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.

Flipkart Laptop Listing - EDA

This repository contains the code and documentation for analyzing the laptop price data extracted from Flipkart. The data has been sourced from here and the corresponding Kaggle and GitHub 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
  • Overview
  • Objectives
  • Scope of Work
  • Deliverables
  • Timeline and Milestones
• Data Analysis
  • Step 1: Data Loading
  • Step 2: Exploratory Data Analysis (EDA)
  • Step 3: Data Cleaning and Preprocessing
  • Step 4: Statistical Analysis
  • Step 5: Visualization
• 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