It's a Python machine learning package
Project description
mlwithmsb: A Versatile Python Package for Outlier Handling in Data Analysis
Effortlessly clean your datasets and ensure data quality with Outlier Cleaner! This user-friendly package empowers you to detect and address outliers using multiple effective methods, tailored to your specific needs.
Key Features:
- Flexible Outlier Detection: Choose from IQR, Z-score, or visual inspection methods to identify outliers based on statistical principles or your expert judgment.
- Customizable Thresholds: Fine-tune the sensitivity of outlier detection to match your dataset's characteristics and analysis goals.
- Visual Exploration: Leverage informative histograms and box plots to visualize data distribution and guide outlier identification in the visual method.
- Streamlined NaN Handling: Automatically remove rows containing missing values to maintain data integrity.
- Easy Integration: Seamlessly incorporate outlier cleaning into your data analysis workflows with a simple function call.
Benefits:
- Improve data quality and accuracy for reliable analysis outcomes.
- Gain deeper insights into your data by identifying and addressing potential anomalies.
- Enhance model performance by mitigating the impact of outliers.
- Foster reproducibility and transparency in your data analysis processes.
Installation:
pip install mlwithmsb
Usage:
import pandas as pd
from mlwithmsb import remove_outliers
# Load your dataset
data = pd.read_csv("your_data.csv")
# Clean the data using your preferred method and threshold
cleaned_data = remove_outliers(data, method="iqr", threshold=1.5) # Example
**Embrace robust data cleaning for accurate and reliable insights—install mlwithmsb today!**
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