# RapidUseML
Minimalistic Machine Learning Toolset used for quick training and usage of various models.
One click for training, one click for prediction.
## Usage:
### Basics:
Prepare basic necessities for usage.
```
import RapidUse # Ensure class is imported.
ml = RapidUse.ML() # Create instance of class.
from pandas import read_csv # Get pandas to read CSVs.
```
### Prediction:
Predicts target value(s) based on input data provided, with automated model identification.
Note: ML.prdict(...) checks the folder and all directories within the folder its located in for the relevant model.
```
input_dataset = read_csv("input_dataset.csv") # Load dataset for pred.
target_column = "column_d" # Prediction target.
prediction_set = ml.predict(input_dataset, target_column) # Try to obtain ML pred.
```
### Training:
Trains many models based on dataset, select top 3 and optimise them for better performance.
```
training_dataset = read_csv("training_dataset.csv") # Load dataset for training.
input_column_names = ["column_a", "column_b", "column_c"] # What features to predict with.
target_column = "column_d" # What component you want predicted.
train_test_ratio = 0.8 # What data % to dedicate to training.
ml.train(training_dataset, input_column_names, target_column, train_test_ratio)
```
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