datacleaner-ck 1.1.1

A practice project combining NumPy, Pandas, Matplotlib, and Seaborn for learning and revision.

🧹 DataCleaner

A simple and flexible Python utility for data cleaning and preprocessing.
It helps you handle missing values, remove duplicates, treat outliers, reduce features, and encode categorical data — all in one place.

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✨ Features

• Missing Values Handling
  • Drop, Mean, Median, Mode, KNN Imputation
• Remove Duplicates
• Outlier Detection & Removal
  • IQR, Z-score, Isolation Forest
• Feature Reduction
  • Low variance feature removal
  • High correlation feature removal
• Encoding Categorical Data
  • Label Encoding
  • One-Hot Encoding
  • Frequency Encoding
  • Auto Encoding (chooses based on cardinality)
• Report Generation – keeps track of all cleaning steps applied

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📦 Installation

Clone the repo:

git clone https://github.com/ck-ahmad/datacleaner.git

Install dependencies:

pip install -r requirements.txt

🚀 Usage Example

import pandas as pd
from cleaner import DataCleaner

# Sample dataset
df = pd.DataFrame({
    "Name": ["Ali", "Sara", "John", "Ali", None],
    "Age": [25, None, 30, 25, 22],
    "Salary": [50000, 60000, None, 50000, 45000],
    "City": ["Lahore", "Karachi", "Karachi", "Lahore", "Islamabad"]
})

# Initialize cleaner
cleaner = DataCleaner(df)

# Apply cleaning steps
cleaned_df = (
    cleaner
    .handle_missing(strategy="mean")   # fill missing values
    .remove_duplicates()               # remove duplicate rows
    .handle_outliers(method="iqr")     # handle outliers
    .remove_low_variance(threshold=0.01)
    .remove_high_correlation(corr_threshold=0.9)
    .encode(method="auto")             # encode categorical columns
    .get_data()
)

# Get report of cleaning steps
report = cleaner.get_report()

print("✅ Cleaned Data:")
print(cleaned_df)

print("\n📝 Cleaning Report:")
print(report)

📋 Example Output

Cleaned Data

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   Age   Salary  City_Karachi  City_Lahore  City_Islamabad
0   25  50000.0             0            1               0
1   25  50000.0             1            0               0
2   22  45000.0             0            0               1

Cleaning Report

 Step  Action
 1     Filled missing Age with mean
 2     Filled missing Salary with mean
 3     Dropped rows with missing in Name
 4     Removed 1 duplicate rows
 5     Removed outliers in Age using IQR
 6     Applied Auto Encoding

🛠️ Requirements

pandas numpy scikit-learn

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pip install pandas numpy scikit-learn

📖 License

This project is licensed under the MIT License – feel free to use and Open To Collabrate For Contibutions in it.

👨‍💻 Author

Ahmad Abdullah 🌐 LinkedIn: linkedin.com/in/ahmad0763 💻 GitHub: github.com/ck-ahmad