Quick Start Guide
Get started with RIDE CLI in minutes!
Basic Usage
1. Launch RIDE
Or load a dataset directly:
This opens the interactive interface:
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RIDE: Rapid Insights Data Engine
RIDE is a free open-source toolkit that lets you perform data analysis
without writing a single line of code and minimal intervention.
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Main Menu:
1. Load Dataset
2. Inspect Data
3. Change Data Type
4. Explore Data
5. Visualize Data
6. Impute Missing Values
7. Feature Encoding
8. Feature Scaling and Transformation
9. Export Data
10. AutoML (Train & Evaluate Models)
'$' Export Data (saves current state)
'exit': Exit RIDE CLI
Enter your choice (1-10, $, exit):
2. Load Your Data
Select option 1 to load your dataset:
Enter your choice (1-10, $, exit): 1
Options:
1. Load your own data
2. Load default data (Pre-loaded)
0. Back to main menu
Enter your choice (0-2): 1
Enter the path to your dataset file (CSV, Excel, Parquet): data/iris.csv
Success! Dataset loaded with 150 rows and 5 columns.
Preview of the first 5 rows:
sepal_length sepal_width petal_length petal_width species
0 5.1 3.5 1.4 0.2 setosa
1 4.9 3.0 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
3 4.6 3.1 1.5 0.2 setosa
4 5.0 3.6 1.4 0.2 setosa
Default Datasets Available
If you choose option 2, you can select from these pre-loaded datasets: - Ames Housing: House prices with 82 features - Camera Dataset: Digital camera specifications - Fish: Fish species measurements - Penguins: Palmer Penguins dataset - Titanic: Passenger survival data
3. Inspect Your Data
Use option 2 to explore your dataset structure:
Enter your choice (1-10, $, exit): 2
Inspection Options:
1. View features and data types
2. View dataset shape
3. Check missing values
4. View data sample
5. View summary statistics
6. Back to main menu
Enter your choice (1-6): 1
Features available in the dataset:
Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species'], dtype='object')
Data types of features:
sepal_length float64
sepal_width float64
petal_length float64
petal_width float64
species object
dtype: object
4. Change Data Types
Convert column data types with option 3:
Enter your choice (1-10, $, exit): 3
Columns available for data type conversion:
--------------------------------------------------
# Column Name Current Type Sample Values
--------------------------------------------------
1 sepal_length float64 5.1, 4.9, 4.7
2 sepal_width float64 3.5, 3.0, 3.2
3 species object setosa, setosa, setosa
--------------------------------------------------
Select data type:
1. String (object)
2. Integer (int8)
3. Integer (int64)
4. Float (float64)
5. DateTime
6. Boolean
5. Explore Your Data
Use option 4 for statistical analysis:
Enter your choice (1-10, $, exit): 4
Exploration Options:
1. Feature correlation analysis
2. Check for normal distribution
3. Detect outliers
4. View skewness
5. View kurtosis
6. Check for imbalanced target variable
7. Back to main menu
Enter your choice (1-7): 1
Top Correlations (excluding self-correlations):
------------------------------------------------------------
Feature Pair Correlation Strength
------------------------------------------------------------
petal_length-petal_width 0.963 Strong
sepal_length-petal_length 0.872 Strong
sepal_length-petal_width 0.818 Strong
------------------------------------------------------------
6. Visualize Data
Create terminal-based visualizations with option 5:
Enter your choice (1-10, $, exit): 5
Visualization Options:
1. Plot histogram
2. Plot scatter plot
3. Back to main menu
Enter your choice (1-3): 1
Available numerical columns for histogram:
--------------------------------------------------
1. sepal_length
2. sepal_width
3. petal_length
4. petal_width
--------------------------------------------------
Enter the column number to plot histogram: 1
Plotting histogram for: sepal_length
[Terminal-based histogram appears]
Statistics for sepal_length:
Mean: 5.84
Median: 5.80
Std Dev: 0.83
Min: 4.30
Max: 7.90
7. Handle Missing Values
Impute missing data with option 6:
Enter your choice (1-10, $, exit): 6
Choice Available to Impute Missing Data:
1. [Press 1] Drop Missing Data
2. [Press 2] Impute Missing Data with Specific Value
3. [Press 3] Impute Missing Data with Mean
4. [Press 4] Impute Missing Data with Median
5. [Press 5] Impute Missing Data based on Distribution
6. [Press 6] Impute Missing Data with Fill Forward Strategy
7. [Press 7] Impute Missing Data with Backward Fill Strategy
8. [Press 8] Impute Missing Data with Nearest Neighbours
Enter your choice: 3
Enter path to save Imputed data: data/imputed_data.csv
Missing Data Imputed and saved successfully
8. Feature Encoding
Transform categorical variables with option 7:
Enter your choice (1-10, $, exit): 7
Categorical columns available:
--------------------------------------------------------
# Column Name Data Type Unique Values Sample Values
--------------------------------------------------------
1 species object 3 setosa, versicolor...
--------------------------------------------------------
Encoding Methods:
1. Label Encoding - Maps each unique value to a number
2. One Hot Encoding - Creates binary columns for each category
3. Ordinal Encoding - Maps values to ordered integers
4. Exit and return to main menu
Select encoding method (1-4): 1
Mapping for column 'species':
setosa → 0
versicolor → 1
virginica → 2
9. Feature Scaling
Scale numerical features with option 8:
Enter your choice (1-10, $, exit): 8
Available Options:
=== SCALING OPTIONS ===
1. Min-Max Scaler [Scales features to a range of [0,1]]
2. Standard Scaler (Z-score) [Scales to mean=0, std=1]
3. Robust Scaler [Recommended if outliers are present]
4. Max Abs Scaler [Scales by dividing by the maximum absolute value]
=== TRANSFORMATION OPTIONS ===
5. Quantile Transformer [Maps to uniform or normal distribution]
6. Log Transformer [Natural logarithm transformation]
7. Reciprocal Transformation [1/x transformation]
8. Square Root Transformation [√x transformation]
9. Exit and return to main menu
Enter your choice (1-9): 2
Enter path to save normalized/transformed data: data/scaled_data.csv
Features scaled and saved successfully
10. Run AutoML
Automatically find the best model with option 10:
Enter your choice (1-10, $, exit): 10
Available columns:
1. sepal_length
2. sepal_width
3. petal_length
4. petal_width
5. species
Enter the number of the target column: 5
Select task type:
1. Classification (target variable has discrete classes)
2. Regression (target variable has continuous values)
Enter your choice (1-2): 1
🤖 AutoML Model Selection 🤖
Task Type: Classification
Target Column: species
🔍 Preprocessing: Missing Value Analysis
Initial Missing Values:
(None found)
🔀 Data Split:
Training set: 120 samples
Testing set: 30 samples
🔍 Evaluating Random Forest Classifier...
✓ Completed - Balanced Accuracy: 1.0000
[More models evaluated...]
📊 Classification Model Comparison:
Balanced Accuracy F1 Score Accuracy
Model
Random Forest 1.000 1.000 1.000
Extra Trees 1.000 1.000 1.000
LightGBM 1.000 1.000 1.000
XGBoost 0.967 0.967 0.967
Logistic Regression 0.967 0.967 0.967
Enter the full path to save the AutoML results: results/automl_results.csv
💾 Results saved to results/automl_results.csv
11. Export Data
Save your processed data with option 9 or '$':
Enter your choice (1-10, $, exit): 9
Enter the path to save the file: processed_data
Export Format Options:
1. CSV (.csv)
2. Excel (.xlsx)
3. Parquet (.parquet)
Choose export format (1-3): 1
Data exported successfully to processed_data.csv
Tips and Tricks
- Use the '$' shortcut to quickly export your current dataset
- Press 'exit' at any time to quit RIDE CLI
- All visualizations are terminal-based using plotext
- You can load files directly when starting RIDE:
ride your-data.csv - Default datasets are available if you want to explore without your own data
- All operations preserve your original data; modifications are saved to new files
Common Workflows
Data Analysis Workflow
- Load dataset
- Inspect data types and missing values
- Explore statistical properties
- Visualize distributions
- Handle missing values
- Encode categorical features
- Scale numerical features
- Export processed data
Machine Learning Workflow
- Load dataset
- Preprocess data (missing values, encoding, scaling)
- Run AutoML to find best model
- Export results for further analysis
Need Help?
- Check the User Guide for detailed instructions
- Report issues on GitHub
- Explore the [API Reference]for advanced usage: