ml-models 0.1.6

This project is based upon basic ml-model testing and building a table.

This repository is stacked with the multiple ml models looped over certain times,

The User can install the ml_models and use the project as shown in driver package.

The data needs to be concise and at last you'll have a table generated over different ml models with different evaluation technique. With this you can directly check the model to use for.

from ml_models import * from ml_models.build_model import build_table

random_forest_classifier= RandomForestClassifier(n_estimators= 10, criterion="entropy")

for decision Tree

Create Decision Tree classifer object

decision_tree = DecisionTreeClassifier()

nn = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(1), random_state=1)

if name == 'main': # reading the csv file df = pd.read_csv("data.csv") X = df.iloc[:, :-1] # Features y = df.iloc[:, -1] # Target variable

li_df = []
for i in range(0,10):
    li_df.append(build_table(X,y,[decision_tree,"Decision Tree",False],
                            [nn, "Artificial Neural Network",True]))

averages = pd.concat([each.stack() for each in li_df],axis=1)\
            .apply(lambda x:x.mean(),axis=1)\
            .unstack()
print(averages)