KeitaTakenouchi

import numpy as np
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from random import randrange


xs = np.array([[1], [3.1], [2.9], [1.3], [2], [4],
              [4.7], [5], [6], [7], [7.1], [7.2]])
ys = np.array([[2], [5.8], [4.2], [2.4], [4], [8],
              [9.2], [9], [10], [10], [11], [12]])

KeitaTakenouchi

#!pip install seaborn

data = {
    "a"   : [1,2,34,4000, 4000, 4000, 40000]
   ,"b"  : [2,1300,3400,3400,3400,3400,340000]
   ,"c"   : [1,10,13,3000,3000,3570,5570,8570,300000]
   ,"d" : [1,110,260, 300, 350, 35077]
}

import seaborn as sns

KeitaTakenouchi

# !pip install torch torchvision torchaudio
# !pip install pytorch_tabular[all]

## Prepare utility functions
from sklearn.datasets import make_classification
def make_mixed_classification(n_samples, n_features, n_categories):
    X,y = make_classification(n_samples=n_samples, n_features=n_features, random_state=42, n_informative=5)
    cat_cols = random.choices(list(range(X.shape[-1])),k=n_categories)
    num_cols = [i for i in range(X.shape[-1]) if i not in cat_cols]
    for col in cat_cols: