Abhayparashar31

def fill_proportionally(col, dataset):
    import random
    random.seed(0)
    
    # getting all unique values (without nan)
    values = dataset[col].dropna().unique()
    
    # getting weights for probability weighting
    weights = dataset[col].value_counts().values / dataset[col].value_counts().values.sum()
    print('Before Imputation Probablity Weights\n',weights)

Abhayparashar31

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,Reshape
from tensorflow.keras.layers import Dropout

def create_model():
  # create model
  model = Sequential()
  model.add(Dense(60, input_shape=(60,), activation='relu',  kernel_regularizer=keras.regularizers.l1(0.01)))
  model.add(Dropout(0.2))

Abhayparashar31

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,Reshape
from tensorflow.keras.layers import Dropout

def create_model():
  model = Sequential()
  model.add(Dense(60, input_shape=(60,), activation='relu'))
  model.add(Dropout(0.2))
  model.add(Dense(30, activation='relu'))

Abhayparashar31

from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img

datagen = ImageDataGenerator(
        rotation_range=40,
        width_shift_range=0.2,
        height_shift_range=0.2,
        brightness_range= [0.5, 1.5],
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.4,

Abhayparashar31

Abhayparashar31

## Base Model
logrec = LogisticRegression()

## Parameter Grid
param_grid = [    
    {'penalty' : ['l1', 'l2', 'elasticnet', 'none'],
    'C' : np.logspace(-4, 4, 20),
    'solver' : ['lbfgs','newton-cg','liblinear','sag','saga'],
    'max_iter' : [100, 800,1000, 1200]
    }

Abhayparashar31

from sklearn.cluster import KMeans

wcss = []
silhouette_scores = []
calinski_harabasz_scores = []
davies_bouldin_scores = []

for i in range(2, 7):
    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)
    y_pred = kmeans.fit_predict(X)

Abhayparashar31

mse  [] ## Regression Problem
accuracy_scores = []   ## Classification Problem

""" Train and Test Data Created Using Cross Validation"""
for train_index,test_index in kFold.split(X,y):
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
    
    print("Train Index:", train_index, "|", "Test Index:", test_index)
    

Abhayparashar31

################## STRATIFIED K FOLD ##########
import numpy as np
""" Creating Sample X and y """
X = np.array([[1,2,3],
              [4,5,6],
              [7,8,9],
              [10,11,12],
              [13,14,15],
              [16,17,18]])

Abhayparashar31

############## LEAVE ONE OUT CROSS VALIDATION ###########
import numpy as np
""" Creating Sample X and y """
X = np.array([[1,2,3],
         [4,5,6],
         [7,8,9],
             [10,11,12],
             [13,14,15],
             [16,17,18]])
y = np.array([0,1,1,1,0,1])