5 ML Model are available to train bassed on provided dataset, user can select one regresion out of 5 for train.
Project description
Installation :
python 3.9 : pip install MLAlgos==1.0.0
python 3.10 : pip install MLAlgos==1.0.1
python 3.11 : pip install MLAlgos==1.0.2
Example:
from MLRegressions import Regressors
import pandas as pd
df = pd.read_csv('Sampledata.csv')
x = df.iloc[:,1:-1].values # Features
y = df.iloc[:,-1].values # Depended Variable
reg = Regressors(x,y,skip_regressor=[],poly_degree=5, test_size=0.2, random_state=0)
obj = reg.fit_models() # To train Models & return class obj [LinearRegression(), LinearRegression(), SVR(), DecisionTreeRegressor(random_state=0), RandomForestRegressor(n_estimators=10, random_state=0)]
Linear Regression : obj[0].predict()
Polynomial Regression : obj[1].predict()
SVR : obj[2].predict()
DecisionTreeRegressor : obj[3].predict()
RandomForestRegressor : obj[4].predict()
data = reg.r2_score() # To get r2_scores data for train test set.
reg.plot_train_data() # To plot graphs for Trained set.
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