https://github.com/shubhamgoyal575/gamezone-data-cleaning
https://github.com/shubhamgoyal575/gamezone-data-cleaning
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/shubhamgoyal575/gamezone-data-cleaning
- Owner: shubhamgoyal575
- Created: 2025-05-30T12:02:02.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-05-30T12:06:15.000Z (5 months ago)
- Last Synced: 2025-05-30T16:51:21.416Z (5 months ago)
- Size: 4.57 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🧹 Virtual Data GameZone - Data Cleaning Project
This project focuses on cleaning and documenting the data from the **Virtual Data GameZone** dataset, which includes:
- 📄 **Orders Sheet** with `21,864` records
- 🌍 **Regions Sheet** with `192` records
The goal was to prepare the data for meaningful analysis by applying industry-standard data cleaning techniques and principles.
---
## 📁 Dataset Overview
### 1. Orders Sheet (`orders`)
| Column Name | Description |
|---------------------------|-------------------------------------|
| `USER_ID` | Unique ID of the customer |
| `ORDER_ID` | Unique ID of the order |
| `PURCHASE_TS` | Timestamp of purchase |
| `SHIP_TS` | Timestamp of shipping |
| `PRODUCT_NAME` | Name of the purchased product |
| `PRODUCT_ID` | Unique ID of the product |
| `USD_PRICE` | Price of the product in USD |
| `PURCHASE_PLATFORM` | Platform used for purchase |
| `MARKETING_CHANNEL` | Marketing channel used |
| `ACCOUNT_CREATION_METHOD` | Account sign-up method |
| `COUNTRY_CODE` | Country code of the user |
### 2. Regions Sheet (`regions`)
| Column Name | Description |
|----------------|----------------------------|
| `COUNTRY_CODE` | Country code reference |
| `REGION` | Associated region |
---
## 🛠️ Cleaning Approach (C.L.E.A.N Framework)
We followed the **C.L.E.A.N** methodology for structured and thoughtful cleaning:
### ✅ C - Conceptualize the Data
- Understood what each row represents (one product order).
- Identified key metrics: `USD_PRICE`, `SHIP_TS - PURCHASE_TS` (delivery time).
- Identified key dimensions: `PRODUCT_NAME`, `COUNTRY_CODE`, `MARKETING_CHANNEL`, etc.
### ✅ L - Locate Solvable Issues
Addressed solvable issues such as:
- Inconsistent date formats
- Null values in categorical fields
- Duplicate `ORDER_ID`s
- Inconsistent `COUNTRY_CODE`s
### ✅ E - Evaluate Unsolvable Issues
- Missing values without a source of truth
- Preserved legitimate outliers after business context review
- Checked business logic (e.g., `SHIP_TS` should not precede `PURCHASE_TS`)
> 🧠 *Imputation is avoided unless there’s a reliable source or strong business logic.*
### ✅ A - Augment the Data
- Added derived columns:
- `SHIP_TIME_DAYS` (Shipping duration)
- `PURCHASE_YEAR`, `PURCHASE_MONTH`
- Merged region information using `COUNTRY_CODE`
### ✅ N - Note and Document
Created an **Issue Log Sheet** with columns:
| Table | Column | Issue | Row_Count | Row_PCT | Solvable? | Resolution |
|-------|--------|-------|-----------|---------|-----------|------------|
---
## 📄 Deliverables
- ✅ `orders_cleaned` – Cleaned & augmented orders data
- ✅ `region_cleaned` – Cleaned regions reference data
- ✅ `issue_log` – Full documentation of identified & resolved issues
---
## 📌 Data Cleaning Philosophy
> **We are not aiming for perfect data — only data that is good enough to analyze, share, and iterate on.**
---
## 📊 Tools Used
- Microsoft Excel / Google Sheets
- Formulas, filters, date-time functions
- Manual inspection and logical validation
---
## 🤔 Final Thoughts
Before jumping into analysis, thoughtful data analysts ask:
> *What am I looking at? What story could this data help me tell?*
This project ensures that the dataset is ready to answer those questions effectively and reliably.
---
## 📬 Contact
For suggestions or feedback, feel free to open an [issue](../../issues) or connect via LinkedIn.