evolveml 0.2.0

A Python ML library that evolves with your data. Batch + Real-time Learning, AutoML, XAI, NLP, RL, Anomaly Detection & more.

evolveml 🚀

A Python ML library that evolves with your data.
Batch + Real-time Learning + Latest 2026 AI Trends — no sklearn needed!

Author: SAPPA VAMSI

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Install

pip install evolveml

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What's Inside (v0.2.0)

🧠 Core Models

Module	Description
DecisionTreeClassifier	Decision Tree from scratch
LinearRegressionModel	Linear Regression
LogisticRegressionModel	Logistic Regression + online updates
NeuralNetwork	Neural Network from scratch

🔄 Real-time / Online Learning

Module	Description
StreamLearner	Learns one sample at a time in real-time

🎯 Ready-to-use Tasks

Module	Description
FraudDetector	Real-time bank fraud detection
ImageClassifier	Image classification
SpamDetector	Email spam detection
StockPredictor	Stock price prediction

🔥 Latest 2026 Trending Modules

Module	Trend	Description
AutoFeatureSelector	AutoML	Auto-selects best features
AnomalyDetector	Edge AI / IoT	Detects anomalies in streams
ConceptDriftDetector	Adaptive ML	Detects data distribution changes
ExplainableModel	XAI	Explains WHY model predicted
ReinforcementAgent	Agentic AI	Q-Learning agent
SentimentAnalyzer	NLP	Real-time text sentiment
TransferLearner	Transfer Learning	Reuse knowledge across tasks

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Quick Examples

🤖 AutoML - Auto Feature Selection

from evolveml import AutoFeatureSelector
selector = AutoFeatureSelector(top_k=5)
X_best = selector.fit_transform(X_train, y_train)
selector.report()

🚨 Anomaly Detection (IoT/Edge)

from evolveml import AnomalyDetector
detector = AnomalyDetector(threshold=2.5)
detector.fit(normal_data)
result = detector.detect(new_sensor_reading)
print(result)  # {'is_anomaly': True, 'status': '🚨 ANOMALY'}

📉 Concept Drift Detection

from evolveml import ConceptDriftDetector
drift = ConceptDriftDetector()
for pred, actual in prediction_stream:
    status = drift.update(pred, actual)
    if status['drift_detected']:
        print("⚠️ Retrain your model!")

🔍 Explainable AI

from evolveml import ExplainableModel, DecisionTreeClassifier
model = DecisionTreeClassifier()
model.fit(X_train, y_train)
xai = ExplainableModel(model, feature_names=['age', 'amount', 'hour'])
xai.fit(X_train, y_train)
xai.explain(X_test[0])

🎮 Reinforcement Learning Agent

from evolveml import ReinforcementAgent
agent = ReinforcementAgent(n_states=100, n_actions=4)
action = agent.act(state)
agent.learn(state, action, reward=+1, next_state=next_state)

💬 Sentiment Analysis

from evolveml import SentimentAnalyzer
analyzer = SentimentAnalyzer()
result = analyzer.analyze("This product is absolutely amazing!")
print(result)  # {'sentiment': 'POSITIVE 😊', 'confidence': 0.87}
analyzer.learn("brilliant", label='positive')  # teach new words

🔁 Transfer Learning

from evolveml import TransferLearner, DecisionTreeClassifier
source = DecisionTreeClassifier()
source.fit(X_source, y_source)
transfer = TransferLearner(source)
transfer.fit(X_target, y_target)  # learns faster with less data!

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License

MIT — Free to use!