How it works
1
Upload data – CSV, Excel, or JSON in the Data tab
2
Configure – Model type, target column, algorithm in Config
3
Train – Click "Start Training" and watch the pipeline execute
4
Evaluate – Review metrics in Results
5
Export Agent – Download a complete Python agent
All processing runs locally. No data leaves your machine.
12 algorithms
6 model types
Exportable agent
Download AI Agent
Get a complete Python agent that works directly with your data
Python Agent Documentation
Complete guide to using your downloaded OpenMind AI Agent.
Quick Start
# 1. Install requirements
pip install pandas numpy scikit-learn openpyxl joblib xgboost
# 2. Save the agent code as openmind_agent.py
# 3. Create a Python script:
from openmind_agent import OpenMindAgent
import pandas as pd
data = pd.read_csv("your_data.csv")
agent = OpenMindAgent(model_type="classification", algorithm="random_forest")
agent.train(data, target_col="target_column")
prediction = agent.predict({"feature1": 10, "feature2": 20})
Installation
pip install pandas numpy scikit-learn openpyxl xlrd joblib
pip install xgboost # optional, better performance
Basic Usage
1. Load your data:
data = agent.load_data("sales_data.csv") # CSV
data = agent.load_data("sales_data.xlsx") # Excel
data = agent.load_data("sales_data.json") # JSON
2. Train the model:
agent.train(data, target_col="price") # Regression
agent.train(data, target_col="purchased") # Classification
3. Make predictions:
# Single prediction
pred = agent.predict({"sqft": 2000, "bedrooms": 3})
# Batch prediction
new_data = pd.read_csv("new_customers.csv")
predictions = agent.predict(new_data)
Model Types
Classification
Predict categories
agent = OpenMindAgent(model_type="classification")
Regression
Predict continuous values
agent = OpenMindAgent(model_type="regression")
Clustering
Group similar data
agent = OpenMindAgent(model_type="clustering")
Anomaly Detection
Detect outliers
agent = OpenMindAgent(model_type="anomaly_detection")
Available Algorithms
Random Forest
Logistic Regression
SVM
KNN
Decision Tree
Naive Bayes
Gradient Boosting
XGBoost
Linear Regression
Ridge Regression
Lasso Regression
Supported File Formats
CSV
Excel (.xlsx, .xls)
JSON
Troubleshooting
• ModuleNotFoundError – Install missing packages: pip install [package_name]
• KeyError: 'target_column' – Check your column name exists
• ValueError: could not convert string – Encode categorical variables