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https://github.com/manjit-baishya-datascience/flipkart-laptop-listing-eda

This project analyzes laptop price data from Flipkart using AutoScraper for web scraping. It includes data loading, EDA, cleaning, statistical analysis, and visualization. The goal is to derive insights for pricing strategies and market positioning. Explore the repository for detailed documentation and code.
https://github.com/manjit-baishya-datascience/flipkart-laptop-listing-eda

data-analysis ecommerce-platform flipkart laptop python

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This project analyzes laptop price data from Flipkart using AutoScraper for web scraping. It includes data loading, EDA, cleaning, statistical analysis, and visualization. The goal is to derive insights for pricing strategies and market positioning. Explore the repository for detailed documentation and code.

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README 

        
# **Flipkart Laptop Listing - EDA**

![download](https://github.com/manjit-baishya-datascience/Flipkart-Laptop-Listing-EDA/assets/127611924/bccebdd2-8543-434c-b689-74ee660c0540)



This repository contains the code and documentation for analyzing the laptop price data extracted from Flipkart. The data has been sourced from [**here**](https://www.kaggle.com/datasets/manjitbaishya001/flipkart-laptop-price-dataset) and the corresponding [**Kaggle**](https://www.kaggle.com/manjitbaishya001/flipkart-laptop-listing-eda) and [**GitHub**](https://github.com/manjit-baishya-datascience/Flipkart-Laptop-Listing---EDA) sources have also been linked.



## **About the Dataset**



This project utilizes the laptop price dataset obtained through web scraping on Flipkart. The scraping process was conducted using **AutoScraper**, and the dataset is stored in a CSV file.



## Contents

- [***Author***](#author)

  - [Overview](#overview)

  - [Objectives](#objectives)

  - [Scope of Work](#scope-of-work)

  - [Deliverables](#deliverables)

  - [Timeline and Milestones](#timeline-and-milestones)

- [***Data Analysis***](#data-analysis)

  - [**Step 1:** Data Loading](#step-1-data-loading)

  - [**Step 2:** Exploratory Data Analysis (EDA)](#step-2-exploratory-data-analysis-eda)

  - [**Step 3:** Data Cleaning and Preprocessing](#step-3-data-cleaning-and-preprocessing)

  - [**Step 4:** Statistical Analysis](#step-4-statistical-analysis)

  - [**Step 5:** Visualization](#step-5-visualization)

- [***Conclusion***](#conclusion)



## **Author**



**Your Name:** Manjit Baishya 


**Start Date:** 26/11/2023 


**Project Status:** Completed 


**End Date:** 27/11/2023 



## **Statement of Work**



### Overview



The project involves loading, cleaning, and analyzing the laptop price dataset obtained from Flipkart. The results will be used to derive insights for pricing strategies and market positioning.



### Objectives



1. Load the laptop price dataset.

2. Conduct Exploratory Data Analysis (EDA).

3. Clean and preprocess the data.

4. Perform statistical analysis.

5. Create visualizations for better understanding.



### Timeline and Milestones



- **Data Loading:** 26th Nov to 26th Nov

- **EDA and Cleaning:** 27th Nov to 28th Nov

- **Statistical Analysis:** 27th Nov to 28th Nov

- **Visualization:** 27th Nov to 28th Nov



## **Data Analysis**



### **Step 1:** Data Loading



#### Loading the Laptop Price Dataset



The first step involves loading the dataset into a suitable data structure for analysis.



### **Step 2:** Exploratory Data Analysis (EDA)



#### Understanding the Dataset



Exploratory Data Analysis (EDA) involves summarizing the main characteristics of the dataset, often with the help of statistical graphics and other data visualization methods.



### **Step 3:** Data Cleaning and Preprocessing



#### Cleaning and Preparing the Dataset



Data cleaning and preprocessing are essential for handling missing values, outliers, and ensuring the dataset is ready for analysis.



### **Step 4:** Statistical Analysis



#### Conducting Statistical Analysis



Statistical analysis involves applying various statistical methods to uncover patterns, relationships, and insights within the dataset.



### **Step 5:** Visualization



#### Creating Visualizations



Visualizations such as plots, charts, and graphs are created to communicate the findings effectively.



## **Conclusion**



In conclusion, this phase of the project focuses on extracting actionable insights from the laptop price dataset obtained from Flipkart. The analysis aims to inform pricing strategies and market positioning based on a thorough understanding of the data.



Feel free to explore the code, documentation, and reports in the repository to gain insights into the entire data analysis process.



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# **THANK YOU**