🚗 Task 1 – Data Cleaning & Preprocessing (Excel-Based)

• Tool Used: Microsoft Excel • Dataset: Car Price Dataset (Kaggle)

---

🎯 Objective Clean a raw dataset using Excel by identifying and fixing:

• ❌ Missing values
• 📄 Duplicate rows
• 🧩 Inconsistent text/casing
• 📅 Non-uniform date formats
• 🧮 Incorrect data types

Goal: Make the data clean, consistent, and ready for analysis.

---

📂 Dataset Overview

	Raw Data	Cleaned Data
Rows	301	298
Columns	16	16
Null Values	3	0
Duplicates	1	0

---

🛠 Excel Cleaning Steps

1️⃣ Missing Values

• Applied filters → found blanks in condition, odometer, color
• Removed 3 rows with critical missing data

2️⃣ Remove Duplicates

• Used Data → Remove Duplicates (across all columns)

3️⃣ Standardized Text Formats

• Applied =PROPER() to fix inconsistent casing (e.g., bmw → Bmw)
• Used Find & Replace to clean up repeated seller formats

4️⃣ Column Header Formatting

• Renamed columns: Year → year, SellingPrice → selling_price, etc.
• Ensured no spaces, consistent lowercase

5️⃣ Date Formatting

• Fixed saledate column to show consistent format → dd-mm-yyyy

6️⃣ Verified Data Types

• Checked that odometer, mmr, and sellingprice are numeric
• Dates set to proper format in Excel

---

✅ Results After Cleaning

• Dataset now has no nulls, no duplicates, and clean, readable formatting.
• All text is standardized, numeric formats are correct, and dates are consistent.
• Ready for analysis or visualization!

---

📁 Files Included in this Repo File Description car_price_raw.xlsx Original raw dataset car_price_cleaned.xlsx Cleaned dataset after all steps README.md Task explanation and documentation screenshots visuals from Excel

🧠 What I Learned

• How to clean data in Excel using built-in tools
• How to handle missing values, remove duplicates, and format text/dates properly
• Why standardization and formatting is crucial before analysis
• Documenting the cleaning process is just as important as doing it

---

📝 Notes

• Excel’s native tools (filters, functions, formatting) are powerful for quick cleaning
• Cleaned data reduces risk of incorrect analysis or model training
• Always save a backup of the original dataset before cleaning!

🔚 Conclusion This task gave me hands-on experience with Excel’s powerful data cleaning tools. I feel confident handling messy datasets and prepping them for real-world analysis. 🧠✅

👨‍💻 Author

Somya Sinha Aspiring Data Analyst | SQL Enthusiast | Excel & Power BI Learner

🔗 www.linkedin.com/in/somyasinha100 📧 somyasinha615@gmail.com