Data Cleaning Project

A collection of 5 data cleaning projects demonstrating data preprocessing, feature engineering, and CSV output.
This repository is designed for portfolio purposes and is suitable for showcasing Python data cleaning skills.

---

Projects Overview

1. House Dataset

Location: Project_HouseNew/
Raw Data: Project_HouseNew/Datasets/HouseNew.csv
Cleaned Data: Project_HouseNew/CleanedDatasets/clean_HouseNew.csv
Script: Project_HouseNew/Scripts/Clean_HouseNew.py

Description:

• Fills missing Address values using mode
• Adds mean price feature (MeanPrice)
• Calculates difference from mean price (DifPrice)
• Saves cleaned dataset as CSV

Sample CSV Output:

Elevator	Floor	Area	Parking	Room	Warehouse	YearOfConstruction	Address	Price	MeanPrice	DifPrice
True	1	311	True	4	True	1396	دروس	31100000000	6159466196	24940533804
True	13	99	True	2	True	1401	دریاچه شهدای خلیج فارس	4700000000	6159466196	-1459466196
True	15	251	True	3	True	1375	شهرک غرب	30120000000	6159466196	23960533804

---

2. NBA Dataset

Location: Project_NBA/
Raw Data: Project_NBA/Datasets/NBA.csv
Cleaned Data: Project_NBA/CleanedDatasets/clean_NBA.csv
Script: Project_NBA/Scripts/Clean_NBA.py

Description:

• Remove empty rows
• Convert Height to inches and centimeters
• Convert Weight to pounds and kilograms
• Fill missing College values using mode
• Fill missing Salary values using mean
• Save cleaned dataset as CSV

Sample CSV Output:

Name	Team	Number	Position	Age	Height	Height_in	Height_cm	Weight	Weight_lb	Weight_kg	College	Salary
Avery Bradley	Boston Celtics	0	PG	25	6-2	74	188	180	180	81.6	Texas	7730337
Jae Crowder	Boston Celtics	99	SF	25	6-6	78	198	235	235	106.6	Marquette	6796117
John Holland	Boston Celtics	30	SG	27	6-5	77	195	205	205	93.0	Boston University	4842684

---

3. Sales Dataset

Location: Project_Sales/
Raw Data: Project_Sales/Datasets/Sales.csv
Cleaned Data: Project_Sales/CleanedDatasets/clean_Sales.csv
Script: Project_Sales/Scripts/Clean_Sales.py

Description:

• Strips extra spaces from CategoryOfBook
• Removes commas , and $ from SalesAmount and converts to float
• Saves cleaned dataset as CSV

Sample CSV Output:

ID	BookID	BookName	CategoryOfBook	SalesAmount
1	BU1032	The Busy Executive's Database Guide	business	299.85
2	BU1111	Cooking with Computers: Surreptitious Balance Sheets	business	298.75
3	BU2075	You Can Combat Computer Stress!	business	104.65

---

4. SMS Spam Dataset

Location: Project_SMSSpam/
Raw Data: Project_SMSSpam/Datasets/SMSSpamCollection
Cleaned Data: Project_SMSSpam/CleanedDatasets/clean_SMSSPAMCollection.csv
Script: Project_SMSSpam/Scripts/SMSSpamCollection.py

Description:

• Removes punctuation from message column
• Calculates length of messages (LenOfMessage)
• Saves cleaned dataset as CSV

Sample CSV Output:

label	clean_message	LenOfMessage
ham	Go until jurong point crazy Available only in bugis n great world la e buffet Cine there got amore wat	111
ham	Ok lar Joking wif u oni	29
spam	Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005 Text FA to 87121 to receive entry questionstd txt rateTCs apply 08452810075over18s	155

---

5. Titanic Dataset

Location: Project_Titanic/
Raw Data: Project_Titanic/Datasets/Titanic.csv
Cleaned Data: Project_Titanic/CleanedDatasets/clean_Titanic.csv
Script: Project_Titanic/Scripts/Titanic.py

Description:

• Drops Cabin column
• Fills missing Age values with mean
• Fills missing Embarked values with mode
• Saves cleaned dataset as CSV

Sample CSV Output:

PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Embarked
1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.25	S
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Thayer)	female	38.0	1	0	PC 17599	71.2833	C
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.925	S

---

Installation

1. Clone the repository:

git clone git@github.com:SamiraSiavash/Data_Cleaning_Project.git
cd Data_Cleaning_Project

2. Create a virtual environment:

python -m venv .venv

3. Activate the virtual environment:

* Windows:

.venv\Scripts\activate

* macOS / Linux:

source .venv/bin/activate

4. Install dependencies:

pip install -r requirements.txt

---

Usage

Run the scripts from each project folder using Python:

python Project_HouseNew/Scripts/Clean_HouseNew.py
python Project_NBA/Scripts/Clean_NBA.py
python Project_Sales/Scripts/Clean_Sales.py
python Project_SMSSpam/Scripts/Clean_SMSSpam.py
python Project_Titanic/Scripts/Clean_Titanic.py

All cleaned CSVs will be saved in the corresponding CleanedDatasets/ folder.

---

Folder Structure

Data_Cleaning_Project/
├── Project_HouseNew/
│   ├── Datasets/
│   ├── CleanedDatasets/
│   └── Scripts/
├── Project_NBA/
│   ├── Datasets/
│   ├── CleanedDatasets/
│   └── Scripts/
├── Project_Sales/
│   ├── Datasets/
│   ├── CleanedDatasets/
│   └── Scripts/
├── Project_SMSSpam/
│   ├── Datasets/
│   ├── CleanedDatasets/
│   └── Scripts/
├── Project_Titanic/
│   ├── Datasets/
│   ├── CleanedDatasets/
│   └── Scripts/
├── .gitignore
├── README.md
└── requirements.txt

---

Notes

• All scripts are independent and can be run separately.
• The project demonstrates basic data cleaning and feature engineering suitable for portfolio purposes.
• Use requirements.txt to ensure consistent dependencies.

---

License

MIT License (optional)

---

Author

Samira Siavash

🔗 GitHub: https://github.com/SamiraSiavash

🔗 LinkedIn: https://linkedin.com/in/samira-siavash