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stringlengths 13
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|---|---|---|---|
88091003/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import StratifiedKFold, GroupKFold
import librosa
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import soundfile as sf
SEED = 42
DATA_PATH = '../input/birdclef-2022/'
AUDIO_PATH = '../input/birdclef-2022/train_audio'
MEAN = np.array([0.485, 0.456, 0.406])
STD = np.array([0.229, 0.224, 0.225])
NUM_WORKERS = 4
CLASSES = sorted(os.listdir(AUDIO_PATH))
NUM_CLASSES = len(CLASSES)
class AudioParams:
"""
Parameters used for the audio data
"""
sr = 32000
duration = 5
n_mels = 224
fmin = 20
fmax = 16000
train = pd.read_csv('../input/birdclef-2022/train_metadata.csv')
train['file_path'] = AUDIO_PATH + '/' + train['filename']
paths = train['file_path'].values
Fold = StratifiedKFold(n_splits=5, shuffle=True, random_state=SEED)
for n, (trn_index, val_index) in enumerate(Fold.split(train, train['primary_label'])):
train.loc[val_index, 'kfold'] = int(n)
train['kfold'] = train['kfold'].astype(int)
train.to_csv('train_folds.csv', index=False)
def compute_melspec(y, params):
"""
Computes a mel-spectrogram and puts it at decibel scale
Arguments:
y {np array} -- signal
params {AudioParams} -- Parameters to use for the spectrogram. Expected to have the attributes sr, n_mels, f_min, f_max
Returns:
np array -- Mel-spectrogram
"""
melspec = librosa.feature.melspectrogram(y=y, sr=params.sr, n_mels=params.n_mels, fmin=params.fmin, fmax=params.fmax)
melspec = librosa.power_to_db(melspec).astype(np.float32)
return melspec
def crop_or_pad(y, length, sr, train=True, probs=None):
"""
Crops an array to a chosen length
Arguments:
y {1D np array} -- Array to crop
length {int} -- Length of the crop
sr {int} -- Sampling rate
Keyword Arguments:
train {bool} -- Whether we are at train time. If so, crop randomly, else return the beginning of y (default: {True})
probs {None or numpy array} -- Probabilities to use to chose where to crop (default: {None})
Returns:
1D np array -- Cropped array
"""
if len(y) <= length:
y = np.concatenate([y, np.zeros(length - len(y))])
else:
if not train:
start = 0
elif probs is None:
start = np.random.randint(len(y) - length)
else:
start = np.random.choice(np.arange(len(probs)), p=probs) + np.random.random()
start = int(sr * start)
y = y[start:start + length]
return y.astype(np.float32)
def mono_to_color(X, eps=1e-06, mean=None, std=None):
"""
Converts a one channel array to a 3 channel one in [0, 255]
Arguments:
X {numpy array [H x W]} -- 2D array to convert
Keyword Arguments:
eps {float} -- To avoid dividing by 0 (default: {1e-6})
mean {None or np array} -- Mean for normalization (default: {None})
std {None or np array} -- Std for normalization (default: {None})
Returns:
numpy array [3 x H x W] -- RGB numpy array
"""
X = np.stack([X, X, X], axis=-1)
mean = mean or X.mean()
std = std or X.std()
X = (X - mean) / (std + eps)
_min, _max = (X.min(), X.max())
if _max - _min > eps:
V = np.clip(X, _min, _max)
V = 255 * (V - _min) / (_max - _min)
V = V.astype(np.uint8)
else:
V = np.zeros_like(X, dtype=np.uint8)
return V
path = train['file_path'][0]
y, sr = sf.read(path, always_2d=True)
y = np.mean(y, 1)
X = compute_melspec(y, AudioParams)
X = mono_to_color(X)
X = X.astype(np.uint8)
plt.imshow(X) | code |
88091003/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_html_output_1.png",
"text_plain_output_1.png"
] | !pip install ../input/torchlibrosa/torchlibrosa-0.0.5-py3-none-any.whl > /dev/null | code |
88091003/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import StratifiedKFold, GroupKFold
import librosa
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import soundfile as sf
SEED = 42
DATA_PATH = '../input/birdclef-2022/'
AUDIO_PATH = '../input/birdclef-2022/train_audio'
MEAN = np.array([0.485, 0.456, 0.406])
STD = np.array([0.229, 0.224, 0.225])
NUM_WORKERS = 4
CLASSES = sorted(os.listdir(AUDIO_PATH))
NUM_CLASSES = len(CLASSES)
class AudioParams:
"""
Parameters used for the audio data
"""
sr = 32000
duration = 5
n_mels = 224
fmin = 20
fmax = 16000
train = pd.read_csv('../input/birdclef-2022/train_metadata.csv')
train['file_path'] = AUDIO_PATH + '/' + train['filename']
paths = train['file_path'].values
Fold = StratifiedKFold(n_splits=5, shuffle=True, random_state=SEED)
for n, (trn_index, val_index) in enumerate(Fold.split(train, train['primary_label'])):
train.loc[val_index, 'kfold'] = int(n)
train['kfold'] = train['kfold'].astype(int)
train.to_csv('train_folds.csv', index=False)
def compute_melspec(y, params):
"""
Computes a mel-spectrogram and puts it at decibel scale
Arguments:
y {np array} -- signal
params {AudioParams} -- Parameters to use for the spectrogram. Expected to have the attributes sr, n_mels, f_min, f_max
Returns:
np array -- Mel-spectrogram
"""
melspec = librosa.feature.melspectrogram(y=y, sr=params.sr, n_mels=params.n_mels, fmin=params.fmin, fmax=params.fmax)
melspec = librosa.power_to_db(melspec).astype(np.float32)
return melspec
def crop_or_pad(y, length, sr, train=True, probs=None):
"""
Crops an array to a chosen length
Arguments:
y {1D np array} -- Array to crop
length {int} -- Length of the crop
sr {int} -- Sampling rate
Keyword Arguments:
train {bool} -- Whether we are at train time. If so, crop randomly, else return the beginning of y (default: {True})
probs {None or numpy array} -- Probabilities to use to chose where to crop (default: {None})
Returns:
1D np array -- Cropped array
"""
if len(y) <= length:
y = np.concatenate([y, np.zeros(length - len(y))])
else:
if not train:
start = 0
elif probs is None:
start = np.random.randint(len(y) - length)
else:
start = np.random.choice(np.arange(len(probs)), p=probs) + np.random.random()
start = int(sr * start)
y = y[start:start + length]
return y.astype(np.float32)
def mono_to_color(X, eps=1e-06, mean=None, std=None):
"""
Converts a one channel array to a 3 channel one in [0, 255]
Arguments:
X {numpy array [H x W]} -- 2D array to convert
Keyword Arguments:
eps {float} -- To avoid dividing by 0 (default: {1e-6})
mean {None or np array} -- Mean for normalization (default: {None})
std {None or np array} -- Std for normalization (default: {None})
Returns:
numpy array [3 x H x W] -- RGB numpy array
"""
X = np.stack([X, X, X], axis=-1)
mean = mean or X.mean()
std = std or X.std()
X = (X - mean) / (std + eps)
_min, _max = (X.min(), X.max())
if _max - _min > eps:
V = np.clip(X, _min, _max)
V = 255 * (V - _min) / (_max - _min)
V = V.astype(np.uint8)
else:
V = np.zeros_like(X, dtype=np.uint8)
return V
path = train['file_path'][0]
y, sr = sf.read(path, always_2d=True)
y = np.mean(y, 1)
X = compute_melspec(y, AudioParams)
X = mono_to_color(X)
X = X.astype(np.uint8)
path = train['file_path'][0]
y, sr = sf.read(path, always_2d=True)
y = np.mean(y, 1)
y = crop_or_pad(y, AudioParams.duration * AudioParams.sr, sr=AudioParams.sr, train=True, probs=None)
X = compute_melspec(y, AudioParams)
X = mono_to_color(X)
X = X.astype(np.uint8)
plt.imshow(X) | code |
88091003/cell_10 | [
"text_plain_output_1.png"
] | from joblib import Parallel, delayed
from sklearn.model_selection import StratifiedKFold, GroupKFold
from tqdm import tqdm
import librosa
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import soundfile as sf
SEED = 42
DATA_PATH = '../input/birdclef-2022/'
AUDIO_PATH = '../input/birdclef-2022/train_audio'
MEAN = np.array([0.485, 0.456, 0.406])
STD = np.array([0.229, 0.224, 0.225])
NUM_WORKERS = 4
CLASSES = sorted(os.listdir(AUDIO_PATH))
NUM_CLASSES = len(CLASSES)
class AudioParams:
"""
Parameters used for the audio data
"""
sr = 32000
duration = 5
n_mels = 224
fmin = 20
fmax = 16000
train = pd.read_csv('../input/birdclef-2022/train_metadata.csv')
train['file_path'] = AUDIO_PATH + '/' + train['filename']
paths = train['file_path'].values
Fold = StratifiedKFold(n_splits=5, shuffle=True, random_state=SEED)
for n, (trn_index, val_index) in enumerate(Fold.split(train, train['primary_label'])):
train.loc[val_index, 'kfold'] = int(n)
train['kfold'] = train['kfold'].astype(int)
train.to_csv('train_folds.csv', index=False)
def compute_melspec(y, params):
"""
Computes a mel-spectrogram and puts it at decibel scale
Arguments:
y {np array} -- signal
params {AudioParams} -- Parameters to use for the spectrogram. Expected to have the attributes sr, n_mels, f_min, f_max
Returns:
np array -- Mel-spectrogram
"""
melspec = librosa.feature.melspectrogram(y=y, sr=params.sr, n_mels=params.n_mels, fmin=params.fmin, fmax=params.fmax)
melspec = librosa.power_to_db(melspec).astype(np.float32)
return melspec
def crop_or_pad(y, length, sr, train=True, probs=None):
"""
Crops an array to a chosen length
Arguments:
y {1D np array} -- Array to crop
length {int} -- Length of the crop
sr {int} -- Sampling rate
Keyword Arguments:
train {bool} -- Whether we are at train time. If so, crop randomly, else return the beginning of y (default: {True})
probs {None or numpy array} -- Probabilities to use to chose where to crop (default: {None})
Returns:
1D np array -- Cropped array
"""
if len(y) <= length:
y = np.concatenate([y, np.zeros(length - len(y))])
else:
if not train:
start = 0
elif probs is None:
start = np.random.randint(len(y) - length)
else:
start = np.random.choice(np.arange(len(probs)), p=probs) + np.random.random()
start = int(sr * start)
y = y[start:start + length]
return y.astype(np.float32)
def mono_to_color(X, eps=1e-06, mean=None, std=None):
"""
Converts a one channel array to a 3 channel one in [0, 255]
Arguments:
X {numpy array [H x W]} -- 2D array to convert
Keyword Arguments:
eps {float} -- To avoid dividing by 0 (default: {1e-6})
mean {None or np array} -- Mean for normalization (default: {None})
std {None or np array} -- Std for normalization (default: {None})
Returns:
numpy array [3 x H x W] -- RGB numpy array
"""
X = np.stack([X, X, X], axis=-1)
mean = mean or X.mean()
std = std or X.std()
X = (X - mean) / (std + eps)
_min, _max = (X.min(), X.max())
if _max - _min > eps:
V = np.clip(X, _min, _max)
V = 255 * (V - _min) / (_max - _min)
V = V.astype(np.uint8)
else:
V = np.zeros_like(X, dtype=np.uint8)
return V
path = train['file_path'][0]
y, sr = sf.read(path, always_2d=True)
y = np.mean(y, 1)
X = compute_melspec(y, AudioParams)
X = mono_to_color(X)
X = X.astype(np.uint8)
path = train['file_path'][0]
y, sr = sf.read(path, always_2d=True)
y = np.mean(y, 1)
y = crop_or_pad(y, AudioParams.duration * AudioParams.sr, sr=AudioParams.sr, train=True, probs=None)
X = compute_melspec(y, AudioParams)
X = mono_to_color(X)
X = X.astype(np.uint8)
def Audio_to_Image(path, params):
y, sr = sf.read(path, always_2d=True)
y = np.mean(y, 1)
y = crop_or_pad(y, params.duration * params.sr, sr=params.sr, train=True, probs=None)
image = compute_melspec(y, params)
image = mono_to_color(image)
image = image.astype(np.uint8)
return image
def save_(path):
save_path = '../working/' + '/'.join(path.split('/')[-2:])
np.save(save_path, Audio_to_Image(path, AudioParams))
NUM_WORKERS = 4
for dir_ in CLASSES:
_ = os.makedirs(dir_, exist_ok=True)
_ = Parallel(n_jobs=NUM_WORKERS)((delayed(save_)(AUDIO_PATH) for AUDIO_PATH in tqdm(paths))) | code |
50237786/cell_21 | [
"text_plain_output_1.png"
] | alphabet = 'abcdefghijklmnopqrstuvwxyz'
key = 'xznlwebgjhqdyvtkfuompciasr'
secret_message = input('Enter your message: ')
secret_message = secret_message.lower()
for c in secret_message:
if c.isalpha():
print(key[alphabet.index(c)], end='')
else:
print(c, end='') | code |
50237786/cell_13 | [
"text_plain_output_1.png"
] | s = ''
for i in range(10):
t = input('Enter a letter: ')
if t == 'a' or t == 'e' or t == 'i' or (t == 'o') or (t == 'u'):
s = s + t
s = input('Enter a string')
s = input('Enter some text: ')
s = input('Enter some text: ')
doubled_s = ''
for c in s:
doubled_s = doubled_s + c * 2 | code |
50237786/cell_9 | [
"text_plain_output_1.png"
] | print('\n' * 9) | code |
50237786/cell_11 | [
"text_plain_output_1.png"
] | s = ''
for i in range(10):
t = input('Enter a letter: ')
if t == 'a' or t == 'e' or t == 'i' or (t == 'o') or (t == 'u'):
s = s + t
s = input('Enter a string')
s = input('Enter some text: ')
for i in range(len(s)):
if s[i] == 'a':
print(i) | code |
50237786/cell_19 | [
"text_plain_output_1.png"
] | s = ''
for i in range(10):
t = input('Enter a letter: ')
if t == 'a' or t == 'e' or t == 'i' or (t == 'o') or (t == 'u'):
s = s + t
s = input('Enter a string')
s = input('Enter some text: ')
s = input('Enter some text: ')
doubled_s = ''
for c in s:
doubled_s = doubled_s + c * 2
s = s.lower()
for c in ',.;:-?!()\'"':
s = s.replace(c, '')
s = input('Enter your decimal number: ')
print(s[s.index('.') + 1:]) | code |
50237786/cell_1 | [
"text_plain_output_1.png"
] | print('-' * 75) | code |
50237786/cell_7 | [
"text_plain_output_1.png"
] | print('Hi\n\nthere!') | code |
50237786/cell_15 | [
"text_plain_output_1.png"
] | name = input('Enter your name: ')
for i in range(len(name)):
print(name[:i + 1], end=' ') | code |
50237786/cell_3 | [
"text_plain_output_1.png"
] | s = ''
for i in range(10):
t = input('Enter a letter: ')
if t == 'a' or t == 'e' or t == 'i' or (t == 'o') or (t == 'u'):
s = s + t
print(s) | code |
50237786/cell_5 | [
"text_plain_output_1.png"
] | s = ''
for i in range(10):
t = input('Enter a letter: ')
if t == 'a' or t == 'e' or t == 'i' or (t == 'o') or (t == 'u'):
s = s + t
s = input('Enter a string')
if s[0].isalpha():
print('Your string starts with a letter')
if not s.isalpha():
print('Your string contains a non-letter.') | code |
128020888/cell_21 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '00': 'Data6', '6f': 'Data7', 'R': 'flag'})
DoS = DoS.dropna()
DoS['Timestamp'] = pd.to_datetime(DoS['Timestamp'], unit='s')
DoS['Timestamp'] = DoS['Timestamp'].apply(lambda x: int(x.timestamp()))
DoS['CAN_ID'] = DoS['CAN_ID'].apply(lambda x: int(x, 16))
DoS['Data0'] = DoS['Data0'].apply(lambda x: int(x, 16))
DoS['Data1'] = DoS['Data1'].apply(lambda x: int(x, 16))
DoS['Data2'] = DoS['Data2'].apply(lambda x: int(x, 16))
DoS['Data3'] = DoS['Data3'].apply(lambda x: int(x, 16))
DoS['Data4'] = DoS['Data4'].apply(lambda x: int(x, 16))
DoS['Data5'] = DoS['Data5'].apply(lambda x: int(x, 16))
DoS['Data6'] = DoS['Data6'].apply(lambda x: int(x, 16))
DoS['Data7'] = DoS['Data7'].apply(lambda x: int(x, 16))
DoS['DLC'] = DoS['DLC'].astype(int)
X = DoS[['Timestamp', 'CAN_ID', 'DLC', 'Data0', 'Data1', 'Data2', 'Data3', 'Data4', 'Data5', 'Data6', 'Data7']]
y = DoS['flag']
from imblearn.over_sampling import SMOTE
smt = SMOTE()
X_sm, y_sm = smt.fit_resample(X, y)
y_sm.value_counts(normalize=True)
DoS1 = pd.concat([X_sm, y_sm], axis=1)
Fuzzy = pd.read_csv('/kaggle/input/car-security/Fuzzy_dataset.csv')
Fuzzy.rename(inplace=True, columns={'1478195721.903877': 'Timestamp', '0545': 'CAN_ID', '8': 'DLC', 'd8': 'Data0', '00': 'Data1', '00.1': 'Data2', '8a': 'Data3', '00.2': 'Data4', '00.3': 'Data5', '00.4': 'Data6', '00.5': 'Data7', 'R': 'flag'})
Fuzzy = Fuzzy.dropna()
print(Fuzzy.dtypes) | code |
128020888/cell_13 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '00': 'Data6', '6f': 'Data7', 'R': 'flag'})
DoS = DoS.dropna()
DoS['Timestamp'] = pd.to_datetime(DoS['Timestamp'], unit='s')
DoS['Timestamp'] = DoS['Timestamp'].apply(lambda x: int(x.timestamp()))
DoS['CAN_ID'] = DoS['CAN_ID'].apply(lambda x: int(x, 16))
DoS['Data0'] = DoS['Data0'].apply(lambda x: int(x, 16))
DoS['Data1'] = DoS['Data1'].apply(lambda x: int(x, 16))
DoS['Data2'] = DoS['Data2'].apply(lambda x: int(x, 16))
DoS['Data3'] = DoS['Data3'].apply(lambda x: int(x, 16))
DoS['Data4'] = DoS['Data4'].apply(lambda x: int(x, 16))
DoS['Data5'] = DoS['Data5'].apply(lambda x: int(x, 16))
DoS['Data6'] = DoS['Data6'].apply(lambda x: int(x, 16))
DoS['Data7'] = DoS['Data7'].apply(lambda x: int(x, 16))
DoS['DLC'] = DoS['DLC'].astype(int)
X = DoS[['Timestamp', 'CAN_ID', 'DLC', 'Data0', 'Data1', 'Data2', 'Data3', 'Data4', 'Data5', 'Data6', 'Data7']]
y = DoS['flag']
from imblearn.over_sampling import SMOTE
smt = SMOTE()
X_sm, y_sm = smt.fit_resample(X, y)
y_sm.value_counts(normalize=True)
DoS1 = pd.concat([X_sm, y_sm], axis=1)
print(DoS1) | code |
128020888/cell_25 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
from imblearn.over_sampling import SMOTE
import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '00': 'Data6', '6f': 'Data7', 'R': 'flag'})
DoS = DoS.dropna()
DoS['Timestamp'] = pd.to_datetime(DoS['Timestamp'], unit='s')
DoS['Timestamp'] = DoS['Timestamp'].apply(lambda x: int(x.timestamp()))
DoS['CAN_ID'] = DoS['CAN_ID'].apply(lambda x: int(x, 16))
DoS['Data0'] = DoS['Data0'].apply(lambda x: int(x, 16))
DoS['Data1'] = DoS['Data1'].apply(lambda x: int(x, 16))
DoS['Data2'] = DoS['Data2'].apply(lambda x: int(x, 16))
DoS['Data3'] = DoS['Data3'].apply(lambda x: int(x, 16))
DoS['Data4'] = DoS['Data4'].apply(lambda x: int(x, 16))
DoS['Data5'] = DoS['Data5'].apply(lambda x: int(x, 16))
DoS['Data6'] = DoS['Data6'].apply(lambda x: int(x, 16))
DoS['Data7'] = DoS['Data7'].apply(lambda x: int(x, 16))
DoS['DLC'] = DoS['DLC'].astype(int)
X = DoS[['Timestamp', 'CAN_ID', 'DLC', 'Data0', 'Data1', 'Data2', 'Data3', 'Data4', 'Data5', 'Data6', 'Data7']]
y = DoS['flag']
from imblearn.over_sampling import SMOTE
smt = SMOTE()
X_sm, y_sm = smt.fit_resample(X, y)
y_sm.value_counts(normalize=True)
DoS1 = pd.concat([X_sm, y_sm], axis=1)
Fuzzy = pd.read_csv('/kaggle/input/car-security/Fuzzy_dataset.csv')
Fuzzy.rename(inplace=True, columns={'1478195721.903877': 'Timestamp', '0545': 'CAN_ID', '8': 'DLC', 'd8': 'Data0', '00': 'Data1', '00.1': 'Data2', '8a': 'Data3', '00.2': 'Data4', '00.3': 'Data5', '00.4': 'Data6', '00.5': 'Data7', 'R': 'flag'})
Fuzzy = Fuzzy.dropna()
Fuzzy['Timestamp'] = pd.to_datetime(Fuzzy['Timestamp'], unit='s')
Fuzzy['Timestamp'] = Fuzzy['Timestamp'].apply(lambda x: int(x.timestamp()))
Fuzzy['CAN_ID'] = Fuzzy['CAN_ID'].apply(lambda x: int(x, 16))
Fuzzy['Data0'] = Fuzzy['Data0'].apply(lambda x: int(x, 16))
Fuzzy['Data1'] = Fuzzy['Data1'].apply(lambda x: int(x, 16))
Fuzzy['Data2'] = Fuzzy['Data2'].apply(lambda x: int(x, 16))
Fuzzy['Data3'] = Fuzzy['Data3'].apply(lambda x: int(x, 16))
Fuzzy['Data4'] = Fuzzy['Data4'].apply(lambda x: int(x, 16))
Fuzzy['Data5'] = Fuzzy['Data5'].apply(lambda x: int(x, 16))
Fuzzy['Data6'] = Fuzzy['Data6'].apply(lambda x: int(x, 16))
Fuzzy['Data7'] = Fuzzy['Data7'].apply(lambda x: int(x, 16))
Fuzzy['DLC'] = Fuzzy['DLC'].astype(int)
X = Fuzzy[['Timestamp', 'CAN_ID', 'DLC', 'Data0', 'Data1', 'Data2', 'Data3', 'Data4', 'Data5', 'Data6', 'Data7']]
y = Fuzzy['flag']
from imblearn.over_sampling import SMOTE
smt = SMOTE()
X_sm, y_sm = smt.fit_resample(X, y)
y_sm.value_counts(normalize=True)
print(X_sm, y_sm) | code |
128020888/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '00': 'Data6', '6f': 'Data7', 'R': 'flag'})
DoS['flag'].value_counts(normalize=True) | code |
128020888/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '00': 'Data6', '6f': 'Data7', 'R': 'flag'})
print(DoS) | code |
128020888/cell_11 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '00': 'Data6', '6f': 'Data7', 'R': 'flag'})
DoS = DoS.dropna()
DoS['Timestamp'] = pd.to_datetime(DoS['Timestamp'], unit='s')
DoS['Timestamp'] = DoS['Timestamp'].apply(lambda x: int(x.timestamp()))
DoS['CAN_ID'] = DoS['CAN_ID'].apply(lambda x: int(x, 16))
DoS['Data0'] = DoS['Data0'].apply(lambda x: int(x, 16))
DoS['Data1'] = DoS['Data1'].apply(lambda x: int(x, 16))
DoS['Data2'] = DoS['Data2'].apply(lambda x: int(x, 16))
DoS['Data3'] = DoS['Data3'].apply(lambda x: int(x, 16))
DoS['Data4'] = DoS['Data4'].apply(lambda x: int(x, 16))
DoS['Data5'] = DoS['Data5'].apply(lambda x: int(x, 16))
DoS['Data6'] = DoS['Data6'].apply(lambda x: int(x, 16))
DoS['Data7'] = DoS['Data7'].apply(lambda x: int(x, 16))
DoS['DLC'] = DoS['DLC'].astype(int)
X = DoS[['Timestamp', 'CAN_ID', 'DLC', 'Data0', 'Data1', 'Data2', 'Data3', 'Data4', 'Data5', 'Data6', 'Data7']]
y = DoS['flag']
from imblearn.over_sampling import SMOTE
smt = SMOTE()
X_sm, y_sm = smt.fit_resample(X, y)
y_sm.value_counts(normalize=True)
print(X_sm, y_sm) | code |
128020888/cell_19 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '00': 'Data6', '6f': 'Data7', 'R': 'flag'})
DoS = DoS.dropna()
DoS['Timestamp'] = pd.to_datetime(DoS['Timestamp'], unit='s')
DoS['Timestamp'] = DoS['Timestamp'].apply(lambda x: int(x.timestamp()))
DoS['CAN_ID'] = DoS['CAN_ID'].apply(lambda x: int(x, 16))
DoS['Data0'] = DoS['Data0'].apply(lambda x: int(x, 16))
DoS['Data1'] = DoS['Data1'].apply(lambda x: int(x, 16))
DoS['Data2'] = DoS['Data2'].apply(lambda x: int(x, 16))
DoS['Data3'] = DoS['Data3'].apply(lambda x: int(x, 16))
DoS['Data4'] = DoS['Data4'].apply(lambda x: int(x, 16))
DoS['Data5'] = DoS['Data5'].apply(lambda x: int(x, 16))
DoS['Data6'] = DoS['Data6'].apply(lambda x: int(x, 16))
DoS['Data7'] = DoS['Data7'].apply(lambda x: int(x, 16))
DoS['DLC'] = DoS['DLC'].astype(int)
X = DoS[['Timestamp', 'CAN_ID', 'DLC', 'Data0', 'Data1', 'Data2', 'Data3', 'Data4', 'Data5', 'Data6', 'Data7']]
y = DoS['flag']
from imblearn.over_sampling import SMOTE
smt = SMOTE()
X_sm, y_sm = smt.fit_resample(X, y)
y_sm.value_counts(normalize=True)
DoS1 = pd.concat([X_sm, y_sm], axis=1)
Fuzzy = pd.read_csv('/kaggle/input/car-security/Fuzzy_dataset.csv')
Fuzzy.rename(inplace=True, columns={'1478195721.903877': 'Timestamp', '0545': 'CAN_ID', '8': 'DLC', 'd8': 'Data0', '00': 'Data1', '00.1': 'Data2', '8a': 'Data3', '00.2': 'Data4', '00.3': 'Data5', '00.4': 'Data6', '00.5': 'Data7', 'R': 'flag'})
Fuzzy['flag'].isnull().sum() | code |
128020888/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '00': 'Data6', '6f': 'Data7', 'R': 'flag'})
DoS = DoS.dropna()
print(DoS.dtypes) | code |
128020888/cell_18 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '00': 'Data6', '6f': 'Data7', 'R': 'flag'})
DoS = DoS.dropna()
DoS['Timestamp'] = pd.to_datetime(DoS['Timestamp'], unit='s')
DoS['Timestamp'] = DoS['Timestamp'].apply(lambda x: int(x.timestamp()))
DoS['CAN_ID'] = DoS['CAN_ID'].apply(lambda x: int(x, 16))
DoS['Data0'] = DoS['Data0'].apply(lambda x: int(x, 16))
DoS['Data1'] = DoS['Data1'].apply(lambda x: int(x, 16))
DoS['Data2'] = DoS['Data2'].apply(lambda x: int(x, 16))
DoS['Data3'] = DoS['Data3'].apply(lambda x: int(x, 16))
DoS['Data4'] = DoS['Data4'].apply(lambda x: int(x, 16))
DoS['Data5'] = DoS['Data5'].apply(lambda x: int(x, 16))
DoS['Data6'] = DoS['Data6'].apply(lambda x: int(x, 16))
DoS['Data7'] = DoS['Data7'].apply(lambda x: int(x, 16))
DoS['DLC'] = DoS['DLC'].astype(int)
X = DoS[['Timestamp', 'CAN_ID', 'DLC', 'Data0', 'Data1', 'Data2', 'Data3', 'Data4', 'Data5', 'Data6', 'Data7']]
y = DoS['flag']
from imblearn.over_sampling import SMOTE
smt = SMOTE()
X_sm, y_sm = smt.fit_resample(X, y)
y_sm.value_counts(normalize=True)
DoS1 = pd.concat([X_sm, y_sm], axis=1)
Fuzzy = pd.read_csv('/kaggle/input/car-security/Fuzzy_dataset.csv')
Fuzzy.rename(inplace=True, columns={'1478195721.903877': 'Timestamp', '0545': 'CAN_ID', '8': 'DLC', 'd8': 'Data0', '00': 'Data1', '00.1': 'Data2', '8a': 'Data3', '00.2': 'Data4', '00.3': 'Data5', '00.4': 'Data6', '00.5': 'Data7', 'R': 'flag'})
Fuzzy['flag'].value_counts(normalize=True) | code |
128020888/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '00': 'Data6', '6f': 'Data7', 'R': 'flag'})
DoS = DoS.dropna()
DoS['Timestamp'] = pd.to_datetime(DoS['Timestamp'], unit='s')
DoS['Timestamp'] = DoS['Timestamp'].apply(lambda x: int(x.timestamp()))
DoS['CAN_ID'] = DoS['CAN_ID'].apply(lambda x: int(x, 16))
DoS['Data0'] = DoS['Data0'].apply(lambda x: int(x, 16))
DoS['Data1'] = DoS['Data1'].apply(lambda x: int(x, 16))
DoS['Data2'] = DoS['Data2'].apply(lambda x: int(x, 16))
DoS['Data3'] = DoS['Data3'].apply(lambda x: int(x, 16))
DoS['Data4'] = DoS['Data4'].apply(lambda x: int(x, 16))
DoS['Data5'] = DoS['Data5'].apply(lambda x: int(x, 16))
DoS['Data6'] = DoS['Data6'].apply(lambda x: int(x, 16))
DoS['Data7'] = DoS['Data7'].apply(lambda x: int(x, 16))
DoS['DLC'] = DoS['DLC'].astype(int)
print(DoS.head()) | code |
128020888/cell_15 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '00': 'Data6', '6f': 'Data7', 'R': 'flag'})
DoS = DoS.dropna()
DoS['Timestamp'] = pd.to_datetime(DoS['Timestamp'], unit='s')
DoS['Timestamp'] = DoS['Timestamp'].apply(lambda x: int(x.timestamp()))
DoS['CAN_ID'] = DoS['CAN_ID'].apply(lambda x: int(x, 16))
DoS['Data0'] = DoS['Data0'].apply(lambda x: int(x, 16))
DoS['Data1'] = DoS['Data1'].apply(lambda x: int(x, 16))
DoS['Data2'] = DoS['Data2'].apply(lambda x: int(x, 16))
DoS['Data3'] = DoS['Data3'].apply(lambda x: int(x, 16))
DoS['Data4'] = DoS['Data4'].apply(lambda x: int(x, 16))
DoS['Data5'] = DoS['Data5'].apply(lambda x: int(x, 16))
DoS['Data6'] = DoS['Data6'].apply(lambda x: int(x, 16))
DoS['Data7'] = DoS['Data7'].apply(lambda x: int(x, 16))
DoS['DLC'] = DoS['DLC'].astype(int)
X = DoS[['Timestamp', 'CAN_ID', 'DLC', 'Data0', 'Data1', 'Data2', 'Data3', 'Data4', 'Data5', 'Data6', 'Data7']]
y = DoS['flag']
from imblearn.over_sampling import SMOTE
smt = SMOTE()
X_sm, y_sm = smt.fit_resample(X, y)
y_sm.value_counts(normalize=True)
DoS1 = pd.concat([X_sm, y_sm], axis=1)
DoS1 = DoS1.dropna()
display(DoS1.dtypes) | code |
128020888/cell_16 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '00': 'Data6', '6f': 'Data7', 'R': 'flag'})
DoS = DoS.dropna()
DoS['Timestamp'] = pd.to_datetime(DoS['Timestamp'], unit='s')
DoS['Timestamp'] = DoS['Timestamp'].apply(lambda x: int(x.timestamp()))
DoS['CAN_ID'] = DoS['CAN_ID'].apply(lambda x: int(x, 16))
DoS['Data0'] = DoS['Data0'].apply(lambda x: int(x, 16))
DoS['Data1'] = DoS['Data1'].apply(lambda x: int(x, 16))
DoS['Data2'] = DoS['Data2'].apply(lambda x: int(x, 16))
DoS['Data3'] = DoS['Data3'].apply(lambda x: int(x, 16))
DoS['Data4'] = DoS['Data4'].apply(lambda x: int(x, 16))
DoS['Data5'] = DoS['Data5'].apply(lambda x: int(x, 16))
DoS['Data6'] = DoS['Data6'].apply(lambda x: int(x, 16))
DoS['Data7'] = DoS['Data7'].apply(lambda x: int(x, 16))
DoS['DLC'] = DoS['DLC'].astype(int)
X = DoS[['Timestamp', 'CAN_ID', 'DLC', 'Data0', 'Data1', 'Data2', 'Data3', 'Data4', 'Data5', 'Data6', 'Data7']]
y = DoS['flag']
from imblearn.over_sampling import SMOTE
smt = SMOTE()
X_sm, y_sm = smt.fit_resample(X, y)
y_sm.value_counts(normalize=True)
DoS1 = pd.concat([X_sm, y_sm], axis=1)
Fuzzy = pd.read_csv('/kaggle/input/car-security/Fuzzy_dataset.csv')
Fuzzy.rename(inplace=True, columns={'1478195721.903877': 'Timestamp', '0545': 'CAN_ID', '8': 'DLC', 'd8': 'Data0', '00': 'Data1', '00.1': 'Data2', '8a': 'Data3', '00.2': 'Data4', '00.3': 'Data5', '00.4': 'Data6', '00.5': 'Data7', 'R': 'flag'})
print(Fuzzy) | code |
128020888/cell_24 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
from imblearn.over_sampling import SMOTE
import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '00': 'Data6', '6f': 'Data7', 'R': 'flag'})
DoS = DoS.dropna()
DoS['Timestamp'] = pd.to_datetime(DoS['Timestamp'], unit='s')
DoS['Timestamp'] = DoS['Timestamp'].apply(lambda x: int(x.timestamp()))
DoS['CAN_ID'] = DoS['CAN_ID'].apply(lambda x: int(x, 16))
DoS['Data0'] = DoS['Data0'].apply(lambda x: int(x, 16))
DoS['Data1'] = DoS['Data1'].apply(lambda x: int(x, 16))
DoS['Data2'] = DoS['Data2'].apply(lambda x: int(x, 16))
DoS['Data3'] = DoS['Data3'].apply(lambda x: int(x, 16))
DoS['Data4'] = DoS['Data4'].apply(lambda x: int(x, 16))
DoS['Data5'] = DoS['Data5'].apply(lambda x: int(x, 16))
DoS['Data6'] = DoS['Data6'].apply(lambda x: int(x, 16))
DoS['Data7'] = DoS['Data7'].apply(lambda x: int(x, 16))
DoS['DLC'] = DoS['DLC'].astype(int)
X = DoS[['Timestamp', 'CAN_ID', 'DLC', 'Data0', 'Data1', 'Data2', 'Data3', 'Data4', 'Data5', 'Data6', 'Data7']]
y = DoS['flag']
from imblearn.over_sampling import SMOTE
smt = SMOTE()
X_sm, y_sm = smt.fit_resample(X, y)
y_sm.value_counts(normalize=True)
DoS1 = pd.concat([X_sm, y_sm], axis=1)
Fuzzy = pd.read_csv('/kaggle/input/car-security/Fuzzy_dataset.csv')
Fuzzy.rename(inplace=True, columns={'1478195721.903877': 'Timestamp', '0545': 'CAN_ID', '8': 'DLC', 'd8': 'Data0', '00': 'Data1', '00.1': 'Data2', '8a': 'Data3', '00.2': 'Data4', '00.3': 'Data5', '00.4': 'Data6', '00.5': 'Data7', 'R': 'flag'})
Fuzzy = Fuzzy.dropna()
Fuzzy['Timestamp'] = pd.to_datetime(Fuzzy['Timestamp'], unit='s')
Fuzzy['Timestamp'] = Fuzzy['Timestamp'].apply(lambda x: int(x.timestamp()))
Fuzzy['CAN_ID'] = Fuzzy['CAN_ID'].apply(lambda x: int(x, 16))
Fuzzy['Data0'] = Fuzzy['Data0'].apply(lambda x: int(x, 16))
Fuzzy['Data1'] = Fuzzy['Data1'].apply(lambda x: int(x, 16))
Fuzzy['Data2'] = Fuzzy['Data2'].apply(lambda x: int(x, 16))
Fuzzy['Data3'] = Fuzzy['Data3'].apply(lambda x: int(x, 16))
Fuzzy['Data4'] = Fuzzy['Data4'].apply(lambda x: int(x, 16))
Fuzzy['Data5'] = Fuzzy['Data5'].apply(lambda x: int(x, 16))
Fuzzy['Data6'] = Fuzzy['Data6'].apply(lambda x: int(x, 16))
Fuzzy['Data7'] = Fuzzy['Data7'].apply(lambda x: int(x, 16))
Fuzzy['DLC'] = Fuzzy['DLC'].astype(int)
X = Fuzzy[['Timestamp', 'CAN_ID', 'DLC', 'Data0', 'Data1', 'Data2', 'Data3', 'Data4', 'Data5', 'Data6', 'Data7']]
y = Fuzzy['flag']
from imblearn.over_sampling import SMOTE
smt = SMOTE()
X_sm, y_sm = smt.fit_resample(X, y)
y_sm.value_counts(normalize=True) | code |
128020888/cell_14 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '00': 'Data6', '6f': 'Data7', 'R': 'flag'})
DoS = DoS.dropna()
DoS['Timestamp'] = pd.to_datetime(DoS['Timestamp'], unit='s')
DoS['Timestamp'] = DoS['Timestamp'].apply(lambda x: int(x.timestamp()))
DoS['CAN_ID'] = DoS['CAN_ID'].apply(lambda x: int(x, 16))
DoS['Data0'] = DoS['Data0'].apply(lambda x: int(x, 16))
DoS['Data1'] = DoS['Data1'].apply(lambda x: int(x, 16))
DoS['Data2'] = DoS['Data2'].apply(lambda x: int(x, 16))
DoS['Data3'] = DoS['Data3'].apply(lambda x: int(x, 16))
DoS['Data4'] = DoS['Data4'].apply(lambda x: int(x, 16))
DoS['Data5'] = DoS['Data5'].apply(lambda x: int(x, 16))
DoS['Data6'] = DoS['Data6'].apply(lambda x: int(x, 16))
DoS['Data7'] = DoS['Data7'].apply(lambda x: int(x, 16))
DoS['DLC'] = DoS['DLC'].astype(int)
X = DoS[['Timestamp', 'CAN_ID', 'DLC', 'Data0', 'Data1', 'Data2', 'Data3', 'Data4', 'Data5', 'Data6', 'Data7']]
y = DoS['flag']
from imblearn.over_sampling import SMOTE
smt = SMOTE()
X_sm, y_sm = smt.fit_resample(X, y)
y_sm.value_counts(normalize=True)
DoS1 = pd.concat([X_sm, y_sm], axis=1)
DoS1 = DoS1.dropna()
print(DoS1) | code |
128020888/cell_22 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '00': 'Data6', '6f': 'Data7', 'R': 'flag'})
DoS = DoS.dropna()
DoS['Timestamp'] = pd.to_datetime(DoS['Timestamp'], unit='s')
DoS['Timestamp'] = DoS['Timestamp'].apply(lambda x: int(x.timestamp()))
DoS['CAN_ID'] = DoS['CAN_ID'].apply(lambda x: int(x, 16))
DoS['Data0'] = DoS['Data0'].apply(lambda x: int(x, 16))
DoS['Data1'] = DoS['Data1'].apply(lambda x: int(x, 16))
DoS['Data2'] = DoS['Data2'].apply(lambda x: int(x, 16))
DoS['Data3'] = DoS['Data3'].apply(lambda x: int(x, 16))
DoS['Data4'] = DoS['Data4'].apply(lambda x: int(x, 16))
DoS['Data5'] = DoS['Data5'].apply(lambda x: int(x, 16))
DoS['Data6'] = DoS['Data6'].apply(lambda x: int(x, 16))
DoS['Data7'] = DoS['Data7'].apply(lambda x: int(x, 16))
DoS['DLC'] = DoS['DLC'].astype(int)
X = DoS[['Timestamp', 'CAN_ID', 'DLC', 'Data0', 'Data1', 'Data2', 'Data3', 'Data4', 'Data5', 'Data6', 'Data7']]
y = DoS['flag']
from imblearn.over_sampling import SMOTE
smt = SMOTE()
X_sm, y_sm = smt.fit_resample(X, y)
y_sm.value_counts(normalize=True)
DoS1 = pd.concat([X_sm, y_sm], axis=1)
Fuzzy = pd.read_csv('/kaggle/input/car-security/Fuzzy_dataset.csv')
Fuzzy.rename(inplace=True, columns={'1478195721.903877': 'Timestamp', '0545': 'CAN_ID', '8': 'DLC', 'd8': 'Data0', '00': 'Data1', '00.1': 'Data2', '8a': 'Data3', '00.2': 'Data4', '00.3': 'Data5', '00.4': 'Data6', '00.5': 'Data7', 'R': 'flag'})
Fuzzy = Fuzzy.dropna()
Fuzzy['Timestamp'] = pd.to_datetime(Fuzzy['Timestamp'], unit='s')
Fuzzy['Timestamp'] = Fuzzy['Timestamp'].apply(lambda x: int(x.timestamp()))
Fuzzy['CAN_ID'] = Fuzzy['CAN_ID'].apply(lambda x: int(x, 16))
Fuzzy['Data0'] = Fuzzy['Data0'].apply(lambda x: int(x, 16))
Fuzzy['Data1'] = Fuzzy['Data1'].apply(lambda x: int(x, 16))
Fuzzy['Data2'] = Fuzzy['Data2'].apply(lambda x: int(x, 16))
Fuzzy['Data3'] = Fuzzy['Data3'].apply(lambda x: int(x, 16))
Fuzzy['Data4'] = Fuzzy['Data4'].apply(lambda x: int(x, 16))
Fuzzy['Data5'] = Fuzzy['Data5'].apply(lambda x: int(x, 16))
Fuzzy['Data6'] = Fuzzy['Data6'].apply(lambda x: int(x, 16))
Fuzzy['Data7'] = Fuzzy['Data7'].apply(lambda x: int(x, 16))
Fuzzy['DLC'] = Fuzzy['DLC'].astype(int)
print(Fuzzy.head()) | code |
128020888/cell_10 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '00': 'Data6', '6f': 'Data7', 'R': 'flag'})
DoS = DoS.dropna()
DoS['Timestamp'] = pd.to_datetime(DoS['Timestamp'], unit='s')
DoS['Timestamp'] = DoS['Timestamp'].apply(lambda x: int(x.timestamp()))
DoS['CAN_ID'] = DoS['CAN_ID'].apply(lambda x: int(x, 16))
DoS['Data0'] = DoS['Data0'].apply(lambda x: int(x, 16))
DoS['Data1'] = DoS['Data1'].apply(lambda x: int(x, 16))
DoS['Data2'] = DoS['Data2'].apply(lambda x: int(x, 16))
DoS['Data3'] = DoS['Data3'].apply(lambda x: int(x, 16))
DoS['Data4'] = DoS['Data4'].apply(lambda x: int(x, 16))
DoS['Data5'] = DoS['Data5'].apply(lambda x: int(x, 16))
DoS['Data6'] = DoS['Data6'].apply(lambda x: int(x, 16))
DoS['Data7'] = DoS['Data7'].apply(lambda x: int(x, 16))
DoS['DLC'] = DoS['DLC'].astype(int)
X = DoS[['Timestamp', 'CAN_ID', 'DLC', 'Data0', 'Data1', 'Data2', 'Data3', 'Data4', 'Data5', 'Data6', 'Data7']]
y = DoS['flag']
from imblearn.over_sampling import SMOTE
smt = SMOTE()
X_sm, y_sm = smt.fit_resample(X, y)
y_sm.value_counts(normalize=True) | code |
128020888/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
DoS = pd.read_csv('/kaggle/input/car-security/DoS_dataset.csv')
DoS.rename(inplace=True, columns={'1478198376.389427': 'Timestamp', '0316': 'CAN_ID', '8': 'DLC', '05': 'Data0', '21': 'Data1', '68': 'Data2', '09': 'Data3', '21.1': 'Data4', '21.2': 'Data5', '00': 'Data6', '6f': 'Data7', 'R': 'flag'})
DoS['flag'].isnull().sum() | code |
334146/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_head = train[:10000]
plt.figure(figsize=(20, 15))
plt.scatter(x=train_head.x, y=train_head.y, c=train_head.time) | code |
334146/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.describe() | code |
334146/cell_2 | [
"text_html_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
334146/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test.describe() | code |
334146/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
plt.figure(figsize=(20, 10))
sns.distplot(bins=200, a=train.accuracy) | code |
334146/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.head() | code |
334146/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_head = train[:10000]
plt.figure(figsize=(20, 10))
plt.scatter(x=train_head.time, y=train_head.accuracy) | code |
334146/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test.head() | code |
49117600/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = data[data.left == 1]
right = data[data.left == 0]
data.shape
data.groupby('left').mean()
pd.crosstab(data.Department, data.left).plot(kind='bar') | code |
49117600/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = data[data.left == 1]
right = data[data.left == 0]
right.shape | code |
49117600/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
data.info() | code |
49117600/cell_30 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
Reg = LogisticRegression()
Reg.fit(X_train, y_train)
Reg.predict(X_test) | code |
49117600/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = data[data.left == 1]
right = data[data.left == 0]
data.shape
data.groupby('left').mean()
sub_salary = data[['satisfaction_level', 'average_montly_hours', 'promotion_last_5years', 'salary']]
sal_dummies = pd.get_dummies(sub_salary.salary, prefix='salary')
df = pd.concat([sub_salary, sal_dummies], axis='columns')
df.drop(columns='salary', inplace=True)
df.head() | code |
49117600/cell_29 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
Reg = LogisticRegression()
Reg.fit(X_train, y_train) | code |
49117600/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = data[data.left == 1]
right = data[data.left == 0]
data.shape
data.groupby('left').mean() | code |
49117600/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
49117600/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = data[data.left == 1]
left.shape | code |
49117600/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = data[data.left == 1]
right = data[data.left == 0]
data.shape
data.groupby('left').mean()
sub_salary = data[['satisfaction_level', 'average_montly_hours', 'promotion_last_5years', 'salary']]
sal_dummies = pd.get_dummies(sub_salary.salary, prefix='salary')
df = pd.concat([sub_salary, sal_dummies], axis='columns')
df.head() | code |
49117600/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = data[data.left == 1]
right = data[data.left == 0]
data.shape
data.groupby('left').mean()
sub_salary = data[['satisfaction_level', 'average_montly_hours', 'promotion_last_5years', 'salary']]
sub_salary.head() | code |
49117600/cell_31 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
Reg = LogisticRegression()
Reg.fit(X_train, y_train)
Reg.predict(X_test)
Reg.score(X_test, y_test) | code |
49117600/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = data[data.left == 1]
right = data[data.left == 0]
data.shape
data.groupby('left').mean()
y = data['left']
y.head() | code |
49117600/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = data[data.left == 1]
right = data[data.left == 0]
data.shape
data.groupby('left').mean()
sub_salary = data[['satisfaction_level', 'average_montly_hours', 'promotion_last_5years', 'salary']]
sal_dummies = pd.get_dummies(sub_salary.salary, prefix='salary')
df = pd.concat([sub_salary, sal_dummies], axis='columns')
df.drop(columns='salary', inplace=True)
X = df
X.head() | code |
49117600/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = data[data.left == 1]
right = data[data.left == 0]
data.shape | code |
49117600/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
left = data[data.left == 1]
right = data[data.left == 0]
data.shape
data.groupby('left').mean()
pd.crosstab(data.salary, data.left).plot(kind='bar') | code |
49117600/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/hr-analytics/HR_comma_sep.csv')
data.head() | code |
106205052/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
b = np.array([1, 1, 0])
c = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
d = np.array([[1], [0], [1]])
e = np.array([1, 1, 0])
f = np.array([[1], [2], [1]])
print(e + f) | code |
106205052/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
b = np.array([1, 1, 0])
c = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
d = np.array([[1], [0], [1]])
print(c + d) | code |
106205052/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
b = np.array([1, 1, 0])
c = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
d = np.array([[1], [0], [1]])
e = np.array([1, 1, 0])
f = np.array([[1], [2], [1]])
a = np.array([1, 2, 4])
b = np.tile(a, (2, 1))
b
a = np.array([1, 2, 3])
a.shape
b = a[0:3, np.newaxis]
b
b.ndim | code |
106205052/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
b = np.array([1, 1, 0])
c = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
d = np.array([[1], [0], [1]])
e = np.array([1, 1, 0])
f = np.array([[1], [2], [1]])
a = np.array([1, 2, 4])
b = np.tile(a, (2, 1))
b
a = np.array([1, 2, 3])
a.shape
b = a[0:3, np.newaxis]
b | code |
106205052/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
b = np.array([1, 1, 0])
c = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
d = np.array([[1], [0], [1]])
print('array1', c)
print('\n')
print('array2', d) | code |
106205052/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
b = np.array([1, 1, 0])
c = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
d = np.array([[1], [0], [1]])
e = np.array([1, 1, 0])
f = np.array([[1], [2], [1]])
a = np.array([1, 2, 4])
b = np.tile(a, (2, 1))
b
a = np.array([1, 2, 3])
a.shape | code |
106205052/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
b = np.array([1, 1, 0])
c = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
d = np.array([[1], [0], [1]])
print(c.shape, d.shape) | code |
106205052/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
b = np.array([1, 1, 0])
c = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
d = np.array([[1], [0], [1]])
e = np.array([1, 1, 0])
f = np.array([[1], [2], [1]])
a = np.array([1, 2, 4])
b = np.tile(a, (2, 1))
b | code |
106205052/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
b = np.array([1, 1, 0])
c = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
d = np.array([[1], [0], [1]])
e = np.array([1, 1, 0])
f = np.array([[1], [2], [1]])
print(e.shape)
print(f.shape) | code |
106205052/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
b = np.array([1, 1, 0])
print(a.shape, b.shape)
print(a + b) | code |
129003546/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
types = data['type'].value_counts()
types.to_frame() | code |
129003546/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data | code |
129003546/cell_34 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
data.dtypes
country = data['country'].value_counts().head(10)
country.to_frame() | code |
129003546/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
data.dtypes | code |
129003546/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
data.dtypes
data.head(2) | code |
129003546/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
types = data['type'].value_counts()
types.to_frame()
x_values, y_values = (types.values, types.index)
director = data['director'].value_counts().head(10)
director.to_frame()
x_values = director.values
y_values = director.index
plt.figure(figsize=(7, 5))
sns.barplot(x=x_values, y=y_values, palette='rainbow')
plt.ylabel('director') | code |
129003546/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
data.dtypes
rating = data['rating']
rating.to_frame() | code |
129003546/cell_39 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
data.dtypes
cast = data['cast'].value_counts().head(10)
cast.to_frame() | code |
129003546/cell_26 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
types = data['type'].value_counts()
types.to_frame()
x_values, y_values = (types.values, types.index)
director = data['director'].value_counts().head(10)
director.to_frame()
x_values = director.values
y_values = director.index
data.dtypes
release_year = data['release_year']
release_year.to_frame()
x_values = release_year.values
plt.figure(figsize=(10, 5))
sns.countplot(x=x_values)
plt.xlabel('year')
plt.xticks(rotation=90) | code |
129003546/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.head(2) | code |
129003546/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
director = data['director'].value_counts().head(10)
director.to_frame() | code |
129003546/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
data.dtypes
data.head(2) | code |
129003546/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum() | code |
129003546/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
types = data['type'].value_counts()
types.to_frame()
x_values, y_values = (types.values, types.index)
plt.figure(figsize=(7, 5))
sns.barplot(data=types, x=x_values, y=y_values)
plt.xlabel('TV Show Movies') | code |
129003546/cell_38 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
data.dtypes
data.head(2) | code |
129003546/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns | code |
129003546/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
data.head(2) | code |
129003546/cell_31 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
types = data['type'].value_counts()
types.to_frame()
x_values, y_values = (types.values, types.index)
director = data['director'].value_counts().head(10)
director.to_frame()
x_values = director.values
y_values = director.index
data.dtypes
release_year = data['release_year']
release_year.to_frame()
x_values = release_year.values
plt.xticks(rotation=90)
rating = data['rating']
rating.to_frame()
x_values = rating.values
y_values = rating.index
plt.figure(figsize=(10, 5))
sns.countplot(x=x_values)
plt.xlabel('year')
plt.xticks(rotation=90) | code |
129003546/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
data.dtypes
release_year = data['release_year']
release_year.to_frame() | code |
129003546/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
data.head(2) | code |
129003546/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum() | code |
129003546/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
data.head(2) | code |
129003546/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes | code |
129003546/cell_36 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('//kaggle//input//netflix-shows//netflix_titles.csv')
data
data.dtypes
data.isnull().sum()
data.dropna(inplace=True)
data.isnull().sum()
types = data['type'].value_counts()
types.to_frame()
x_values, y_values = (types.values, types.index)
director = data['director'].value_counts().head(10)
director.to_frame()
x_values = director.values
y_values = director.index
data.dtypes
release_year = data['release_year']
release_year.to_frame()
x_values = release_year.values
plt.xticks(rotation=90)
rating = data['rating']
rating.to_frame()
x_values = rating.values
y_values = rating.index
plt.xticks(rotation=90)
country = data['country'].value_counts().head(10)
country.to_frame()
x_values = country.values
y_values = country.index
plt.figure(figsize=(10, 5))
sns.barplot(x=x_values, y=y_values, palette='rainbow')
plt.ylabel('Country')
plt.xticks(rotation=90) | code |
2023611/cell_21 | [
"text_plain_output_1.png"
] | from matplotlib import cm
import h5py
import matplotlib.pylab as plt
import numpy as np
import pandas as pd
data = pd.read_csv('../input/letters.csv')
files = data['file']
letters = data['letter']
backgrounds = data['background']
f = h5py.File('../input/LetterColorImages.h5', 'r')
keys = list(f.keys())
keys
backgrounds = np.array(f[keys[0]])
tensors = np.array(f[keys[1]])
targets = np.array(f[keys[2]])
# Plot letter images
fig, ax = plt.subplots(figsize=(18, 3), nrows=1, ncols=5, sharex=True, sharey=True,)
ax = ax.flatten()
for i in range(5):
image = tensors[i*200]/255
ax[i].imshow(image)
ax[0].set_xticks([])
ax[0].set_yticks([])
plt.tight_layout()
plt.gcf()
ax[2].set_title('Examples of letters', fontsize=25);
fig, ax = plt.subplots(figsize=(18, 3), nrows=1, ncols=5, sharex=True, sharey=True)
ax = ax.flatten()
for i in range(5):
image = x_test[i * 10].reshape(32, 32)
ax[i].imshow(image, cmap=cm.bone)
ax[0].set_xticks([])
ax[0].set_yticks([])
plt.tight_layout()
plt.gcf()
ax[2].set_title('Examples of original grayscaled letters', fontsize=25) | code |
2023611/cell_13 | [
"image_output_1.png"
] | from keras.utils import to_categorical
import h5py
import numpy as np
import pandas as pd
data = pd.read_csv('../input/letters.csv')
files = data['file']
letters = data['letter']
backgrounds = data['background']
f = h5py.File('../input/LetterColorImages.h5', 'r')
keys = list(f.keys())
keys
backgrounds = np.array(f[keys[0]])
tensors = np.array(f[keys[1]])
targets = np.array(f[keys[2]])
tensors = tensors.astype('float32') / 255
gray_tensors = np.dot(tensors[..., :3], [0.299, 0.587, 0.114])
gray_tensors = gray_tensors.reshape(-1, 32, 32, 1)
cat_targets = to_categorical(np.array(targets - 1), 33)
cat_targets.shape
backgrounds = to_categorical(backgrounds, 2)
backgrounds.shape
back_targets = np.concatenate((cat_targets, backgrounds), axis=1)
back_targets.shape | code |
2023611/cell_6 | [
"image_output_1.png"
] | import h5py
import numpy as np
import pandas as pd
data = pd.read_csv('../input/letters.csv')
files = data['file']
letters = data['letter']
backgrounds = data['background']
f = h5py.File('../input/LetterColorImages.h5', 'r')
keys = list(f.keys())
keys
backgrounds = np.array(f[keys[0]])
tensors = np.array(f[keys[1]])
targets = np.array(f[keys[2]])
print('Tensor shape:', tensors.shape)
print('Target shape', targets.shape)
print('Background shape:', backgrounds.shape) | code |
2023611/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import math
import tensorflow as tf
from sklearn.model_selection import train_test_split
from keras.utils import to_categorical
import h5py
import cv2
from keras.models import Sequential, load_model, Model
from keras.layers import Input, UpSampling2D
from keras.layers import Dense, LSTM, GlobalAveragePooling1D, GlobalAveragePooling2D
from keras.layers import Activation, Flatten, Dropout, BatchNormalization
from keras.layers import Conv2D, MaxPooling2D, GlobalMaxPooling2D
import matplotlib.pylab as plt
from matplotlib import cm | code |
2023611/cell_11 | [
"text_plain_output_1.png"
] | from keras.utils import to_categorical
import h5py
import numpy as np
import pandas as pd
data = pd.read_csv('../input/letters.csv')
files = data['file']
letters = data['letter']
backgrounds = data['background']
f = h5py.File('../input/LetterColorImages.h5', 'r')
keys = list(f.keys())
keys
backgrounds = np.array(f[keys[0]])
tensors = np.array(f[keys[1]])
targets = np.array(f[keys[2]])
tensors = tensors.astype('float32') / 255
gray_tensors = np.dot(tensors[..., :3], [0.299, 0.587, 0.114])
gray_tensors = gray_tensors.reshape(-1, 32, 32, 1)
cat_targets = to_categorical(np.array(targets - 1), 33)
cat_targets.shape | code |
2023611/cell_7 | [
"image_output_1.png"
] | import h5py
import matplotlib.pylab as plt
import numpy as np
import pandas as pd
data = pd.read_csv('../input/letters.csv')
files = data['file']
letters = data['letter']
backgrounds = data['background']
f = h5py.File('../input/LetterColorImages.h5', 'r')
keys = list(f.keys())
keys
backgrounds = np.array(f[keys[0]])
tensors = np.array(f[keys[1]])
targets = np.array(f[keys[2]])
fig, ax = plt.subplots(figsize=(18, 3), nrows=1, ncols=5, sharex=True, sharey=True)
ax = ax.flatten()
for i in range(5):
image = tensors[i * 200] / 255
ax[i].imshow(image)
ax[0].set_xticks([])
ax[0].set_yticks([])
plt.tight_layout()
plt.gcf()
ax[2].set_title('Examples of letters', fontsize=25) | code |
2023611/cell_18 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, GlobalMaxPooling2D
from keras.layers import Input, UpSampling2D
from keras.models import Sequential, load_model, Model
def autoencoder():
inputs = Input(shape=(32, 32, 1))
x = Conv2D(32, 3, activation='relu', padding='same')(inputs)
x = MaxPooling2D(padding='same')(x)
x = Conv2D(16, 3, activation='relu', padding='same')(x)
x = MaxPooling2D(padding='same')(x)
x = Conv2D(8, 3, activation='relu', padding='same')(x)
encoded = MaxPooling2D(padding='same')(x)
x = Conv2D(8, 3, activation='relu', padding='same')(encoded)
x = UpSampling2D()(x)
x = Conv2D(16, 3, activation='relu', padding='same')(x)
x = UpSampling2D()(x)
x = Conv2D(32, 3, activation='relu', padding='same')(x)
x = UpSampling2D()(x)
decoded = Conv2D(1, 3, activation='sigmoid', padding='same')(x)
autoencoder = Model(inputs, decoded)
autoencoder.compile(optimizer='nadam', loss='binary_crossentropy')
return autoencoder
autoencoder = autoencoder()
autoencoder.summary() | code |
2023611/cell_22 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, GlobalMaxPooling2D
from keras.layers import Input, UpSampling2D
from keras.models import Sequential, load_model, Model
from matplotlib import cm
import h5py
import matplotlib.pylab as plt
import numpy as np
import pandas as pd
data = pd.read_csv('../input/letters.csv')
files = data['file']
letters = data['letter']
backgrounds = data['background']
f = h5py.File('../input/LetterColorImages.h5', 'r')
keys = list(f.keys())
keys
backgrounds = np.array(f[keys[0]])
tensors = np.array(f[keys[1]])
targets = np.array(f[keys[2]])
# Plot letter images
fig, ax = plt.subplots(figsize=(18, 3), nrows=1, ncols=5, sharex=True, sharey=True,)
ax = ax.flatten()
for i in range(5):
image = tensors[i*200]/255
ax[i].imshow(image)
ax[0].set_xticks([])
ax[0].set_yticks([])
plt.tight_layout()
plt.gcf()
ax[2].set_title('Examples of letters', fontsize=25);
def autoencoder():
inputs = Input(shape=(32, 32, 1))
x = Conv2D(32, 3, activation='relu', padding='same')(inputs)
x = MaxPooling2D(padding='same')(x)
x = Conv2D(16, 3, activation='relu', padding='same')(x)
x = MaxPooling2D(padding='same')(x)
x = Conv2D(8, 3, activation='relu', padding='same')(x)
encoded = MaxPooling2D(padding='same')(x)
x = Conv2D(8, 3, activation='relu', padding='same')(encoded)
x = UpSampling2D()(x)
x = Conv2D(16, 3, activation='relu', padding='same')(x)
x = UpSampling2D()(x)
x = Conv2D(32, 3, activation='relu', padding='same')(x)
x = UpSampling2D()(x)
decoded = Conv2D(1, 3, activation='sigmoid', padding='same')(x)
autoencoder = Model(inputs, decoded)
autoencoder.compile(optimizer='nadam', loss='binary_crossentropy')
return autoencoder
autoencoder = autoencoder()
autoencoder.summary()
autoencoder_history = autoencoder.fit(x_train, x_train, epochs=200, batch_size=64, verbose=0, validation_data=(x_valid, x_valid))
x_test_decoded = autoencoder.predict(x_test)
# Plot original grayscaled images
fig, ax = plt.subplots(figsize=(18, 3), nrows=1, ncols=5, sharex=True, sharey=True,)
ax = ax.flatten()
for i in range(5):
image = x_test[i*10].reshape(32,32)
ax[i].imshow(image, cmap=cm.bone)
ax[0].set_xticks([])
ax[0].set_yticks([])
plt.tight_layout()
plt.gcf()
ax[2].set_title('Examples of original grayscaled letters', fontsize=25);
fig, ax = plt.subplots(figsize=(18, 3), nrows=1, ncols=5, sharex=True, sharey=True)
ax = ax.flatten()
for i in range(5):
image = x_test_decoded[i * 10].reshape(32, 32)
ax[i].imshow(image, cmap=cm.bone)
ax[0].set_xticks([])
ax[0].set_yticks([])
plt.tight_layout()
plt.gcf()
ax[2].set_title('Examples of decoded grayscaled letters', fontsize=25) | code |
2023611/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import h5py
import numpy as np
import pandas as pd
data = pd.read_csv('../input/letters.csv')
files = data['file']
letters = data['letter']
backgrounds = data['background']
f = h5py.File('../input/LetterColorImages.h5', 'r')
keys = list(f.keys())
keys
backgrounds = np.array(f[keys[0]])
tensors = np.array(f[keys[1]])
targets = np.array(f[keys[2]])
tensors = tensors.astype('float32') / 255
gray_tensors = np.dot(tensors[..., :3], [0.299, 0.587, 0.114])
gray_tensors = gray_tensors.reshape(-1, 32, 32, 1)
print('Grayscaled Tensor shape:', gray_tensors.shape) | code |
2023611/cell_12 | [
"text_plain_output_1.png"
] | from keras.utils import to_categorical
import h5py
import numpy as np
import pandas as pd
data = pd.read_csv('../input/letters.csv')
files = data['file']
letters = data['letter']
backgrounds = data['background']
f = h5py.File('../input/LetterColorImages.h5', 'r')
keys = list(f.keys())
keys
backgrounds = np.array(f[keys[0]])
tensors = np.array(f[keys[1]])
targets = np.array(f[keys[2]])
backgrounds = to_categorical(backgrounds, 2)
backgrounds.shape | code |
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