path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
105178234/cell_32 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum()
df.duplicated().sum()
df = df.drop_duplicates(keep='first')
df.duplicated().sum()
df.shape
df.head() | code |
105178234/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum()
df.duplicated().sum()
df = df.drop_duplicates(keep='first')
df.duplicated().sum()
df.shape
df.head() | code |
105178234/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum() | code |
105178234/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum()
df.duplicated().sum() | code |
105178234/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.head() | code |
105178234/cell_35 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum()
df.duplicated().sum()
df = df.drop_duplicates(keep='first')
df.duplicated().sum()
df.shape
df[df['target'] == 1][['num_char', 'NUm_words', 'Num_sentence']].describe() | code |
105178234/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum()
df.duplicated().sum()
df = df.drop_duplicates(keep='first')
df.duplicated().sum()
df.shape
import matplotlib.pyplot as plt
plt.pie(df['target'].value_counts(), labels=['ham', 'spam'], autopct='%0.3f') | code |
105178234/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.head() | code |
105178234/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.head(3) | code |
105178234/cell_37 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum()
df.duplicated().sum()
df = df.drop_duplicates(keep='first')
df.duplicated().sum()
df.shape
import matplotlib.pyplot as plt
plt.figure(figsize=(12, 6))
sns.histplot(df[df['target'] == 0]['NUm_words'], color='green')
sns.histplot(df[df['target'] == 1]['NUm_words'], color='red') | code |
50227013/cell_9 | [
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import MaxPooling2D,Flatten,Dense,LSTM,Dropout
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import numpy as np
import pandas as pd
import os
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D, Flatten, Dense, LSTM, Dropout
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
tpu = None
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
tpu_strategy = tf.distribute.experimental.TPUStrategy(tpu)
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset_train = pd.read_csv('../input/gooogle-stock-price/Google_Stock_Price_Train.csv')
training_set = dataset_train.iloc[:, 1:2].values
from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler(feature_range=(0, 1))
training_set_scaled = sc.fit_transform(training_set)
X_train = []
y_train = []
for i in range(60, 1258):
X_train.append(training_set_scaled[i - 60:i, 0])
y_train.append(training_set_scaled[i, 0])
X_train, y_train = (np.array(X_train), np.array(y_train))
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
X_train.shape[1]
with tpu_strategy.scope():
regressor = Sequential()
regressor.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units=50, return_sequences=True))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units=50, return_sequences=True))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units=50))
regressor.add(Dropout(0.2))
regressor.add(Dense(units=1))
regressor.compile(optimizer='adam', loss='mean_squared_error')
regressor.fit(X_train, y_train, epochs=500, batch_size=32)
regressor.summary()
regressor.save('regressorEpochs500_batchSize32.h5')
dataset_test = pd.read_csv('../input/gooogle-stock-price/Google_Stock_Price_Test.csv')
real_stock_price = dataset_test.iloc[:, 1:2].values
dataset_total = pd.concat((dataset_train['Open'], dataset_test['Open']), axis=0)
inputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:].values
inputs = inputs.reshape(-1, 1)
inputs = sc.transform(inputs)
X_test = []
for i in range(60, len(inputs)):
X_test.append(inputs[i - 60:i, 0])
X_test = np.array(X_test)
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
predicted_stock_price = regressor.predict(X_test)
predicted_stock_price = sc.inverse_transform(predicted_stock_price)
plt.plot(real_stock_price, color='red', label='Real Google Stock Price')
plt.plot(predicted_stock_price, color='blue', label='Predicted Google Stock Price')
plt.title('Google Stock Price Prediction')
plt.xlabel('Date')
plt.ylabel('Google Stock Price')
plt.legend()
plt.show() | code |
50227013/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset_train = pd.read_csv('../input/gooogle-stock-price/Google_Stock_Price_Train.csv')
training_set = dataset_train.iloc[:, 1:2].values
from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler(feature_range=(0, 1))
training_set_scaled = sc.fit_transform(training_set)
X_train = []
y_train = []
for i in range(60, 1258):
X_train.append(training_set_scaled[i - 60:i, 0])
y_train.append(training_set_scaled[i, 0])
X_train, y_train = (np.array(X_train), np.array(y_train))
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
X_train.shape[1] | code |
50227013/cell_6 | [
"text_plain_output_1.png"
] | print('done') | code |
50227013/cell_2 | [
"text_plain_output_1.png"
] | import tensorflow as tf
import numpy as np
import pandas as pd
import os
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D, Flatten, Dense, LSTM, Dropout
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
print('Running on TPU ', tpu.master())
except ValueError:
tpu = None
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
tpu_strategy = tf.distribute.experimental.TPUStrategy(tpu) | code |
50227013/cell_1 | [
"text_plain_output_1.png"
] | # !pip install tensorflow==2.2-rc1
!pip install tensorflow | code |
50227013/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import MaxPooling2D,Flatten,Dense,LSTM,Dropout
from tensorflow.keras.models import Sequential
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import numpy as np
import pandas as pd
import os
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D, Flatten, Dense, LSTM, Dropout
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
tpu = None
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
tpu_strategy = tf.distribute.experimental.TPUStrategy(tpu)
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset_train = pd.read_csv('../input/gooogle-stock-price/Google_Stock_Price_Train.csv')
training_set = dataset_train.iloc[:, 1:2].values
from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler(feature_range=(0, 1))
training_set_scaled = sc.fit_transform(training_set)
X_train = []
y_train = []
for i in range(60, 1258):
X_train.append(training_set_scaled[i - 60:i, 0])
y_train.append(training_set_scaled[i, 0])
X_train, y_train = (np.array(X_train), np.array(y_train))
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
X_train.shape[1]
with tpu_strategy.scope():
regressor = Sequential()
regressor.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units=50, return_sequences=True))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units=50, return_sequences=True))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units=50))
regressor.add(Dropout(0.2))
regressor.add(Dense(units=1))
regressor.compile(optimizer='adam', loss='mean_squared_error')
regressor.fit(X_train, y_train, epochs=500, batch_size=32)
regressor.summary() | code |
50227013/cell_5 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import MaxPooling2D,Flatten,Dense,LSTM,Dropout
from tensorflow.keras.models import Sequential
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import numpy as np
import pandas as pd
import os
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D, Flatten, Dense, LSTM, Dropout
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
tpu = None
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
tpu_strategy = tf.distribute.experimental.TPUStrategy(tpu)
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset_train = pd.read_csv('../input/gooogle-stock-price/Google_Stock_Price_Train.csv')
training_set = dataset_train.iloc[:, 1:2].values
from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler(feature_range=(0, 1))
training_set_scaled = sc.fit_transform(training_set)
X_train = []
y_train = []
for i in range(60, 1258):
X_train.append(training_set_scaled[i - 60:i, 0])
y_train.append(training_set_scaled[i, 0])
X_train, y_train = (np.array(X_train), np.array(y_train))
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
X_train.shape[1]
with tpu_strategy.scope():
regressor = Sequential()
regressor.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units=50, return_sequences=True))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units=50, return_sequences=True))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units=50))
regressor.add(Dropout(0.2))
regressor.add(Dense(units=1))
regressor.compile(optimizer='adam', loss='mean_squared_error')
regressor.fit(X_train, y_train, epochs=500, batch_size=32) | code |
33096624/cell_13 | [
"image_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
bs = 64
data = ImageDataBunch.from_folder(path, valid_pct=0.2, ds_tfms=get_transforms(), size=bs).normalize(imagenet_stats)
arch = resnet50
learn = cnn_learner(data, arch, metrics=[error_rate, accuracy], model_dir='/kaggle/working').to_fp16()
learn.model_dir = '/kaggle/working'
learn.lr_find() | code |
33096624/cell_25 | [
"text_plain_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
bs = 64
data = ImageDataBunch.from_folder(path, valid_pct=0.2, ds_tfms=get_transforms(), size=bs).normalize(imagenet_stats)
arch = resnet50
learn = cnn_learner(data, arch, metrics=[error_rate, accuracy], model_dir='/kaggle/working').to_fp16()
learn.model_dir = '/kaggle/working'
learn.lr_find()
lr = learn.recorder.min_grad_lr
lr
learn.fit_one_cycle(5, lr)
learn.save('stage-1-50')
learn.load('stage-1-50')
learn.unfreeze()
learn.lr_find()
lr1 = learn.recorder.min_grad_lr
lr1
learn.fit_one_cycle(5, max_lr=slice(lr1 / 100, lr1 / 10, lr1)) | code |
33096624/cell_4 | [
"text_plain_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls() | code |
33096624/cell_23 | [
"text_plain_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
bs = 64
data = ImageDataBunch.from_folder(path, valid_pct=0.2, ds_tfms=get_transforms(), size=bs).normalize(imagenet_stats)
arch = resnet50
learn = cnn_learner(data, arch, metrics=[error_rate, accuracy], model_dir='/kaggle/working').to_fp16()
learn.model_dir = '/kaggle/working'
learn.lr_find()
lr = learn.recorder.min_grad_lr
lr
learn.fit_one_cycle(5, lr)
learn.save('stage-1-50')
learn.load('stage-1-50')
learn.unfreeze()
learn.lr_find()
learn.recorder.plot(suggestion=True) | code |
33096624/cell_20 | [
"text_plain_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
bs = 64
data = ImageDataBunch.from_folder(path, valid_pct=0.2, ds_tfms=get_transforms(), size=bs).normalize(imagenet_stats)
arch = resnet50
learn = cnn_learner(data, arch, metrics=[error_rate, accuracy], model_dir='/kaggle/working').to_fp16()
learn.model_dir = '/kaggle/working'
learn.lr_find()
lr = learn.recorder.min_grad_lr
lr
learn.fit_one_cycle(5, lr)
learn.save('stage-1-50')
learn.load('stage-1-50') | code |
33096624/cell_1 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_8.png",
"text_plain_output_2.png",
"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 |
33096624/cell_7 | [
"text_html_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
bs = 64
data = ImageDataBunch.from_folder(path, valid_pct=0.2, ds_tfms=get_transforms(), size=bs).normalize(imagenet_stats)
data.show_batch(3, figsize=(7, 6)) | code |
33096624/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
bs = 64
data = ImageDataBunch.from_folder(path, valid_pct=0.2, ds_tfms=get_transforms(), size=bs).normalize(imagenet_stats)
arch = resnet50
learn = cnn_learner(data, arch, metrics=[error_rate, accuracy], model_dir='/kaggle/working').to_fp16()
learn.model_dir = '/kaggle/working'
learn.lr_find()
lr = learn.recorder.min_grad_lr
lr
learn.fit_one_cycle(5, lr) | code |
33096624/cell_8 | [
"text_plain_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
bs = 64
data = ImageDataBunch.from_folder(path, valid_pct=0.2, ds_tfms=get_transforms(), size=bs).normalize(imagenet_stats)
data.show_batch(4, figsize=(7, 6)) | code |
33096624/cell_15 | [
"text_plain_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
bs = 64
data = ImageDataBunch.from_folder(path, valid_pct=0.2, ds_tfms=get_transforms(), size=bs).normalize(imagenet_stats)
arch = resnet50
learn = cnn_learner(data, arch, metrics=[error_rate, accuracy], model_dir='/kaggle/working').to_fp16()
learn.model_dir = '/kaggle/working'
learn.lr_find()
lr = learn.recorder.min_grad_lr
lr | code |
33096624/cell_16 | [
"text_plain_output_1.png"
] | torch.cuda.is_available() | code |
33096624/cell_17 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | torch.cuda.is_available()
torch.backends.cudnn.enabled | code |
33096624/cell_24 | [
"text_html_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
bs = 64
data = ImageDataBunch.from_folder(path, valid_pct=0.2, ds_tfms=get_transforms(), size=bs).normalize(imagenet_stats)
arch = resnet50
learn = cnn_learner(data, arch, metrics=[error_rate, accuracy], model_dir='/kaggle/working').to_fp16()
learn.model_dir = '/kaggle/working'
learn.lr_find()
lr = learn.recorder.min_grad_lr
lr
learn.fit_one_cycle(5, lr)
learn.save('stage-1-50')
learn.load('stage-1-50')
learn.unfreeze()
learn.lr_find()
lr1 = learn.recorder.min_grad_lr
lr1 | code |
33096624/cell_14 | [
"image_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
bs = 64
data = ImageDataBunch.from_folder(path, valid_pct=0.2, ds_tfms=get_transforms(), size=bs).normalize(imagenet_stats)
arch = resnet50
learn = cnn_learner(data, arch, metrics=[error_rate, accuracy], model_dir='/kaggle/working').to_fp16()
learn.model_dir = '/kaggle/working'
learn.lr_find()
learn.recorder.plot(suggestion=True) | code |
33096624/cell_22 | [
"text_plain_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
bs = 64
data = ImageDataBunch.from_folder(path, valid_pct=0.2, ds_tfms=get_transforms(), size=bs).normalize(imagenet_stats)
arch = resnet50
learn = cnn_learner(data, arch, metrics=[error_rate, accuracy], model_dir='/kaggle/working').to_fp16()
learn.model_dir = '/kaggle/working'
learn.lr_find()
lr = learn.recorder.min_grad_lr
lr
learn.fit_one_cycle(5, lr)
learn.save('stage-1-50')
learn.load('stage-1-50')
learn.unfreeze()
learn.lr_find() | code |
33096624/cell_10 | [
"text_plain_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
path = Path('/kaggle/input/pretrained-pytorch-models/resnet50-19c8e357.pth')
path.cwd() | code |
33096624/cell_27 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | path = Path('../input/normal-vs-camouflage-clothes/8k_normal_vs_camouflage_clothes_images')
path.ls()
bs = 64
data = ImageDataBunch.from_folder(path, valid_pct=0.2, ds_tfms=get_transforms(), size=bs).normalize(imagenet_stats)
arch = resnet50
learn = cnn_learner(data, arch, metrics=[error_rate, accuracy], model_dir='/kaggle/working').to_fp16()
learn.model_dir = '/kaggle/working'
learn.lr_find()
lr = learn.recorder.min_grad_lr
lr
learn.fit_one_cycle(5, lr)
learn.save('stage-1-50')
learn.load('stage-1-50')
learn.unfreeze()
learn.lr_find()
lr1 = learn.recorder.min_grad_lr
lr1
learn.fit_one_cycle(5, max_lr=slice(lr1 / 100, lr1 / 10, lr1))
learn.save('model-2')
learn.load('model-2') | code |
2030951/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/diabetes.csv')
data.head() | code |
2030951/cell_20 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('../input/diabetes.csv')
correlations = data.corr()
correlations['Outcome'].sort_values(ascending=False)
def visualise(data):
fig, ax = plt.subplots()
ax.scatter(data.iloc[:,1].values, data.iloc[:,5].values)
ax.set_title('Highly Correlated Features')
ax.set_xlabel('Plasma glucose concentration')
ax.set_ylabel('Body mass index')
visualise(data)
data[['Glucose', 'BMI']] = data[['Glucose', 'BMI']].replace(0, np.NaN)
data.dropna(inplace=True)
X = data[['Glucose', 'BMI']].values
y = data[['Outcome']].values
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X = sc.fit_transform(X)
print(X[0:10, :]) | code |
2030951/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/diabetes.csv')
correlations = data.corr()
correlations['Outcome'].sort_values(ascending=False) | code |
2030951/cell_26 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train.ravel())
y_pred = model.predict(X_test)
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
print(cm) | code |
2030951/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('../input/diabetes.csv')
correlations = data.corr()
correlations['Outcome'].sort_values(ascending=False)
def visualise(data):
fig, ax = plt.subplots()
ax.scatter(data.iloc[:,1].values, data.iloc[:,5].values)
ax.set_title('Highly Correlated Features')
ax.set_xlabel('Plasma glucose concentration')
ax.set_ylabel('Body mass index')
visualise(data)
data[['Glucose', 'BMI']] = data[['Glucose', 'BMI']].replace(0, np.NaN)
data.dropna(inplace=True)
X = data[['Glucose', 'BMI']].values
y = data[['Outcome']].values
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X = sc.fit_transform(X)
mean = np.mean(X, axis=0)
print('Mean: (%d, %d)' % (mean[0], mean[1]))
standard_deviation = np.std(X, axis=0)
print('Standard deviation: (%d, %d)' % (standard_deviation[0], standard_deviation[1])) | code |
2030951/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train.ravel())
y_pred = model.predict(X_test)
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
def precision_recall(y_test, y_pred):
cm = confusion_matrix(y_test, y_pred)
tp = cm[0, 0]
fp = cm[0, 1]
fn = cm[1, 0]
prec = tp / (tp + fp)
rec = tp / (tp + fn)
return (prec, rec)
precision, recall = precision_recall(y_test, y_pred)
print('Precision: %f Recall %f' % (precision, recall)) | code |
2030951/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/diabetes.csv')
correlations = data.corr()
correlations['Outcome'].sort_values(ascending=False)
def visualise(data):
fig, ax = plt.subplots()
ax.scatter(data.iloc[:, 1].values, data.iloc[:, 5].values)
ax.set_title('Highly Correlated Features')
ax.set_xlabel('Plasma glucose concentration')
ax.set_ylabel('Body mass index')
visualise(data) | code |
2030951/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('../input/diabetes.csv')
correlations = data.corr()
correlations['Outcome'].sort_values(ascending=False)
def visualise(data):
fig, ax = plt.subplots()
ax.scatter(data.iloc[:,1].values, data.iloc[:,5].values)
ax.set_title('Highly Correlated Features')
ax.set_xlabel('Plasma glucose concentration')
ax.set_ylabel('Body mass index')
visualise(data)
data[['Glucose', 'BMI']] = data[['Glucose', 'BMI']].replace(0, np.NaN)
data.dropna(inplace=True)
visualise(data) | code |
2017076/cell_13 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('../input/date_info.csv')
relation = pd.read_csv('../input/store_id_relation.csv')
air_store_info = pd.read_csv('../input/air_store_info.csv')
hpg_store_info = pd.read_csv('../input/hpg_store_info.csv')
air_store_info.info()
air_store_info.head() | code |
2017076/cell_9 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('../input/date_info.csv')
relation = pd.read_csv('../input/store_id_relation.csv')
air_store_info = pd.read_csv('../input/air_store_info.csv')
hpg_store_info = pd.read_csv('../input/hpg_store_info.csv')
visits.info()
visits.describe() | code |
2017076/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('../input/date_info.csv')
relation = pd.read_csv('../input/store_id_relation.csv')
air_store_info = pd.read_csv('../input/air_store_info.csv')
hpg_store_info = pd.read_csv('../input/hpg_store_info.csv')
hpg_reserve.head() | code |
2017076/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('../input/date_info.csv')
relation = pd.read_csv('../input/store_id_relation.csv')
air_store_info = pd.read_csv('../input/air_store_info.csv')
hpg_store_info = pd.read_csv('../input/hpg_store_info.csv')
cols = ['store_id', 'visit_datetime', 'reserve_datetime', 'reserve_visitors']
air_reserve.columns = cols
hpg_reserve.columns = cols
reserves = pd.DataFrame(columns=cols)
reserves = pd.concat([air_reserve, hpg_reserve])
reserves['visit_datetime'] = pd.to_datetime(reserves['visit_datetime'])
reserves['reserve_datetime'] = pd.to_datetime(reserves['reserve_datetime'])
print('Number of restaurants from AirREGI = ', str(len(air_reserve['store_id'].unique())))
print('Number of restaurants from hpg = ', str(len(hpg_reserve['store_id'].unique()))) | code |
2017076/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('../input/date_info.csv')
relation = pd.read_csv('../input/store_id_relation.csv')
air_store_info = pd.read_csv('../input/air_store_info.csv')
hpg_store_info = pd.read_csv('../input/hpg_store_info.csv')
dates.head() | code |
2017076/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('../input/date_info.csv')
relation = pd.read_csv('../input/store_id_relation.csv')
air_store_info = pd.read_csv('../input/air_store_info.csv')
hpg_store_info = pd.read_csv('../input/hpg_store_info.csv')
sns.countplot(x='visitors', data=visits) | code |
2017076/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('../input/date_info.csv')
relation = pd.read_csv('../input/store_id_relation.csv')
air_store_info = pd.read_csv('../input/air_store_info.csv')
hpg_store_info = pd.read_csv('../input/hpg_store_info.csv')
print('Areas:\n')
air_store_info['air_area_name'].unique() | code |
2017076/cell_16 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('../input/date_info.csv')
relation = pd.read_csv('../input/store_id_relation.csv')
air_store_info = pd.read_csv('../input/air_store_info.csv')
hpg_store_info = pd.read_csv('../input/hpg_store_info.csv')
hpg_store_info.info()
hpg_store_info.head() | code |
2017076/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('../input/date_info.csv')
relation = pd.read_csv('../input/store_id_relation.csv')
air_store_info = pd.read_csv('../input/air_store_info.csv')
hpg_store_info = pd.read_csv('../input/hpg_store_info.csv')
air_reserve.head() | code |
2017076/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('../input/date_info.csv')
relation = pd.read_csv('../input/store_id_relation.csv')
air_store_info = pd.read_csv('../input/air_store_info.csv')
hpg_store_info = pd.read_csv('../input/hpg_store_info.csv')
print('Cuisines:')
air_store_info['air_genre_name'].unique() | code |
2017076/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('../input/date_info.csv')
relation = pd.read_csv('../input/store_id_relation.csv')
air_store_info = pd.read_csv('../input/air_store_info.csv')
hpg_store_info = pd.read_csv('../input/hpg_store_info.csv')
relation.info() | code |
2017076/cell_5 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('../input/date_info.csv')
relation = pd.read_csv('../input/store_id_relation.csv')
air_store_info = pd.read_csv('../input/air_store_info.csv')
hpg_store_info = pd.read_csv('../input/hpg_store_info.csv')
cols = ['store_id', 'visit_datetime', 'reserve_datetime', 'reserve_visitors']
air_reserve.columns = cols
hpg_reserve.columns = cols
reserves = pd.DataFrame(columns=cols)
reserves = pd.concat([air_reserve, hpg_reserve])
reserves['visit_datetime'] = pd.to_datetime(reserves['visit_datetime'])
reserves['reserve_datetime'] = pd.to_datetime(reserves['reserve_datetime'])
reserves.info()
reserves.describe() | code |
32071161/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 |
72062686/cell_2 | [
"text_plain_output_1.png"
] | !pip install dtreeviz
!pip install graphviz | code |
72062686/cell_10 | [
"text_plain_output_1.png"
] | from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier
iris = datasets.load_iris()
model = DecisionTreeClassifier(random_state=42)
model.fit(iris.data, iris.target)
model.predict(iris.data) | code |
72062686/cell_12 | [
"text_plain_output_1.png"
] | from dtreeviz.trees import dtreeviz
from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier
iris = datasets.load_iris()
model = DecisionTreeClassifier(random_state=42)
model.fit(iris.data, iris.target)
model.predict(iris.data)
viz = dtreeviz(model, iris.data, iris.target, target_name='target', feature_names=iris.feature_names, class_names=list(iris.target_names))
viz.save('regression.svg')
viz | code |
72104768/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
from functools import partial
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import StratifiedKFold
import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam, Nadam
from tensorflow.keras.layers import Input, Embedding, Reshape, GlobalAveragePooling1D
from tensorflow.keras.layers import Flatten, concatenate, Concatenate, Lambda, Dropout, SpatialDropout1D
from tensorflow.keras.layers import Reshape, MaxPooling1D, BatchNormalization, AveragePooling1D, Conv1D
from tensorflow.keras.layers import Activation, LeakyReLU
from tensorflow.keras.optimizers import SGD, Adam, Nadam
from tensorflow.keras.models import Model, load_model
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from tensorflow.keras.regularizers import l2, l1_l2
from keras.losses import MeanSquaredError
from tensorflow.keras.utils import get_custom_objects
from tensorflow.keras.layers import Activation, LeakyReLU
from tabular import gelu, Mish, mish
from tabular import TabularTransformer, DataGenerator | code |
72104768/cell_1 | [
"text_plain_output_1.png"
] | ##### DEEP LEARNING FOR TABULAR DATA ##########################
# The functions used in this Kernel are based on:
# https://github.com/lmassaron/deep_learning_for_tabular_data
# You can watch the full tutorial presented at the DEVFEST 2019
# explaining how to process tabular data with TensorFlow:
# https://www.youtube.com/watch?v=nQgUt_uADSE
################################################################
!wget https://raw.githubusercontent.com/lmassaron/deep_learning_for_tabular_data/master/tabular.py | code |
72104768/cell_15 | [
"text_plain_output_1.png"
] | from functools import partial
from keras.losses import MeanSquaredError
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import StratifiedKFold
from tabular import TabularTransformer, DataGenerator
from tabular import gelu, Mish, mish
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Flatten, concatenate, Concatenate, Lambda, Dropout, SpatialDropout1D
from tensorflow.keras.layers import Input, Embedding, Reshape, GlobalAveragePooling1D
from tensorflow.keras.layers import Reshape, MaxPooling1D,BatchNormalization, AveragePooling1D, Conv1D
from tensorflow.keras.models import Model, load_model
from tensorflow.keras.optimizers import Adam, Nadam
from tensorflow.keras.optimizers import SGD, Adam, Nadam
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
X = pd.read_csv('../input/30-days-of-ml/train.csv')
X_test = pd.read_csv('../input/30-days-of-ml/test.csv')
y = X.target
X = X.set_index('id').drop('target', axis='columns')
X_test = X_test.set_index('id')
y_stratified = pd.cut(y, bins=10, labels=False)
categoricals = [item for item in X.columns if 'cat' in item]
cat_values = np.unique(X[categoricals].values)
cat_dict = dict(zip(cat_values, range(len(cat_values))))
X[categoricals] = X[categoricals].replace(cat_dict)
X_test[categoricals] = X_test[categoricals].replace(cat_dict)
numeric_variables = [item for item in X.columns if 'cont' in item]
categorical_variables = categoricals
ordinal_variables = categorical_variables
def tabular_dnn(numeric_variables, categorical_variables=None, categorical_counts=None, feature_selection_dropout=0.2, categorical_dropout=0.1, first_dense=256, second_dense=256, dense_dropout=0.2, activation_type=gelu):
numerical_inputs = Input(shape=(numeric_variables,))
numerical_normalization = BatchNormalization()(numerical_inputs)
numerical_feature_selection = Dropout(feature_selection_dropout)(numerical_normalization)
if categorical_variables is not None:
categorical_inputs = []
categorical_embeddings = []
for category in categorical_variables:
categorical_inputs.append(Input(shape=[1], name=category))
category_counts = categorical_counts[category]
categorical_embeddings.append(Embedding(category_counts + 1, int(np.log1p(category_counts) + 1), name=category + '_embed')(categorical_inputs[-1]))
categorical_logits = Concatenate(name='categorical_conc')([Flatten()(SpatialDropout1D(categorical_dropout)(cat_emb)) for cat_emb in categorical_embeddings])
x = concatenate([numerical_feature_selection, categorical_logits])
else:
x = numerical_feature_selection
x = Dense(first_dense, activation=activation_type)(x)
x = Dropout(dense_dropout)(x)
x = Dense(second_dense, activation=activation_type)(x)
x = Dropout(dense_dropout)(x)
output = Dense(1)(x)
if categorical_variables is not None:
model = Model([numerical_inputs] + categorical_inputs, output)
else:
model = Model([numerical_inputs], output)
return model
# Useful functions
def RMSE(y_true, y_pred):
return tf.py_function(partial(mean_squared_error, squared=False), (y_true, y_pred), tf.double)
def compile_model(model, loss, metrics, optimizer):
model.compile(loss=loss, metrics=metrics, optimizer=optimizer)
return model
def plot_keras_history(history, measures):
"""
history: Keras training history
measures = list of names of measures
"""
rows = len(measures) // 2 + len(measures) % 2
fig, panels = plt.subplots(rows, 2, figsize=(15, 5))
plt.subplots_adjust(top = 0.99, bottom=0.01, hspace=0.4, wspace=0.2)
try:
panels = [item for sublist in panels for item in sublist]
except:
pass
for k, measure in enumerate(measures):
panel = panels[k]
panel.set_title(measure + ' history')
panel.plot(history.epoch, history.history[measure], label="Train "+measure)
panel.plot(history.epoch, history.history["val_"+measure], label="Validation "+measure)
panel.set(xlabel='epochs', ylabel=measure)
panel.legend()
plt.show(fig)
measure_to_monitor = 'val_RMSE'
modality = 'min'
early_stopping = EarlyStopping(monitor=measure_to_monitor, mode=modality, patience=3, verbose=0)
model_checkpoint = ModelCheckpoint('best.model', monitor=measure_to_monitor, mode=modality, save_best_only=True, verbose=0)
skf = StratifiedKFold(n_splits=Config.folds, shuffle=True, random_state=Config.seed)
score = list()
oof = np.zeros(len(X))
best_iteration = list()
for fold, (train_idx, test_idx) in enumerate(skf.split(X, y_stratified)):
tb = TabularTransformer(numeric=numeric_variables, ordinal=[], lowcat=[], highcat=categorical_variables)
tb.fit(X.iloc[train_idx])
sizes = tb.shape(X.iloc[train_idx])
categorical_levels = dict(zip(categorical_variables, sizes[1:]))
model = tabular_dnn(numeric_variables=sizes[0], categorical_variables=categorical_variables, categorical_counts=categorical_levels, feature_selection_dropout=0.0, categorical_dropout=0.0, first_dense=64, second_dense=64, dense_dropout=0.0, activation_type='relu')
model = compile_model(model, loss='mean_squared_error', metrics=[MeanSquaredError(name='MSE'), RMSE], optimizer=Adam(learning_rate=0.0001))
train_batch = DataGenerator(X.iloc[train_idx], y[train_idx], tabular_transformer=tb, batch_size=Config.batch_size, shuffle=True)
history = model.fit(train_batch, validation_data=(tb.transform(X.iloc[test_idx]), y[test_idx]), epochs=Config.epochs, callbacks=[model_checkpoint, early_stopping], verbose=1)
best_iteration.append(np.argmin(history.history['val_RMSE']) + 1)
preds = model.predict(tb.transform(X.iloc[test_idx]), verbose=1, batch_size=1024).flatten()
oof[test_idx] = preds
score.append(mean_squared_error(y_true=y[test_idx], y_pred=preds, squared=False))
tb = TabularTransformer(numeric=numeric_variables, ordinal=[], lowcat=[], highcat=categorical_variables)
tb.fit(X)
sizes = tb.shape(X)
categorical_levels = dict(zip(categorical_variables, sizes[1:]))
print(f'Input array sizes: {sizes}')
print(f'Categorical levels: {categorical_levels}\n')
model = tabular_dnn(numeric_variables=sizes[0], categorical_variables=categorical_variables, categorical_counts=categorical_levels, feature_selection_dropout=0.0, categorical_dropout=0.0, first_dense=64, second_dense=64, dense_dropout=0.0, activation_type='relu')
model = compile_model(model, loss='mean_squared_error', metrics=[MeanSquaredError(name='MSE'), RMSE], optimizer=Adam(learning_rate=0.0001))
train_batch = DataGenerator(X, y, tabular_transformer=tb, batch_size=Config.batch_size, shuffle=True)
best_epochs = int(np.median(best_iteration))
print(f'Training for {best_epochs} epochs')
history = model.fit(train_batch, epochs=best_epochs, verbose=1) | code |
72104768/cell_14 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from functools import partial
from keras.losses import MeanSquaredError
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import StratifiedKFold
from tabular import TabularTransformer, DataGenerator
from tabular import gelu, Mish, mish
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Flatten, concatenate, Concatenate, Lambda, Dropout, SpatialDropout1D
from tensorflow.keras.layers import Input, Embedding, Reshape, GlobalAveragePooling1D
from tensorflow.keras.layers import Reshape, MaxPooling1D,BatchNormalization, AveragePooling1D, Conv1D
from tensorflow.keras.models import Model, load_model
from tensorflow.keras.optimizers import Adam, Nadam
from tensorflow.keras.optimizers import SGD, Adam, Nadam
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
X = pd.read_csv('../input/30-days-of-ml/train.csv')
X_test = pd.read_csv('../input/30-days-of-ml/test.csv')
y = X.target
X = X.set_index('id').drop('target', axis='columns')
X_test = X_test.set_index('id')
y_stratified = pd.cut(y, bins=10, labels=False)
categoricals = [item for item in X.columns if 'cat' in item]
cat_values = np.unique(X[categoricals].values)
cat_dict = dict(zip(cat_values, range(len(cat_values))))
X[categoricals] = X[categoricals].replace(cat_dict)
X_test[categoricals] = X_test[categoricals].replace(cat_dict)
numeric_variables = [item for item in X.columns if 'cont' in item]
categorical_variables = categoricals
ordinal_variables = categorical_variables
def tabular_dnn(numeric_variables, categorical_variables=None, categorical_counts=None, feature_selection_dropout=0.2, categorical_dropout=0.1, first_dense=256, second_dense=256, dense_dropout=0.2, activation_type=gelu):
numerical_inputs = Input(shape=(numeric_variables,))
numerical_normalization = BatchNormalization()(numerical_inputs)
numerical_feature_selection = Dropout(feature_selection_dropout)(numerical_normalization)
if categorical_variables is not None:
categorical_inputs = []
categorical_embeddings = []
for category in categorical_variables:
categorical_inputs.append(Input(shape=[1], name=category))
category_counts = categorical_counts[category]
categorical_embeddings.append(Embedding(category_counts + 1, int(np.log1p(category_counts) + 1), name=category + '_embed')(categorical_inputs[-1]))
categorical_logits = Concatenate(name='categorical_conc')([Flatten()(SpatialDropout1D(categorical_dropout)(cat_emb)) for cat_emb in categorical_embeddings])
x = concatenate([numerical_feature_selection, categorical_logits])
else:
x = numerical_feature_selection
x = Dense(first_dense, activation=activation_type)(x)
x = Dropout(dense_dropout)(x)
x = Dense(second_dense, activation=activation_type)(x)
x = Dropout(dense_dropout)(x)
output = Dense(1)(x)
if categorical_variables is not None:
model = Model([numerical_inputs] + categorical_inputs, output)
else:
model = Model([numerical_inputs], output)
return model
# Useful functions
def RMSE(y_true, y_pred):
return tf.py_function(partial(mean_squared_error, squared=False), (y_true, y_pred), tf.double)
def compile_model(model, loss, metrics, optimizer):
model.compile(loss=loss, metrics=metrics, optimizer=optimizer)
return model
def plot_keras_history(history, measures):
"""
history: Keras training history
measures = list of names of measures
"""
rows = len(measures) // 2 + len(measures) % 2
fig, panels = plt.subplots(rows, 2, figsize=(15, 5))
plt.subplots_adjust(top = 0.99, bottom=0.01, hspace=0.4, wspace=0.2)
try:
panels = [item for sublist in panels for item in sublist]
except:
pass
for k, measure in enumerate(measures):
panel = panels[k]
panel.set_title(measure + ' history')
panel.plot(history.epoch, history.history[measure], label="Train "+measure)
panel.plot(history.epoch, history.history["val_"+measure], label="Validation "+measure)
panel.set(xlabel='epochs', ylabel=measure)
panel.legend()
plt.show(fig)
measure_to_monitor = 'val_RMSE'
modality = 'min'
early_stopping = EarlyStopping(monitor=measure_to_monitor, mode=modality, patience=3, verbose=0)
model_checkpoint = ModelCheckpoint('best.model', monitor=measure_to_monitor, mode=modality, save_best_only=True, verbose=0)
skf = StratifiedKFold(n_splits=Config.folds, shuffle=True, random_state=Config.seed)
score = list()
oof = np.zeros(len(X))
best_iteration = list()
for fold, (train_idx, test_idx) in enumerate(skf.split(X, y_stratified)):
tb = TabularTransformer(numeric=numeric_variables, ordinal=[], lowcat=[], highcat=categorical_variables)
tb.fit(X.iloc[train_idx])
sizes = tb.shape(X.iloc[train_idx])
categorical_levels = dict(zip(categorical_variables, sizes[1:]))
model = tabular_dnn(numeric_variables=sizes[0], categorical_variables=categorical_variables, categorical_counts=categorical_levels, feature_selection_dropout=0.0, categorical_dropout=0.0, first_dense=64, second_dense=64, dense_dropout=0.0, activation_type='relu')
model = compile_model(model, loss='mean_squared_error', metrics=[MeanSquaredError(name='MSE'), RMSE], optimizer=Adam(learning_rate=0.0001))
train_batch = DataGenerator(X.iloc[train_idx], y[train_idx], tabular_transformer=tb, batch_size=Config.batch_size, shuffle=True)
history = model.fit(train_batch, validation_data=(tb.transform(X.iloc[test_idx]), y[test_idx]), epochs=Config.epochs, callbacks=[model_checkpoint, early_stopping], verbose=1)
best_iteration.append(np.argmin(history.history['val_RMSE']) + 1)
preds = model.predict(tb.transform(X.iloc[test_idx]), verbose=1, batch_size=1024).flatten()
oof[test_idx] = preds
score.append(mean_squared_error(y_true=y[test_idx], y_pred=preds, squared=False))
print('Average RMSE %0.3f ± %0.3f' % (np.mean(score), np.std(score)))
print('RMSE OOF %0.3f' % mean_squared_error(y_true=y, y_pred=oof, squared=False)) | code |
88086649/cell_21 | [
"text_html_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
import pandas as pd
df = pd.read_csv('../input/fish-market/Fish.csv')
df.isna().sum()
corr = df.corr()
corr
X = df.drop('Weight', axis=1)
y = df['Weight']
(X.shape, y.shape)
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [0])], remainder='passthrough')
transformed_X = ct.fit_transform(X, y)
transformed_X[0] | code |
88086649/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fish-market/Fish.csv')
df.isna().sum()
df['Species'].value_counts() | code |
88086649/cell_25 | [
"image_output_1.png"
] | (X_train.shape, X_test.shape, y_train.shape, y_test.shape) | code |
88086649/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
y_pred[:10] | code |
88086649/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
from sklearn.metrics import r2_score
print('R2 Score', r2_score(y_test, y_pred)) | code |
88086649/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fish-market/Fish.csv')
df.describe() | code |
88086649/cell_40 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_log_error, r2_score
from sklearn.metrics import r2_score
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from sklearn.ensemble import RandomForestRegressor
reg = RandomForestRegressor(n_estimators=200)
reg.fit(X_train, y_train)
y_pred_reg = reg.predict(X_test)
from sklearn.metrics import mean_squared_log_error, r2_score
print('Mean Squared Log Error', mean_squared_log_error(y_test, y_pred_reg))
print('R2 Score', r2_score(y_test, y_pred_reg)) | code |
88086649/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fish-market/Fish.csv')
df.isna().sum()
corr = df.corr()
corr | code |
88086649/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fish-market/Fish.csv')
df.info() | code |
88086649/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fish-market/Fish.csv')
df.isna().sum()
corr = df.corr()
corr
X = df.drop('Weight', axis=1)
y = df['Weight']
(X.shape, y.shape)
(type(X), type(y)) | code |
88086649/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fish-market/Fish.csv')
df.isna().sum() | code |
88086649/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fish-market/Fish.csv')
df.isna().sum()
corr = df.corr()
corr
sns.boxplot(x='Species', y='Weight', data=df) | code |
88086649/cell_38 | [
"text_plain_output_1.png"
] | import numpy as np
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
np.array(y_test)
np.array(y_test) | code |
88086649/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fish-market/Fish.csv')
df.isna().sum()
corr = df.corr()
corr
X = df.drop('Weight', axis=1)
y = df['Weight']
(X.shape, y.shape) | code |
88086649/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from sklearn.ensemble import RandomForestRegressor
reg = RandomForestRegressor(n_estimators=200)
reg.fit(X_train, y_train) | code |
88086649/cell_31 | [
"text_plain_output_1.png"
] | import numpy as np
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
np.array(y_test) | code |
88086649/cell_22 | [
"image_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
import pandas as pd
df = pd.read_csv('../input/fish-market/Fish.csv')
df.isna().sum()
corr = df.corr()
corr
X = df.drop('Weight', axis=1)
y = df['Weight']
(X.shape, y.shape)
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [0])], remainder='passthrough')
transformed_X = ct.fit_transform(X, y)
transformed_X | code |
88086649/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fish-market/Fish.csv')
df.isna().sum()
df['Species'].value_counts().plot.bar() | code |
88086649/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train) | code |
88086649/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from sklearn.ensemble import RandomForestRegressor
reg = RandomForestRegressor(n_estimators=200)
reg.fit(X_train, y_train)
y_pred_reg = reg.predict(X_test)
y_pred_reg | code |
88086649/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fish-market/Fish.csv')
df.isna().sum()
corr = df.corr()
corr
plt.figure(figsize=(11, 8))
sns.heatmap(corr, cmap='Greens', annot=True)
plt.show() | code |
88086649/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fish-market/Fish.csv')
df.head() | code |
130011577/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df['official_death_date'] = pd.to_datetime(df['official_death_date'])
thai_accident_df = df.dropna(subset='accident_date').copy()
thai_accident_df['accident_date'] = pd.to_datetime(thai_accident_df['accident_date'])
thai_accident_df.dtypes
thai_accident_df.describe().T
import matplotlib.pyplot as plt
import seaborn as sns
thai_accident_df = df.dropna(subset='accident_date').copy()
thai_accident_df['accident_date'] = pd.to_datetime(thai_accident_df['accident_date'])
thai_accident_df['year'], thai_accident_df['month'], thai_accident_df['day'] = (thai_accident_df['accident_date'].dt.year, thai_accident_df['accident_date'].dt.month, thai_accident_df['accident_date'].dt.day)
def thai_accident_from_to(from_date=thai_accident_df['accident_date'].min(), to_date=thai_accident_df['accident_date'].max()):
df = thai_accident_df[(thai_accident_df['accident_date'] >= from_date) & (thai_accident_df['accident_date'] < to_date)]
return df
counts = thai_accident_df.groupby(['year', 'month']).size().reset_index(name='count')
print(counts.tail())
plt.figure(figsize=(14, 7))
sns.lineplot(data=counts, x='month', y='count', hue='year')
plt.show() | code |
130011577/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df['official_death_date'] = pd.to_datetime(df['official_death_date'])
thai_accident_df = df.dropna(subset='accident_date').copy()
print(thai_accident_df.shape) | code |
130011577/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df['official_death_date'] = pd.to_datetime(df['official_death_date'])
thai_accident_df = df.dropna(subset='accident_date').copy()
thai_accident_df['accident_date'] = pd.to_datetime(thai_accident_df['accident_date'])
thai_accident_df.dtypes
print(thai_accident_df.isnull().sum())
thai_accident_df.describe().T | code |
130011577/cell_2 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df.tail() | code |
130011577/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df['official_death_date'] = pd.to_datetime(df['official_death_date'])
thai_accident_df = df.dropna(subset='accident_date').copy()
thai_accident_df['accident_date'] = pd.to_datetime(thai_accident_df['accident_date'])
thai_accident_df.dtypes
thai_accident_df.describe().T
import matplotlib.pyplot as plt
import seaborn as sns
thai_accident_df = df.dropna(subset='accident_date').copy()
thai_accident_df['accident_date'] = pd.to_datetime(thai_accident_df['accident_date'])
thai_accident_df['year'], thai_accident_df['month'], thai_accident_df['day'] = (thai_accident_df['accident_date'].dt.year, thai_accident_df['accident_date'].dt.month, thai_accident_df['accident_date'].dt.day)
def thai_accident_from_to(from_date=thai_accident_df['accident_date'].min(), to_date=thai_accident_df['accident_date'].max()):
df = thai_accident_df[(thai_accident_df['accident_date'] >= from_date) & (thai_accident_df['accident_date'] < to_date)]
return df
counts = thai_accident_df.groupby(['year', 'month']).size().reset_index(name='count')
gender_count = thai_accident_df['gender'].value_counts().reset_index()
gender_count.columns = ['gender', 'g_count']
gender_count['%'] = gender_count['g_count'] / gender_count['g_count'].sum() * 100
print('## Accident by year ##')
year_df = thai_accident_df['accident_date'].dt.year.value_counts().reset_index(name='c')
year_df.columns = ['year', 'count']
plt.title('All accident occur each year')
sns.barplot(data=year_df, x='year', y='count')
plt.ylabel('count')
plt.xlabel('Year')
plt.show()
plt.title('All appear accident_date')
sns.lineplot(data=year_df, x='year', y='count')
plt.ylabel('count')
plt.xlabel('Year')
plt.show()
plt.figure(figsize=(14, 7))
plt.title('Use Histogram')
sns.histplot(data=thai_accident_df['year'], discrete=True, element='step')
plt.show()
print('Describe accident by year')
print(thai_accident_df['year'].value_counts().describe()) | code |
130011577/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import geopandas as gpd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
130011577/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df['official_death_date'] = pd.to_datetime(df['official_death_date'])
thai_accident_df = df.dropna(subset='accident_date').copy()
thai_accident_df['accident_date'] = pd.to_datetime(thai_accident_df['accident_date'])
thai_accident_df.dtypes
thai_accident_df.describe().T
import matplotlib.pyplot as plt
import seaborn as sns
thai_accident_df = df.dropna(subset='accident_date').copy()
thai_accident_df['accident_date'] = pd.to_datetime(thai_accident_df['accident_date'])
thai_accident_df['year'], thai_accident_df['month'], thai_accident_df['day'] = (thai_accident_df['accident_date'].dt.year, thai_accident_df['accident_date'].dt.month, thai_accident_df['accident_date'].dt.day)
def thai_accident_from_to(from_date=thai_accident_df['accident_date'].min(), to_date=thai_accident_df['accident_date'].max()):
df = thai_accident_df[(thai_accident_df['accident_date'] >= from_date) & (thai_accident_df['accident_date'] < to_date)]
return df
print('Ready for Data visualization') | code |
130011577/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
print(f'Chech Datatype\n{df.dtypes}')
print('\nShape check')
print(df.shape)
print()
print(df.isnull().sum())
df['official_death_date'] = pd.to_datetime(df['official_death_date']) | code |
130011577/cell_10 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df['official_death_date'] = pd.to_datetime(df['official_death_date'])
thai_accident_df = df.dropna(subset='accident_date').copy()
thai_accident_df['accident_date'] = pd.to_datetime(thai_accident_df['accident_date'])
thai_accident_df.dtypes
thai_accident_df.describe().T
import matplotlib.pyplot as plt
import seaborn as sns
thai_accident_df = df.dropna(subset='accident_date').copy()
thai_accident_df['accident_date'] = pd.to_datetime(thai_accident_df['accident_date'])
thai_accident_df['year'], thai_accident_df['month'], thai_accident_df['day'] = (thai_accident_df['accident_date'].dt.year, thai_accident_df['accident_date'].dt.month, thai_accident_df['accident_date'].dt.day)
def thai_accident_from_to(from_date=thai_accident_df['accident_date'].min(), to_date=thai_accident_df['accident_date'].max()):
df = thai_accident_df[(thai_accident_df['accident_date'] >= from_date) & (thai_accident_df['accident_date'] < to_date)]
return df
counts = thai_accident_df.groupby(['year', 'month']).size().reset_index(name='count')
print('Simple data below\n')
print(f"All accident data from {thai_accident_df['accident_date'].min().date()} to {thai_accident_df['accident_date'].max().date()} \n{thai_accident_df.shape[0]} cases\n")
print('# By Gender')
gender_count = thai_accident_df['gender'].value_counts().reset_index()
gender_count.columns = ['gender', 'g_count']
gender_count['%'] = gender_count['g_count'] / gender_count['g_count'].sum() * 100
print(gender_count)
print('\n# By Vehicle type')
print(thai_accident_df['vehicle_type'].value_counts())
print('\n# By province')
print(thai_accident_df['province_en'].value_counts())
print(thai_accident_df['province_en'].value_counts().describe()) | code |
130011577/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df['official_death_date'] = pd.to_datetime(df['official_death_date'])
thai_accident_df = df.dropna(subset='accident_date').copy()
thai_accident_df['accident_date'] = pd.to_datetime(thai_accident_df['accident_date'])
thai_accident_df.dtypes
thai_accident_df.describe().T
import matplotlib.pyplot as plt
import seaborn as sns
thai_accident_df = df.dropna(subset='accident_date').copy()
thai_accident_df['accident_date'] = pd.to_datetime(thai_accident_df['accident_date'])
thai_accident_df['year'], thai_accident_df['month'], thai_accident_df['day'] = (thai_accident_df['accident_date'].dt.year, thai_accident_df['accident_date'].dt.month, thai_accident_df['accident_date'].dt.day)
def thai_accident_from_to(from_date=thai_accident_df['accident_date'].min(), to_date=thai_accident_df['accident_date'].max()):
df = thai_accident_df[(thai_accident_df['accident_date'] >= from_date) & (thai_accident_df['accident_date'] < to_date)]
return df
counts = thai_accident_df.groupby(['year', 'month']).size().reset_index(name='count')
gender_count = thai_accident_df['gender'].value_counts().reset_index()
gender_count.columns = ['gender', 'g_count']
gender_count['%'] = gender_count['g_count'] / gender_count['g_count'].sum() * 100
year_df = thai_accident_df['accident_date'].dt.year.value_counts().reset_index(name='c')
year_df.columns = ['year', 'count']
print('Try......')
fig, ax = plt.subplots(1, 2, figsize=(12, 5))
fig.suptitle('1st try subplots', y=1.05)
sns.histplot(ax=ax[0], x=thai_accident_df['year'], discrete=True)
ax[0].set_title('Plot using histogram', y=1.05)
ax[0].bar_label(ax[0].containers[1], rotation=45)
sns.barplot(ax=ax[1], data=year_df, x='year', y='count')
ax[1].set_title('Plot using barplot', y=1.05)
ax[1].bar_label(ax[1].containers[0], rotation=35)
ax[1].set_xticklabels(ax[1].get_xticklabels(), rotation=45)
plt.show() | code |
130011577/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/thailand-fatal-road-accident/thailand_fatal_raod_accident_2011_2022.csv')
df['official_death_date'] = pd.to_datetime(df['official_death_date'])
thai_accident_df = df.dropna(subset='accident_date').copy()
thai_accident_df['accident_date'] = pd.to_datetime(thai_accident_df['accident_date'])
thai_accident_df.dtypes | code |
16127029/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/loan.csv', low_memory=False)
data = data[(data.loan_status == 'Fully Paid') | (data.loan_status == 'Default')]
data['target'] = data.loan_status == 'Fully Paid'
data.shape
x = data.loan_amnt
term_groups = data.groupby('term')
term_groups['int_rate'].mean()
grade_groups = data.groupby('grade')
grade_groups['int_rate'].mean()
total_loaned = grade_groups['funded_amnt'].sum()
print(total_loaned) | code |
16127029/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/loan.csv', low_memory=False)
data = data[(data.loan_status == 'Fully Paid') | (data.loan_status == 'Default')]
data['target'] = data.loan_status == 'Fully Paid'
data.shape
x = data.loan_amnt
term_groups = data.groupby('term')
term_groups['int_rate'].mean()
term_groups['int_rate'].std() | code |
16127029/cell_30 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/loan.csv', low_memory=False)
data = data[(data.loan_status == 'Fully Paid') | (data.loan_status == 'Default')]
data['target'] = data.loan_status == 'Fully Paid'
data.shape
x = data.loan_amnt
term_groups = data.groupby('term')
term_groups['int_rate'].mean()
grade_groups = data.groupby('grade')
grade_groups['int_rate'].mean()
X = pd.get_dummies(data[['term', 'verification_status', 'purpose', 'policy_code', 'loan_amnt', 'funded_amnt', 'funded_amnt_inv', 'int_rate', 'emp_length', 'addr_state']])
count_class_0, count_class_1 = data.target.value_counts()
data_class_0 = data[data['target'] == 1]
data_class_1 = data[data['target'] == 0]
data_class_0_under = data_class_0.sample(count_class_1)
data_test_under = pd.concat([data_class_0_under, data_class_1], axis=0)
data_class_1_over = data_class_1.sample(count_class_0, replace=True)
data_test_over = pd.concat([data_class_0, data_class_1_over], axis=0)
print('Random over-sampling:')
print(data_test_over.target.value_counts())
data_test_over.target.value_counts().plot(kind='bar', title='Count (target )') | code |
16127029/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/loan.csv', low_memory=False)
data = data[(data.loan_status == 'Fully Paid') | (data.loan_status == 'Default')]
data['target'] = data.loan_status == 'Fully Paid'
data.shape
x = data.loan_amnt
term_groups = data.groupby('term')
term_groups['int_rate'].mean()
grade_groups = data.groupby('grade')
grade_groups['int_rate'].mean()
X = pd.get_dummies(data[['term', 'verification_status', 'purpose', 'policy_code', 'loan_amnt', 'funded_amnt', 'funded_amnt_inv', 'int_rate', 'emp_length', 'addr_state']])
X.shape
y = data['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
X_train.shape | code |
16127029/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/loan.csv', low_memory=False)
data = data[(data.loan_status == 'Fully Paid') | (data.loan_status == 'Default')]
data['target'] = data.loan_status == 'Fully Paid'
data.shape
print(f"The mean of loan amount: {data['loan_amnt'].mean()}")
print(f"The median of loan amount: {data['loan_amnt'].median()}")
print(f"The maximum of loan amount: {data['loan_amnt'].max()}")
print(f"The standard deviation of loan amount: {data['loan_amnt'].std()}") | code |
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