path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
1009871/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
train.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
train.head() | code |
105176805/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd
import openpyxl
import yfinance as yf
import datetime
import time
import requests
import io
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
90128404/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Dense, Conv2D, Flatten, MaxPooling2D, Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.utils import to_categorical
import matplotlib.pyplot as plot
import numpy as np
import pandas as pd
import seaborn as sb
data = pd.read_csv('../input/hardfakevsrealfaces/data.csv')
height, width = (128, 128)
X = np.empty((data.shape[0], height, width, 3))
for i in range(data.shape[0]):
img = load_img('../input/hardfakevsrealfaces/{}/{}.jpg'.format(data.loc[i, 'label'], data.loc[i, 'images_id']), target_size=(height, width))
X[i] = img_to_array(img)
X.shape
def changeLabels(x):
return labels[x]
labels = data.label.unique()
labels = {labels[i]: i for i in range(labels.size)}
y = data.label.apply(changeLabels)
y[:5]
y = to_categorical(y, len(labels))
y = y.astype(int)
y[:5]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=8)
(X_train.shape, y_train.shape)
model = Sequential()
model.add(Conv2D(64, kernel_size=3, activation='relu', input_shape=(height, width, 3)))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Conv2D(32, kernel_size=3, activation='relu'))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Conv2D(32, kernel_size=3, activation='relu'))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.45))
model.add(Dense(2, activation='softmax'))
epochs = 8
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
stats = model.fit(X_train, y_train, epochs=epochs, validation_split=0.2)
model.summary()
stats_df = pd.DataFrame(stats.history)
stats_df['epoch'] = list(range(1, epochs + 1))
stats_df = pd.DataFrame(stats.history)
stats_df['epoch'] = list(range(1, epochs + 1))
plot.figure(figsize=(10, 8))
sb.lineplot(y='accuracy', x='epoch', data=stats_df, color='deeppink', linewidth=2.5, label='Training accuracy')
sb.lineplot(y='val_accuracy', x='epoch', data=stats_df, color='darkturquoise', linewidth=2.5, label='Validation accuracy')
plot.grid()
plot.legend()
plot.title('Training and validation accuracy') | code |
90128404/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from tensorflow.keras.preprocessing.image import load_img, img_to_array
import numpy as np
import pandas as pd
data = pd.read_csv('../input/hardfakevsrealfaces/data.csv')
height, width = (128, 128)
X = np.empty((data.shape[0], height, width, 3))
for i in range(data.shape[0]):
img = load_img('../input/hardfakevsrealfaces/{}/{}.jpg'.format(data.loc[i, 'label'], data.loc[i, 'images_id']), target_size=(height, width))
X[i] = img_to_array(img)
X.shape
def changeLabels(x):
return labels[x]
labels = data.label.unique()
labels = {labels[i]: i for i in range(labels.size)}
y = data.label.apply(changeLabels)
y[:5] | code |
90128404/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.utils import to_categorical
import numpy as np
import pandas as pd
data = pd.read_csv('../input/hardfakevsrealfaces/data.csv')
height, width = (128, 128)
X = np.empty((data.shape[0], height, width, 3))
for i in range(data.shape[0]):
img = load_img('../input/hardfakevsrealfaces/{}/{}.jpg'.format(data.loc[i, 'label'], data.loc[i, 'images_id']), target_size=(height, width))
X[i] = img_to_array(img)
X.shape
def changeLabels(x):
return labels[x]
labels = data.label.unique()
labels = {labels[i]: i for i in range(labels.size)}
y = data.label.apply(changeLabels)
y[:5]
y = to_categorical(y, len(labels))
y = y.astype(int)
y[:5] | code |
90128404/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/hardfakevsrealfaces/data.csv')
data.head() | code |
90128404/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Dense, Conv2D, Flatten, MaxPooling2D, Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.utils import to_categorical
import numpy as np
import pandas as pd
data = pd.read_csv('../input/hardfakevsrealfaces/data.csv')
height, width = (128, 128)
X = np.empty((data.shape[0], height, width, 3))
for i in range(data.shape[0]):
img = load_img('../input/hardfakevsrealfaces/{}/{}.jpg'.format(data.loc[i, 'label'], data.loc[i, 'images_id']), target_size=(height, width))
X[i] = img_to_array(img)
X.shape
def changeLabels(x):
return labels[x]
labels = data.label.unique()
labels = {labels[i]: i for i in range(labels.size)}
y = data.label.apply(changeLabels)
y[:5]
y = to_categorical(y, len(labels))
y = y.astype(int)
y[:5]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=8)
(X_train.shape, y_train.shape)
model = Sequential()
model.add(Conv2D(64, kernel_size=3, activation='relu', input_shape=(height, width, 3)))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Conv2D(32, kernel_size=3, activation='relu'))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Conv2D(32, kernel_size=3, activation='relu'))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.45))
model.add(Dense(2, activation='softmax'))
epochs = 8
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
stats = model.fit(X_train, y_train, epochs=epochs, validation_split=0.2)
model.summary() | code |
90128404/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.utils import to_categorical
import numpy as np
import pandas as pd
data = pd.read_csv('../input/hardfakevsrealfaces/data.csv')
height, width = (128, 128)
X = np.empty((data.shape[0], height, width, 3))
for i in range(data.shape[0]):
img = load_img('../input/hardfakevsrealfaces/{}/{}.jpg'.format(data.loc[i, 'label'], data.loc[i, 'images_id']), target_size=(height, width))
X[i] = img_to_array(img)
X.shape
def changeLabels(x):
return labels[x]
labels = data.label.unique()
labels = {labels[i]: i for i in range(labels.size)}
y = data.label.apply(changeLabels)
y[:5]
y = to_categorical(y, len(labels))
y = y.astype(int)
y[:5]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=8)
(X_train.shape, y_train.shape) | code |
90128404/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow.keras.preprocessing.image import load_img, img_to_array
import numpy as np
import pandas as pd
data = pd.read_csv('../input/hardfakevsrealfaces/data.csv')
height, width = (128, 128)
X = np.empty((data.shape[0], height, width, 3))
for i in range(data.shape[0]):
img = load_img('../input/hardfakevsrealfaces/{}/{}.jpg'.format(data.loc[i, 'label'], data.loc[i, 'images_id']), target_size=(height, width))
X[i] = img_to_array(img)
X.shape | code |
90128404/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Dense, Conv2D, Flatten, MaxPooling2D, Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.utils import to_categorical
import numpy as np
import pandas as pd
data = pd.read_csv('../input/hardfakevsrealfaces/data.csv')
height, width = (128, 128)
X = np.empty((data.shape[0], height, width, 3))
for i in range(data.shape[0]):
img = load_img('../input/hardfakevsrealfaces/{}/{}.jpg'.format(data.loc[i, 'label'], data.loc[i, 'images_id']), target_size=(height, width))
X[i] = img_to_array(img)
X.shape
def changeLabels(x):
return labels[x]
labels = data.label.unique()
labels = {labels[i]: i for i in range(labels.size)}
y = data.label.apply(changeLabels)
y[:5]
y = to_categorical(y, len(labels))
y = y.astype(int)
y[:5]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=8)
(X_train.shape, y_train.shape)
model = Sequential()
model.add(Conv2D(64, kernel_size=3, activation='relu', input_shape=(height, width, 3)))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Conv2D(32, kernel_size=3, activation='relu'))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Conv2D(32, kernel_size=3, activation='relu'))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.45))
model.add(Dense(2, activation='softmax'))
epochs = 8
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
stats = model.fit(X_train, y_train, epochs=epochs, validation_split=0.2)
model.summary()
print('Accuracy:')
_, accuracy = model.evaluate(X_test, y_test) | code |
90128404/cell_10 | [
"text_html_output_1.png"
] | from tensorflow.keras.layers import Dense, Conv2D, Flatten, MaxPooling2D, Dropout
from tensorflow.keras.models import Sequential
model = Sequential()
model.add(Conv2D(64, kernel_size=3, activation='relu', input_shape=(height, width, 3)))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Conv2D(32, kernel_size=3, activation='relu'))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Conv2D(32, kernel_size=3, activation='relu'))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.45))
model.add(Dense(2, activation='softmax')) | code |
90128404/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Dense, Conv2D, Flatten, MaxPooling2D, Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.utils import to_categorical
import matplotlib.pyplot as plot
import numpy as np
import pandas as pd
import seaborn as sb
data = pd.read_csv('../input/hardfakevsrealfaces/data.csv')
height, width = (128, 128)
X = np.empty((data.shape[0], height, width, 3))
for i in range(data.shape[0]):
img = load_img('../input/hardfakevsrealfaces/{}/{}.jpg'.format(data.loc[i, 'label'], data.loc[i, 'images_id']), target_size=(height, width))
X[i] = img_to_array(img)
X.shape
def changeLabels(x):
return labels[x]
labels = data.label.unique()
labels = {labels[i]: i for i in range(labels.size)}
y = data.label.apply(changeLabels)
y[:5]
y = to_categorical(y, len(labels))
y = y.astype(int)
y[:5]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=8)
(X_train.shape, y_train.shape)
model = Sequential()
model.add(Conv2D(64, kernel_size=3, activation='relu', input_shape=(height, width, 3)))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Conv2D(32, kernel_size=3, activation='relu'))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Conv2D(32, kernel_size=3, activation='relu'))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.45))
model.add(Dense(2, activation='softmax'))
epochs = 8
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
stats = model.fit(X_train, y_train, epochs=epochs, validation_split=0.2)
model.summary()
stats_df = pd.DataFrame(stats.history)
stats_df['epoch'] = list(range(1, epochs + 1))
plot.figure(figsize=(10, 8))
sb.lineplot(y='loss', x='epoch', data=stats_df, color='deeppink', linewidth=2.5, label='Training loss')
sb.lineplot(y='val_loss', x='epoch', data=stats_df, color='darkturquoise', linewidth=2.5, label='Validation loss')
plot.grid()
plot.legend()
plot.title('Training and validation loss') | code |
1003217/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
print('Skewness: %f' % train['SalePrice'].skew())
print('Kurtosis: %f' % train['SalePrice'].kurt()) | code |
1003217/cell_33 | [
"text_html_output_1.png"
] | import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
#box plot overallqual/saleprice
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
prices = pd.DataFrame({'price': train['SalePrice'], 'log(price + 1)': np.log1p(train['SalePrice'])})
corr = train.select_dtypes(include=['float64', 'int64']).iloc[:, 1:].corr()
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull().sum() / train.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
missing_data.head(20) | code |
1003217/cell_29 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
#box plot overallqual/saleprice
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
corr = train.select_dtypes(include=['float64', 'int64']).iloc[:, 1:].corr()
plt.figure(figsize=(12, 6))
sns.countplot(x='Neighborhood', data=train)
xt = plt.xticks(rotation=45) | code |
1003217/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
train['SalePrice'].describe() | code |
1003217/cell_28 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
#box plot overallqual/saleprice
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
corr = train.select_dtypes(include=['float64', 'int64']).iloc[:, 1:].corr()
cor_dict = corr['SalePrice'].to_dict()
del cor_dict['SalePrice']
print('List the numerical features decendingly by their correlation with Sale Price:\n')
for ele in sorted(cor_dict.items(), key=lambda x: -abs(x[1])):
print('{0}: \t{1}'.format(*ele)) | code |
1003217/cell_15 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
sns.distplot(train['SalePrice']) | code |
1003217/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y='SalePrice', data=data)
fig.axis(ymin=0, ymax=800000) | code |
1003217/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
#box plot overallqual/saleprice
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
prices = pd.DataFrame({'price': train['SalePrice'], 'log(price + 1)': np.log1p(train['SalePrice'])})
prices.hist() | code |
1003217/cell_31 | [
"image_output_1.png"
] | from sklearn.model_selection import cross_val_score
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
#box plot overallqual/saleprice
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
prices = pd.DataFrame({'price': train['SalePrice'], 'log(price + 1)': np.log1p(train['SalePrice'])})
from sklearn.linear_model import LinearRegression, Ridge, RidgeCV, ElasticNet, LassoCV, LassoLarsCV
from sklearn.model_selection import cross_val_score
def rmse_cv(model):
rmse = np.sqrt(-cross_val_score(model, X_train, y, scoring='neg_mean_squared_error', cv=5))
return rmse
corr = train.select_dtypes(include=['float64', 'int64']).iloc[:, 1:].corr()
xt = plt.xticks(rotation=45)
k = 10
cols = corr.nlargest(k, 'SalePrice')['SalePrice'].index
cm = np.corrcoef(train[cols].values.T)
sns.set(font_scale=1.25)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
plt.show() | code |
1003217/cell_14 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
data.plot.scatter(x=var, y='SalePrice', ylim=(0, 800000)) | code |
1003217/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
train.head() | code |
1003217/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
#box plot overallqual/saleprice
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
corr = train.select_dtypes(include=['float64', 'int64']).iloc[:, 1:].corr()
plt.figure(figsize=(12, 12))
sns.heatmap(corr, vmax=1, square=True) | code |
1003217/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
train['SalePrice'].describe() | code |
1009496/cell_9 | [
"image_output_1.png"
] | from glob import glob
import cv2
import matplotlib.pylab as plt
import numpy as np # linear algebra
import os
import os
from glob import glob
TRAIN_DATA = '../input/train'
type_1_files = glob(os.path.join(TRAIN_DATA, 'Type_1', '*.jpg'))
type_1_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_1')) + 1:-4] for s in type_1_files])
type_2_files = glob(os.path.join(TRAIN_DATA, 'Type_2', '*.jpg'))
type_2_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_2')) + 1:-4] for s in type_2_files])
type_3_files = glob(os.path.join(TRAIN_DATA, 'Type_3', '*.jpg'))
type_3_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_3')) + 1:-4] for s in type_3_files])
def get_filename(image_id, image_type):
"""
Method to get image file path from its id and type
"""
try:
['Type_1', 'Type_2', 'Type_3'].index(image_type)
except:
raise Exception("Image type '%s' is not recognized" % image_type)
ext = 'jpg'
data_path = os.path.join(TRAIN_DATA, image_type)
return os.path.join(data_path, '{}.{}'.format(image_id, ext))
import cv2
def get_image_data(image_id, image_type):
"""
Method to get image data as np.array specifying image id and type
"""
fname = get_filename(image_id, image_type)
img = cv2.imread(fname)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
return img
img = get_image_data('497', 'Type_1')
tile_size = (256, 256)
n = 10
m = int(np.floor(len(type_1_ids) / n))
complete_image = np.zeros((m * (tile_size[0] + 2), n * (tile_size[1] + 2), 3), dtype=np.uint8)
train_ids = sorted(type_1_ids)
counter = 0
for i in range(m):
ys = i * (tile_size[1] + 2)
ye = ys + tile_size[1]
for j in range(n):
xs = j * (tile_size[0] + 2)
xe = xs + tile_size[0]
image_id = train_ids[counter]
counter += 1
img = get_image_data(image_id, 'Type_1')
img = cv2.resize(img, dsize=tile_size)
complete_image[ys:ye, xs:xe] = img[:, :, :]
plt_st(20, 20)
_ = plt.imshow(complete_image) | code |
1009496/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1009496/cell_8 | [
"text_plain_output_1.png"
] | from glob import glob
import cv2
import numpy as np # linear algebra
import os
import os
from glob import glob
TRAIN_DATA = '../input/train'
type_1_files = glob(os.path.join(TRAIN_DATA, 'Type_1', '*.jpg'))
type_1_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_1')) + 1:-4] for s in type_1_files])
type_2_files = glob(os.path.join(TRAIN_DATA, 'Type_2', '*.jpg'))
type_2_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_2')) + 1:-4] for s in type_2_files])
type_3_files = glob(os.path.join(TRAIN_DATA, 'Type_3', '*.jpg'))
type_3_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_3')) + 1:-4] for s in type_3_files])
def get_filename(image_id, image_type):
"""
Method to get image file path from its id and type
"""
try:
['Type_1', 'Type_2', 'Type_3'].index(image_type)
except:
raise Exception("Image type '%s' is not recognized" % image_type)
ext = 'jpg'
data_path = os.path.join(TRAIN_DATA, image_type)
return os.path.join(data_path, '{}.{}'.format(image_id, ext))
import cv2
def get_image_data(image_id, image_type):
"""
Method to get image data as np.array specifying image id and type
"""
fname = get_filename(image_id, image_type)
img = cv2.imread(fname)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
return img
img = get_image_data('497', 'Type_1')
tile_size = (256, 256)
n = 10
m = int(np.floor(len(type_1_ids) / n))
complete_image = np.zeros((m * (tile_size[0] + 2), n * (tile_size[1] + 2), 3), dtype=np.uint8)
train_ids = sorted(type_1_ids)
counter = 0
for i in range(m):
ys = i * (tile_size[1] + 2)
ye = ys + tile_size[1]
for j in range(n):
xs = j * (tile_size[0] + 2)
xe = xs + tile_size[0]
image_id = train_ids[counter]
counter += 1
img = get_image_data(image_id, 'Type_1')
img = cv2.resize(img, dsize=tile_size)
complete_image[ys:ye, xs:xe] = img[:, :, :]
print(complete_image.shape) | code |
1009496/cell_3 | [
"text_plain_output_1.png"
] | from glob import glob
import numpy as np # linear algebra
import os
import os
from glob import glob
TRAIN_DATA = '../input/train'
type_1_files = glob(os.path.join(TRAIN_DATA, 'Type_1', '*.jpg'))
type_1_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_1')) + 1:-4] for s in type_1_files])
type_2_files = glob(os.path.join(TRAIN_DATA, 'Type_2', '*.jpg'))
type_2_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_2')) + 1:-4] for s in type_2_files])
type_3_files = glob(os.path.join(TRAIN_DATA, 'Type_3', '*.jpg'))
type_3_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_3')) + 1:-4] for s in type_3_files])
print(len(type_1_files), len(type_2_files), len(type_3_files))
print('Type 1', type_1_ids[:10])
print('Type 2', type_2_ids[:10])
print('Type 3', type_3_ids[:10]) | code |
1009496/cell_5 | [
"text_plain_output_1.png"
] | from glob import glob
import cv2
import numpy as np # linear algebra
import os
import os
from glob import glob
TRAIN_DATA = '../input/train'
type_1_files = glob(os.path.join(TRAIN_DATA, 'Type_1', '*.jpg'))
type_1_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_1')) + 1:-4] for s in type_1_files])
type_2_files = glob(os.path.join(TRAIN_DATA, 'Type_2', '*.jpg'))
type_2_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_2')) + 1:-4] for s in type_2_files])
type_3_files = glob(os.path.join(TRAIN_DATA, 'Type_3', '*.jpg'))
type_3_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_3')) + 1:-4] for s in type_3_files])
def get_filename(image_id, image_type):
"""
Method to get image file path from its id and type
"""
try:
['Type_1', 'Type_2', 'Type_3'].index(image_type)
except:
raise Exception("Image type '%s' is not recognized" % image_type)
ext = 'jpg'
data_path = os.path.join(TRAIN_DATA, image_type)
return os.path.join(data_path, '{}.{}'.format(image_id, ext))
import cv2
def get_image_data(image_id, image_type):
"""
Method to get image data as np.array specifying image id and type
"""
fname = get_filename(image_id, image_type)
img = cv2.imread(fname)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
return img
img = get_image_data('497', 'Type_1')
print(img.shape) | code |
50230145/cell_30 | [
"text_html_output_1.png"
] | from bs4 import BeautifulSoup
import numpy as np
import pandas as pd
import requests
titles = []
years = []
urls = []
ranks = [i for i in range(1, 1001)]
def JazzStandardsTable(url):
r = requests.get(url)
soup = BeautifulSoup(r.content, 'html.parser')
for i in range(25, 125):
titles.append(soup.find_all('a')[i].get_text())
for i in range(25, 125):
urls.append(soup.find_all('a')[i].get('href'))
for i in range(0, 100):
years.append(soup.find_all('tr', class_='JSContentsLine')[i].get_text().split('\xa0')[0][-4:])
url = 'https://www.jazzstandards.com/compositions/index.htm'
JazzStandardsTable(url)
for i in range(2, 11):
index = url.find('index')
url = url[:index + 5] + str(i) + '.htm'
JazzStandardsTable(url)
title = pd.Series(titles, name='Title')
year = pd.Series(years, name='Year')
rank = pd.Series(ranks, name='Rank')
url = pd.Series(urls, name='URL')
JazzStandards = pd.concat([rank, title, year, url], axis=1)
music = []
lyrics = []
for url in urls:
r = requests.get(url)
soup = BeautifulSoup(r.content, 'html.parser')
music.append(soup.find('table', id='table33').find_all('tr', class_='JSQuote')[-2].get_text().strip().split('\n')[2:])
lyrics.append(soup.find('table', id='table33').find_all('tr', class_='JSQuote')[-1].get_text().strip().split('\n')[2:])
Music = pd.Series(music, name='Composer(s)')
Lyrics = pd.Series(lyrics, name='Lyricist(s)')
Music = Music.apply(lambda x: np.nan if len(x) == 0 else x)
Music.fillna(Lyrics, inplace=True)
JazzStandards = pd.concat([JazzStandards, Music, Lyrics], axis=1)
JazzStandards = JazzStandards[['Rank', 'Title', 'Year', 'Composer(s)', 'Lyricist(s)', 'URL']]
JazzStandards.head(60) | code |
50230145/cell_14 | [
"text_html_output_1.png"
] | from bs4 import BeautifulSoup
import pandas as pd
import requests
titles = []
years = []
urls = []
ranks = [i for i in range(1, 1001)]
def JazzStandardsTable(url):
r = requests.get(url)
soup = BeautifulSoup(r.content, 'html.parser')
for i in range(25, 125):
titles.append(soup.find_all('a')[i].get_text())
for i in range(25, 125):
urls.append(soup.find_all('a')[i].get('href'))
for i in range(0, 100):
years.append(soup.find_all('tr', class_='JSContentsLine')[i].get_text().split('\xa0')[0][-4:])
url = 'https://www.jazzstandards.com/compositions/index.htm'
JazzStandardsTable(url)
for i in range(2, 11):
index = url.find('index')
url = url[:index + 5] + str(i) + '.htm'
JazzStandardsTable(url)
title = pd.Series(titles, name='Title')
year = pd.Series(years, name='Year')
rank = pd.Series(ranks, name='Rank')
url = pd.Series(urls, name='URL')
JazzStandards = pd.concat([rank, title, year, url], axis=1)
JazzStandards | code |
73079773/cell_30 | [
"image_output_11.png",
"image_output_24.png",
"image_output_46.png",
"image_output_25.png",
"image_output_47.png",
"image_output_17.png",
"image_output_30.png",
"image_output_14.png",
"image_output_39.png",
"image_output_28.png",
"image_output_23.png",
"image_output_34.png",
"image_output_13.png",
"image_output_40.png",
"image_output_5.png",
"image_output_48.png",
"image_output_18.png",
"image_output_21.png",
"image_output_7.png",
"image_output_31.png",
"image_output_20.png",
"image_output_32.png",
"image_output_4.png",
"image_output_42.png",
"image_output_35.png",
"image_output_41.png",
"image_output_36.png",
"image_output_8.png",
"image_output_37.png",
"image_output_16.png",
"image_output_27.png",
"image_output_6.png",
"image_output_45.png",
"image_output_12.png",
"image_output_22.png",
"image_output_3.png",
"image_output_29.png",
"image_output_44.png",
"image_output_43.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_33.png",
"image_output_50.png",
"image_output_15.png",
"image_output_49.png",
"image_output_9.png",
"image_output_19.png",
"image_output_38.png",
"image_output_26.png"
] | from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.backend import clear_session
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
from tensorflow.keras.layers import Convolution2D,MaxPooling2D, Dense, Flatten, InputLayer
from tensorflow.keras.models import Sequential, Model, load_model
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import zipfile
import zipfile
input_path = '/kaggle/input/dogs-vs-cats'
work_path = '/kaggle/working/data'
train_path = os.path.join(input_path, 'train.zip')
test_path = os.path.join(input_path, 'test1.zip')
with zipfile.ZipFile(train_path, 'r') as zip_ref:
zip_ref.extractall(work_path)
with zipfile.ZipFile(test_path, 'r') as zip_ref:
zip_ref.extractall(work_path)
data_dir = work_path
train_dir = data_dir + '/train'
test_dir = data_dir + '/test1'
df = pd.DataFrame()
fnames = os.listdir(train_dir)
class_name = []
for name in fnames:
class_name.append(name.split('.')[0])
data = {'filename': fnames, 'class': class_name}
df = pd.DataFrame(data)
df = df.sample(frac=1)
train_datagen = ImageDataGenerator(rescale=1 / 255, rotation_range=20, shear_range=0.2, width_shift_range=0.2, height_shift_range=0.2, horizontal_flip=True, zoom_range=0.2)
valid_datagen = ImageDataGenerator(rescale=1 / 255)
idx = int(0.8 * len(df))
train_df = df.iloc[:idx]
valid_df = df.iloc[idx:]
target = (224, 224)
train_set = train_datagen.flow_from_dataframe(train_df, directory=train_dir, shuffle=True, target_size=target, batch_size=64, class_mode='binary')
valid_set = valid_datagen.flow_from_dataframe(valid_df, directory=train_dir, shuffle=False, target_size=target, batch_size=32, class_mode='binary')
clear_session()
model = Sequential([InputLayer(input_shape=target + (3,)), Convolution2D(16, 3, activation='relu'), MaxPooling2D(2), Convolution2D(32, 3, activation='relu'), MaxPooling2D(2), Convolution2D(64, 3, activation='relu'), MaxPooling2D(2), Flatten(), Dense(512, activation='relu'), Dense(1, activation='sigmoid')])
model.summary()
opt = SGD(learning_rate=0.05, momentum=0.9, nesterov=True)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['acc'])
model.optimizer.get_config()
clear_session()
model = VGG16(include_top=False, input_shape=target + (3,))
for layer in model.layers:
layer.trainable = False
flat1 = Flatten()(model.layers[-1].output)
class1 = Dense(128, activation='relu', kernel_initializer='he_uniform')(flat1)
output = Dense(1, activation='sigmoid')(class1)
model = Model(inputs=model.inputs, outputs=output)
model.summary()
opt = SGD(learning_rate=0.001, momentum=0.9)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['acc'])
model.optimizer.get_config()
checkpoint = ModelCheckpoint('temp_model.h5', monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto')
early = EarlyStopping(monitor='val_acc', min_delta=0, patience=10, verbose=1, mode='auto')
history = model.fit(train_set, validation_data=valid_set, steps_per_epoch=train_set.n // train_set.batch_size, validation_steps=valid_set.n // valid_set.batch_size, epochs=50, callbacks=[checkpoint, early])
legend = ['train', 'validation']
model.save('my_model-3_block-aug-50_epoch.h5')
model = load_model('temp_model.h5')
layer_outputs = []
for layer in model.layers:
if 'conv' not in layer.name:
continue
layer_outputs.append(layer.output)
activation_model = Model(inputs=model.input, outputs=layer_outputs)
def preprocess(img):
img = cv2.resize(img,target)
img = img/255
return np.array(img)
def predict(img):
img = preprocess(img)
img = img.reshape((1,)+img.shape)
probability = model.predict(img)
return probability
def getLabel(probability):
if probability<0.5:
probability=0
else:
probability=1
return list(train_set.class_indices)[probability]
def visualize(img):
img = preprocess(img)
img = img.reshape((1,)+img.shape)
fmaps = activation_model.predict(img)
for i in range(len(fmaps)):
activation = fmaps[i]
fig = plt.figure(figsize=(20,15))
fig.suptitle(layer_outputs[i].name)
for j in range(min(8*8,activation.shape[-1])):
plt.subplot(8,8,j+1)
plt.imshow(activation[0,:,:,j],cmap='gray')
plt.show()
WIN_SIZES=[]
for i in range(100,260,20):
WIN_SIZES.append(i)
def get_box(img,step=20,win_sizes=WIN_SIZES):
best_box = None
best_distance = 1
raw_prob = predict(img)
if (raw_prob<0.5):
raw_prob=0
else:
raw_prob=1
for win in win_sizes:
print("Run with window size:",str(win))
for top in range(0,img.shape[0]-win+1,step):
for left in range(0,img.shape[1]-win+1,step):
box = (left,top,left+win,top+win)
crop = img[box[1]:box[3],box[0]:box[2]]
prob = predict(crop)
distance = abs(raw_prob-prob)
if (distance<best_distance):
best_box = box
best_distance = distance
return (best_box, best_distance)
test_fnames = os.listdir(test_dir)
random.shuffle(test_fnames)
result = []
nPic = 50
for fnames in test_fnames:
pred_path = os.path.join(test_dir, fnames)
img = cv2.imread(pred_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
title = getLabel(predict(img))
plt.imshow(img)
plt.title(title)
plt.show()
nPic -= 1
if nPic == 0:
break | code |
73079773/cell_2 | [
"image_output_2.png",
"image_output_1.png"
] | code |
|
73079773/cell_18 | [
"text_plain_output_1.png"
] | from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.backend import clear_session
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
from tensorflow.keras.layers import Convolution2D,MaxPooling2D, Dense, Flatten, InputLayer
from tensorflow.keras.models import Sequential, Model, load_model
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
import pandas as pd
import zipfile
import zipfile
input_path = '/kaggle/input/dogs-vs-cats'
work_path = '/kaggle/working/data'
train_path = os.path.join(input_path, 'train.zip')
test_path = os.path.join(input_path, 'test1.zip')
with zipfile.ZipFile(train_path, 'r') as zip_ref:
zip_ref.extractall(work_path)
with zipfile.ZipFile(test_path, 'r') as zip_ref:
zip_ref.extractall(work_path)
data_dir = work_path
train_dir = data_dir + '/train'
test_dir = data_dir + '/test1'
df = pd.DataFrame()
fnames = os.listdir(train_dir)
class_name = []
for name in fnames:
class_name.append(name.split('.')[0])
data = {'filename': fnames, 'class': class_name}
df = pd.DataFrame(data)
df = df.sample(frac=1)
train_datagen = ImageDataGenerator(rescale=1 / 255, rotation_range=20, shear_range=0.2, width_shift_range=0.2, height_shift_range=0.2, horizontal_flip=True, zoom_range=0.2)
valid_datagen = ImageDataGenerator(rescale=1 / 255)
idx = int(0.8 * len(df))
train_df = df.iloc[:idx]
valid_df = df.iloc[idx:]
target = (224, 224)
train_set = train_datagen.flow_from_dataframe(train_df, directory=train_dir, shuffle=True, target_size=target, batch_size=64, class_mode='binary')
valid_set = valid_datagen.flow_from_dataframe(valid_df, directory=train_dir, shuffle=False, target_size=target, batch_size=32, class_mode='binary')
clear_session()
model = Sequential([InputLayer(input_shape=target + (3,)), Convolution2D(16, 3, activation='relu'), MaxPooling2D(2), Convolution2D(32, 3, activation='relu'), MaxPooling2D(2), Convolution2D(64, 3, activation='relu'), MaxPooling2D(2), Flatten(), Dense(512, activation='relu'), Dense(1, activation='sigmoid')])
model.summary()
opt = SGD(learning_rate=0.05, momentum=0.9, nesterov=True)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['acc'])
model.optimizer.get_config()
clear_session()
model = VGG16(include_top=False, input_shape=target + (3,))
for layer in model.layers:
layer.trainable = False
flat1 = Flatten()(model.layers[-1].output)
class1 = Dense(128, activation='relu', kernel_initializer='he_uniform')(flat1)
output = Dense(1, activation='sigmoid')(class1)
model = Model(inputs=model.inputs, outputs=output)
model.summary()
opt = SGD(learning_rate=0.001, momentum=0.9)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['acc'])
model.optimizer.get_config()
checkpoint = ModelCheckpoint('temp_model.h5', monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto')
early = EarlyStopping(monitor='val_acc', min_delta=0, patience=10, verbose=1, mode='auto')
history = model.fit(train_set, validation_data=valid_set, steps_per_epoch=train_set.n // train_set.batch_size, validation_steps=valid_set.n // valid_set.batch_size, epochs=50, callbacks=[checkpoint, early])
legend = ['train', 'validation']
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('Accuracy')
plt.xlabel('epochs')
plt.ylabel('acc')
plt.legend(legend, loc='upper left')
plt.show()
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Binary cross-entropy loss')
plt.xlabel('epochs')
plt.ylabel('loss')
plt.legend(legend, loc='upper left')
plt.show() | code |
73079773/cell_16 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.backend import clear_session
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
from tensorflow.keras.layers import Convolution2D,MaxPooling2D, Dense, Flatten, InputLayer
from tensorflow.keras.models import Sequential, Model, load_model
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import pandas as pd
import zipfile
import zipfile
input_path = '/kaggle/input/dogs-vs-cats'
work_path = '/kaggle/working/data'
train_path = os.path.join(input_path, 'train.zip')
test_path = os.path.join(input_path, 'test1.zip')
with zipfile.ZipFile(train_path, 'r') as zip_ref:
zip_ref.extractall(work_path)
with zipfile.ZipFile(test_path, 'r') as zip_ref:
zip_ref.extractall(work_path)
data_dir = work_path
train_dir = data_dir + '/train'
test_dir = data_dir + '/test1'
df = pd.DataFrame()
fnames = os.listdir(train_dir)
class_name = []
for name in fnames:
class_name.append(name.split('.')[0])
data = {'filename': fnames, 'class': class_name}
df = pd.DataFrame(data)
df = df.sample(frac=1)
train_datagen = ImageDataGenerator(rescale=1 / 255, rotation_range=20, shear_range=0.2, width_shift_range=0.2, height_shift_range=0.2, horizontal_flip=True, zoom_range=0.2)
valid_datagen = ImageDataGenerator(rescale=1 / 255)
idx = int(0.8 * len(df))
train_df = df.iloc[:idx]
valid_df = df.iloc[idx:]
target = (224, 224)
train_set = train_datagen.flow_from_dataframe(train_df, directory=train_dir, shuffle=True, target_size=target, batch_size=64, class_mode='binary')
valid_set = valid_datagen.flow_from_dataframe(valid_df, directory=train_dir, shuffle=False, target_size=target, batch_size=32, class_mode='binary')
clear_session()
model = Sequential([InputLayer(input_shape=target + (3,)), Convolution2D(16, 3, activation='relu'), MaxPooling2D(2), Convolution2D(32, 3, activation='relu'), MaxPooling2D(2), Convolution2D(64, 3, activation='relu'), MaxPooling2D(2), Flatten(), Dense(512, activation='relu'), Dense(1, activation='sigmoid')])
model.summary()
opt = SGD(learning_rate=0.05, momentum=0.9, nesterov=True)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['acc'])
model.optimizer.get_config()
clear_session()
model = VGG16(include_top=False, input_shape=target + (3,))
for layer in model.layers:
layer.trainable = False
flat1 = Flatten()(model.layers[-1].output)
class1 = Dense(128, activation='relu', kernel_initializer='he_uniform')(flat1)
output = Dense(1, activation='sigmoid')(class1)
model = Model(inputs=model.inputs, outputs=output)
model.summary()
opt = SGD(learning_rate=0.001, momentum=0.9)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['acc'])
model.optimizer.get_config()
checkpoint = ModelCheckpoint('temp_model.h5', monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto')
early = EarlyStopping(monitor='val_acc', min_delta=0, patience=10, verbose=1, mode='auto')
history = model.fit(train_set, validation_data=valid_set, steps_per_epoch=train_set.n // train_set.batch_size, validation_steps=valid_set.n // valid_set.batch_size, epochs=50, callbacks=[checkpoint, early]) | code |
73079773/cell_24 | [
"text_plain_output_1.png"
] | from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.backend import clear_session
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
from tensorflow.keras.layers import Convolution2D,MaxPooling2D, Dense, Flatten, InputLayer
from tensorflow.keras.models import Sequential, Model, load_model
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import pandas as pd
import zipfile
import zipfile
input_path = '/kaggle/input/dogs-vs-cats'
work_path = '/kaggle/working/data'
train_path = os.path.join(input_path, 'train.zip')
test_path = os.path.join(input_path, 'test1.zip')
with zipfile.ZipFile(train_path, 'r') as zip_ref:
zip_ref.extractall(work_path)
with zipfile.ZipFile(test_path, 'r') as zip_ref:
zip_ref.extractall(work_path)
data_dir = work_path
train_dir = data_dir + '/train'
test_dir = data_dir + '/test1'
df = pd.DataFrame()
fnames = os.listdir(train_dir)
class_name = []
for name in fnames:
class_name.append(name.split('.')[0])
data = {'filename': fnames, 'class': class_name}
df = pd.DataFrame(data)
df = df.sample(frac=1)
train_datagen = ImageDataGenerator(rescale=1 / 255, rotation_range=20, shear_range=0.2, width_shift_range=0.2, height_shift_range=0.2, horizontal_flip=True, zoom_range=0.2)
valid_datagen = ImageDataGenerator(rescale=1 / 255)
idx = int(0.8 * len(df))
train_df = df.iloc[:idx]
valid_df = df.iloc[idx:]
target = (224, 224)
train_set = train_datagen.flow_from_dataframe(train_df, directory=train_dir, shuffle=True, target_size=target, batch_size=64, class_mode='binary')
valid_set = valid_datagen.flow_from_dataframe(valid_df, directory=train_dir, shuffle=False, target_size=target, batch_size=32, class_mode='binary')
clear_session()
model = Sequential([InputLayer(input_shape=target + (3,)), Convolution2D(16, 3, activation='relu'), MaxPooling2D(2), Convolution2D(32, 3, activation='relu'), MaxPooling2D(2), Convolution2D(64, 3, activation='relu'), MaxPooling2D(2), Flatten(), Dense(512, activation='relu'), Dense(1, activation='sigmoid')])
model.summary()
opt = SGD(learning_rate=0.05, momentum=0.9, nesterov=True)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['acc'])
model.optimizer.get_config()
clear_session()
model = VGG16(include_top=False, input_shape=target + (3,))
for layer in model.layers:
layer.trainable = False
flat1 = Flatten()(model.layers[-1].output)
class1 = Dense(128, activation='relu', kernel_initializer='he_uniform')(flat1)
output = Dense(1, activation='sigmoid')(class1)
model = Model(inputs=model.inputs, outputs=output)
model.summary()
opt = SGD(learning_rate=0.001, momentum=0.9)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['acc'])
model.optimizer.get_config()
checkpoint = ModelCheckpoint('temp_model.h5', monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto')
early = EarlyStopping(monitor='val_acc', min_delta=0, patience=10, verbose=1, mode='auto')
history = model.fit(train_set, validation_data=valid_set, steps_per_epoch=train_set.n // train_set.batch_size, validation_steps=valid_set.n // valid_set.batch_size, epochs=50, callbacks=[checkpoint, early])
model.save('my_model-3_block-aug-50_epoch.h5')
model = load_model('temp_model.h5')
print('Accuracy:', model.evaluate(valid_set)) | code |
73079773/cell_14 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.backend import clear_session
from tensorflow.keras.layers import Convolution2D,MaxPooling2D, Dense, Flatten, InputLayer
from tensorflow.keras.models import Sequential, Model, load_model
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import pandas as pd
import zipfile
import zipfile
input_path = '/kaggle/input/dogs-vs-cats'
work_path = '/kaggle/working/data'
train_path = os.path.join(input_path, 'train.zip')
test_path = os.path.join(input_path, 'test1.zip')
with zipfile.ZipFile(train_path, 'r') as zip_ref:
zip_ref.extractall(work_path)
with zipfile.ZipFile(test_path, 'r') as zip_ref:
zip_ref.extractall(work_path)
data_dir = work_path
train_dir = data_dir + '/train'
test_dir = data_dir + '/test1'
df = pd.DataFrame()
fnames = os.listdir(train_dir)
class_name = []
for name in fnames:
class_name.append(name.split('.')[0])
data = {'filename': fnames, 'class': class_name}
df = pd.DataFrame(data)
df = df.sample(frac=1)
train_datagen = ImageDataGenerator(rescale=1 / 255, rotation_range=20, shear_range=0.2, width_shift_range=0.2, height_shift_range=0.2, horizontal_flip=True, zoom_range=0.2)
valid_datagen = ImageDataGenerator(rescale=1 / 255)
idx = int(0.8 * len(df))
train_df = df.iloc[:idx]
valid_df = df.iloc[idx:]
target = (224, 224)
train_set = train_datagen.flow_from_dataframe(train_df, directory=train_dir, shuffle=True, target_size=target, batch_size=64, class_mode='binary')
valid_set = valid_datagen.flow_from_dataframe(valid_df, directory=train_dir, shuffle=False, target_size=target, batch_size=32, class_mode='binary')
clear_session()
model = Sequential([InputLayer(input_shape=target + (3,)), Convolution2D(16, 3, activation='relu'), MaxPooling2D(2), Convolution2D(32, 3, activation='relu'), MaxPooling2D(2), Convolution2D(64, 3, activation='relu'), MaxPooling2D(2), Flatten(), Dense(512, activation='relu'), Dense(1, activation='sigmoid')])
model.summary()
opt = SGD(learning_rate=0.05, momentum=0.9, nesterov=True)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['acc'])
model.optimizer.get_config()
clear_session()
model = VGG16(include_top=False, input_shape=target + (3,))
for layer in model.layers:
layer.trainable = False
flat1 = Flatten()(model.layers[-1].output)
class1 = Dense(128, activation='relu', kernel_initializer='he_uniform')(flat1)
output = Dense(1, activation='sigmoid')(class1)
model = Model(inputs=model.inputs, outputs=output)
model.summary()
opt = SGD(learning_rate=0.001, momentum=0.9)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['acc'])
model.optimizer.get_config() | code |
73079773/cell_10 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
import pandas as pd
import zipfile
import zipfile
input_path = '/kaggle/input/dogs-vs-cats'
work_path = '/kaggle/working/data'
train_path = os.path.join(input_path, 'train.zip')
test_path = os.path.join(input_path, 'test1.zip')
with zipfile.ZipFile(train_path, 'r') as zip_ref:
zip_ref.extractall(work_path)
with zipfile.ZipFile(test_path, 'r') as zip_ref:
zip_ref.extractall(work_path)
data_dir = work_path
train_dir = data_dir + '/train'
test_dir = data_dir + '/test1'
df = pd.DataFrame()
fnames = os.listdir(train_dir)
class_name = []
for name in fnames:
class_name.append(name.split('.')[0])
data = {'filename': fnames, 'class': class_name}
df = pd.DataFrame(data)
df = df.sample(frac=1)
train_datagen = ImageDataGenerator(rescale=1 / 255, rotation_range=20, shear_range=0.2, width_shift_range=0.2, height_shift_range=0.2, horizontal_flip=True, zoom_range=0.2)
valid_datagen = ImageDataGenerator(rescale=1 / 255)
idx = int(0.8 * len(df))
train_df = df.iloc[:idx]
valid_df = df.iloc[idx:]
target = (224, 224)
train_set = train_datagen.flow_from_dataframe(train_df, directory=train_dir, shuffle=True, target_size=target, batch_size=64, class_mode='binary')
valid_set = valid_datagen.flow_from_dataframe(valid_df, directory=train_dir, shuffle=False, target_size=target, batch_size=32, class_mode='binary') | code |
73079773/cell_12 | [
"text_plain_output_1.png"
] | from tensorflow.keras.backend import clear_session
from tensorflow.keras.layers import Convolution2D,MaxPooling2D, Dense, Flatten, InputLayer
from tensorflow.keras.models import Sequential, Model, load_model
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import pandas as pd
import zipfile
import zipfile
input_path = '/kaggle/input/dogs-vs-cats'
work_path = '/kaggle/working/data'
train_path = os.path.join(input_path, 'train.zip')
test_path = os.path.join(input_path, 'test1.zip')
with zipfile.ZipFile(train_path, 'r') as zip_ref:
zip_ref.extractall(work_path)
with zipfile.ZipFile(test_path, 'r') as zip_ref:
zip_ref.extractall(work_path)
data_dir = work_path
train_dir = data_dir + '/train'
test_dir = data_dir + '/test1'
df = pd.DataFrame()
fnames = os.listdir(train_dir)
class_name = []
for name in fnames:
class_name.append(name.split('.')[0])
data = {'filename': fnames, 'class': class_name}
df = pd.DataFrame(data)
df = df.sample(frac=1)
train_datagen = ImageDataGenerator(rescale=1 / 255, rotation_range=20, shear_range=0.2, width_shift_range=0.2, height_shift_range=0.2, horizontal_flip=True, zoom_range=0.2)
valid_datagen = ImageDataGenerator(rescale=1 / 255)
idx = int(0.8 * len(df))
train_df = df.iloc[:idx]
valid_df = df.iloc[idx:]
target = (224, 224)
train_set = train_datagen.flow_from_dataframe(train_df, directory=train_dir, shuffle=True, target_size=target, batch_size=64, class_mode='binary')
valid_set = valid_datagen.flow_from_dataframe(valid_df, directory=train_dir, shuffle=False, target_size=target, batch_size=32, class_mode='binary')
clear_session()
model = Sequential([InputLayer(input_shape=target + (3,)), Convolution2D(16, 3, activation='relu'), MaxPooling2D(2), Convolution2D(32, 3, activation='relu'), MaxPooling2D(2), Convolution2D(64, 3, activation='relu'), MaxPooling2D(2), Flatten(), Dense(512, activation='relu'), Dense(1, activation='sigmoid')])
model.summary()
opt = SGD(learning_rate=0.05, momentum=0.9, nesterov=True)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['acc'])
model.optimizer.get_config() | code |
128008433/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_train_pred = knn.predict(X_train)
y_test_pred = knn.predict(X_test)
train_acc_knn = accuracy_score(y_train, y_train_pred)
test_acc_knn = accuracy_score(y_test, y_test_pred)
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_train_pred = dt.predict(X_train)
y_test_pred = dt.predict(X_test)
train_acc_dt = accuracy_score(y_train, y_train_pred)
test_acc_dt = accuracy_score(y_test, y_test_pred)
svm = SVC()
svm.fit(X_train, y_train)
y_train_pred = svm.predict(X_train)
y_test_pred = svm.predict(X_test)
train_acc_svm = accuracy_score(y_train, y_train_pred)
test_acc_svm = accuracy_score(y_test, y_test_pred)
lr = LogisticRegression()
lr.fit(X_train, y_train)
y_train_pred = lr.predict(X_train)
y_test_pred = lr.predict(X_test)
train_acc_lr = accuracy_score(y_train, y_train_pred)
test_acc_lr = accuracy_score(y_test, y_test_pred)
print('Potential of overfitting for KNN: ', train_acc_knn - test_acc_knn)
print('Potential of overfitting for Decision Tree: ', train_acc_dt - test_acc_dt)
print('Potential of overfitting for SVM: ', train_acc_svm - test_acc_svm)
print('Potential of overfitting for Logistic Regression: ', train_acc_lr - test_acc_lr) | code |
128008433/cell_13 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
data | code |
128008433/cell_23 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_train_pred = knn.predict(X_train)
y_test_pred = knn.predict(X_test)
train_acc_knn = accuracy_score(y_train, y_train_pred)
test_acc_knn = accuracy_score(y_test, y_test_pred)
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_train_pred = dt.predict(X_train)
y_test_pred = dt.predict(X_test)
train_acc_dt = accuracy_score(y_train, y_train_pred)
test_acc_dt = accuracy_score(y_test, y_test_pred)
svm = SVC()
svm.fit(X_train, y_train)
y_train_pred = svm.predict(X_train)
y_test_pred = svm.predict(X_test)
train_acc_svm = accuracy_score(y_train, y_train_pred)
test_acc_svm = accuracy_score(y_test, y_test_pred)
lr = LogisticRegression()
lr.fit(X_train, y_train)
y_train_pred = lr.predict(X_train)
y_test_pred = lr.predict(X_test)
train_acc_lr = accuracy_score(y_train, y_train_pred)
test_acc_lr = accuracy_score(y_test, y_test_pred)
from sklearn.metrics import confusion_matrix
knn_cm = confusion_matrix(y_test, y_test_pred)
print('KNN confusion matrix:\n', knn_cm)
dt_cm = confusion_matrix(y_test, y_test_pred)
print('Decision Tree confusion matrix:\n', dt_cm)
svm_cm = confusion_matrix(y_test, y_test_pred)
print('SVM confusion matrix:\n', svm_cm)
lr_cm = confusion_matrix(y_test, y_test_pred)
print('Logistic Regression confusion matrix:\n', lr_cm) | code |
128008433/cell_30 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import recall_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import MinMaxScaler, StandardScaler, LabelEncoder
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_train_pred = knn.predict(X_train)
y_test_pred = knn.predict(X_test)
train_acc_knn = accuracy_score(y_train, y_train_pred)
test_acc_knn = accuracy_score(y_test, y_test_pred)
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_train_pred = dt.predict(X_train)
y_test_pred = dt.predict(X_test)
train_acc_dt = accuracy_score(y_train, y_train_pred)
test_acc_dt = accuracy_score(y_test, y_test_pred)
svm = SVC()
svm.fit(X_train, y_train)
y_train_pred = svm.predict(X_train)
y_test_pred = svm.predict(X_test)
train_acc_svm = accuracy_score(y_train, y_train_pred)
test_acc_svm = accuracy_score(y_test, y_test_pred)
lr = LogisticRegression()
lr.fit(X_train, y_train)
y_train_pred = lr.predict(X_train)
y_test_pred = lr.predict(X_test)
train_acc_lr = accuracy_score(y_train, y_train_pred)
test_acc_lr = accuracy_score(y_test, y_test_pred)
from sklearn.preprocessing import MinMaxScaler, StandardScaler, LabelEncoder
scaler = MinMaxScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
knn = KNeighborsClassifier()
knn.fit(X_train_scaled, y_train)
y_train_pred = knn.predict(X_train_scaled)
y_test_pred = knn.predict(X_test_scaled)
train_acc_knn_scaled = accuracy_score(y_train, y_train_pred)
test_acc_knn_scaled = accuracy_score(y_test, y_test_pred)
train_recall_knn_scaled = recall_score(y_train, y_train_pred)
test_recall_knn_scaled = recall_score(y_test, y_test_pred)
dt = DecisionTreeClassifier()
dt.fit(X_train_scaled, y_train)
y_train_pred = dt.predict(X_train_scaled)
y_test_pred = dt.predict(X_test_scaled)
train_acc_dt_scaled = accuracy_score(y_train, y_train_pred)
test_acc_dt_scaled = accuracy_score(y_test, y_test_pred)
train_recall_dt_scaled = recall_score(y_train, y_train_pred)
test_recall_dt_scaled = recall_score(y_test, y_test_pred)
print('Training accuracy of Decision Tree (scaled data): ', train_acc_dt_scaled)
print('Testing accuracy of Decision Tree (scaled data): ', test_acc_dt_scaled)
print('Training recall of Decision Tree (scaled data): ', train_recall_dt_scaled)
print('Testing recall of Decision Tree (scaled data): ', test_recall_dt_scaled) | code |
128008433/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import recall_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import MinMaxScaler, StandardScaler, LabelEncoder
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_train_pred = knn.predict(X_train)
y_test_pred = knn.predict(X_test)
train_acc_knn = accuracy_score(y_train, y_train_pred)
test_acc_knn = accuracy_score(y_test, y_test_pred)
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_train_pred = dt.predict(X_train)
y_test_pred = dt.predict(X_test)
train_acc_dt = accuracy_score(y_train, y_train_pred)
test_acc_dt = accuracy_score(y_test, y_test_pred)
svm = SVC()
svm.fit(X_train, y_train)
y_train_pred = svm.predict(X_train)
y_test_pred = svm.predict(X_test)
train_acc_svm = accuracy_score(y_train, y_train_pred)
test_acc_svm = accuracy_score(y_test, y_test_pred)
lr = LogisticRegression()
lr.fit(X_train, y_train)
y_train_pred = lr.predict(X_train)
y_test_pred = lr.predict(X_test)
train_acc_lr = accuracy_score(y_train, y_train_pred)
test_acc_lr = accuracy_score(y_test, y_test_pred)
from sklearn.preprocessing import MinMaxScaler, StandardScaler, LabelEncoder
scaler = MinMaxScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
knn = KNeighborsClassifier()
knn.fit(X_train_scaled, y_train)
y_train_pred = knn.predict(X_train_scaled)
y_test_pred = knn.predict(X_test_scaled)
train_acc_knn_scaled = accuracy_score(y_train, y_train_pred)
test_acc_knn_scaled = accuracy_score(y_test, y_test_pred)
train_recall_knn_scaled = recall_score(y_train, y_train_pred)
test_recall_knn_scaled = recall_score(y_test, y_test_pred)
print('Training accuracy of KNN (scaled data): ', train_acc_knn_scaled)
print('Testing accuracy of KNN (scaled data): ', test_acc_knn_scaled)
print('Training recall of KNN (scaled data): ', train_recall_knn_scaled)
print('Testing recall of KNN (scaled data): ', test_recall_knn_scaled) | code |
128008433/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_train_pred = knn.predict(X_train)
y_test_pred = knn.predict(X_test)
train_acc_knn = accuracy_score(y_train, y_train_pred)
test_acc_knn = accuracy_score(y_test, y_test_pred)
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_train_pred = dt.predict(X_train)
y_test_pred = dt.predict(X_test)
train_acc_dt = accuracy_score(y_train, y_train_pred)
test_acc_dt = accuracy_score(y_test, y_test_pred)
svm = SVC()
svm.fit(X_train, y_train)
y_train_pred = svm.predict(X_train)
y_test_pred = svm.predict(X_test)
train_acc_svm = accuracy_score(y_train, y_train_pred)
test_acc_svm = accuracy_score(y_test, y_test_pred)
lr = LogisticRegression()
lr.fit(X_train, y_train)
y_train_pred = lr.predict(X_train)
y_test_pred = lr.predict(X_test)
train_acc_lr = accuracy_score(y_train, y_train_pred)
test_acc_lr = accuracy_score(y_test, y_test_pred)
lr = LogisticRegression()
lr.fit(X, y) | code |
128008433/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
data | code |
128008433/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_train_pred = knn.predict(X_train)
y_test_pred = knn.predict(X_test)
train_acc_knn = accuracy_score(y_train, y_train_pred)
test_acc_knn = accuracy_score(y_test, y_test_pred)
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_train_pred = dt.predict(X_train)
y_test_pred = dt.predict(X_test)
train_acc_dt = accuracy_score(y_train, y_train_pred)
test_acc_dt = accuracy_score(y_test, y_test_pred)
svm = SVC()
svm.fit(X_train, y_train)
y_train_pred = svm.predict(X_train)
y_test_pred = svm.predict(X_test)
train_acc_svm = accuracy_score(y_train, y_train_pred)
test_acc_svm = accuracy_score(y_test, y_test_pred)
lr = LogisticRegression()
lr.fit(X_train, y_train)
y_train_pred = lr.predict(X_train)
y_test_pred = lr.predict(X_test)
train_acc_lr = accuracy_score(y_train, y_train_pred)
test_acc_lr = accuracy_score(y_test, y_test_pred)
print('Training accuracy of Logistic Regression: ', train_acc_lr)
print('Testing accuracy of Logistic Regression: ', test_acc_lr) | code |
128008433/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_train_pred = knn.predict(X_train)
y_test_pred = knn.predict(X_test)
train_acc_knn = accuracy_score(y_train, y_train_pred)
test_acc_knn = accuracy_score(y_test, y_test_pred)
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_train_pred = dt.predict(X_train)
y_test_pred = dt.predict(X_test)
train_acc_dt = accuracy_score(y_train, y_train_pred)
test_acc_dt = accuracy_score(y_test, y_test_pred)
svm = SVC()
svm.fit(X_train, y_train)
y_train_pred = svm.predict(X_train)
y_test_pred = svm.predict(X_test)
train_acc_svm = accuracy_score(y_train, y_train_pred)
test_acc_svm = accuracy_score(y_test, y_test_pred)
print('Training accuracy of SVM: ', train_acc_svm)
print('Testing accuracy of SVM: ', test_acc_svm) | code |
128008433/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape | code |
128008433/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_train_pred = knn.predict(X_train)
y_test_pred = knn.predict(X_test)
train_acc_knn = accuracy_score(y_train, y_train_pred)
test_acc_knn = accuracy_score(y_test, y_test_pred)
print('Training accuracy of KNN: ', train_acc_knn)
print('Testing accuracy of KNN: ', test_acc_knn) | code |
128008433/cell_17 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_train_pred = knn.predict(X_train)
y_test_pred = knn.predict(X_test)
train_acc_knn = accuracy_score(y_train, y_train_pred)
test_acc_knn = accuracy_score(y_test, y_test_pred)
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_train_pred = dt.predict(X_train)
y_test_pred = dt.predict(X_test)
train_acc_dt = accuracy_score(y_train, y_train_pred)
test_acc_dt = accuracy_score(y_test, y_test_pred)
print('Training accuracy of Decision Tree: ', train_acc_dt)
print('Testing accuracy of Decision Tree: ', test_acc_dt) | code |
128008433/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import recall_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import MinMaxScaler, StandardScaler, LabelEncoder
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_train_pred = knn.predict(X_train)
y_test_pred = knn.predict(X_test)
train_acc_knn = accuracy_score(y_train, y_train_pred)
test_acc_knn = accuracy_score(y_test, y_test_pred)
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_train_pred = dt.predict(X_train)
y_test_pred = dt.predict(X_test)
train_acc_dt = accuracy_score(y_train, y_train_pred)
test_acc_dt = accuracy_score(y_test, y_test_pred)
svm = SVC()
svm.fit(X_train, y_train)
y_train_pred = svm.predict(X_train)
y_test_pred = svm.predict(X_test)
train_acc_svm = accuracy_score(y_train, y_train_pred)
test_acc_svm = accuracy_score(y_test, y_test_pred)
lr = LogisticRegression()
lr.fit(X_train, y_train)
y_train_pred = lr.predict(X_train)
y_test_pred = lr.predict(X_test)
train_acc_lr = accuracy_score(y_train, y_train_pred)
test_acc_lr = accuracy_score(y_test, y_test_pred)
from sklearn.preprocessing import MinMaxScaler, StandardScaler, LabelEncoder
scaler = MinMaxScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
knn = KNeighborsClassifier()
knn.fit(X_train_scaled, y_train)
y_train_pred = knn.predict(X_train_scaled)
y_test_pred = knn.predict(X_test_scaled)
train_acc_knn_scaled = accuracy_score(y_train, y_train_pred)
test_acc_knn_scaled = accuracy_score(y_test, y_test_pred)
train_recall_knn_scaled = recall_score(y_train, y_train_pred)
test_recall_knn_scaled = recall_score(y_test, y_test_pred)
dt = DecisionTreeClassifier()
dt.fit(X_train_scaled, y_train)
y_train_pred = dt.predict(X_train_scaled)
y_test_pred = dt.predict(X_test_scaled)
train_acc_dt_scaled = accuracy_score(y_train, y_train_pred)
test_acc_dt_scaled = accuracy_score(y_test, y_test_pred)
train_recall_dt_scaled = recall_score(y_train, y_train_pred)
test_recall_dt_scaled = recall_score(y_test, y_test_pred)
svm = SVC()
svm.fit(X_train_scaled, y_train)
y_train_pred = svm.predict(X_train_scaled)
y_test_pred = svm.predict(X_test_scaled)
train_acc_svm_scaled = accuracy_score(y_train, y_train_pred)
test_acc_svm_scaled = accuracy_score(y_test, y_test_pred)
train_recall_svm_scaled = recall_score(y_train, y_train_pred)
test_recall_svm_scaled = recall_score(y_test, y_test_pred)
print('Training accuracy of SVM (scaled data): ', train_acc_svm_scaled)
print('Testing accuracy of SVM (scaled data): ', test_acc_svm_scaled)
print('Training recall of SVM (scaled data): ', train_recall_svm_scaled)
print('Testing recall of SVM (scaled data): ', test_recall_svm_scaled) | code |
128008433/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import recall_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_train_pred = knn.predict(X_train)
y_test_pred = knn.predict(X_test)
train_acc_knn = accuracy_score(y_train, y_train_pred)
test_acc_knn = accuracy_score(y_test, y_test_pred)
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_train_pred = dt.predict(X_train)
y_test_pred = dt.predict(X_test)
train_acc_dt = accuracy_score(y_train, y_train_pred)
test_acc_dt = accuracy_score(y_test, y_test_pred)
svm = SVC()
svm.fit(X_train, y_train)
y_train_pred = svm.predict(X_train)
y_test_pred = svm.predict(X_test)
train_acc_svm = accuracy_score(y_train, y_train_pred)
test_acc_svm = accuracy_score(y_test, y_test_pred)
lr = LogisticRegression()
lr.fit(X_train, y_train)
y_train_pred = lr.predict(X_train)
y_test_pred = lr.predict(X_test)
train_acc_lr = accuracy_score(y_train, y_train_pred)
test_acc_lr = accuracy_score(y_test, y_test_pred)
from sklearn.metrics import recall_score
knn_recall = recall_score(y_test, y_test_pred)
print('KNN recall score:', knn_recall)
dt_recall = recall_score(y_test, y_test_pred)
print('Decision Tree recall score:', dt_recall)
svm_recall = recall_score(y_test, y_test_pred)
print('SVM recall score:', svm_recall)
lr_recall = recall_score(y_test, y_test_pred)
print('Logistic Regression recall score:', lr_recall) | code |
128008433/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
print('Training set shape:', X_train.shape)
print('Testing set shape:', X_test.shape) | code |
128008433/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape | code |
128020267/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/amazon-fine-food-reviews/Reviews.csv', index_col='Id')
data = data.drop({'ProductId', 'UserId', 'ProfileName', 'HelpfulnessNumerator', 'HelpfulnessDenominator', 'Time', 'Summary'}, axis=1)
data.Score = ['positive' if i >= 4 else 'negative' for i in data.Score]
data.head() | code |
128020267/cell_2 | [
"text_html_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 |
128020267/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/amazon-fine-food-reviews/Reviews.csv', index_col='Id')
data.head() | code |
128020267/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/amazon-fine-food-reviews/Reviews.csv', index_col='Id')
data.describe() | code |
128020267/cell_16 | [
"text_html_output_1.png"
] | !pip install gensim pandas
import pandas as pd
import gensim | code |
128020267/cell_17 | [
"text_html_output_1.png"
] | import gensim
import pandas as pd
import pandas as pd
import re
data = pd.read_csv('../input/amazon-fine-food-reviews/Reviews.csv', index_col='Id')
data = data.drop({'ProductId', 'UserId', 'ProfileName', 'HelpfulnessNumerator', 'HelpfulnessDenominator', 'Time', 'Summary'}, axis=1)
data.Score = ['positive' if i >= 4 else 'negative' for i in data.Score]
import re
data.Text = [i.lower() for i in data.Text]
data.Text = [re.sub('[^\\w\\s]', '', i) for i in data.Text]
def preprocess_text(text):
tokens = gensim.utils.simple_preprocess(text)
return tokens
data['tokens'] = data['Text'].apply(preprocess_text)
data.head(25) | code |
128020267/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import re
data = pd.read_csv('../input/amazon-fine-food-reviews/Reviews.csv', index_col='Id')
data = data.drop({'ProductId', 'UserId', 'ProfileName', 'HelpfulnessNumerator', 'HelpfulnessDenominator', 'Time', 'Summary'}, axis=1)
data.Score = ['positive' if i >= 4 else 'negative' for i in data.Score]
import re
data.Text = [i.lower() for i in data.Text]
data.Text = [re.sub('[^\\w\\s]', '', i) for i in data.Text]
data.head() | code |
128020267/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/amazon-fine-food-reviews/Reviews.csv', index_col='Id')
data = data.drop({'ProductId', 'UserId', 'ProfileName', 'HelpfulnessNumerator', 'HelpfulnessDenominator', 'Time', 'Summary'}, axis=1)
data.head() | code |
129012188/cell_20 | [
"text_plain_output_1.png"
] | from copy import deepcopy
from copy import deepcopy
from datasets import list_metrics,load_metric
from random import randint
from random import randint,shuffle
from sentence_transformers import SentenceTransformer, util
from sklearn.metrics import confusion_matrix
import numpy as np
import pandas as pd
import pandas as pd
import plotly.express as px
def performance(y_ture,y_pred):
f1_metric = load_metric("f1")
re_metric = load_metric("recall")
pre_metric = load_metric("precision")
type_c_int = list(set(np.concatenate([y_ture, y_pred])))
type_c = [str(i) for i in type_c_int]
f1_m_list = []
re_m_list = []
pre_m_list = []
for i in type_c_int:
bi_ture = list(y_ture == i)
bi_pred = list(y_pred == i)
f1_m_results = f1_metric.compute(predictions=bi_pred, references=bi_ture, average="macro")
re_m_results = re_metric.compute(predictions=bi_pred, references=bi_ture, average="macro")
pre_m_results = pre_metric.compute(predictions=bi_pred, references=bi_ture, average="macro")
f1_m_list.append(f1_m_results["f1"])
re_m_list.append(re_m_results["recall"])
pre_m_list.append(pre_m_results["precision"])
data = {'Class_type':type_c_int,'F1-macro':f1_m_list,'Recall-macro':re_m_list,'Precision-macro':pre_m_list}
df = pd.DataFrame(data)
display(df)
z = confusion_matrix(y_ture, y_pred)
x_lab = type_c
fig = px.imshow(z,
text_auto=True,
labels=dict(x="True label", y="Predicted label", color="times"),
x=x_lab,
y=x_lab)
# fig.show()
Accuarcy = sum([1 for i in range(len(y_ture)) if y_pred[i] == y_ture[i]])/len(y_ture)
print("Accuarcy is", Accuarcy)
return z,fig
cf_matrix_test, figure_test = performance([1, 3, 1, 2, 2, 1], [2, 3, 1, 3, 3, 2])
def read_and_split_the_excel(QA_path):
"""
:func: 根据xlsx文件获取问题list和答案list（需要更新openyxl）
:param path: 文件路径
:return: 问题list，答案list
"""
df1 = pd.read_excel(QA_path)
question_list = df1.iloc[:, 0].tolist()
answer_list = df1.iloc[:, 1].tolist()
return (question_list, answer_list)
def read_and_split_the_01(zero_one_path):
"""
:func: 根据xlsx文件获取原始list和测试list和label
:param path: 文件路径
:return: 问题list，答案list
"""
df1 = pd.read_csv(zero_one_path)
Sen1_list = df1.iloc[:, 0].tolist()
Sen2_list = df1.iloc[:, 1].tolist()
label_list = df1.iloc[:, 2].tolist()
return (Sen1_list, Sen2_list, label_list)
def shuffle_without_repeated(list_):
temp_list = deepcopy(list_)
m = len(temp_list)
m = m - 1
for i_current in range(m, 1, -1):
rest = i_current - 1
i_replace = randint(0, rest)
temp_list[i_current], temp_list[i_replace] = (temp_list[i_replace], temp_list[i_current])
return temp_list
def obtain_shuffle_01(ori_list):
shuffle_q_list = shuffle_without_repeated(ori_list)
shuffle_label_list = [0] * len(shuffle_q_list)
return (ori_list, shuffle_q_list, shuffle_label_list)
question_list = ['The cat sits outside', 'A man is playing guitar', 'The new movie is awesome', 'The new opera is nice']
obtain_shuffle_01(question_list)
def read_qa_and_expand_training_set(QA_path, zero_one_path):
question_list, answer_list = read_and_split_the_excel(QA_path)
Sen1_list, Sen2_list, label_list = read_and_split_the_01(zero_one_path)
return (question_list, answer_list, Sen1_list, Sen2_list, label_list)
QA_path = '../input/uic-cn-admission/CN_QA_dataset_all.xlsx'
zero_one_path = '/kaggle/input/01-uic-rm-dup/df_test.csv'
question_list, answer_list, Sen1_list, Sen1_list_index, Sen2_list, label_list = read_qa_and_expand_training_set(QA_path, zero_one_path)
from sentence_transformers import SentenceTransformer, util
import pandas as pd
from copy import deepcopy
from random import randint
from termcolor import colored
def SBERT_get_reply(model, query, question_list, answer_list, question_list_emb, topk_SBERT, threshold_SBERT):
queries = [query]
query_embeddings = model.encode(queries, convert_to_tensor=True)
index_ranked = []
tensor_scores = []
cosine_scores = util.pytorch_cos_sim(query_embeddings, question_list_emb)[0]
results = zip(range(len(cosine_scores)), cosine_scores)
results = sorted(results, key=lambda x: x[1], reverse=True)
for index, tensor_score in results:
index_ranked.append(question_list[index])
tensor_scores.append(tensor_score)
topk_idx_SBERT = index_ranked[:topk_SBERT]
return (topk_idx_SBERT, tensor_scores)
def use_model_qa(model_path, QA_path, zero_one_path):
model = SentenceTransformer(model_path, device='cuda')
topk_SBERT = 3
threshold_SBERT = 0.6
question_list, answer_list = read_and_split_the_excel(QA_path)
question_embeddings = model.encode(question_list, convert_to_tensor=True)
question_list, answer_list, Sen1_list, Sen2_list, label_list = read_qa_and_expand_training_set(QA_path, zero_one_path)
predict_result = []
df = pd.read_csv(zero_one_path)
match = []
score = []
for index, test_query in enumerate(Sen2_list):
topk_idx_SBERT, tensor_scores = SBERT_get_reply(model, test_query, question_list, answer_list, question_embeddings, topk_SBERT, threshold_SBERT)
match.append(topk_idx_SBERT[0])
score.append(tensor_scores[0].cpu().numpy())
if topk_idx_SBERT[0] == Sen1_list[index]:
prediction = 1
else:
prediction = 0
predict_result.append(prediction)
new = pd.DataFrame({'roberta_fine_tune_match': match, 'roberta_fine_tune_score': score, 'roberta_fine_tune_label': predict_result})
merged_df = pd.concat([df, new], axis=1)
merged_df.to_excel('UIC问题匹配结果比较roberta.xlsx', index=None)
cf_matrix_test, figure_test = performance(label_list, predict_result)
return cf_matrix_test
def SBERT_QA_test(model_path):
QA_path = '../input/uic-cn-admission/CN_QA_dataset_all.xlsx'
zero_one_path = '/kaggle/input/01-uic-rm-dup/df_test.csv'
model_path = '/kaggle/input/sbert-fine-tune/roberta_fine_tune'
SBERT_QA_test(model_path) | code |
129012188/cell_6 | [
"text_plain_output_100.png",
"text_plain_output_201.png",
"text_plain_output_261.png",
"text_plain_output_84.png",
"text_plain_output_322.png",
"text_plain_output_205.png",
"text_plain_output_271.png",
"text_plain_output_56.png",
"text_plain_output_158.png",
"text_plain_output_223.png",
"text_plain_output_218.png",
"text_plain_output_264.png",
"text_plain_output_282.png",
"text_plain_output_287.png",
"text_plain_output_232.png",
"text_plain_output_181.png",
"text_plain_output_137.png",
"text_plain_output_139.png",
"text_plain_output_35.png",
"text_plain_output_258.png",
"text_plain_output_130.png",
"text_plain_output_117.png",
"text_plain_output_286.png",
"text_plain_output_262.png",
"text_plain_output_278.png",
"text_plain_output_254.png",
"text_plain_output_307.png",
"text_plain_output_98.png",
"text_plain_output_236.png",
"text_plain_output_195.png",
"text_plain_output_219.png",
"text_plain_output_237.png",
"text_plain_output_43.png",
"text_plain_output_284.png",
"text_plain_output_187.png",
"text_plain_output_309.png",
"text_plain_output_78.png",
"text_plain_output_143.png",
"text_plain_output_106.png",
"text_plain_output_37.png",
"text_plain_output_138.png",
"text_plain_output_192.png",
"text_plain_output_184.png",
"text_plain_output_274.png",
"text_plain_output_172.png",
"text_plain_output_147.png",
"text_plain_output_327.png",
"text_plain_output_256.png",
"text_plain_output_90.png",
"text_plain_output_79.png",
"text_plain_output_5.png",
"text_plain_output_75.png",
"text_plain_output_48.png",
"text_plain_output_116.png",
"text_plain_output_128.png",
"text_plain_output_30.png",
"text_plain_output_167.png",
"text_plain_output_213.png",
"text_plain_output_73.png",
"text_plain_output_126.png",
"text_plain_output_321.png",
"text_plain_output_272.png",
"text_plain_output_115.png",
"text_plain_output_316.png",
"text_plain_output_15.png",
"text_plain_output_133.png",
"text_plain_output_198.png",
"text_plain_output_178.png",
"text_plain_output_226.png",
"text_plain_output_154.png",
"text_plain_output_234.png",
"text_plain_output_114.png",
"text_plain_output_157.png",
"text_plain_output_317.png",
"text_plain_output_251.png",
"text_plain_output_70.png",
"text_plain_output_9.png",
"text_plain_output_44.png",
"text_plain_output_203.png",
"text_plain_output_119.png",
"text_plain_output_86.png",
"text_plain_output_244.png",
"text_plain_output_118.png",
"text_plain_output_131.png",
"text_plain_output_40.png",
"text_plain_output_123.png",
"text_plain_output_74.png",
"text_plain_output_190.png",
"text_plain_output_302.png",
"text_plain_output_31.png",
"text_plain_output_281.png",
"text_plain_output_20.png",
"text_plain_output_273.png",
"text_plain_output_263.png",
"text_plain_output_102.png",
"text_plain_output_229.png",
"text_plain_output_111.png",
"text_plain_output_222.png",
"text_plain_output_101.png",
"text_plain_output_169.png",
"text_plain_output_144.png",
"text_plain_output_161.png",
"text_plain_output_305.png",
"text_plain_output_275.png",
"text_plain_output_301.png",
"text_plain_output_132.png",
"text_plain_output_60.png",
"text_plain_output_221.png",
"text_plain_output_155.png",
"text_plain_output_68.png",
"text_plain_output_4.png",
"text_plain_output_65.png",
"text_plain_output_64.png",
"text_plain_output_215.png",
"text_plain_output_189.png",
"text_plain_output_13.png",
"text_plain_output_200.png",
"text_plain_output_107.png",
"text_plain_output_312.png",
"text_plain_output_248.png",
"text_html_output_2.png",
"text_plain_output_318.png",
"text_plain_output_52.png",
"text_plain_output_66.png",
"text_plain_output_243.png",
"text_plain_output_45.png",
"text_plain_output_300.png",
"text_plain_output_257.png",
"text_plain_output_277.png",
"text_plain_output_171.png",
"text_plain_output_14.png",
"text_plain_output_159.png",
"text_plain_output_32.png",
"text_plain_output_304.png",
"text_plain_output_88.png",
"text_plain_output_240.png",
"text_plain_output_29.png",
"text_plain_output_140.png",
"text_plain_output_280.png",
"text_plain_output_129.png",
"text_plain_output_242.png",
"text_plain_output_289.png",
"text_plain_output_255.png",
"text_plain_output_160.png",
"text_plain_output_58.png",
"text_plain_output_49.png",
"text_plain_output_63.png",
"text_plain_output_260.png",
"text_plain_output_294.png",
"text_plain_output_27.png",
"text_plain_output_320.png",
"text_plain_output_177.png",
"text_plain_output_76.png",
"text_plain_output_108.png",
"text_plain_output_54.png",
"text_plain_output_142.png",
"text_plain_output_10.png",
"text_plain_output_269.png",
"text_plain_output_276.png",
"text_plain_output_6.png",
"text_plain_output_326.png",
"text_plain_output_153.png",
"text_plain_output_170.png",
"text_plain_output_92.png",
"text_plain_output_57.png",
"text_plain_output_120.png",
"text_plain_output_24.png",
"text_plain_output_21.png",
"application_vnd.jupyter.stderr_output_325.png",
"text_plain_output_104.png",
"text_plain_output_270.png",
"text_plain_output_47.png",
"text_plain_output_121.png",
"text_plain_output_25.png",
"text_plain_output_134.png",
"text_plain_output_77.png",
"text_plain_output_288.png",
"text_plain_output_18.png",
"text_plain_output_183.png",
"text_plain_output_266.png",
"text_plain_output_149.png",
"text_plain_output_208.png",
"text_plain_output_50.png",
"text_plain_output_36.png",
"text_plain_output_207.png",
"text_plain_output_96.png",
"text_plain_output_87.png",
"text_plain_output_3.png",
"text_plain_output_217.png",
"text_plain_output_180.png",
"text_plain_output_141.png",
"text_plain_output_210.png",
"text_plain_output_112.png",
"text_plain_output_152.png",
"text_plain_output_225.png",
"text_plain_output_191.png",
"text_plain_output_259.png",
"text_plain_output_290.png",
"text_plain_output_283.png",
"text_plain_output_247.png",
"text_plain_output_113.png",
"text_plain_output_324.png",
"text_plain_output_22.png",
"text_plain_output_188.png",
"text_plain_output_81.png",
"text_plain_output_69.png",
"text_plain_output_175.png",
"text_plain_output_165.png",
"text_plain_output_146.png",
"text_plain_output_145.png",
"text_plain_output_125.png",
"text_plain_output_197.png",
"text_plain_output_315.png",
"text_plain_output_38.png",
"text_plain_output_7.png",
"text_plain_output_214.png",
"text_plain_output_166.png",
"text_plain_output_314.png",
"text_plain_output_91.png",
"text_plain_output_308.png",
"text_plain_output_245.png",
"text_plain_output_16.png",
"text_plain_output_174.png",
"text_plain_output_212.png",
"text_plain_output_230.png",
"text_plain_output_265.png",
"text_html_output_1.png",
"text_plain_output_59.png",
"text_plain_output_206.png",
"text_plain_output_103.png",
"text_plain_output_71.png",
"text_plain_output_8.png",
"text_plain_output_122.png",
"text_plain_output_211.png",
"text_plain_output_182.png",
"text_plain_output_26.png",
"text_plain_output_310.png",
"text_plain_output_220.png",
"text_plain_output_109.png",
"text_plain_output_238.png",
"text_plain_output_41.png",
"text_plain_output_34.png",
"text_plain_output_253.png",
"text_plain_output_291.png",
"text_plain_output_168.png",
"text_plain_output_204.png",
"text_plain_output_241.png",
"text_plain_output_231.png",
"text_plain_output_209.png",
"text_plain_output_185.png",
"text_plain_output_85.png",
"text_plain_output_42.png",
"text_plain_output_110.png",
"text_plain_output_67.png",
"text_plain_output_297.png",
"text_plain_output_53.png",
"text_plain_output_313.png",
"text_plain_output_224.png",
"text_plain_output_193.png",
"text_plain_output_23.png",
"text_plain_output_173.png",
"text_plain_output_235.png",
"text_plain_output_151.png",
"text_plain_output_89.png",
"text_plain_output_299.png",
"text_plain_output_51.png",
"text_plain_output_252.png",
"text_plain_output_296.png",
"text_plain_output_28.png",
"text_plain_output_72.png",
"text_plain_output_99.png",
"text_plain_output_163.png",
"text_plain_output_179.png",
"text_plain_output_162.png",
"text_plain_output_136.png",
"text_plain_output_246.png",
"text_plain_output_2.png",
"text_plain_output_239.png",
"text_plain_output_127.png",
"text_plain_output_311.png",
"text_plain_output_295.png",
"text_plain_output_279.png",
"text_plain_output_196.png",
"text_plain_output_97.png",
"text_plain_output_227.png",
"text_plain_output_1.png",
"text_plain_output_33.png",
"text_plain_output_150.png",
"text_plain_output_39.png",
"text_plain_output_176.png",
"text_plain_output_186.png",
"text_plain_output_233.png",
"text_plain_output_228.png",
"text_plain_output_55.png",
"text_plain_output_293.png",
"text_plain_output_268.png",
"text_plain_output_199.png",
"text_plain_output_319.png",
"text_plain_output_82.png",
"text_plain_output_202.png",
"text_plain_output_93.png",
"text_plain_output_19.png",
"text_plain_output_105.png",
"text_plain_output_80.png",
"text_plain_output_94.png",
"text_plain_output_164.png",
"text_plain_output_249.png",
"text_plain_output_216.png",
"text_plain_output_124.png",
"text_plain_output_17.png",
"text_plain_output_148.png",
"text_plain_output_323.png",
"text_plain_output_250.png",
"text_plain_output_11.png",
"text_plain_output_12.png",
"text_plain_output_267.png",
"text_plain_output_194.png",
"text_plain_output_62.png",
"text_plain_output_303.png",
"text_plain_output_95.png",
"text_plain_output_156.png",
"text_plain_output_298.png",
"text_plain_output_61.png",
"text_plain_output_83.png",
"text_plain_output_292.png",
"text_plain_output_135.png",
"text_plain_output_285.png",
"text_plain_output_306.png",
"text_plain_output_46.png"
] | !pip install -U sentence-transformers
!pip install openpyxl | code |
50237666/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
import os
def mkdir(p):
if not os.path.exists(p):
os.mkdir(p)
def link(src, dst):
if not os.path.exists(dst):
os.symlink(src, dst, target_is_directory=True)
os.mkdir('../input/fruits/fruits-360/smallImages')
classes = ['Banana', 'Strawerry', 'Raspberry']
train_from_path = os.path.abspath('../large_files/fruits-360/Training') | code |
50237666/cell_3 | [
"text_plain_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
import os
def mkdir(p):
if not os.path.exists(p):
os.mkdir(p)
def link(src, dst):
if not os.path.exists(dst):
os.symlink(src, dst, target_is_directory=True)
os.mkdir('../input/fruits/fruits-360/smallImages')
classes = ['Banana', 'Strawerry', 'Raspberry']
train_from_path = os.path.abspath('../large_files/fruits-360/Training')
os.makedir('../input/fruits/fruits-360/smallImages') | code |
50237666/cell_5 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from glob import glob
from keras.applications.vgg16 import VGG16
from keras.layers import Input, Lambda, Dense, Flatten
from keras.models import Model
from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import confusion_matrix
from utils import plot_confusion_matrix
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
IMAGE_SIZE = [100, 100]
epochs = 5
batch_size = 32
train_path = '../input/fruits/fruits-360/Training'
valid_path = '../input/fruits/fruits-360/Test'
image_files = glob(train_path + '/*/*.jp*g')
valid_image_files = glob(valid_path + '/*/*.jp*g')
folders = glob(train_path + '/*')
plt.imshow(image.load_img(np.random.choice(image_files)))
plt.show()
vgg = VGG16(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)
for layer in vgg.layers:
layer.trainable = False
x = Flatten()(vgg.output)
prediction = Dense(len(folders), activation='softmax')(x)
model = Model(inputs=vgg.input, outputs=prediction)
model.summary()
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
gen = ImageDataGenerator(rotation_range=20, width_shift_range=0.1, height_shift_range=0.1, shear_range=0.1, zoom_range=0.2, horizontal_flip=True, vertical_flip=True)
test_gen = gen.flow_from_directory(valid_path, target_size=IMAGE_SIZE)
print(test_gen.class_indices)
labels = [None] * len(test_gen.class_indices)
for k, v in test_gen.class_indices.items():
labels[v] = k
for x, y in test_gen:
print('min:', x[0].min(), 'max:', x[0].max())
plt.title(labels[np.argmax(y[0])])
plt.imshow(x[0])
plt.show()
break
train_generator = gen.flow_from_directory(train_path, target_size=IMAGE_SIZE, shuffle=True, batch_size=batch_size)
valid_generator = gen.flow_from_directory(valid_path, target_size=IMAGE_SIZE, shuffle=True, batch_size=batch_size)
r = model.fit_generator(train_generator, validation_data=valid_generator, epochs=epochs, steps_per_epoch=len(image_files) // batch_size, validation_steps=len(valid_image_files) // batch_size)
def get_confusion_matrix(data_path, N):
print('Generating Confusion Matrix', N)
predictions = []
targets = []
i = 0
for x, y in gen.flow_from_directory(data_path, target_size=IMAGE_SIZE, shuffle=False, batch_size=batch_size * 2):
i += 1
if i % 50 == 0:
print(i)
p = model.predict(x)
p = np.argmax(p, axis=1)
y = np.argmax(y, axis=1)
predictions = np.concatenate((predictions, p))
targets = np.concatenate((targets, y))
if len(targets) >= N:
break
cm = confusion_matrix(targets, predictions)
return cm
cm = get_confusion_matrix(train_path, len(image_files))
print(cm)
valid_cm = get_confusion_matrix(valid_path, len(valid_image_files))
print(valid_cm)
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='val loss')
plt.legend()
plt.show()
plt.plot(r.history['acc'], label='train acc')
plt.plot(r.history['val_acc'], label='val acc')
plt.legend()
plt.show()
from utils import plot_confusion_matrix
plot_confusion_matrix(cm, labels, title='Train Confusion Matrix')
plot_confusion_matrix(valid_cm, labels, title='Validation Confusion Matrix') | code |
128006002/cell_42 | [
"text_html_output_2.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import precision_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import precision_score
import xgboost as xgb
def build_random_forest(x_train, y_train, x_test, y_test, n_estimators):
rndforest = RandomForestClassifier(n_estimators=n_estimators, n_jobs=-1)
rndforest.fit(x_train, y_train)
print('Cantidad de estimadores:', n_estimators)
print('TRAINING ACCURACY:', rndforest.score(x_train, y_train))
mean_score = rndforest.score(x_test, y_test)
print('Accuracy: ', mean_score)
print('-------------------------------------')
precision = precision_score(y_test, rndforest.predict(x_test), average='macro')
print('Precisión del Random Forest: ', precision)
build_random_forest(x_train, y_train.values.ravel(), x_test, y_test.values.ravel(), n_estimators=250) | code |
128006002/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_data = all_data.drop('salary', axis=1)
all_data = all_data.drop('salary_currency', axis=1)
all_data = all_data.drop('employee_residence', axis=1)
all_data.nunique()
for columna in ['work_year', 'experience_level', 'employment_type', 'company_size', 'remote_ratio', 'job_title']:
print(all_data[columna].unique()) | code |
128006002/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_data | code |
128006002/cell_6 | [
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
diferencias = all_data['employee_residence'].compare(all_data['company_location'])
print(diferencias) | code |
128006002/cell_29 | [
"text_html_output_1.png"
] | from matplotlib import pyplot
from matplotlib import pyplot
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
from sklearn.metrics import r2_score
from sklearn.tree import DecisionTreeRegressor
import xgboost as xgb
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
from sklearn.tree import DecisionTreeClassifier
from matplotlib import pyplot
def evaluate_decision_tree(x_train, y_train, x_test, y_test):
train_scores = []
test_scores = []
train_scores_mse, test_scores_mse = ([], [])
values = [i for i in range(1, 20)]
for i in values:
model = DecisionTreeRegressor(max_depth=i)
model.fit(x_train, y_train)
train_predict = model.predict(x_train)
train_r2 = round(r2_score(y_train, train_predict), 3)
train_scores.append(train_r2)
train_mse = round(mean_squared_error(y_train, train_predict), 3)
train_scores_mse.append(train_mse)
test_predict = model.predict(x_test)
test_r2 = round(r2_score(y_test, test_predict), 3)
test_scores.append(test_r2)
test_mse = round(mean_squared_error(y_test, test_predict), 3)
test_scores_mse.append(test_mse)
import xgboost as xgb
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
from matplotlib import pyplot
def evaluate_xgboost(x_train, y_train, x_test, y_test):
train_scores = []
test_scores = []
train_scores_mse, test_scores_mse = ([], [])
values = [i for i in range(1, 5)]
for i in values:
model = xgb.XGBRegressor(max_depth=i, objective='reg:squarederror')
model.fit(x_train, y_train)
train_predict = model.predict(x_train)
train_r2 = round(r2_score(y_train, train_predict), 3)
train_scores.append(train_r2)
train_mse = round(mean_squared_error(y_train, train_predict), 3)
train_scores_mse.append(train_mse)
test_predict = model.predict(x_test)
test_r2 = round(r2_score(y_test, test_predict), 3)
test_scores.append(test_r2)
test_mse = round(mean_squared_error(y_test, test_predict), 3)
test_scores_mse.append(test_mse)
evaluate_xgboost(x_train, y_train, x_test, y_test) | code |
128006002/cell_26 | [
"text_html_output_1.png"
] | from matplotlib import pyplot
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
from sklearn.tree import DecisionTreeClassifier
from matplotlib import pyplot
def evaluate_decision_tree(x_train, y_train, x_test, y_test):
train_scores = []
test_scores = []
train_scores_mse, test_scores_mse = ([], [])
values = [i for i in range(1, 20)]
for i in values:
model = DecisionTreeRegressor(max_depth=i)
model.fit(x_train, y_train)
train_predict = model.predict(x_train)
train_r2 = round(r2_score(y_train, train_predict), 3)
train_scores.append(train_r2)
train_mse = round(mean_squared_error(y_train, train_predict), 3)
train_scores_mse.append(train_mse)
test_predict = model.predict(x_test)
test_r2 = round(r2_score(y_test, test_predict), 3)
test_scores.append(test_r2)
test_mse = round(mean_squared_error(y_test, test_predict), 3)
test_scores_mse.append(test_mse)
evaluate_decision_tree(x_train, y_train, x_test, y_test) | code |
128006002/cell_41 | [
"text_html_output_1.png"
] | print(x_train.shape, x_test.shape, y_train.shape, y_test.shape) | code |
128006002/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 |
128006002/cell_18 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_data = all_data.drop('salary', axis=1)
all_data = all_data.drop('salary_currency', axis=1)
all_data = all_data.drop('employee_residence', axis=1)
all_data.nunique()
all_data = all_data.drop('salary_in_usd', axis=1)
df_reg = pd.get_dummies(all_data, drop_first=True, columns=['experience_level', 'employment_type', 'company_size', 'job_title', 'company_location'])
df_reg | code |
128006002/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_data = all_data.drop('salary', axis=1)
all_data = all_data.drop('salary_currency', axis=1)
all_data = all_data.drop('employee_residence', axis=1)
all_data.nunique()
all_data = all_data.drop('salary_in_usd', axis=1)
df_reg = pd.get_dummies(all_data, drop_first=True, columns=['experience_level', 'employment_type', 'company_size', 'job_title', 'company_location'])
df_reg
aux = df_reg
scaler = MinMaxScaler()
aux = pd.DataFrame(scaler.fit_transform(aux), columns=aux.columns)
df_clas = pd.get_dummies(all_data, drop_first=True, columns=['experience_level', 'employment_type', 'company_size', 'company_location'])
df_clas | code |
128006002/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_data = all_data.drop('salary', axis=1)
all_data = all_data.drop('salary_currency', axis=1)
all_data = all_data.drop('employee_residence', axis=1)
all_data.nunique() | code |
128006002/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_data = all_data.drop('salary', axis=1)
all_data = all_data.drop('salary_currency', axis=1)
all_data = all_data.drop('employee_residence', axis=1)
all_data.nunique()
all_data = all_data.drop('salary_in_usd', axis=1)
job_counts = all_data['job_title'].value_counts()
print(job_counts) | code |
128006002/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_data = all_data.drop('salary', axis=1)
all_data = all_data.drop('salary_currency', axis=1)
all_data = all_data.drop('employee_residence', axis=1)
all_data.nunique()
all_data = all_data.drop('salary_in_usd', axis=1)
sel_jobs = ['Data Engineer', 'Data Scientist', 'Data Analyst', 'Machine Learning Engineer', 'Analytics Engineer']
all_data['job_title'] = all_data['job_title'].apply(lambda x: x if x in sel_jobs else 'Otro')
all_data | code |
128006002/cell_38 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from collections import Counter
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_data = all_data.drop('salary', axis=1)
all_data = all_data.drop('salary_currency', axis=1)
all_data = all_data.drop('employee_residence', axis=1)
all_data.nunique()
all_data = all_data.drop('salary_in_usd', axis=1)
df_reg = pd.get_dummies(all_data, drop_first=True, columns=['experience_level', 'employment_type', 'company_size', 'job_title', 'company_location'])
df_reg
aux = df_reg
scaler = MinMaxScaler()
aux = pd.DataFrame(scaler.fit_transform(aux), columns=aux.columns)
target = 'salary_in_euro'
X = aux.loc[:, aux.columns != target]
y = aux.loc[:, aux.columns == target]
df_clas = pd.get_dummies(all_data, drop_first=True, columns=['experience_level', 'employment_type', 'company_size', 'company_location'])
df_clas
from collections import Counter
import plotly.graph_objects as go
def plot_class_distribution(df, class_col):
categories = sorted(df[class_col].unique(), reverse=False)
hist = Counter(df[class_col])
fig = go.Figure(layout=go.Layout(height=400, width=600))
fig.add_trace(go.Bar(x=categories, y=[hist[cat] for cat in categories]))
target = 'job_title'
X = df_clas.loc[:, df_clas.columns != target]
y = df_clas.loc[:, df_clas.columns == target]
plot_class_distribution(y, 'job_title') | code |
128006002/cell_35 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from collections import Counter
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_data = all_data.drop('salary', axis=1)
all_data = all_data.drop('salary_currency', axis=1)
all_data = all_data.drop('employee_residence', axis=1)
all_data.nunique()
all_data = all_data.drop('salary_in_usd', axis=1)
df_reg = pd.get_dummies(all_data, drop_first=True, columns=['experience_level', 'employment_type', 'company_size', 'job_title', 'company_location'])
df_reg
aux = df_reg
scaler = MinMaxScaler()
aux = pd.DataFrame(scaler.fit_transform(aux), columns=aux.columns)
df_clas = pd.get_dummies(all_data, drop_first=True, columns=['experience_level', 'employment_type', 'company_size', 'company_location'])
df_clas
from collections import Counter
import plotly.graph_objects as go
def plot_class_distribution(df, class_col):
categories = sorted(df[class_col].unique(), reverse=False)
hist = Counter(df[class_col])
fig = go.Figure(layout=go.Layout(height=400, width=600))
fig.add_trace(go.Bar(x=categories, y=[hist[cat] for cat in categories]))
plot_class_distribution(df_clas, 'job_title') | code |
128006002/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_data = all_data.drop('salary', axis=1)
all_data = all_data.drop('salary_currency', axis=1)
all_data = all_data.drop('employee_residence', axis=1)
all_data.nunique()
all_data = all_data.drop('salary_in_usd', axis=1)
all_data | code |
128006002/cell_24 | [
"text_plain_output_1.png"
] | print(x_train.shape, x_test.shape, y_train.shape, y_test.shape) | code |
128006002/cell_22 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_data = all_data.drop('salary', axis=1)
all_data = all_data.drop('salary_currency', axis=1)
all_data = all_data.drop('employee_residence', axis=1)
all_data.nunique()
all_data = all_data.drop('salary_in_usd', axis=1)
df_reg = pd.get_dummies(all_data, drop_first=True, columns=['experience_level', 'employment_type', 'company_size', 'job_title', 'company_location'])
df_reg
aux = df_reg
scaler = MinMaxScaler()
aux = pd.DataFrame(scaler.fit_transform(aux), columns=aux.columns)
target = 'salary_in_euro'
X = aux.loc[:, aux.columns != target]
y = aux.loc[:, aux.columns == target]
y | code |
128006002/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import missingno as msno
import missingno as msno
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_data = all_data.drop('salary', axis=1)
all_data = all_data.drop('salary_currency', axis=1)
all_data = all_data.drop('employee_residence', axis=1)
all_data.nunique()
fig, ax = plt.subplots(figsize=(14, 5))
graph = msno.matrix(all_data, ax=ax, sparkline=False)
ax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha='center')
plt.show() | code |
128006002/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_data = all_data.drop('salary', axis=1)
all_data = all_data.drop('salary_currency', axis=1)
all_data = all_data.drop('employee_residence', axis=1)
all_data.nunique()
tasa_conversion = 0.9
all_data['salary_in_euro'] = (all_data['salary_in_usd'] * tasa_conversion).round(0).astype(int)
all_data | code |
32068608/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_tests = pd.read_csv('../input/covid19-in-turkey/test_numbers.csv')
data = pd.read_csv('../input/covid19-in-turkey/covid_19_data_tr.csv')
data.rename(columns={'Country/Region': 'Ülke', 'Last_Update': 'Tarih', 'Confirmed': 'Vaka_Sayisi', 'Deaths': 'Vefat_Sayisi', 'Recovered': 'Tedavi_Sayisi'}, inplace=True)
data.drop('Province/State', axis=1, inplace=True)
test_sayisi = data_tests.iloc[0, 4:].values
test_sayisi.sort()
data['Test_Sayisi'] = test_sayisi
data.columns
vaka_artis = [0]
vefat_artis = [0]
iyilesen_artis = [0]
for i in range(len(data) - 1):
vaka_artis.append(data['Vaka_Sayisi'][i + 1] - data['Vaka_Sayisi'][i])
vefat_artis.append(data['Vefat_Sayisi'][i + 1] - data['Vefat_Sayisi'][i])
iyilesen_artis.append(data['Tedavi_Sayisi'][i + 1] - data['Tedavi_Sayisi'][i])
data['Vaka_Artış_Sayısı'] = vaka_artis
data['Vefat_Artış_Sayısı'] = vefat_artis
data['Tedavi_Artış_Sayısı'] = iyilesen_artis
data | code |
32068608/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_tests = pd.read_csv('../input/covid19-in-turkey/test_numbers.csv')
data = pd.read_csv('../input/covid19-in-turkey/covid_19_data_tr.csv')
data.rename(columns={'Country/Region': 'Ülke', 'Last_Update': 'Tarih', 'Confirmed': 'Vaka_Sayisi', 'Deaths': 'Vefat_Sayisi', 'Recovered': 'Tedavi_Sayisi'}, inplace=True)
data.drop('Province/State', axis=1, inplace=True)
test_sayisi = data_tests.iloc[0, 4:].values
test_sayisi.sort()
data['Test_Sayisi'] = test_sayisi
data.columns
vaka_artis = [0]
vefat_artis = [0]
iyilesen_artis = [0]
for i in range(len(data) - 1):
vaka_artis.append(data['Vaka_Sayisi'][i + 1] - data['Vaka_Sayisi'][i])
vefat_artis.append(data['Vefat_Sayisi'][i + 1] - data['Vefat_Sayisi'][i])
iyilesen_artis.append(data['Tedavi_Sayisi'][i + 1] - data['Tedavi_Sayisi'][i])
data['Vaka_Artış_Sayısı'] = vaka_artis
data['Vefat_Artış_Sayısı'] = vefat_artis
data['Tedavi_Artış_Sayısı'] = iyilesen_artis
data.info() | code |
32068608/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_tests = pd.read_csv('../input/covid19-in-turkey/test_numbers.csv')
data = pd.read_csv('../input/covid19-in-turkey/covid_19_data_tr.csv')
data.info() | code |
32068608/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_tests = pd.read_csv('../input/covid19-in-turkey/test_numbers.csv')
data = pd.read_csv('../input/covid19-in-turkey/covid_19_data_tr.csv')
data_tests | code |
32068608/cell_2 | [
"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 os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
32068608/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_tests = pd.read_csv('../input/covid19-in-turkey/test_numbers.csv')
data = pd.read_csv('../input/covid19-in-turkey/covid_19_data_tr.csv')
data.rename(columns={'Country/Region': 'Ülke', 'Last_Update': 'Tarih', 'Confirmed': 'Vaka_Sayisi', 'Deaths': 'Vefat_Sayisi', 'Recovered': 'Tedavi_Sayisi'}, inplace=True)
data.drop('Province/State', axis=1, inplace=True)
test_sayisi = data_tests.iloc[0, 4:].values
test_sayisi.sort()
data['Test_Sayisi'] = test_sayisi
data.columns
vaka_artis = [0]
vefat_artis = [0]
iyilesen_artis = [0]
for i in range(len(data) - 1):
vaka_artis.append(data['Vaka_Sayisi'][i + 1] - data['Vaka_Sayisi'][i])
vefat_artis.append(data['Vefat_Sayisi'][i + 1] - data['Vefat_Sayisi'][i])
iyilesen_artis.append(data['Tedavi_Sayisi'][i + 1] - data['Tedavi_Sayisi'][i])
data['Vaka_Artış_Sayısı'] = vaka_artis
data['Vefat_Artış_Sayısı'] = vefat_artis
data['Tedavi_Artış_Sayısı'] = iyilesen_artis
data.tail(1) | code |
32068608/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_tests = pd.read_csv('../input/covid19-in-turkey/test_numbers.csv')
data = pd.read_csv('../input/covid19-in-turkey/covid_19_data_tr.csv')
data.rename(columns={'Country/Region': 'Ülke', 'Last_Update': 'Tarih', 'Confirmed': 'Vaka_Sayisi', 'Deaths': 'Vefat_Sayisi', 'Recovered': 'Tedavi_Sayisi'}, inplace=True)
data.drop('Province/State', axis=1, inplace=True)
test_sayisi = data_tests.iloc[0, 4:].values
test_sayisi.sort()
data['Test_Sayisi'] = test_sayisi
data.columns | code |
32068608/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_tests = pd.read_csv('../input/covid19-in-turkey/test_numbers.csv')
data = pd.read_csv('../input/covid19-in-turkey/covid_19_data_tr.csv')
data.rename(columns={'Country/Region': 'Ülke', 'Last_Update': 'Tarih', 'Confirmed': 'Vaka_Sayisi', 'Deaths': 'Vefat_Sayisi', 'Recovered': 'Tedavi_Sayisi'}, inplace=True)
data.drop('Province/State', axis=1, inplace=True)
test_sayisi = data_tests.iloc[0, 4:].values
test_sayisi.sort()
data['Test_Sayisi'] = test_sayisi
data.columns
vaka_artis = [0]
vefat_artis = [0]
iyilesen_artis = [0]
for i in range(len(data) - 1):
vaka_artis.append(data['Vaka_Sayisi'][i + 1] - data['Vaka_Sayisi'][i])
vefat_artis.append(data['Vefat_Sayisi'][i + 1] - data['Vefat_Sayisi'][i])
iyilesen_artis.append(data['Tedavi_Sayisi'][i + 1] - data['Tedavi_Sayisi'][i])
data['Vaka_Artış_Sayısı'] = vaka_artis
data['Vefat_Artış_Sayısı'] = vefat_artis
data['Tedavi_Artış_Sayısı'] = iyilesen_artis
data.info() | code |
32068608/cell_15 | [
"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_tests = pd.read_csv('../input/covid19-in-turkey/test_numbers.csv')
data = pd.read_csv('../input/covid19-in-turkey/covid_19_data_tr.csv')
data.rename(columns={'Country/Region': 'Ülke', 'Last_Update': 'Tarih', 'Confirmed': 'Vaka_Sayisi', 'Deaths': 'Vefat_Sayisi', 'Recovered': 'Tedavi_Sayisi'}, inplace=True)
data.drop('Province/State', axis=1, inplace=True)
test_sayisi = data_tests.iloc[0, 4:].values
test_sayisi.sort()
data['Test_Sayisi'] = test_sayisi
data.columns
vaka_artis = [0]
vefat_artis = [0]
iyilesen_artis = [0]
for i in range(len(data) - 1):
vaka_artis.append(data['Vaka_Sayisi'][i + 1] - data['Vaka_Sayisi'][i])
vefat_artis.append(data['Vefat_Sayisi'][i + 1] - data['Vefat_Sayisi'][i])
iyilesen_artis.append(data['Tedavi_Sayisi'][i + 1] - data['Tedavi_Sayisi'][i])
data['Vaka_Artış_Sayısı'] = vaka_artis
data['Vefat_Artış_Sayısı'] = vefat_artis
data['Tedavi_Artış_Sayısı'] = iyilesen_artis
date_x = data.Tarih
vaka_l = data.Vaka_Sayisi
vefat_l = data.Vefat_Sayisi
iyileşen_l = data.Tedavi_Sayisi
test_l = data.Test_Sayisi
fgr = plt.figure(figsize=(20, 10), dpi=150, facecolor='w')
ax = fgr.add_subplot(111)
ax.patch.set_facecolor('w')
ax.patch.set_alpha(1)
plt.plot(date_x, vaka_l, color='orange', linewidth=2, alpha=1, label='VAKA SAYISI')
plt.plot(date_x, vefat_l, color='red', linewidth=2, alpha=1, label='VEFAT SAYISI')
plt.plot(date_x, iyileşen_l, color='blue', linewidth=2, alpha=1, label='İYİLEŞEN SAYISI')
plt.plot(date_x, test_l, color='black', linewidth=0.7, alpha=0.5, label='GÜNLÜK YAPILAN TEST SAYISI')
plt.scatter(date_x, vaka_l, color='orange', linewidth=0.5, alpha=1)
plt.scatter(date_x, vefat_l, color='red', linewidth=0.5, alpha=1)
plt.scatter(date_x, iyileşen_l, color='blue', linewidth=0.5, alpha=1)
plt.scatter(date_x, test_l, color='gray', linewidth=0.1, alpha=0.5)
plt.title("TÜRKİYE'DEKİ GÜNCEL SON DURUM")
plt.xticks(rotation='vertical')
plt.xlabel('TARİH')
plt.ylabel('SAYI')
plt.legend(loc=0)
plt.grid(color='black', linestyle='--', linewidth=0.5, alpha=0.5, dash_joinstyle='bevel')
plt.show() | code |
32068608/cell_17 | [
"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)
data_tests = pd.read_csv('../input/covid19-in-turkey/test_numbers.csv')
data = pd.read_csv('../input/covid19-in-turkey/covid_19_data_tr.csv')
data.rename(columns={'Country/Region': 'Ülke', 'Last_Update': 'Tarih', 'Confirmed': 'Vaka_Sayisi', 'Deaths': 'Vefat_Sayisi', 'Recovered': 'Tedavi_Sayisi'}, inplace=True)
data.drop('Province/State', axis=1, inplace=True)
test_sayisi = data_tests.iloc[0, 4:].values
test_sayisi.sort()
data['Test_Sayisi'] = test_sayisi
data.columns
vaka_artis = [0]
vefat_artis = [0]
iyilesen_artis = [0]
for i in range(len(data) - 1):
vaka_artis.append(data['Vaka_Sayisi'][i + 1] - data['Vaka_Sayisi'][i])
vefat_artis.append(data['Vefat_Sayisi'][i + 1] - data['Vefat_Sayisi'][i])
iyilesen_artis.append(data['Tedavi_Sayisi'][i + 1] - data['Tedavi_Sayisi'][i])
data['Vaka_Artış_Sayısı'] = vaka_artis
data['Vefat_Artış_Sayısı'] = vefat_artis
data['Tedavi_Artış_Sayısı'] = iyilesen_artis
date_x = data.Tarih
vaka_l = data.Vaka_Sayisi
vefat_l = data.Vefat_Sayisi
iyileşen_l = data.Tedavi_Sayisi
test_l = data.Test_Sayisi
fgr = plt.figure(figsize=(20, 10), dpi=150, facecolor='w')
ax = fgr.add_subplot(111)
ax.patch.set_facecolor('w')
ax.patch.set_alpha(1)
plt.plot(date_x,vaka_l,color='orange',linewidth = 2, alpha=1 ,label = "VAKA SAYISI");
plt.plot(date_x,vefat_l,color='red',linewidth = 2, alpha=1 ,label = "VEFAT SAYISI");
plt.plot(date_x,iyileşen_l,color='blue',linewidth = 2, alpha=1 ,label = "İYİLEŞEN SAYISI");
plt.plot(date_x,test_l,color='black',linewidth = 0.7, alpha=0.5 ,label = "GÜNLÜK YAPILAN TEST SAYISI");
plt.scatter(date_x,vaka_l,color='orange',linewidth = 0.5, alpha=1);
plt.scatter(date_x,vefat_l,color='red',linewidth = 0.5, alpha=1 );
plt.scatter(date_x,iyileşen_l,color='blue',linewidth = 0.5, alpha=1);
plt.scatter(date_x,test_l,color='gray',linewidth = 0.1, alpha=0.5);
plt.title('TÜRKİYE\'DEKİ GÜNCEL SON DURUM')
plt.xticks(rotation='vertical')
plt.xlabel('TARİH')
plt.ylabel('SAYI')
plt.legend(loc = 0)
plt.grid(color='black', linestyle="--", linewidth=0.5,alpha=0.5 ,dash_joinstyle = "bevel")
plt.show()
date_x = data.Tarih
vaka_l = data.Vaka_Artış_Sayısı
vefat_l = data.Vefat_Artış_Sayısı
iyileşen_l = data.Tedavi_Artış_Sayısı
test_l = data.Test_Sayisi
fig, ((ax1, ax4), (ax2, ax3)) = plt.subplots(2, 2, dpi=150, figsize=(20, 10), sharex='col')
fig.suptitle('GÜNLÜK ARTIŞ MİKTARLARI')
ax1.plot(date_x, vaka_l, '-o', color='orange', linewidth=2, alpha=1)
ax2.plot(date_x, vefat_l, '-o', color='red', linewidth=2, alpha=1)
ax3.plot(date_x, iyileşen_l, '-o', color='blue', linewidth=2, alpha=1)
ax4.plot(date_x, test_l, '-o', color='blue', linewidth=2, alpha=1)
ax1.set_xticklabels(date_x, rotation=90)
ax2.set_xticklabels(date_x, rotation=90)
ax3.set_xticklabels(date_x, rotation=90)
ax4.set_xticklabels(date_x, rotation=90)
ax1.grid(color='black', linestyle='--', linewidth=1, alpha=0.5, dash_joinstyle='bevel')
ax2.grid(color='black', linestyle='--', linewidth=1, alpha=0.5, dash_joinstyle='bevel')
ax3.grid(color='black', linestyle='--', linewidth=1, alpha=0.5, dash_joinstyle='bevel')
ax4.grid(color='black', linestyle='--', linewidth=1, alpha=0.5, dash_joinstyle='bevel')
ax1.set_title('VAKA SAYISI')
ax2.set_title('VEFAT SAYISI')
ax3.set_title('İYİLEŞEN SAYISI')
ax4.set_title('TEST SAYISI')
plt.show() | code |
32068608/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_tests = pd.read_csv('../input/covid19-in-turkey/test_numbers.csv')
data = pd.read_csv('../input/covid19-in-turkey/covid_19_data_tr.csv')
data.rename(columns={'Country/Region': 'Ülke', 'Last_Update': 'Tarih', 'Confirmed': 'Vaka_Sayisi', 'Deaths': 'Vefat_Sayisi', 'Recovered': 'Tedavi_Sayisi'}, inplace=True)
data.drop('Province/State', axis=1, inplace=True)
data.info() | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.