AnLOF 1.0.0

Automatic Outliers Handler Library

AnLOF

AnLOF is a machine learning utility library build to automatically handle outliers Effectively.

AnLOF uses multiple outliers handling tools and helps you automatically select the best-performing method.

The Ooutliers Handling Tools Used are:

• IQR Score
• Z-score
• Winsorization
• Median Scoring
• Mean Scoring
• Isolation Forest
• Boxcox Transformation
• k-Nearest Neighbors
• XGBRegression
• LGBMRegresion
• CatBoostRegression
• Standard Scaler
• Robust Scaler
• MinMax Scaler
• Log Transformation
• Quantile Normalization

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Features

• Automatic outlier detection & handling
• Multiple preprocessing strategies
• Model-based evaluation
• Selects best method based on chosen metric
• Get your dataset Ready with the best methods applied

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Hyperparameters

• X_train : Your training features set
• X_val : Your validation features set
• y_train : your training target set
• t_val : your validation target set
• features : Features that contains outliers. [Note : Not all the feautures of your dataset]
• base_model : The model based on which you want to evaluate the tools performance and select the best one
• metric : The evaluation metric you use
• higher_is_better : "YES" for metrics where hihger is better for example accuracy_score or f1_score "NO" otherwise.

Author

This Project is developed and managed by MurtazaA2010. For any help email him at: [e-mail]

Installation

pip install AnLOF


```python
from AnLOF.AnLOF_module import AnLOF
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

anlof = AnLOF(
    X_train,
    X_val,
    y_train,
    y_val,
    features=["feature1", "feature2"],  # features containing outliers
    base_model=LinearRegression,
    metric=mean_squared_error,
    higher_is_better=False
)

best_X_train, best_X_val, best_method, performance_df = anlof.forward()

## best_method : the method with the best score
## performace_df: contains the performance of all the methods 

print("Best method:", best_method)
print("Best X_train:")
print(best_X_train.head())

print("Best X_val:")
print(best_X_val.head())

print("Performance comparison:")
print(performance_df)