https://github.com/harshitwaldia/exploratory-data-analysis

Exploratory Data Analysis with data cleaning, visualization, and insights discovery.
https://github.com/harshitwaldia/exploratory-data-analysis

exploratory-data-analysis jypyternotebook outlier-detection python sentiment-analysis textblob wordcloud-visualization

Last synced: about 2 months ago
JSON representation

Exploratory Data Analysis with data cleaning, visualization, and insights discovery.

• Host: GitHub
• URL: https://github.com/harshitwaldia/exploratory-data-analysis
• Owner: HarshitWaldia
• Created: 2025-09-26T04:21:37.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2025-09-26T04:45:25.000Z (9 months ago)
• Last Synced: 2025-09-26T06:20:44.393Z (9 months ago)
• Topics: exploratory-data-analysis, jypyternotebook, outlier-detection, python, sentiment-analysis, textblob, wordcloud-visualization
• Language: Jupyter Notebook
• Homepage:
• Size: 6.05 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# 📊 Amazon Product Reviews - Exploratory Data Analysis (EDA)

## 📌 Overview

This project performs **Exploratory Data Analysis (EDA)** on an Amazon product dataset.  

The dataset contains product details, prices, discounts, ratings, reviews, and user information.  

The goal of this analysis is to:

- Understand the structure and quality of the dataset.  

- Identify trends in pricing, discounting, and ratings.  

- Explore customer review patterns.  

- Detect potential issues like missing values, duplicates, or imbalances.  

---

## 🗂️ Dataset Description

The dataset includes the following key columns:

| Column | Description |

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

| `product_id` | Unique identifier for each product |

| `product_name` | Name/description of the product |

| `category` | Product category (e.g., Electronics, Accessories) |

| `discounted_price` | Selling price after discount |

| `actual_price` | Original price before discount |

| `discount_percentage` | Percentage discount offered |

| `rating` | Customer rating (out of 5) |

| `rating_count` | Number of ratings |

| `about_product` | Short description/features |

| `user_id` | Unique ID of reviewer |

| `user_name` | Name of reviewer |

| `review_id` | Unique ID of review |

| `review_title` | Title of review |

| `review_content` | Full review text |

| `img_link` | Product image link |

| `product_link` | Product page link |

---

## 🔍 Steps in EDA

### 1. **Data Inspection**

- Used `.info()` to check data types, null values, and dataset size.  

- Found that most columns are complete, with very few missing values.  

### 2. **Descriptive Statistics**

- `.describe()` applied to both numeric and categorical columns.  

- Found **mean ≈ median** in prices → data is fairly symmetric.  

- Ratings cluster around **4.1**, showing positive bias.  

### 3. **Correlation Analysis**

- Computed correlation matrix for numeric features.  

- Observed strong negative correlation between `discount_percentage` and `discounted_price`.  

- Weak/no correlation between `rating` and price → ratings are not price-driven.  

### 4. **Visualizations**

- **Bar Chart**: Average rating per category.  

- **Boxplot**: Discount % distribution across categories.  

- **Scatterplot**: Discounted price vs rating.  

- **Word Cloud**: Most frequent terms in reviews.  

- **Heatmap**: Correlations between numeric features.  

### 5. **Data Quality Checks**

- Found duplicate product IDs (same product reviewed multiple times).  

- Prices and discounts stored as strings (`₹`, `%`) → cleaned and converted to numeric.  

---

## 📈 Insights

- Many products receive **4★ or higher** → customer reviews skew positive.  

- Discounts are widely offered (~50% most frequent).  

- Certain categories dominate the dataset (e.g., Electronics & Accessories).  

- Some reviews and users appear multiple times → dataset contains duplicate/overlapping entries.  

---

## 🛠️ Tools & Libraries

- **Python 3**  

- **Pandas** → data cleaning & manipulation  

- **NumPy** → numerical operations  

- **Matplotlib / Seaborn** → data visualization  

- **WordCloud** → review text analysis  

---

## 📌 How to Run

1. Clone the repository:  

   ```

   git clone https://github.com/HarshitWaldia/Exploratory-Data-Analysis.git

   cd Exploratory-Data-Analysis

   ```

2. Install required libraries:

  ```

  pip install -r requirements.txt

  ```

3.Open the Jupyter Notebook:

  ```

  jupyter notebook Amazon_EDA.ipynb

  ```

4. Run the cells step by step to reproduce the analysis.

## 🚀 Future Work

- **Build a recommendation system using ratings & categories.**

- **Perform sentiment analysis on review text.**

- **Use ML models to predict product ratings based on price & discount.**

## 👨‍💻 Author

**Harshit Waldia**