Intelligent conversational machine learning pipeline integrating LangChain Agent and XGBoost for automating data science workflows
In this tutorial, we combine the analytical capabilities of XGBoost with the conversational intelligence of LangChain. We built an end-to-end pipeline that can generate synthetic datasets, train XGBoost models, evaluate their performance, and visualize key insights, all orchestrated through the modular LangChain tool. In doing so, we demonstrate how conversational AI can interact seamlessly with machine learning workflows, enabling agents to intelligently manage the entire ML lifecycle in a structured and human-like manner. Through this process, we experienced how the integration of inference-driven automation can make machine learning interactive and explainable. Check The complete code is here.
!pip install langchain langchain-community langchain-core xgboost scikit-learn pandas numpy matplotlib seaborn
import pandas as pd
import numpy as np
import xgboost as xgb
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns
from langchain.tools import Tool
from langchain.agents import AgentType, initialize_agent
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain_community.llms.fake import FakeListLLM
import jsonWe start by installing and importing all the basic libraries required for this tutorial. We use LangChain for agent AI integration, XGBoost and scikit-learn for machine learning, and Pandas, NumPy, and Seaborn for data processing and visualization. Check The complete code is here.
class DataManager:
"""Manages dataset generation and preprocessing"""
def __init__(self, n_samples=1000, n_features=20, random_state=42):
self.n_samples = n_samples
self.n_features = n_features
self.random_state = random_state
self.X_train, self.X_test, self.y_train, self.y_test = None, None, None, None
self.feature_names = [f'feature_{i}' for i in range(n_features)]
def generate_data(self):
"""Generate synthetic classification dataset"""
X, y = make_classification(
n_samples=self.n_samples,
n_features=self.n_features,
n_informative=15,
n_redundant=5,
random_state=self.random_state
)
self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(
X, y, test_size=0.2, random_state=self.random_state
)
return f"Dataset generated: {self.X_train.shape[0]} train samples, {self.X_test.shape[0]} test samples"
def get_data_summary(self):
"""Return summary statistics of the dataset"""
if self.X_train is None:
return "No data generated yet. Please generate data first."
summary = {
"train_samples": self.X_train.shape[0],
"test_samples": self.X_test.shape[0],
"features": self.X_train.shape[1],
"class_distribution": {
"train": {0: int(np.sum(self.y_train == 0)), 1: int(np.sum(self.y_train == 1))},
"test": {0: int(np.sum(self.y_test == 0)), 1: int(np.sum(self.y_test == 1))}
}
}
return json.dumps(summary, indent=2)We define the DataManager class to handle dataset generation and preprocessing tasks. Here, we use scikit-learn’s make_classification function to create comprehensive classification data, split it into training and test sets, and generate a concise summary containing sample counts, feature dimensions, and class distributions. Check The complete code is here.
class XGBoostManager:
"""Manages XGBoost model training and evaluation"""
def __init__(self):
self.model = None
self.predictions = None
self.accuracy = None
self.feature_importance = None
def train_model(self, X_train, y_train, params=None):
"""Train XGBoost classifier"""
if params is None:
params = {
'max_depth': 6,
'learning_rate': 0.1,
'n_estimators': 100,
'objective': 'binary:logistic',
'random_state': 42
}
self.model = xgb.XGBClassifier(**params)
self.model.fit(X_train, y_train)
return f"Model trained successfully with {params['n_estimators']} estimators"
def evaluate_model(self, X_test, y_test):
"""Evaluate model performance"""
if self.model is None:
return "No model trained yet. Please train model first."
self.predictions = self.model.predict(X_test)
self.accuracy = accuracy_score(y_test, self.predictions)
report = classification_report(y_test, self.predictions, output_dict=True)
result = {
"accuracy": float(self.accuracy),
"precision": float(report['1']['precision']),
"recall": float(report['1']['recall']),
"f1_score": float(report['1']['f1-score'])
}
return json.dumps(result, indent=2)
def get_feature_importance(self, feature_names, top_n=10):
"""Get top N most important features"""
if self.model is None:
return "No model trained yet."
importance = self.model.feature_importances_
feature_imp_df = pd.DataFrame({
'feature': feature_names,
'importance': importance
}).sort_values('importance', ascending=False)
return feature_imp_df.head(top_n).to_string()
def visualize_results(self, X_test, y_test, feature_names):
"""Create visualizations for model results"""
if self.model is None:
print("No model trained yet.")
return
fig, axes = plt.subplots(2, 2, figsize=(15, 12))
cm = confusion_matrix(y_test, self.predictions)
sns.heatmap(cm, annot=True, fmt="d", cmap='Blues', ax=axes[0, 0])
axes[0, 0].set_title('Confusion Matrix')
axes[0, 0].set_ylabel('True Label')
axes[0, 0].set_xlabel('Predicted Label')
importance = self.model.feature_importances_
indices = np.argsort(importance)[-10:]
axes[0, 1].barh(range(10), importance[indices])
axes[0, 1].set_yticks(range(10))
axes[0, 1].set_yticklabels([feature_names[i] for i in indices])
axes[0, 1].set_title('Top 10 Feature Importances')
axes[0, 1].set_xlabel('Importance')
axes[1, 0].hist([y_test, self.predictions], label=['True', 'Predicted'], bins=2)
axes[1, 0].set_title('True vs Predicted Distribution')
axes[1, 0].legend()
axes[1, 0].set_xticks([0, 1])
train_sizes = [0.2, 0.4, 0.6, 0.8, 1.0]
train_scores = [0.7, 0.8, 0.85, 0.88, 0.9]
axes[1, 1].plot(train_sizes, train_scores, marker="o")
axes[1, 1].set_title('Learning Curve (Simulated)')
axes[1, 1].set_xlabel('Training Set Size')
axes[1, 1].set_ylabel('Accuracy')
axes[1, 1].grid(True)
plt.tight_layout()
plt.show()We implement XGBoostManager to train, evaluate, and interpret our classifier end-to-end. We fit XGBClassifier, calculate accuracy and per-class metrics, extract the most important feature importance, and visualize the results using confusion matrices, importance charts, distribution comparisons, and simple learning curve views. Check The complete code is here.
def create_ml_agent(data_manager, xgb_manager):
"""Create LangChain agent with ML tools"""
tools = [
Tool(
name="GenerateData",
func=lambda x: data_manager.generate_data(),
description="Generate synthetic dataset for training. No input needed."
),
Tool(
name="DataSummary",
func=lambda x: data_manager.get_data_summary(),
description="Get summary statistics of the dataset. No input needed."
),
Tool(
name="TrainModel",
func=lambda x: xgb_manager.train_model(
data_manager.X_train, data_manager.y_train
),
description="Train XGBoost model on the dataset. No input needed."
),
Tool(
name="EvaluateModel",
func=lambda x: xgb_manager.evaluate_model(
data_manager.X_test, data_manager.y_test
),
description="Evaluate trained model performance. No input needed."
),
Tool(
name="FeatureImportance",
func=lambda x: xgb_manager.get_feature_importance(
data_manager.feature_names, top_n=10
),
description="Get top 10 most important features. No input needed."
)
]
return toolsWe define the create_ml_agent function to integrate machine learning tasks into the LangChain ecosystem. Here, we encapsulate key operations, data generation, aggregation, model training, evaluation, and feature analysis into the LangChain tool, enabling conversational agents to seamlessly execute end-to-end ML workflows through natural language instructions. Check The complete code is here.
def run_tutorial():
"""Execute the complete tutorial"""
print("=" * 80)
print("ADVANCED LANGCHAIN + XGBOOST TUTORIAL")
print("=" * 80)
data_mgr = DataManager(n_samples=1000, n_features=20)
xgb_mgr = XGBoostManager()
tools = create_ml_agent(data_mgr, xgb_mgr)
print("n1. Generating Dataset...")
result = tools[0].func("")
print(result)
print("n2. Dataset Summary:")
summary = tools[1].func("")
print(summary)
print("n3. Training XGBoost Model...")
train_result = tools[2].func("")
print(train_result)
print("n4. Evaluating Model:")
eval_result = tools[3].func("")
print(eval_result)
print("n5. Top Feature Importances:")
importance = tools[4].func("")
print(importance)
print("n6. Generating Visualizations...")
xgb_mgr.visualize_results(
data_mgr.X_test,
data_mgr.y_test,
data_mgr.feature_names
)
print("n" + "=" * 80)
print("TUTORIAL COMPLETE!")
print("=" * 80)
print("nKey Takeaways:")
print("- LangChain tools can wrap ML operations")
print("- XGBoost provides powerful gradient boosting")
print("- Agent-based approach enables conversational ML pipelines")
print("- Easy integration with existing ML workflows")
if __name__ == "__main__":
run_tutorial()We use run_tutorial() to orchestrate a complete workflow in which we generate data, train and evaluate XGBoost models, and surface feature importance. We then visualized the results and printed key takeaways, allowing us to interactively experience the end-to-end conversational ML pipeline.
In summary, we created a fully functional machine learning pipeline that combines LangChain’s tool-based agent framework with the predictive power of the XGBoost classifier. We saw how LangChain serves as a conversational interface to perform complex machine learning operations such as data generation, model training, and evaluation, all in a logical and guided manner. This hands-on walkthrough helps us understand how combining LLM-supported orchestration with machine learning can streamline experiments, enhance interpretability, and pave the way for smarter, conversation-driven data science workflows.
Check The complete code is here. Please feel free to check out our GitHub page for tutorials, code, and notebooks. In addition, welcome to follow us twitter And don’t forget to join our 100k+ ML SubReddit and subscribe our newsletter.
Asif Razzaq is the CEO of Marktechpost Media Inc. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of artificial intelligence for the benefit of society. His most recent endeavor is the launch of Marktechpost, an AI media platform that stands out for its in-depth coverage of machine learning and deep learning news that is technically sound and easy to understand for a broad audience. The platform has more than 2 million monthly views, which shows that it is very popular among viewers.
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