_project_using_python
- Owner: anuj-kshatriya
- Created: 2025-03-29T14:55:10.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-03-29T15:22:08.000Z (3 months ago)
- Last Synced: 2025-03-29T15:32:50.334Z (3 months ago)
- Topics: data-analysis-python, dataanalysisusingpython, graphical-data, pandas, python, pythonlibrarires, pythonproject
- Language: Jupyter Notebook
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Iphone_Sales_Data_analysis_project_using_python
# 🍏 Apple Product Sales Analysis
## 📂 Dataset Overview
The dataset includes key details on:
- **Products** 📱💻 – Apple devices (iPhones, MacBooks, iPads, etc.), categories, and pricing.
- **Customers** 🧑💻 – Purchase behavior, demographics, and locations.
- **Sales Transactions** 💰 – Order date, quantity sold, revenue, and discounts.
## 🔍 Key Analyses & Insights
✅ **Sales Trends** – Identifying top-selling products and seasonal trends.
✅ **Revenue Analysis** – Determining high-revenue products and customer segments.
✅ **Customer Insights** – Analyzing buying patterns and regional demand.
✅ **Data Cleaning & Transformation** – Handling missing values, duplicates, and inconsistencies.
✅ **Data Visualization** 📊 – Using graphs to represent trends and insights.
## 📊 Visualizations & Graphs
I used **Matplotlib & Seaborn** to create:
📈 **Sales trend graphs** – Line charts showing sales performance over time.
📊 **Product comparison charts** – Bar plots for revenue and unit sales of different products.
🗺️ **Regional sales heatmaps** – Showing sales distribution across different locations.
## 🛠️ Technologies Used
- **Python (Pandas, Matplotlib, Seaborn, plotly, NumPy)** for analysis & visualization.
- **Jupyter Notebook** for writing, running, and documenting the project.
- **Data Cleaning & Preprocessing** to enhance data quality.
## 🚀 Future Enhancements
- Implement **time-series forecasting** for future sales predictions.
- Create **interactive dashboards** with **Plotly**.