https://github.com/aryan4codes/amazon_dataset_s4ds
EDA of Amazon Sales Dataset
https://github.com/aryan4codes/amazon_dataset_s4ds
Last synced: over 1 year ago
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EDA of Amazon Sales Dataset
- Host: GitHub
- URL: https://github.com/aryan4codes/amazon_dataset_s4ds
- Owner: aryan4codes
- Created: 2023-08-17T16:19:33.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-08-24T18:34:58.000Z (almost 3 years ago)
- Last Synced: 2025-02-08T17:30:30.268Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 2.6 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Amazon Dataset Exploratory Data Analysis (EDA) - S4DS Task
This repository contains the code and findings from an exploratory data analysis (EDA) performed on the Amazon Dataset. The analysis was conducted as part of the S4DS Task by Aryan Rajpurkar.
## Overview
In this project, the Amazon Products dataset was analyzed to gain insights into various aspects of the products available on Amazon. The analysis covered data cleaning, handling missing values, data visualization, and correlation analysis. The goal was to uncover patterns, trends, and relationships within the dataset.
## Files
- `amazon-dataset-eda.ipynb`: Jupyter Notebook containing the code for data cleaning, analysis, and visualizations.
- `Amazon_Products.csv`: The original dataset used for analysis.
## Project Steps
1. Importing Data and Libraries: Loading the dataset and necessary libraries for analysis.
2. Filling/Dropping Null Values: Handling missing values for various columns in the dataset.
3. Converting Specific Columns: Converting specific columns to suitable data types for analysis.
4. Handling Price Outlier: Identifying and handling an outlier in the 'price' column.
5. EDA: Exploratory data analysis covering various aspects of the dataset:
- Distribution of Prices
- Number of Products vs Top Manufacturers
- Correlation between Review Ratings and Price
- Availability of Products
- Most Frequently Used Words in Description
- Top Categories of Sales
- Price vs Availability
- Most Expensive Products
- Related Products Analysis
- Number of Reviews vs Number of Answered Questions
- Distribution of Products Across Categories
## Usage
1. Clone the repository to your local machine.
2. Install the required libraries (e.g., pandas, matplotlib, seaborn, wordcloud) if not already installed.
3. Open and run the Jupyter Notebook `amazon-dataset-eda.ipynb` to reproduce the analysis.
## Acknowledgments
This project was completed as part of the S4DS Task. The dataset was sourced from [Kaggle](https://www.kaggle.com/) and all credits for the data go to its contributors.
## Contact
For any questions or suggestions, feel free to contact Aryan Rajpurkar on Linkedin