https://github.com/samiksha29-patil/flipkart-mobiles-data-analysis-visualization-in-python

This project analyzes Flipkart Mobiles Dataset to extract useful insights about mobile phones, their pricing, ratings, discounts, and customer reviews. The analysis and visualization are done using Python to understand market trends and customer preferences.
https://github.com/samiksha29-patil/flipkart-mobiles-data-analysis-visualization-in-python

data-analysis data-visualization matplotlib numpy pandas python seaborn

Last synced: 3 months ago
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This project analyzes Flipkart Mobiles Dataset to extract useful insights about mobile phones, their pricing, ratings, discounts, and customer reviews. The analysis and visualization are done using Python to understand market trends and customer preferences.

• Host: GitHub
• URL: https://github.com/samiksha29-patil/flipkart-mobiles-data-analysis-visualization-in-python
• Owner: samiksha29-patil
• Created: 2025-09-05T18:13:12.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2025-09-07T11:55:13.000Z (11 months ago)
• Last Synced: 2025-09-07T13:21:04.691Z (11 months ago)
• Topics: data-analysis, data-visualization, matplotlib, numpy, pandas, python, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 169 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 📱 Flipkart Mobiles Data Analysis & Visualization

## 📌 Project Overview  

This project analyzes **Flipkart Mobiles Dataset** to extract useful insights about mobile phones, their pricing, ratings, discounts, and customer reviews. The analysis and visualization are done using **Python** to understand market trends and customer preferences.  

## 🎯 Objectives  

- Analyze **mobile ratings and reviews**  

- Compare **MRP vs. MSP (discounted price)**  

- Study **brand-wise popularity** (Realme, Redmi, Poco, Apple, Vivo, etc.)  

- Identify **best deals** based on discounts  

- Visualize **customer preferences** using charts  

## 🛠️ Tools & Libraries  

- `pandas` → Data cleaning & manipulation  

- `numpy` → Numerical computations  

- `matplotlib` → Data visualization  

- `seaborn` → Advanced visualizations  

## 📂 Dataset Description  

The dataset used is: **`flipkart_mobiles.csv`**  

| Column Name    | Description |

|----------------|-------------|

| Name           | Mobile phone model name |

| Brand          | Mobile phone brand (Realme, Redmi, Poco, Apple, Vivo, etc.) |

| Ratings        | Average customer rating (out of 5) |

| No_of_rat      | Number of ratings |

| No_of_rev      | Number of reviews |

| Product_f      | Product features |

| MSP            | Market Selling Price (discounted price) |

| MRP            | Maximum Retail Price |

| Discount       | Discount percentage offered |

---

## 📊 Visualizations

[![flipkart1](https://github.com/samiksha29-patil/Flipkart-Mobiles-Data-Analysis-Visualization-in-Python/blob/main/flipkart1.png)](https://github.com/samiksha29-patil/Flipkart-Mobiles-Data-Analysis-Visualization-in-Python/blob/main/flipkart1.png)  

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[![flipkart2](https://github.com/samiksha29-patil/Flipkart-Mobiles-Data-Analysis-Visualization-in-Python/blob/main/flipkart2.png)](https://github.com/samiksha29-patil/Flipkart-Mobiles-Data-Analysis-Visualization-in-Python/blob/main/flipkart2.png)  

---

[![flipkart3](https://github.com/samiksha29-patil/Flipkart-Mobiles-Data-Analysis-Visualization-in-Python/blob/main/flipkart3.png)](https://github.com/samiksha29-patil/Flipkart-Mobiles-Data-Analysis-Visualization-in-Python/blob/main/flipkart3.png)  

---

[![flipkart5](https://github.com/samiksha29-patil/Flipkart-Mobiles-Data-Analysis-Visualization-in-Python/blob/main/flipkart5.png)](https://github.com/samiksha29-patil/Flipkart-Mobiles-Data-Analysis-Visualization-in-Python/blob/main/flipkart5.png)  

---

## 📊 Key Visualizations & Insights  

- **Brand-wise Sales Analysis** → Popular mobile brands  

- **Ratings Distribution** → Most phones have ratings above 4.0  

- **MRP vs. MSP Comparison** → Highlights biggest discounts  

- **Discount Trends** → Budget phones have higher discounts  

- **Top Rated Phones** → Apple & premium segment show high ratings  

## 🚀 Steps Performed  

1. **Data Cleaning & Preprocessing**  

   - Removed missing/duplicate values  

   - Converted price columns to numeric  

   - Extracted brand and features  

2. **Exploratory Data Analysis (EDA)**  

   - Rating distributions  

   - Brand-wise comparisons  

   - Price vs. Discount analysis  

3. **Visualization**  

   - Bar plots for brand popularity  

   - Scatter plots for MRP vs. MSP  

   - Heatmaps for correlations  

   - Histograms for ratings  

## 📌 Results & Insights  

- **Realme & Redmi** dominate with maximum models & ratings  

- **Apple** has high ratings but low discounts  

- **Discount %** is highest for budget phones (₹7k–₹15k)  

- Customers prefer mobiles with **ratings > 4.2** and heavy discounts  

---

✨ This project provides **valuable insights into customer behavior, pricing strategies, and brand competition** in the Flipkart smartphone market.