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MLJAR Studio
Quick start
Install
Uninstall
New notebook
Install new packages
AI Assistant
Code Recipes
Python
Display Python version
Install Package
Get current time
Get working directory
Get environment variable
Add .env variable
Load secrets from .env
Random integer
Time delay
Check if file exists
List files
Read file
Write to file
Append to file
Delete file
Copy file
Delete directory
Read JSON from file
Write JSON to file
Pretty print JSON
Markdown
Read data
Sample datasets
Read CSV
Read Excel
Read XML
Load Pickle
Read HTML
Read Parquet
Read SPSS
Read Stata
Read SAS
Write data
Write CSV
Write Parquet
Save to Pickle
Postgresql
Define a new connection
Connect to database
Show all tables
Show all columns
Raw query to Pandas
Raw query
Run select query
Run insert query
Create table
Drop table
Data wrangling
DataFrame info
DataFrame describe
Display DataFrame
Select X,y
Select Columns
Filter rows
Delete Column
Split to train/test
Check missing values
Fill missing values
Use imputer on new data
Categorical to integer
Use encoder on new data
matplotlib
Scatter plot
Scikit-learn
Decision Tree
Random Forest
k-Nearest Neighbors
Train Model
Compute Predictions
Visualize Decision Tree
Hyper Parameters Search
Feature Importance
Compute Metric
Confusion Matrix
ROC Curve
Precision-Recall
Lift Chart
Calibration PLot
MLJAR AutoML
Train AutoML
Predict with AutoML
AutoML report
Load AutoML
Images Operations
Remove background from single image
Remove background from multiple images
Show Image
OpenAI
Client connection
AI Image Generator
Chat Completion
Cookbook
MLJAR AutoML
Use MLJAR AutoML to train Machine Learning pipeline on tabular data.
Train AutoML
Train Machine Learning pipeline with AutoML in Python
Predict with AutoML
Compute predictions on new data with AutoML
AutoML report
Display AutoML report from training with details about each model
Load AutoML
Load AutoML models from disk and use them to compute predictions on new data
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