https://github.com/vvipjain/iphone-sales-analysis
Iphone Sales Analysis
https://github.com/vvipjain/iphone-sales-analysis
jupyter-notebook numpy numpy-arrays numpy-library pandas pandas-dataframe pandas-library pandas-python plotly plotly-express plotly-python python python3
Last synced: 3 months ago
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Iphone Sales Analysis
- Host: GitHub
- URL: https://github.com/vvipjain/iphone-sales-analysis
- Owner: VVipJain
- Created: 2024-08-07T07:05:13.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-08-07T07:16:45.000Z (11 months ago)
- Last Synced: 2025-02-10T05:14:14.327Z (4 months ago)
- Topics: jupyter-notebook, numpy, numpy-arrays, numpy-library, pandas, pandas-dataframe, pandas-library, pandas-python, plotly, plotly-express, plotly-python, python, python3
- Language: Jupyter Notebook
- Homepage:
- Size: 145 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Iphone-Sales-Analysis
This repository contains a comprehensive analysis of iPhone sales data using Python, Numpy,Pandas, and Plotly. The project aims to provide insights into sales performance, customer demographics, and product trends through interactive visualizations.
INTRODUCTION -> In this project, we analyze iPhone sales data to uncover patterns and trends. By leveraging the powerful data manipulation capabilities of Pandas and the interactive visualization features of Plotly, we aim to provide valuable insights into various aspects of iPhone sales, including regional performance, customer preferences, and sales trends. This project is ideal for data analysts, business managers, and anyone interested in retail and technology analytics.
DATASET -> The dataset used in this analysis contains detailed information about iPhone sales transactions, including product details, sales amounts, customer demographics, and more. The data is stored in a CSV file named apple_products.csv.
ANALYSIS -> The analysis is divided into several sections:
* Loading and Cleaning Data: Importing the dataset and performing initial cleaning operations such as handling missing values and converting data types.
* Data Exploration: Exploring the dataset to understand its structure, including summary statistics and unique values.
* Sales Performance: Analyzing sales performance across different regions, stores, and customer types.
* Customer Demographics: Understanding customer demographics, including age and gender distribution.
* Product Trends: Identifying top-selling iPhone models, seasonal trends, and product performance over time.
VISUALISATIONS -> We use Plotly to create interactive visualizations. Some of the key visualizations include:
Bar Charts: Sales performance by highest rated and highest reviewed iphones.
Scatter Plots: Relation between sale price and discount VS number of ratings .
