autoflowml 0.1.0

An end-to-end Python library for automated data preprocessing and model selection, designed to streamline ML workflows

AutoFlowML

Automated data preprocessing and model selection for production and analytics.

Overview

AutoFlowML automates the tedious parts of machine learning:

Data Cleaning (missing values, outliers, duplicates)

Feature Engineering (encoding, scaling, selection)

Model Selection (AutoML with optimized hyperparameters)

Built for data scientists who want to focus on insights, not boilerplate code.

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Installation

pip install autoflowml

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Getting Started

from autoflowml import CleanIt, NullFixer, run_tiny_automl
import pandas as pd

# Load your dataset
df = pd.read_csv("data.csv")

# Step 1: Clean the data
df_clean = CleanIt(df).full_clean()

# Step 2: Fix missing values
df_imputed = NullFixer(df_clean).nullfix_knn(n_neighbors=5)

# Step 3: Train the best model
run_tiny_automl(df_imputed, target_column="target", problem_type="regression")

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Show Off More Features!

AutoFlowML is built for production. Here's how to remove outliers, encode features, and save models.

from autoflowml import AutoOutlier, CategoricalMaster
from joblib import dump, load

# Step 1: Outlier removal
df_no_outliers = AutoOutlier(method="isolation_forest").fit_transform(df_imputed)

# Step 2: Categorical encoding
encoded_df = CategoricalMaster(df_no_outliers, target_column="target").encode_auto()

# Step 3: Train the model
model = run_tiny_automl(encoded_df, target_column="target", problem_type="classification")

# Step 4: Save the model
dump(model, "best_model.pkl")

# Step 5: Load and predict
model = load("best_model.pkl")
predictions = model.predict(encoded_df.drop("target", axis=1))

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3rd Party Integrations

AutoFlowML plays well with popular ML libraries:

• evalml for automated model selection and hyperparameter tuning
• category_encoders for powerful encoding techniques
• Fully compatible with scikit-learn pipelines

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Feature Highlights

Analytics + Cleaning

• Rename messy columns
• Remove duplicates
• Fix null values with KNN, MICE, or time-aware methods

Outlier Detection

• Supports Z-score, IQR, Isolation Forest

Encoding Support

• One-Hot, Binary, and Target encoding built-in

AutoML

• Regression and Classification
• Feature selection and model tuning with evalml

Ready for Deployment

• Serialize models using joblib
• Predict on individual rows or full DataFrames

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Feature Learning (Advanced)

You can use deep learning to extract useful features, then combine them with traditional models for superior performance. Use feature_learning=True in run_tiny_automl() (coming soon).

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Categorical Ensembling (Coming Soon)

Train one model per category with train_categorical_ensemble(), perfect for use-cases like one model per region/store/customer.

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Documentation

Full documentation coming soon. Until then, refer to examples in this README or explore the API directly.

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What This Project Automates

• Column cleanup, outlier detection, and data imputation
• Encoding categorical variables
• Automated model selection and evaluation
• Feature importance and selection
• Serialization for production

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Running Tests

If contributing or debugging, run:

pytest tests/

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Why AutoFlowML?

• Saves hours of preprocessing
• Production-ready pipelines
• Transparent, extensible design
• Lightning-fast single-row predictions