import pandas as pd

df = pd.read_csv('~/datasets/kaggle/kc_house_data.csv')

df.head()

import numpy as np
from sklearn.metrics import accuracy_score

y_pred = [0, 2, 1, 3]
y_true = [0, 1, 2, 3]
score1 = accuracy_score(y_true, y_pred)    
print(score1)

0.5

score2 = accuracy_score(y_true, y_pred, normalize=False)
print(score2)

2

# !pip install spacy

import spacy

spacy.__version__

'3.8.2'

# !python -m spacy download en_core_web_sm

import re

# Load the spaCy model
nlp = spacy.load("en_core_web_sm")

# Sample text
text = "California Xgb is a great tool for machine learning. Another example is AI. \
NTLK is very slow and not recommended high speed …

import pandas as pd

data = {
    'city' : ['Toronto', 'Montreal', 'Waterloo'],
    'points' : [80, 70, 90]
}

data

{'city': ['Toronto', 'Montreal', 'Waterloo'], 'points': [80, 70, 90]}

type(data)

dict

df = pd.DataFrame(data)

df

import pandas as pd

data = {
    'city' : ['Toronto', 'Montreal', 'Waterloo'],
    'points' : [80, 70, 90]
}

data

{'city': ['Toronto', 'Montreal', 'Waterloo'], 'points': [80, 70, 90]}

type(data)

dict

df = pd.DataFrame(data)

df

from numba import njit, prange
import numpy as np

@njit(parallel=True)
def add_even_indices(A):
    s = 0
    # Without "parallel=True" in the jit-decorator
    # the prange statement is equivalent to range
    for i in prange(A.shape[0]):
        if(i % 2 == 0):
            s += A[i]
    return s

abc = add_even_indices(np.array …

from numba import njit, prange
import numpy as np

@njit(parallel=True)
def add_even_numbers(A):
    s = 0
    # Without "parallel=True" in the jit-decorator
    # the prange statement is equivalent to range
    for i in prange(A.shape[0]):
        if(A[i] % 2 == 0):
            s += A[i]
    return s

abc = add_even_numbers(np …

import tensorflow as tf

import os

# Just disables the warning, doesn't enable AVX/FMA
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

a = tf.Variable(0)

b = tf.constant(1)

c = tf.add(a, b)

c

<tf.Tensor 'Add_2 …

text = 'Chapter 2'

# Add Spaces Of Padding To The Left
format(text, '>20')

'           Chapter 2'

# Add Spaces Of Padding To The Right
format(text, '<20')

'Chapter 2           '

# Add Spaces Of Padding On Each Side
format(text, '^20')

'     Chapter 2      '

# Add * Of Padding On …

# !pip install datasets

from transformers import AutoModelForTokenClassification, AutoTokenizer, Trainer, TrainingArguments
from datasets import load_dataset

# Load data
dataset = load_dataset("json", data_files="train_data.json")

Generating train split: 0 examples [00:00, ? examples/s]

dataset

DatasetDict({
    train: Dataset({
        features: ['text', 'entities'],
        num_rows: 1
    })
})

# Load model and tokenizer
# model = AutoModelForTokenClassification.from_pretrained("urchade/gliner_medium-v2.1 …