def DTC(df,target):
    import numpy as np
    import pandas as pd
    from sklearn import grid_search
    from sklearn.tree import DecisionTreeClassifier

    #Splitting the data into train and test
    x_train,x_test,y_train,y_test = ms.train_test_split(df,target,test_size=0.3,random_state=123)

    dtc = DecisionTreeClassifier(criterion='gini',min_samples_split=2,random_state=123)
    dtc.fit(x_train,y_train)

    pred_dt_train = dtc.predict(x_train)
    pred_dt_test=dtc.predict(x_test)
    print(dtc.score(x_train,y_train))
    print(dtc.score(x_test,y_test))

    #parameter tuning using grid search
    parameters = {'max_depth': list(range(3,20)),'min_samples_leaf':list(range(2,8))}
    dtc_cv = grid_search.GridSearchCV(DecisionTreeClassifier(random_state=123), parameters)
    dtc_cv.fit(x_train,y_train)
    tree_model = dtc_cv.best_estimator_
    print("\n Tuning with Grid Search \n")
    print(tree_model)
    print ("\n best score \n",dtc_cv.best_score_,"\n best params \n", dtc_cv.best_params_)