path
stringlengths 13
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sequencelengths 1
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stringlengths 0
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stringclasses 1
value |
|---|---|---|---|
88099842/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import wfdb
data = '../input/mit-bih-arrhythmia-database/'
patients = ['100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '111', '112', '113', '114', '115', '116', '117', '118', '119', '121', '122', '123', '124', '200', '201', '202', '203', '205', '207', '208', '209', '210', '212', '213', '214', '215', '217', '219', '220', '221', '222', '223', '228', '230', '231', '232', '233', '234']
dataframe = pd.DataFrame()
for pts in patients:
file = data + pts
annotation = wfdb.rdann(file, 'atr')
sym = annotation.symbol
values, counts = np.unique(sym, return_counts=True)
df_sub = pd.DataFrame({'symbol': values, 'Counts': counts, 'Patient Number': [pts] * len(counts)})
dataframe = pd.concat([dataframe, df_sub], axis=0)
ax = sns.countplot(dataframe.symbol)
nonbeat = ['[', '!', ']', 'x', '(', ')', 'p', 't', 'u', '`', "'", '^', '|', '~', '+', 's', 'T', '*', 'D', '=', '"', '@', 'Q', '?']
abnormal = ['L', 'R', 'V', '/', 'A', 'f', 'F', 'j', 'a', 'E', 'J', 'e', 'S']
normal = ['N']
dataframe['category'] = -1
dataframe.loc[dataframe.symbol == 'N', 'category'] = 0
dataframe.loc[dataframe.symbol.isin(abnormal), 'category'] = 1
dataframe.groupby('category').Counts.sum()
dataframe = dataframe.loc[~(dataframe['category'] == -1)]
dataframe.groupby('category').Counts.sum() | code |
88099842/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import wfdb
data = '../input/mit-bih-arrhythmia-database/'
patients = ['100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '111', '112', '113', '114', '115', '116', '117', '118', '119', '121', '122', '123', '124', '200', '201', '202', '203', '205', '207', '208', '209', '210', '212', '213', '214', '215', '217', '219', '220', '221', '222', '223', '228', '230', '231', '232', '233', '234']
dataframe = pd.DataFrame()
for pts in patients:
file = data + pts
annotation = wfdb.rdann(file, 'atr')
sym = annotation.symbol
values, counts = np.unique(sym, return_counts=True)
df_sub = pd.DataFrame({'symbol': values, 'Counts': counts, 'Patient Number': [pts] * len(counts)})
dataframe = pd.concat([dataframe, df_sub], axis=0)
ax = sns.countplot(dataframe.symbol)
nonbeat = ['[', '!', ']', 'x', '(', ')', 'p', 't', 'u', '`', "'", '^', '|', '~', '+', 's', 'T', '*', 'D', '=', '"', '@', 'Q', '?']
abnormal = ['L', 'R', 'V', '/', 'A', 'f', 'F', 'j', 'a', 'E', 'J', 'e', 'S']
normal = ['N']
dataframe['category'] = -1
dataframe.loc[dataframe.symbol == 'N', 'category'] = 0
dataframe.loc[dataframe.symbol.isin(abnormal), 'category'] = 1
dataframe.groupby('category').Counts.sum() | code |
88099842/cell_8 | [
"image_output_2.png",
"image_output_1.png"
] | import os
import os
print(os.listdir('../input')) | code |
88099842/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import wfdb
data = '../input/mit-bih-arrhythmia-database/'
patients = ['100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '111', '112', '113', '114', '115', '116', '117', '118', '119', '121', '122', '123', '124', '200', '201', '202', '203', '205', '207', '208', '209', '210', '212', '213', '214', '215', '217', '219', '220', '221', '222', '223', '228', '230', '231', '232', '233', '234']
dataframe = pd.DataFrame()
for pts in patients:
file = data + pts
annotation = wfdb.rdann(file, 'atr')
sym = annotation.symbol
values, counts = np.unique(sym, return_counts=True)
df_sub = pd.DataFrame({'symbol': values, 'Counts': counts, 'Patient Number': [pts] * len(counts)})
dataframe = pd.concat([dataframe, df_sub], axis=0)
ax = sns.countplot(dataframe.symbol) | code |
88099842/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import wfdb
data = '../input/mit-bih-arrhythmia-database/'
patients = ['100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '111', '112', '113', '114', '115', '116', '117', '118', '119', '121', '122', '123', '124', '200', '201', '202', '203', '205', '207', '208', '209', '210', '212', '213', '214', '215', '217', '219', '220', '221', '222', '223', '228', '230', '231', '232', '233', '234']
dataframe = pd.DataFrame()
for pts in patients:
file = data + pts
annotation = wfdb.rdann(file, 'atr')
sym = annotation.symbol
values, counts = np.unique(sym, return_counts=True)
df_sub = pd.DataFrame({'symbol': values, 'Counts': counts, 'Patient Number': [pts] * len(counts)})
dataframe = pd.concat([dataframe, df_sub], axis=0)
ax = sns.countplot(dataframe.symbol)
dataframe | code |
88099842/cell_3 | [
"text_html_output_1.png"
] | !pip install wfdb | code |
88099842/cell_5 | [
"text_plain_output_1.png"
] | pip install matplotlib==3.1.3 | code |
1003427/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | train_data = pd.read_csv('/Users/apple/Desktop/Data science/datasets/train.csv')
test_data = pd.read_csv('/Users/apple/Desktop/Data science/datasets/test.csv')
gender_submission = pd.read_csv('/Users/apple/Desktop/Data science/datasets/gender_submission.csv') | code |
1003427/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | train_data = pd.read_csv('/Users/apple/Desktop/Data science/datasets/train.csv')
test_data = pd.read_csv('/Users/apple/Desktop/Data science/datasets/test.csv')
gender_submission = pd.read_csv('/Users/apple/Desktop/Data science/datasets/gender_submission.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1) | code |
122265203/cell_21 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow import keras
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import tensorflow as tf
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citation_Request.txt':
continue
files.append(file)
files
size_number = []
size = []
for file in files:
path = os.path.join(train, file)
size_number.append(len(os.listdir(path)))
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
size.append(image.shape)
pd.DataFrame(size_number, columns=['size'], index=files)
pd.DataFrame(pd.Series(size).value_counts(), columns=['Number'])
train_image = []
label = []
for file in files:
path = os.path.join(train, file)
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
image = cv2.resize(image, (100, 100))
train_image.append(image)
label.append(file)
pd.DataFrame(label, columns=['label'])
for n, i in enumerate(np.random.randint(0, len(train_image), 100)):
plt.axis('off')
code = {}
label_uniqe = list(pd.unique(label))
for i in range(5):
code[label_uniqe[i]] = i
code
def get_Name(N):
for x, y in code.items():
if y == N:
return x
label2 = []
for i in label:
label2.append(code[i])
label2 = np.array(label2)
pd.DataFrame(label2)
train_image = np.array(train_image)
X_train, X_test, y_train, y_test = train_test_split(train_image, label2, test_size=0.1, random_state=44, shuffle=True)
shape = (100, 100, 3)
num_class = 5
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(filters=32, kernel_size=(3, 3), activation=tf.nn.relu, input_shape=shape))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation=tf.nn.relu))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Conv2D(filters=128, kernel_size=(3, 3), activation=tf.nn.relu))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(512, activation=tf.nn.relu))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Dense(128, activation=tf.nn.relu))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Dense(num_class, activation=tf.nn.softmax))
model.summary()
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
hist = model.fit(X_train, y_train, epochs=5) | code |
122265203/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citation_Request.txt':
continue
files.append(file)
files
size_number = []
size = []
for file in files:
path = os.path.join(train, file)
size_number.append(len(os.listdir(path)))
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
size.append(image.shape)
pd.DataFrame(size_number, columns=['size'], index=files)
pd.DataFrame(pd.Series(size).value_counts(), columns=['Number'])
train_image = []
label = []
for file in files:
path = os.path.join(train, file)
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
image = cv2.resize(image, (100, 100))
train_image.append(image)
label.append(file)
pd.DataFrame(label, columns=['label'])
for n, i in enumerate(np.random.randint(0, len(train_image), 100)):
plt.axis('off')
code = {}
label_uniqe = list(pd.unique(label))
for i in range(5):
code[label_uniqe[i]] = i
code
def get_Name(N):
for x, y in code.items():
if y == N:
return x
label2 = []
for i in label:
label2.append(code[i])
label2 = np.array(label2)
pd.DataFrame(label2) | code |
122265203/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
import os
import pandas as pd
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citation_Request.txt':
continue
files.append(file)
files
size_number = []
size = []
for file in files:
path = os.path.join(train, file)
size_number.append(len(os.listdir(path)))
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
size.append(image.shape)
pd.DataFrame(size_number, columns=['size'], index=files)
pd.DataFrame(pd.Series(size).value_counts(), columns=['Number']) | code |
122265203/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow import keras
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import tensorflow as tf
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citation_Request.txt':
continue
files.append(file)
files
size_number = []
size = []
for file in files:
path = os.path.join(train, file)
size_number.append(len(os.listdir(path)))
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
size.append(image.shape)
pd.DataFrame(size_number, columns=['size'], index=files)
pd.DataFrame(pd.Series(size).value_counts(), columns=['Number'])
train_image = []
label = []
for file in files:
path = os.path.join(train, file)
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
image = cv2.resize(image, (100, 100))
train_image.append(image)
label.append(file)
pd.DataFrame(label, columns=['label'])
for n, i in enumerate(np.random.randint(0, len(train_image), 100)):
plt.axis('off')
code = {}
label_uniqe = list(pd.unique(label))
for i in range(5):
code[label_uniqe[i]] = i
code
def get_Name(N):
for x, y in code.items():
if y == N:
return x
label2 = []
for i in label:
label2.append(code[i])
label2 = np.array(label2)
pd.DataFrame(label2)
train_image = np.array(train_image)
X_train, X_test, y_train, y_test = train_test_split(train_image, label2, test_size=0.1, random_state=44, shuffle=True)
shape = (100, 100, 3)
num_class = 5
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(filters=32, kernel_size=(3, 3), activation=tf.nn.relu, input_shape=shape))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation=tf.nn.relu))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Conv2D(filters=128, kernel_size=(3, 3), activation=tf.nn.relu))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(512, activation=tf.nn.relu))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Dense(128, activation=tf.nn.relu))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Dense(num_class, activation=tf.nn.softmax))
model.summary()
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
hist = model.fit(X_train, y_train, epochs=5)
score, acc = model.evaluate(X_test, y_test)
y_pred = model.predict(X_test)
y_pred | code |
122265203/cell_4 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names']) | code |
122265203/cell_23 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow import keras
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import tensorflow as tf
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citation_Request.txt':
continue
files.append(file)
files
size_number = []
size = []
for file in files:
path = os.path.join(train, file)
size_number.append(len(os.listdir(path)))
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
size.append(image.shape)
pd.DataFrame(size_number, columns=['size'], index=files)
pd.DataFrame(pd.Series(size).value_counts(), columns=['Number'])
train_image = []
label = []
for file in files:
path = os.path.join(train, file)
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
image = cv2.resize(image, (100, 100))
train_image.append(image)
label.append(file)
pd.DataFrame(label, columns=['label'])
for n, i in enumerate(np.random.randint(0, len(train_image), 100)):
plt.axis('off')
code = {}
label_uniqe = list(pd.unique(label))
for i in range(5):
code[label_uniqe[i]] = i
code
def get_Name(N):
for x, y in code.items():
if y == N:
return x
label2 = []
for i in label:
label2.append(code[i])
label2 = np.array(label2)
pd.DataFrame(label2)
train_image = np.array(train_image)
X_train, X_test, y_train, y_test = train_test_split(train_image, label2, test_size=0.1, random_state=44, shuffle=True)
shape = (100, 100, 3)
num_class = 5
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(filters=32, kernel_size=(3, 3), activation=tf.nn.relu, input_shape=shape))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation=tf.nn.relu))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Conv2D(filters=128, kernel_size=(3, 3), activation=tf.nn.relu))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(512, activation=tf.nn.relu))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Dense(128, activation=tf.nn.relu))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Dense(num_class, activation=tf.nn.softmax))
model.summary()
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
hist = model.fit(X_train, y_train, epochs=5)
hist_ = pd.DataFrame(hist.history)
hist_
plt.plot(hist_['accuracy'], label='Accuracy')
plt.plot(hist_['loss'], label='Loss')
plt.title('Accuracy && LOSS')
plt.legend() | code |
122265203/cell_20 | [
"text_html_output_1.png"
] | from tensorflow import keras
import tensorflow as tf
shape = (100, 100, 3)
num_class = 5
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(filters=32, kernel_size=(3, 3), activation=tf.nn.relu, input_shape=shape))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation=tf.nn.relu))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Conv2D(filters=128, kernel_size=(3, 3), activation=tf.nn.relu))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(512, activation=tf.nn.relu))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Dense(128, activation=tf.nn.relu))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Dense(num_class, activation=tf.nn.softmax))
model.summary()
tf.keras.utils.plot_model(model, to_file='model.png', show_shapes=True, show_layer_names=True, show_dtype=True, dpi=120) | code |
122265203/cell_6 | [
"text_html_output_1.png"
] | import cv2
import os
import pandas as pd
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citation_Request.txt':
continue
files.append(file)
files
size_number = []
size = []
for file in files:
path = os.path.join(train, file)
size_number.append(len(os.listdir(path)))
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
size.append(image.shape)
pd.DataFrame(size_number, columns=['size'], index=files) | code |
122265203/cell_26 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow import keras
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import tensorflow as tf
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citation_Request.txt':
continue
files.append(file)
files
size_number = []
size = []
for file in files:
path = os.path.join(train, file)
size_number.append(len(os.listdir(path)))
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
size.append(image.shape)
pd.DataFrame(size_number, columns=['size'], index=files)
pd.DataFrame(pd.Series(size).value_counts(), columns=['Number'])
train_image = []
label = []
for file in files:
path = os.path.join(train, file)
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
image = cv2.resize(image, (100, 100))
train_image.append(image)
label.append(file)
pd.DataFrame(label, columns=['label'])
for n, i in enumerate(np.random.randint(0, len(train_image), 100)):
plt.axis('off')
code = {}
label_uniqe = list(pd.unique(label))
for i in range(5):
code[label_uniqe[i]] = i
code
def get_Name(N):
for x, y in code.items():
if y == N:
return x
label2 = []
for i in label:
label2.append(code[i])
label2 = np.array(label2)
pd.DataFrame(label2)
train_image = np.array(train_image)
X_train, X_test, y_train, y_test = train_test_split(train_image, label2, test_size=0.1, random_state=44, shuffle=True)
shape = (100, 100, 3)
num_class = 5
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(filters=32, kernel_size=(3, 3), activation=tf.nn.relu, input_shape=shape))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation=tf.nn.relu))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Conv2D(filters=128, kernel_size=(3, 3), activation=tf.nn.relu))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(512, activation=tf.nn.relu))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Dense(128, activation=tf.nn.relu))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Dense(num_class, activation=tf.nn.softmax))
model.summary()
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
hist = model.fit(X_train, y_train, epochs=5)
hist_ = pd.DataFrame(hist.history)
hist_
score, acc = model.evaluate(X_test, y_test)
y_pred = model.predict(X_test)
y_pred
pred_Name = []
pred_number = []
for row in y_pred:
N = np.argmax(row)
pred_Name.append(get_Name(N))
pred_number.append(N)
pd.DataFrame(pred_Name, columns=['pred Names']) | code |
122265203/cell_11 | [
"text_plain_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citation_Request.txt':
continue
files.append(file)
files
size_number = []
size = []
for file in files:
path = os.path.join(train, file)
size_number.append(len(os.listdir(path)))
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
size.append(image.shape)
pd.DataFrame(size_number, columns=['size'], index=files)
pd.DataFrame(pd.Series(size).value_counts(), columns=['Number'])
train_image = []
label = []
for file in files:
path = os.path.join(train, file)
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
image = cv2.resize(image, (100, 100))
train_image.append(image)
label.append(file)
pd.DataFrame(label, columns=['label'])
plt.figure(figsize=(50, 50))
for n, i in enumerate(np.random.randint(0, len(train_image), 100)):
plt.subplot(10, 10, n + 1)
plt.imshow(train_image[i])
plt.axis('off')
plt.title(label[i], fontsize=25) | code |
122265203/cell_19 | [
"text_html_output_1.png"
] | from tensorflow import keras
import tensorflow as tf
shape = (100, 100, 3)
num_class = 5
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(filters=32, kernel_size=(3, 3), activation=tf.nn.relu, input_shape=shape))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation=tf.nn.relu))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Conv2D(filters=128, kernel_size=(3, 3), activation=tf.nn.relu))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(512, activation=tf.nn.relu))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Dense(128, activation=tf.nn.relu))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Dense(num_class, activation=tf.nn.softmax))
model.summary() | code |
122265203/cell_7 | [
"text_html_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sns
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citation_Request.txt':
continue
files.append(file)
files
size_number = []
size = []
for file in files:
path = os.path.join(train, file)
size_number.append(len(os.listdir(path)))
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
size.append(image.shape)
pd.DataFrame(size_number, columns=['size'], index=files)
plt.figure(figsize=(5, 5))
sns.countplot(x=size_number, hue=files) | code |
122265203/cell_28 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow import keras
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import tensorflow as tf
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citation_Request.txt':
continue
files.append(file)
files
size_number = []
size = []
for file in files:
path = os.path.join(train, file)
size_number.append(len(os.listdir(path)))
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
size.append(image.shape)
pd.DataFrame(size_number, columns=['size'], index=files)
pd.DataFrame(pd.Series(size).value_counts(), columns=['Number'])
train_image = []
label = []
for file in files:
path = os.path.join(train, file)
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
image = cv2.resize(image, (100, 100))
train_image.append(image)
label.append(file)
pd.DataFrame(label, columns=['label'])
for n, i in enumerate(np.random.randint(0, len(train_image), 100)):
plt.axis('off')
code = {}
label_uniqe = list(pd.unique(label))
for i in range(5):
code[label_uniqe[i]] = i
code
def get_Name(N):
for x, y in code.items():
if y == N:
return x
label2 = []
for i in label:
label2.append(code[i])
label2 = np.array(label2)
pd.DataFrame(label2)
train_image = np.array(train_image)
X_train, X_test, y_train, y_test = train_test_split(train_image, label2, test_size=0.1, random_state=44, shuffle=True)
shape = (100, 100, 3)
num_class = 5
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(filters=32, kernel_size=(3, 3), activation=tf.nn.relu, input_shape=shape))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation=tf.nn.relu))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Conv2D(filters=128, kernel_size=(3, 3), activation=tf.nn.relu))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(512, activation=tf.nn.relu))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Dense(128, activation=tf.nn.relu))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Dense(num_class, activation=tf.nn.softmax))
model.summary()
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
hist = model.fit(X_train, y_train, epochs=5)
hist_ = pd.DataFrame(hist.history)
hist_
score, acc = model.evaluate(X_test, y_test)
y_pred = model.predict(X_test)
y_pred
pred_Name = []
pred_number = []
for row in y_pred:
N = np.argmax(row)
pred_Name.append(get_Name(N))
pred_number.append(N)
pd.DataFrame(pred_Name, columns=['pred Names'])
plt.figure(figsize=(40, 60))
n = 1
for i in range(100):
plt.subplot(20, 5, n)
plt.imshow(X_test[i])
plt.axis('off')
ti = get_Name(y_test[i]) + ' predict ' + pred_Name[i]
plt.title(ti, fontsize=25)
plt.legend()
n += 1 | code |
122265203/cell_8 | [
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sns
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citation_Request.txt':
continue
files.append(file)
files
size_number = []
size = []
for file in files:
path = os.path.join(train, file)
size_number.append(len(os.listdir(path)))
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
size.append(image.shape)
pd.DataFrame(size_number, columns=['size'], index=files)
plt.figure(figsize=(10, 10))
plt.pie(x=size_number, labels=files, autopct='%1.1f%%') | code |
122265203/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citation_Request.txt':
continue
files.append(file)
files
size_number = []
size = []
for file in files:
path = os.path.join(train, file)
size_number.append(len(os.listdir(path)))
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
size.append(image.shape)
pd.DataFrame(size_number, columns=['size'], index=files)
pd.DataFrame(pd.Series(size).value_counts(), columns=['Number'])
train_image = []
label = []
for file in files:
path = os.path.join(train, file)
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
image = cv2.resize(image, (100, 100))
train_image.append(image)
label.append(file)
pd.DataFrame(label, columns=['label'])
for n, i in enumerate(np.random.randint(0, len(train_image), 100)):
plt.axis('off')
code = {}
label_uniqe = list(pd.unique(label))
for i in range(5):
code[label_uniqe[i]] = i
code
def get_Name(N):
for x, y in code.items():
if y == N:
return x
label2 = []
for i in label:
label2.append(code[i])
label2 = np.array(label2)
pd.DataFrame(label2)
train_image = np.array(train_image)
X_train, X_test, y_train, y_test = train_test_split(train_image, label2, test_size=0.1, random_state=44, shuffle=True)
print('X_train shape is ', X_train.shape)
print('X_test shape is ', X_test.shape)
print('y_train shape is ', y_train.shape)
print('y_test shape is ', y_test.shape) | code |
122265203/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow import keras
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import tensorflow as tf
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citation_Request.txt':
continue
files.append(file)
files
size_number = []
size = []
for file in files:
path = os.path.join(train, file)
size_number.append(len(os.listdir(path)))
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
size.append(image.shape)
pd.DataFrame(size_number, columns=['size'], index=files)
pd.DataFrame(pd.Series(size).value_counts(), columns=['Number'])
train_image = []
label = []
for file in files:
path = os.path.join(train, file)
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
image = cv2.resize(image, (100, 100))
train_image.append(image)
label.append(file)
pd.DataFrame(label, columns=['label'])
for n, i in enumerate(np.random.randint(0, len(train_image), 100)):
plt.axis('off')
code = {}
label_uniqe = list(pd.unique(label))
for i in range(5):
code[label_uniqe[i]] = i
code
def get_Name(N):
for x, y in code.items():
if y == N:
return x
label2 = []
for i in label:
label2.append(code[i])
label2 = np.array(label2)
pd.DataFrame(label2)
train_image = np.array(train_image)
X_train, X_test, y_train, y_test = train_test_split(train_image, label2, test_size=0.1, random_state=44, shuffle=True)
shape = (100, 100, 3)
num_class = 5
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(filters=32, kernel_size=(3, 3), activation=tf.nn.relu, input_shape=shape))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation=tf.nn.relu))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Conv2D(filters=128, kernel_size=(3, 3), activation=tf.nn.relu))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(512, activation=tf.nn.relu))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Dense(128, activation=tf.nn.relu))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Dense(num_class, activation=tf.nn.softmax))
model.summary()
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
hist = model.fit(X_train, y_train, epochs=5)
score, acc = model.evaluate(X_test, y_test)
print('Test Loss =', score)
print('Test Accuracy =', acc) | code |
122265203/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow import keras
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import tensorflow as tf
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citation_Request.txt':
continue
files.append(file)
files
size_number = []
size = []
for file in files:
path = os.path.join(train, file)
size_number.append(len(os.listdir(path)))
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
size.append(image.shape)
pd.DataFrame(size_number, columns=['size'], index=files)
pd.DataFrame(pd.Series(size).value_counts(), columns=['Number'])
train_image = []
label = []
for file in files:
path = os.path.join(train, file)
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
image = cv2.resize(image, (100, 100))
train_image.append(image)
label.append(file)
pd.DataFrame(label, columns=['label'])
for n, i in enumerate(np.random.randint(0, len(train_image), 100)):
plt.axis('off')
code = {}
label_uniqe = list(pd.unique(label))
for i in range(5):
code[label_uniqe[i]] = i
code
def get_Name(N):
for x, y in code.items():
if y == N:
return x
label2 = []
for i in label:
label2.append(code[i])
label2 = np.array(label2)
pd.DataFrame(label2)
train_image = np.array(train_image)
X_train, X_test, y_train, y_test = train_test_split(train_image, label2, test_size=0.1, random_state=44, shuffle=True)
shape = (100, 100, 3)
num_class = 5
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(filters=32, kernel_size=(3, 3), activation=tf.nn.relu, input_shape=shape))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation=tf.nn.relu))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Conv2D(filters=128, kernel_size=(3, 3), activation=tf.nn.relu))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(512, activation=tf.nn.relu))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Dense(128, activation=tf.nn.relu))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Dense(num_class, activation=tf.nn.softmax))
model.summary()
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
hist = model.fit(X_train, y_train, epochs=5)
hist_ = pd.DataFrame(hist.history)
hist_ | code |
122265203/cell_10 | [
"text_html_output_1.png"
] | import cv2
import os
import pandas as pd
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citation_Request.txt':
continue
files.append(file)
files
size_number = []
size = []
for file in files:
path = os.path.join(train, file)
size_number.append(len(os.listdir(path)))
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
size.append(image.shape)
pd.DataFrame(size_number, columns=['size'], index=files)
pd.DataFrame(pd.Series(size).value_counts(), columns=['Number'])
train_image = []
label = []
for file in files:
path = os.path.join(train, file)
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
image = cv2.resize(image, (100, 100))
train_image.append(image)
label.append(file)
pd.DataFrame(label, columns=['label']) | code |
122265203/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow import keras
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import tensorflow as tf
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citation_Request.txt':
continue
files.append(file)
files
size_number = []
size = []
for file in files:
path = os.path.join(train, file)
size_number.append(len(os.listdir(path)))
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
size.append(image.shape)
pd.DataFrame(size_number, columns=['size'], index=files)
pd.DataFrame(pd.Series(size).value_counts(), columns=['Number'])
train_image = []
label = []
for file in files:
path = os.path.join(train, file)
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
image = cv2.resize(image, (100, 100))
train_image.append(image)
label.append(file)
pd.DataFrame(label, columns=['label'])
for n, i in enumerate(np.random.randint(0, len(train_image), 100)):
plt.axis('off')
code = {}
label_uniqe = list(pd.unique(label))
for i in range(5):
code[label_uniqe[i]] = i
code
def get_Name(N):
for x, y in code.items():
if y == N:
return x
label2 = []
for i in label:
label2.append(code[i])
label2 = np.array(label2)
pd.DataFrame(label2)
train_image = np.array(train_image)
X_train, X_test, y_train, y_test = train_test_split(train_image, label2, test_size=0.1, random_state=44, shuffle=True)
shape = (100, 100, 3)
num_class = 5
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(filters=32, kernel_size=(3, 3), activation=tf.nn.relu, input_shape=shape))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation=tf.nn.relu))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Conv2D(filters=128, kernel_size=(3, 3), activation=tf.nn.relu))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D((3, 3)))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(512, activation=tf.nn.relu))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Dense(128, activation=tf.nn.relu))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Dense(num_class, activation=tf.nn.softmax))
model.summary()
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
hist = model.fit(X_train, y_train, epochs=5)
hist_ = pd.DataFrame(hist.history)
hist_
score, acc = model.evaluate(X_test, y_test)
y_pred = model.predict(X_test)
y_pred
pred_Name = []
pred_number = []
for row in y_pred:
N = np.argmax(row)
pred_Name.append(get_Name(N))
pred_number.append(N)
pd.DataFrame(pred_Name, columns=['pred Names'])
pd.DataFrame(pred_number, columns=['pred Number']) | code |
122265203/cell_12 | [
"text_html_output_1.png"
] | import cv2
import os
import pandas as pd
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citation_Request.txt':
continue
files.append(file)
files
size_number = []
size = []
for file in files:
path = os.path.join(train, file)
size_number.append(len(os.listdir(path)))
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
size.append(image.shape)
pd.DataFrame(size_number, columns=['size'], index=files)
pd.DataFrame(pd.Series(size).value_counts(), columns=['Number'])
train_image = []
label = []
for file in files:
path = os.path.join(train, file)
for img in os.listdir(path):
image = cv2.imread(os.path.join(path, img))
image = cv2.resize(image, (100, 100))
train_image.append(image)
label.append(file)
pd.DataFrame(label, columns=['label'])
code = {}
label_uniqe = list(pd.unique(label))
for i in range(5):
code[label_uniqe[i]] = i
code | code |
122265203/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import os
import pandas as pd
train = '/kaggle/input/rice-image-dataset/Rice_Image_Dataset'
file_names = os.listdir(train)
pd.DataFrame(file_names, columns=['Names'])
files = []
for file in file_names:
if file == 'Rice_Citation_Request.txt':
continue
files.append(file)
files | code |
89142662/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/jena-climate/jena_climate_2009_2016.csv')
data
data.isna().sum()
data.duplicated().sum()
data.duplicated(subset=['Date Time']).sum()
data = data.drop_duplicates()
data.duplicated().sum()
data.isna().sum() | code |
89142662/cell_9 | [
"application_vnd.jupyter.stderr_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/jena-climate/jena_climate_2009_2016.csv')
data
data.isna().sum()
data.duplicated().sum()
data.duplicated(subset=['Date Time']).sum()
data.head(20) | code |
89142662/cell_4 | [
"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/jena-climate/jena_climate_2009_2016.csv')
data
data['T (degC)'].plot(figsize=(15, 10)) | code |
89142662/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)
data = pd.read_csv('../input/jena-climate/jena_climate_2009_2016.csv')
data
data.isna().sum() | code |
89142662/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/jena-climate/jena_climate_2009_2016.csv')
data
data.isna().sum()
data.duplicated().sum()
data.duplicated(subset=['Date Time']).sum()
data = data.drop_duplicates()
data.duplicated().sum() | code |
89142662/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 |
89142662/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/jena-climate/jena_climate_2009_2016.csv')
data
data.isna().sum()
data.duplicated().sum() | code |
89142662/cell_8 | [
"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/jena-climate/jena_climate_2009_2016.csv')
data
data.isna().sum()
data.duplicated().sum()
data.duplicated(subset=['Date Time']).sum() | code |
89142662/cell_15 | [
"text_plain_output_1.png"
] | (len(data_hourly), data_hourly.isna().sum()) | code |
89142662/cell_3 | [
"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/jena-climate/jena_climate_2009_2016.csv')
data | code |
89142662/cell_14 | [
"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/jena-climate/jena_climate_2009_2016.csv')
data
data.isna().sum()
data.duplicated().sum()
data.duplicated(subset=['Date Time']).sum()
data = data.drop_duplicates()
data.duplicated().sum()
data.isna().sum()
data.index = pd.to_datetime(data['Date Time'], format='%d.%m.%Y %H:%M:%S')
data | code |
89142662/cell_12 | [
"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/jena-climate/jena_climate_2009_2016.csv')
data
data.isna().sum()
data.duplicated().sum()
data.duplicated(subset=['Date Time']).sum()
data = data.drop_duplicates()
data.duplicated().sum()
data.head(20) | code |
89142662/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/jena-climate/jena_climate_2009_2016.csv')
data
data.info() | code |
90154574/cell_13 | [
"text_html_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/housedata/output.csv')
corr = df.corr()
x = df[['bathrooms', 'bedrooms', 'sqft_above', 'sqft_living']]
y = df['price']
regr = linear_model.LinearRegression()
regr.fit(x.values, y)
print('Intercept :', regr.intercept_)
print('Coefficient :', regr.coef_)
print(x) | code |
90154574/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/housedata/output.csv')
corr = df.corr()
plt.figure(figsize=(15, 10))
sns.heatmap(corr, vmax=0.8, annot=True, fmt='.2f')
plt.show() | code |
90154574/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/housedata/output.csv')
df.head() | code |
90154574/cell_20 | [
"image_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/housedata/output.csv')
corr = df.corr()
x = df[['bathrooms', 'bedrooms', 'sqft_above', 'sqft_living']]
y = df['price']
regr = linear_model.LinearRegression()
regr.fit(x.values, y)
df.isnull().sum()
def function(x, a):
f = a[2] * x * x + a[1] * x + a[0]
return f
def grad(x, a):
g = 2 * a[2] * x + a[1]
return g
x = df[['bathrooms', 'bedrooms', 'sqft_above', 'sqft_living']]
y = df['price']
f = function(x, y)
plt.scatter(x, f)
plt.plot(x, f)
plt.xlabel('X')
plt.ylabel('f(X)') | code |
90154574/cell_26 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/housedata/output.csv')
corr = df.corr()
x = df[['bathrooms', 'bedrooms', 'sqft_above', 'sqft_living']]
y = df['price']
regr = linear_model.LinearRegression()
regr.fit(x.values, y)
predicted = regr.predict([[2, 3, 1680, 2180]])
predicted = regr.predict([[2.75, 4.0, 1400, 2490]])
predicted = regr.predict([[8.0, 7.0, 9410, 13540]])
predicted = regr.predict([[1.0, 0.0, 0.0, 0.0]])
predicted = regr.predict([[0.0, 1.0, 0.0, 0.0]])
predicted = regr.predict([[1.0, 0.0, 1.0, 0.0]])
predicted = regr.predict([[0.0, 0.0, 0.0, 1.0]])
df.isnull().sum()
def function(x, a):
f = a[2] * x * x + a[1] * x + a[0]
return f
def grad(x, a):
g = 2 * a[2] * x + a[1]
return g
x = df[['bathrooms', 'bedrooms', 'sqft_above', 'sqft_living']]
y = df['price']
f = function(x, y)
x = df[['bedrooms']]
y = df['price']
import numpy as np
def find_theta(X, y):
m = X.shape[0]
X = np.append(X, np.ones((m,1)), axis=1)
theta = np.dot(np.linalg.inv(np.dot(X.T, X)), np.dot(X.T, y))
return theta
def predict(X):
X = np.append(X, np.ones((x.shape[0],1)), axis=1)
preds = np.dot(X, theta)
return preds
theta = find_theta(x, y)
print(theta)
preds = predict(x)
fig = plt.figure()
plt.plot(x, y, 'b.')
plt.plot(x, preds, 'c-')
plt.xlabel('X - Input')
plt.ylabel('y - target/true')
x = df[['bedrooms']]
y = df['price']
regr2 = linear_model.LinearRegression()
regr2.fit(x.values, y)
arr = []
index = []
for i in range(0, 9, 1):
predicted = regr2.predict([[i]])
arr.append(predicted[0])
index.append(i)
fig = plt.figure()
plt.plot(x, y, 'b.')
plt.plot(index, arr, 'c-') | code |
90154574/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/housedata/output.csv')
df.hist(figsize=(20, 20))
plt.show() | code |
90154574/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/housedata/output.csv')
corr = df.corr()
df.isnull().sum() | code |
90154574/cell_24 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/housedata/output.csv')
corr = df.corr()
x = df[['bathrooms', 'bedrooms', 'sqft_above', 'sqft_living']]
y = df['price']
regr = linear_model.LinearRegression()
regr.fit(x.values, y)
df.isnull().sum()
def function(x, a):
f = a[2] * x * x + a[1] * x + a[0]
return f
def grad(x, a):
g = 2 * a[2] * x + a[1]
return g
x = df[['bathrooms', 'bedrooms', 'sqft_above', 'sqft_living']]
y = df['price']
f = function(x, y)
x = df[['bedrooms']]
y = df['price']
import numpy as np
def find_theta(X, y):
m = X.shape[0]
X = np.append(X, np.ones((m, 1)), axis=1)
theta = np.dot(np.linalg.inv(np.dot(X.T, X)), np.dot(X.T, y))
return theta
def predict(X):
X = np.append(X, np.ones((x.shape[0], 1)), axis=1)
preds = np.dot(X, theta)
return preds
theta = find_theta(x, y)
print(theta)
preds = predict(x)
fig = plt.figure()
plt.plot(x, y, 'b.')
plt.plot(x, preds, 'c-')
plt.xlabel('X - Input')
plt.ylabel('y - target/true') | code |
90154574/cell_14 | [
"text_html_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/housedata/output.csv')
corr = df.corr()
x = df[['bathrooms', 'bedrooms', 'sqft_above', 'sqft_living']]
y = df['price']
regr = linear_model.LinearRegression()
regr.fit(x.values, y)
predicted = regr.predict([[2, 3, 1680, 2180]])
print('predicted with [2.0,3.0,1680,2180] so, price :', predicted)
predicted = regr.predict([[2.75, 4.0, 1400, 2490]])
print('predicted with [2.75,4.0,1400,2490] so, price :', predicted)
predicted = regr.predict([[8.0, 7.0, 9410, 13540]])
print('predicted with [8.0,7.0,9410,13540] so, price :', predicted)
predicted = regr.predict([[1.0, 0.0, 0.0, 0.0]])
print('predicted with bathrooms in 1 unit so, price :', predicted)
predicted = regr.predict([[0.0, 1.0, 0.0, 0.0]])
print('predicted with bedrooms in 1 unit so, price :', predicted)
predicted = regr.predict([[1.0, 0.0, 1.0, 0.0]])
print('predicted with sqft_above in 1 unit so, price :', predicted)
predicted = regr.predict([[0.0, 0.0, 0.0, 1.0]])
print('predicted with sqft_living in 1 unit so, price :', predicted) | code |
90154574/cell_22 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/housedata/output.csv')
corr = df.corr()
x = df[['bathrooms', 'bedrooms', 'sqft_above', 'sqft_living']]
y = df['price']
regr = linear_model.LinearRegression()
regr.fit(x.values, y)
df.isnull().sum()
def function(x, a):
f = a[2] * x * x + a[1] * x + a[0]
return f
def grad(x, a):
g = 2 * a[2] * x + a[1]
return g
x = df[['bathrooms', 'bedrooms', 'sqft_above', 'sqft_living']]
y = df['price']
f = function(x, y)
x = df[['bedrooms']]
y = df['price']
plt.plot(x, y, 'r.') | code |
90154574/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/housedata/output.csv')
df.describe() | code |
18116806/cell_21 | [
"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)
import scipy as scipy
import seaborn as sns
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
scipy.stats.kurtosis(hosp.age)
hosp.isnull().sum()
plt.xticks(rotation=90)
scipy.stats.chisquare(hosp.age)
scipy.stats.pearsonr(hosp.age, hosp.admit_type)
scipy.stats.skew(hosp.age, axis=0, bias=True, nan_policy='propagate')
numpy.histogram(hosp.age, bins=40, range=None, normed=None, weights=None, density=None) | code |
18116806/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)
import scipy as scipy
import seaborn as sns
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
scipy.stats.kurtosis(hosp.age)
hosp.isnull().sum()
plt.figure(figsize=(20, 10))
sns.countplot(x='age', data=hosp, palette='bwr')
plt.title('Distibution of Age')
plt.xticks(rotation=90)
plt.show() | code |
18116806/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy as scipy
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
scipy.stats.describe(hosp.age) | code |
18116806/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes | code |
18116806/cell_20 | [
"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)
import scipy as scipy
import seaborn as sns
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
scipy.stats.kurtosis(hosp.age)
hosp.isnull().sum()
plt.xticks(rotation=90)
scipy.stats.chisquare(hosp.age)
scipy.stats.pearsonr(hosp.age, hosp.admit_type)
scipy.stats.skew(hosp.age, axis=0, bias=True, nan_policy='propagate') | code |
18116806/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
hosp.head(5) | code |
18116806/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.info() | code |
18116806/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import scipy as scipy
from scipy import stats
import os
print(os.listdir('../input')) | code |
18116806/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
hosp['AdmitDiagnosis'].unique().shape | code |
18116806/cell_18 | [
"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)
import scipy as scipy
import seaborn as sns
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
scipy.stats.kurtosis(hosp.age)
hosp.isnull().sum()
plt.xticks(rotation=90)
scipy.stats.chisquare(hosp.age)
scipy.stats.pearsonr(hosp.age, hosp.admit_type) | code |
18116806/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
hosp['age'].unique().shape | code |
18116806/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy as scipy
import seaborn as sns
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
scipy.stats.kurtosis(hosp.age)
hosp.isnull().sum()
plt.xticks(rotation=90)
scipy.stats.chisquare(hosp.age) | code |
18116806/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.describe() | code |
18116806/cell_14 | [
"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)
import scipy as scipy
import seaborn as sns
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
scipy.stats.kurtosis(hosp.age)
hosp.isnull().sum()
plt.xticks(rotation=90)
plt.figure(figsize=(20, 20))
sns.heatmap(cbar=False, annot=True, data=hosp.corr() * 100, cmap='coolwarm')
plt.title('% Corelation Matrix')
plt.show() | code |
18116806/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy as scipy
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
scipy.stats.kurtosis(hosp.age) | code |
18116806/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy as scipy
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
scipy.stats.kurtosis(hosp.age)
hosp.isnull().sum() | code |
18116806/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape | code |
105210311/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.duplicated().sum()
data = passengers.copy()
data.drop('ID', axis=1, inplace=True)
data.isnull().sum()
fill_list = data['Arrival Delay'].dropna()
data['Arrival Delay'] = data['Arrival Delay'].fillna(pd.Series(np.random.choice(fill_list, size=len(data.index))))
data.isnull().sum()
sns.heatmap(data.isnull()) | code |
105210311/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls | code |
105210311/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.duplicated().sum()
data = passengers.copy()
data.drop('ID', axis=1, inplace=True)
data.isnull().sum()
fill_list = data['Arrival Delay'].dropna()
data['Arrival Delay'] = data['Arrival Delay'].fillna(pd.Series(np.random.choice(fill_list, size=len(data.index))))
data.isnull().sum()
data.shape | code |
105210311/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.duplicated().sum()
data = passengers.copy()
data.drop('ID', axis=1, inplace=True)
data.isnull().sum()
fill_list = data['Arrival Delay'].dropna()
data['Arrival Delay'] = data['Arrival Delay'].fillna(pd.Series(np.random.choice(fill_list, size=len(data.index))))
data.isnull().sum() | code |
105210311/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.head() | code |
105210311/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.duplicated().sum()
data = passengers.copy()
data.drop('ID', axis=1, inplace=True)
data.isnull().sum()
fill_list = data['Arrival Delay'].dropna()
data['Arrival Delay'] = data['Arrival Delay'].fillna(pd.Series(np.random.choice(fill_list, size=len(data.index))))
data.isnull().sum()
data.shape
data = data[data['Flight Distance'] < 4000]
data.shape | code |
105210311/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.duplicated().sum()
data = passengers.copy()
data.drop('ID', axis=1, inplace=True)
data.isnull().sum()
fill_list = data['Arrival Delay'].dropna()
data['Arrival Delay'] = data['Arrival Delay'].fillna(pd.Series(np.random.choice(fill_list, size=len(data.index))))
data.isnull().sum()
data.shape
sns.set(rc={'figure.figsize': (8, 4)})
sns.scatterplot(x='Flight Distance', y='Satisfaction', data=data) | code |
105210311/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.duplicated().sum()
passengers.describe() | code |
105210311/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.duplicated().sum()
data = passengers.copy()
data.drop('ID', axis=1, inplace=True)
data.isnull().sum()
fill_list = data['Arrival Delay'].dropna()
data['Arrival Delay'] = data['Arrival Delay'].fillna(pd.Series(np.random.choice(fill_list, size=len(data.index))))
sns.displot(data=data, x='Arrival Delay', kind='kde') | code |
105210311/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.info() | code |
105210311/cell_28 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.duplicated().sum()
data = passengers.copy()
data.drop('ID', axis=1, inplace=True)
data.isnull().sum()
fill_list = data['Arrival Delay'].dropna()
data['Arrival Delay'] = data['Arrival Delay'].fillna(pd.Series(np.random.choice(fill_list, size=len(data.index))))
data.isnull().sum()
data.shape
sns.set(rc={'figure.figsize': (8, 4)})
data = data[data['Flight Distance'] < 4000]
sns.set(rc={'figure.figsize': (8, 4)})
sns.scatterplot(x='Flight Distance', y='Satisfaction', data=data) | code |
105210311/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape | code |
105210311/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.duplicated().sum()
data = passengers.copy()
data.drop('ID', axis=1, inplace=True)
data.isnull().sum() | code |
105210311/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.duplicated().sum()
data = passengers.copy()
data.drop('ID', axis=1, inplace=True)
data.isnull().sum()
sns.displot(data=data, x='Arrival Delay', kind='kde') | code |
105210311/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.duplicated().sum()
data = passengers.copy()
data.drop('ID', axis=1, inplace=True)
data.isnull().sum()
fill_list = data['Arrival Delay'].dropna()
data['Arrival Delay'] = data['Arrival Delay'].fillna(pd.Series(np.random.choice(fill_list, size=len(data.index))))
data.isnull().sum()
data.plot(kind='box', subplots=True, figsize=(18, 15), layout=(4, 5))
plt.show() | code |
105210311/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.duplicated().sum() | code |
105210311/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns
passengers.shape
nulls = passengers.isnull().sum()
nulls
passengers.duplicated().sum()
plt.figure(figsize=(16, 10))
sns.heatmap(passengers.corr(), cbar=True, annot=True) | code |
105210311/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
passengers = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv', sep=',', encoding='utf-8')
passengers.columns | code |
72081346/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
from xgboost import XGBRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
test = pd.read_csv('../input/30-days-of-ml/train.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.shape
target = train.target
X_trainfull, X_validfull, y_train, y_valid = train_test_split(train, target, train_size=0.8, test_size=0.2, random_state=42)
categorical_cols = [cname for cname in X_trainfull.columns if X_trainfull[cname].dtype == 'object']
numerical_cols = [cname for cname in X_trainfull.columns if X_trainfull[cname].dtype in ['int64', 'float64']]
my_cols = categorical_cols + numerical_cols
X_train = X_trainfull[my_cols].copy()
X_valid = X_validfull[my_cols].copy()
X_test = test[my_cols].copy()
numeric = SimpleImputer(strategy='constant')
cat = Pipeline(steps=[('imputer', SimpleImputer(strategy='constant')), ('onehot', OneHotEncoder(handle_unknown='ignore'))])
preprocessor = ColumnTransformer(transformers=[('num', numeric, numerical_cols), ('cat', cat, categorical_cols)])
model = XGBRegressor()
clf = Pipeline(steps=[('preprocessor', preprocessor), ('model', model)])
clf.fit(X_train, y_train)
predictions = clf.predict(X_valid)
from sklearn.model_selection import cross_val_score
scores = -1 * cross_val_score(clf, train, target, cv=5, scoring='neg_mean_absolute_error')
print('MAE scores:\n', scores) | code |
72081346/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
test = pd.read_csv('../input/30-days-of-ml/train.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.shape | code |
72081346/cell_11 | [
"text_html_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
from xgboost import XGBRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
test = pd.read_csv('../input/30-days-of-ml/train.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
categorical_cols = [cname for cname in X_trainfull.columns if X_trainfull[cname].dtype == 'object']
numerical_cols = [cname for cname in X_trainfull.columns if X_trainfull[cname].dtype in ['int64', 'float64']]
my_cols = categorical_cols + numerical_cols
X_train = X_trainfull[my_cols].copy()
X_valid = X_validfull[my_cols].copy()
X_test = test[my_cols].copy()
numeric = SimpleImputer(strategy='constant')
cat = Pipeline(steps=[('imputer', SimpleImputer(strategy='constant')), ('onehot', OneHotEncoder(handle_unknown='ignore'))])
preprocessor = ColumnTransformer(transformers=[('num', numeric, numerical_cols), ('cat', cat, categorical_cols)])
model = XGBRegressor()
clf = Pipeline(steps=[('preprocessor', preprocessor), ('model', model)])
clf.fit(X_train, y_train) | code |
72081346/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 |
72081346/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
test = pd.read_csv('../input/30-days-of-ml/train.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.shape
target = train.target
X_trainfull, X_validfull, y_train, y_valid = train_test_split(train, target, train_size=0.8, test_size=0.2, random_state=42)
target | code |
72081346/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
test = pd.read_csv('../input/30-days-of-ml/train.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train | code |
104123689/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os
"\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n" | code |
104123689/cell_7 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import json
import matplotlib.pylab as plt
import rasterio
image = '/kaggle/input/hubmap-organ-segmentation/train_images/15329.tiff'
tiff = rasterio.open(image)
img = tiff.read()
boundry = '/kaggle/input/hubmap-organ-segmentation/train_annotations/15329.json'
with open(boundry) as json_file:
data = json.load(json_file)
rasterio.plot.show(tiff, title='15329')
ig, ax = plt.subplots()
ax.imshow(img[2, :, :])
for cord in data[0]:
plt.scatter(cord[0], cord[1], color='red', alpha=0.05)
plt.show()
print('look at a sample image and mark it with the annotation to find the area of interest marked in red') | code |
104123689/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import rasterio
image = '/kaggle/input/hubmap-organ-segmentation/train_images/15329.tiff'
tiff = rasterio.open(image)
img = tiff.read() | code |
128048094/cell_42 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data = data.dropna()
data_encoded = pd.get_dummies(data, columns=['Property_Area'])
model_knn = KNeighborsClassifier()
model_knn.fit(X_train, y_train)
model_svm = LinearSVC()
model_svm.fit(X_train, y_train)
model_tree = DecisionTreeClassifier()
model_tree.fit(X_train, y_train)
model_logistic = LogisticRegression()
model_logistic.fit(X_train, y_train)
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report
cm_knn = confusion_matrix(y_test, model_knn.predict(X_test))
cm_tree = confusion_matrix(y_test, model_tree.predict(X_test))
cm_svm = confusion_matrix(y_test, model_svm.predict(X_test))
cm_logistic = confusion_matrix(y_test, model_logistic.predict(X_test))
y_pred = model_logistic.predict(X_test)
arr = np.array(y_pred)
compare_pre_act = pd.DataFrame(arr, columns=['Prediction'])
y_true = y_test.values
flatten_y_true = y_true.flatten()
df_pred = pd.DataFrame({'y_true': flatten_y_true, 'y_pred': y_pred})
df_pred
model_knn = KNeighborsClassifier()
model_knn.fit(X_train, y_train)
model_tree = DecisionTreeClassifier()
model_tree.fit(X_train, y_train)
model_logistic = LogisticRegression()
model_logistic.fit(X_train, y_train)
cm_knn = confusion_matrix(y_test, model_knn.predict(X_test))
cm_tree = confusion_matrix(y_test, model_tree.predict(X_test))
print(classification_report(y_test, model_logistic.predict(X_test)))
cm_logistic = confusion_matrix(y_test, model_logistic.predict(X_test))
ConfusionMatrixDisplay(cm_logistic).plot()
plt.show() | code |
128048094/cell_33 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data = data.dropna()
data_encoded = pd.get_dummies(data, columns=['Property_Area'])
from sklearn.model_selection import train_test_split
data_y = data_encoded[['Loan_Status']]
data_x = data_encoded.drop(columns=['Loan_Status', 'Loan_ID'])
X_train, X_test, y_train, y_test = train_test_split(data_x, data_y, test_size=0.2, random_state=1)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
data_encoded = data_encoded.drop(columns=['Loan_ID'])
cols_to_scale = [col for col in data_encoded.columns if col != 'Loan_Status']
data_std = data_encoded[cols_to_scale].copy()
data_std[cols_to_scale] = scaler.fit_transform(data_std[cols_to_scale])
data_std['Loan_Status'] = data_encoded['Loan_Status']
data_std.head(10) | code |
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