mertbozkir

mertbozkir

# Function that runs the requested algorithm and returns the accuracy metrics
def fit_ml_algo(algo, X_train, y_train, cv):
    
    # One Pass
    model = algo.fit(X_train, y_train)
    acc = round(model.score(X_train, y_train) * 100, 2)
    
    # Cross Validation
    train_pred = model_selection.cross_val_predict(algo,
                                                  X_train,

mertbozkir

def CodelandUsernameValidation(strParam):
  if (4<len(strParam)<25) or (strParam[0] != str) or strParam[-1] == "_":
    return False
  
  return True

# keep this function call here 
print(CodelandUsernameValidation(input()))

"""

mertbozkir

import numpy as np
class Solution:
    def areaEdge(self, h_w: int, Cuts: List[int]) -> int:
        Cuts.insert(len(Cuts), h_w)
    
        temp_list = Cuts[:-1]
        temp_list.insert(0,0)
        
        Cuts.sort()
        temp_list.sort()

mertbozkir

class Solution:
    def reverse(self, x: int) -> int:
        ex = str(x)
        if ex[0] == "-":
            ex2 = ex[1:]
            print(ex2[::-1])
            if not((-2)**31 < int(ex2[::-1]) < 2**31 - 1):
                return 0
            return ex[0] + str(int(ex2[::-1]))
        else:

mertbozkir

def search(nums, target) -> int:
        high = len(nums) - 1
        low, mid  = 0, 0
        
        while low <= high:
            mid = (high + low)// 2
        
            if nums[mid] < target:
                low = mid + 1
            

mertbozkir

mertbozkir

def val_curve_params(model, X, y, param_name, param_range, scoring = 'roc_auc', cv = 10):
    train_score, test_score = validation_curve(
    model, X = X, y = y, param_name = param_name, param_range = param_range, scoring = scoring, cv = cv)
    
    mean_train_score = np.mean(train_score, axis = 1)
    mean_test_score = np.mean(test_score, axis = 1)
    
    plt.plot(param_range, mean_train_score,
            label = 'Training Score', color = 'b')
    plt.plot(param_range, mean_test_score,

mertbozkir

def plot_importance(model, features, num = len(X), save = False):
    feature_imp = pd.DataFrame({'Value': model.feature_importances_, 'Feature': features.columns})
    plt.figure(figsize = (10, 10))
    sns.set(font_scale = 1)
    sns.barplot(x = 'Value', y = 'Feature', 
                data = feature_imp.sort_values(by = 'Value', ascending = False)[0:num])
    plt.title('Features')
    plt.tight_layout()
    plt.show()
    if save:

mertbozkir

# Based on evan's prompt
# Shows the exit status of the last command if non-zero
# Uses "#" instead of "»" when running with elevated privileges
# local venv_prompt='$(virtualenv_prompt_info)'
# PROMPT='%{${fg_bold[blue]}%}$(virtualenv_prompt_info)'
PROMPT="%m %{${fg_bold[red]}%}::%{${fg[green]}%} %3~%{${reset_color}%} $(virtualenv_prompt_info)%(0?. . ${fg[red]}%? )%{${fg[blue]}%}» "
# PROMPT="%{${fg_bold[white]}%} %m %{${fg_bold[red]}%}:: %{${fg[green]}%}%3~%(0?. . %{${fg[red]}%}%? )%{${fg[blue]}%}$(virtualenv_prompt_info)» %{${reset_color}%}"
ZSH_THEME_VIRTUALENV_PREFIX=""
ZSH_THEME_VIRTUALENV_SUFFIX=""