okanyenigun

import numpy as np
import open3d as o3d
import open3d_tutorial as o3dtut

#get mesh data
bunny = o3d.data.BunnyMesh()
mesh  = o3d.io.read_triangle_mesh(bunny.path)

#visualize mesh
mesh.scale(1 / np.max(mesh.get_max_bound() - mesh.get_min_bound()),

okanyenigun

import lightgbm as lgb
from sklearn.model_selection import GridSearchCV

param_grid = {
    'objective': ["binary"],
    'metric': ['binary_logloss'],
    'boosting': ['gbdt','dart'],
    'lambda_l1': [0, 0.1, 0.01],
    'bagging_fraction': [0.7, 0.9],
    'bagging_freq': [0,1],

okanyenigun

from abc import ABC, abstractmethod

class IObservable(ABC):
    
    @abstractmethod
    def subscribe(self, observer):
        """subscription"""
    
    @abstractmethod
    def unsubscribe(self, observer):

okanyenigun

import os
import pandas as pd
import numpy as np
np.set_printoptions(precision=4)
import catboost
print(catboost.__version__)

#dataset

from catboost.datasets import amazon

okanyenigun

import numpy as np
from collections import Counter

class Knn:
    
    def __init__(self, k=5):
        self.k = k
    
    def fit(self,X,y):
        self.X_train = X

okanyenigun

// to start a spark session
import org.apache.spark.sql.SparkSession
// to use lineer regression model
import org.apache.spark.ml.regression.LinearRegression

//set logging to level of ERROR
import org.apache.log4j._
Logger.getLogger("org").setLevel(Level.ERROR)

//start a spark Session

okanyenigun

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.pipeline import make_pipeline
from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import QuantileTransformer
from sklearn.metrics import roc_curve, auc

plt.style.use('bmh')
plt.rcParams['figure.figsize'] = (10, 10)
title_config = {'fontsize': 20, 'y': 1.05}

okanyenigun

import nltk
nltk.download('stopwords')
import pandas as pd
import numpy as np
from nltk.corpus import stopwords
import string
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB

okanyenigun

from abc import ABC, abstractmethod

class Ticket(ABC):
    
    def method(self):
        #read plate
        plate = self.read_plate()
        #check record
        record = self.check_record(plate)
        #decide price

okanyenigun

import xgboost as xgb
import numpy as np
from sklearn.datasets import make_regression, make_gaussian_quantiles
from sklearn.metrics import mean_squared_error, confusion_matrix
from sklearn.model_selection import train_test_split

#REGRESSION
#generate regression data
X, y, _ = make_regression(n_samples=10000,#number of samples
                                      n_features=10,#number of features