TryMLEasy is available in two ways: as a permanently deployed web app on Streamlit Cloud that you can open in any browser right now, or as a locally-hosted app that you run from your own machine. Both options give you the identical full-featured interface — choose whichever fits your workflow. No account, sign-up, or API key is needed for either option.Documentation Index
Fetch the complete documentation index at: https://mintlify.com/Gurneet1928/TryMLEasy/llms.txt
Use this file to discover all available pages before exploring further.
Option 1 — Use the Live App
The fastest way to try TryMLEasy is to visit the hosted deployment directly. Everything runs on Streamlit’s infrastructure — nothing to install. Open your browser and navigate to:Option 2 — Run Locally
Running TryMLEasy locally gives you full control over the source code and lets you experiment with or extend the app. You’ll need Python 3.8 or newer andpip installed on your system.
Install all dependencies
Train Your First Model
Once the app is open — whether via the cloud URL or your local server — follow these steps to go from raw CSV to trained model results.Only CSV files with numerical columns are currently supported. If your dataset contains text or categorical columns, encode them numerically before uploading.
Upload your CSV dataset
On the Main Page, click the file uploader and select your
.csv file (or use sample_data.csv from the repo root for a quick test). Once a file is selected, the Next button becomes active. Click it to proceed.Review your dataset
The Show Dataset page displays your full dataframe so you can verify it loaded correctly — column names, row counts, and values are all visible. Click Next (Feature Selection) when you’re satisfied.
Configure feature selection
On the Feature Selection page:
- Select the feature columns you want to use as model inputs (multi-select).
- Choose the target column — the variable the model will predict.
- Optionally pick a scaler (Standard Scaler, MinMax Scaler, Robust Scaler, or Normalization) to preprocess your features.
- Use the test ratio slider to set the proportion of data held out for evaluation (e.g., 0.2 for an 80/20 train-test split).
Choose Traditional ML Model
On the Model Selection page, select Traditional ML Model as your model type and click Next.
Select and train a model
On the Traditional ML Model page:
- Choose Classification or Regression depending on your target variable.
- Pick a specific model from the dropdown (e.g., Logistic Regression for classification, or Linear Regression for regression).
- Click Train Model.
Review your results
After training, the results page displays:
- Evaluation metrics — Accuracy, F1 Score, Precision, and Recall for classification; MSE, Explained Variance, and Max Error for regression.
- Predicted vs. True scatter plot — an interactive Plotly chart comparing the model’s predictions against actual target values on the test set.