Flipkart Mobiles Dataset Analysis Project

Overview

The Flipkart Mobiles Dataset Analysis Project leverages a dataset sourced from Kaggle to showcase the application of data analysis and visualization using Power BI Desktop. This project features dynamic dashboards and interactive reports that offer comprehensive insights into mobile phones available in the Indian market.

Key Features

• Price Range Analysis: Visualize and analyze the distribution of mobile prices across different segments.
• Brand Performance: Examine the number of product offerings by various brands and identify which brands cater to multiple market segments.
• Specification Trends: Discover common specifications such as RAM and storage configurations across different brands.
• Premium Offerings: Compare premium offerings from top brands to understand their market positioning.
• Color Preferences: Analyze the most frequently offered colors for mobile phones by different brands.
• Brand Comparison: Compare two brands based on their specifications to determine competitive positioning.
• Rating and Price Correlation: Investigate if higher-rated mobiles are typically more expensive or premium.
• Rating Consistency: Evaluate if a brand maintains a rating above 4 for all its products.

Getting Started

1. Download and Install Power BI Desktop:

   • Obtain Power BI Desktop from the official Power BI website.

2. Download the Dataset:

   • Download the dataset from Kaggle. You can find it here. The dataset should be saved as flipkart_mobiles.csv.

3. Open the Power BI File:

   • Launch Power BI Desktop.
   • Open the Power BI file (Flipkart_Mobiles_Analysis.pbix) included in the repository.

4. Load the Dataset:

   • In Power BI Desktop, load the flipkart_mobiles.csv file to start analyzing the data.

5. Explore Dashboards and Reports:

   • Navigate through the interactive dashboards and reports to gain insights into the dataset.

Dataset

The dataset used in this project contains mobile phone specifications scraped from Flipkart. It includes 3,114 samples with attributes such as brand, model, color, memory, storage, rating, selling price, and original price.

Acknowledgements

• Kaggle: For providing the dataset through their platform.
• Microsoft Power BI: For providing the powerful tools used for data visualization and analysis.