path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
104121949/cell_19 | [
"text_plain_output_1.png"
] | age2 = {'a': 3, 'b': 6, 'c': 9}
age2 | code |
104121949/cell_8 | [
"text_plain_output_1.png"
] | marks = {'Rahul': 23, 'Joe': 15, 'Venkat': {'Section1': 12, 'Section2': 15, 'Section3': 22}}
marks['Venkat'] | code |
104121949/cell_15 | [
"text_plain_output_1.png"
] | age = {}
type(age)
age = {'Rahul': 23, 'Joe': 15, 'Venkat': 32}
a = age.get('Rohit')
print(a) | code |
104121949/cell_17 | [
"text_plain_output_1.png"
] | age = {}
type(age)
age = {'Rahul': 23, 'Joe': 15, 'Venkat': 32}
a = age.get('Rohit')
age['Rohit'] = 18
age | code |
104121949/cell_14 | [
"text_plain_output_1.png"
] | age = {}
type(age)
age = {'Rahul': 23, 'Joe': 15, 'Venkat': 32}
age | code |
104121949/cell_22 | [
"text_plain_output_1.png"
] | age = {}
type(age)
age = {'Rahul': 23, 'Joe': 15, 'Venkat': 32}
a = age.get('Rohit')
age2 = {'a': 3, 'b': 6, 'c': 9}
age.update(age2)
age.pop('c') | code |
104121949/cell_10 | [
"text_plain_output_1.png"
] | marks = {'Rahul': 23, 'Joe': 15, 'Venkat': {'Section1': 12, 'Section2': 15, 'Section3': 22}}
for i in marks:
print(i) | code |
104121949/cell_27 | [
"text_plain_output_1.png"
] | n = int(input('Enter the number'))
d = {}
for i in range(1, 1 + n):
d[i] = i * i
print(d) | code |
104121949/cell_12 | [
"text_plain_output_1.png"
] | marks = {'Rahul': 23, 'Joe': 15, 'Venkat': {'Section1': 12, 'Section2': 15, 'Section3': 22}}
marks.keys()
marks.values() | code |
104121949/cell_5 | [
"text_plain_output_1.png"
] | age = {}
type(age)
age = {'Rahul': 23, 'Joe': 15, 'Venkat': 32}
age['Venkat'] | code |
128010513/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt #visualisation
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
data.shape
data.dtypes
data.drop(['id', 'date', 'lat', 'long'], axis=1, inplace=True)
column_names = ['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'sqft_living15', 'sqft_lot15']
Q1 = data.quantile(0.25)
Q3 = data.quantile(0.75)
IQR = Q3 - Q1
upper = data[~(data > Q3 + 1.5 * IQR)].max()
lower = data[~(data < Q1 - 1.5 * IQR)].min()
df = np.where(data > upper, data.mean(), np.where(data < lower, data.mean(), data))
data = pd.DataFrame(df, columns=data.columns)
plt.figure(figsize=(10, 5))
c = data.corr()
sns.heatmap(c, cmap='BrBG', annot=True)
c | code |
128010513/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
data.shape
data.dtypes
data.drop(['id', 'date', 'lat', 'long'], axis=1, inplace=True)
data.hist(bins=10, figsize=(15, 10), xlabelsize=7, ylabelsize=7) | code |
128010513/cell_6 | [
"text_html_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
data.shape
data.dtypes | code |
128010513/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
data.shape
data.dtypes
data.drop(['id', 'date', 'lat', 'long'], axis=1, inplace=True)
Q1 = data.quantile(0.25)
Q3 = data.quantile(0.75)
IQR = Q3 - Q1
print(IQR)
upper = data[~(data > Q3 + 1.5 * IQR)].max()
lower = data[~(data < Q1 - 1.5 * IQR)].min()
df = np.where(data > upper, data.mean(), np.where(data < lower, data.mean(), data))
data = pd.DataFrame(df, columns=data.columns) | code |
128010513/cell_19 | [
"image_output_11.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_15.png",
"image_output_9.png"
] | from scipy import stats
from sklearn import preprocessing
from sklearn.linear_model import LinearRegression
from sklearn.metrics import accuracy_score, jaccard_score, mean_absolute_error, mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt #visualisation
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
data.shape
data.dtypes
data.drop(['id', 'date', 'lat', 'long'], axis=1, inplace=True)
column_names = ['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'sqft_living15', 'sqft_lot15']
Q1 = data.quantile(0.25)
Q3 = data.quantile(0.75)
IQR = Q3 - Q1
upper = data[~(data > Q3 + 1.5 * IQR)].max()
lower = data[~(data < Q1 - 1.5 * IQR)].min()
df = np.where(data > upper, data.mean(), np.where(data < lower, data.mean(), data))
data = pd.DataFrame(df, columns=data.columns)
pearson_coef_values = []
p_values = []
for name in column_names:
pearson_coef, p_value = stats.pearsonr(data[name], data['price'])
pearson_coef_values.append(pearson_coef)
p_values.append(p_value)
pearson_corelation = pd.DataFrame({'Feature': column_names, 'Pearson Coefficient Values': pearson_coef_values, 'P Values': p_values})
pearson_corelation.sort_values('Pearson Coefficient Values')
c = data.corr()
c
X = data[['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'sqft_living15', 'sqft_lot15']]
X = preprocessing.StandardScaler().fit(X).transform(X)
Y = data['price']
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=1)
from sklearn.linear_model import LinearRegression
linearReg = LinearRegression().fit(x_train, y_train)
predictions = linearReg.predict(x_test)
from sklearn.metrics import accuracy_score, jaccard_score, mean_absolute_error, mean_squared_error, r2_score
LinearRegression_MAE = mean_absolute_error(y_test, predictions)
LinearRegression_MSE = mean_squared_error(y_test, predictions)
LinearRegression_R2 = r2_score(y_test, predictions)
Report = pd.DataFrame({'Mean Absolute Error': LinearRegression_MAE, 'Mean Squared Error': LinearRegression_MSE, 'R Squared': LinearRegression_R2}, index=[0])
Report.head() | code |
128010513/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
data.shape
data.dtypes
data.drop(['id', 'date', 'lat', 'long'], axis=1, inplace=True)
data.describe() | code |
128010513/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import preprocessing
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt #visualisation
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
data.shape
data.dtypes
data.drop(['id', 'date', 'lat', 'long'], axis=1, inplace=True)
column_names = ['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'sqft_living15', 'sqft_lot15']
Q1 = data.quantile(0.25)
Q3 = data.quantile(0.75)
IQR = Q3 - Q1
upper = data[~(data > Q3 + 1.5 * IQR)].max()
lower = data[~(data < Q1 - 1.5 * IQR)].min()
df = np.where(data > upper, data.mean(), np.where(data < lower, data.mean(), data))
data = pd.DataFrame(df, columns=data.columns)
c = data.corr()
c
X = data[['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'sqft_living15', 'sqft_lot15']]
X = preprocessing.StandardScaler().fit(X).transform(X)
Y = data['price']
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=1)
print('number of test samples :', x_test.shape[0])
print('number of training samples:', x_train.shape[0]) | code |
128010513/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
data.head() | code |
128010513/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt #visualisation
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
data.shape
data.dtypes
data.drop(['id', 'date', 'lat', 'long'], axis=1, inplace=True)
column_names = ['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'sqft_living15', 'sqft_lot15']
for column in column_names:
plt.figure(figsize=(17, 3))
sns.boxplot(data=data, x=column) | code |
128010513/cell_12 | [
"text_plain_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt #visualisation
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
data.shape
data.dtypes
data.drop(['id', 'date', 'lat', 'long'], axis=1, inplace=True)
column_names = ['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'sqft_living15', 'sqft_lot15']
Q1 = data.quantile(0.25)
Q3 = data.quantile(0.75)
IQR = Q3 - Q1
upper = data[~(data > Q3 + 1.5 * IQR)].max()
lower = data[~(data < Q1 - 1.5 * IQR)].min()
df = np.where(data > upper, data.mean(), np.where(data < lower, data.mean(), data))
data = pd.DataFrame(df, columns=data.columns)
pearson_coef_values = []
p_values = []
for name in column_names:
pearson_coef, p_value = stats.pearsonr(data[name], data['price'])
pearson_coef_values.append(pearson_coef)
p_values.append(p_value)
pearson_corelation = pd.DataFrame({'Feature': column_names, 'Pearson Coefficient Values': pearson_coef_values, 'P Values': p_values})
pearson_corelation.sort_values('Pearson Coefficient Values')
pearson_corelation.head(30) | code |
128010513/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/housesalesprediction/kc_house_data.csv')
data.shape | code |
88077915/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
train_data = pd.read_csv('/kaggle/input/porto-seguro-safe-driver-prediction/train.csv')
test_data = pd.read_csv('/kaggle/input/porto-seguro-safe-driver-prediction/test.csv')
def check_data(data):
new_dataframe = pd.concat([data.head(5), data.tail(5)], axis=0)
return new_dataframe
check_data(train_data) | code |
88077915/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 |
88077915/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
train_data = pd.read_csv('/kaggle/input/porto-seguro-safe-driver-prediction/train.csv')
test_data = pd.read_csv('/kaggle/input/porto-seguro-safe-driver-prediction/test.csv')
print(train_data.info())
print(test_data.info())
print(train_data.head(10))
print(test_data.head(10))
print(train_data.tail(7))
print(test_data.tail(7)) | code |
88077915/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
train_data = pd.read_csv('/kaggle/input/porto-seguro-safe-driver-prediction/train.csv')
test_data = pd.read_csv('/kaggle/input/porto-seguro-safe-driver-prediction/test.csv')
print(train_data.info())
print(train_data.head(1))
print(train_data.tail(1))
print(test_data.info())
print(test_data.head(1))
print(test_data.tail(1)) | code |
128023079/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from google.colab import files
from google.colab import files
files.upload() | code |
128023079/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import re
import pandas as pd
import librosa
import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.ensemble import AdaBoostClassifier
from sklearn.metrics import accuracy_score, classification_report
from scipy.fft import fft
import seaborn as sns
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as f
import torch.optim as optim
from torch.utils.data import DataLoader, random_split, TensorDataset | code |
128023079/cell_3 | [
"text_plain_output_1.png"
] | ! pip install -q kaggle | code |
17138453/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from torch.utils.data import TensorDataset, DataLoader, Dataset
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import torch
import torch.nn as nn
labels = pd.read_csv('../input/train.csv')
sub = pd.read_csv('../input/sample_submission.csv')
train_path = '../input/train/train/'
test_path = '../input/test/test/'
dtrain, dval = train_test_split(labels, stratify=labels.has_cactus, test_size=0.1)
(dtrain.shape, dval.shape)
class MyDataset(Dataset):
def __init__(self, df_data, data_dir='./', transform=None):
super().__init__()
self.df = df_data.values
self.data_dir = data_dir
self.transform = transform
def __len__(self):
return len(self.df)
def __getitem__(self, index):
img_name, label = self.df[index]
img_path = os.path.join(self.data_dir, img_name)
image = cv2.imread(img_path)
if self.transform is not None:
image = self.transform(image)
return (image, label)
class VGG(nn.Module):
def __init__(self, cfg):
super(VGG, self).__init__()
self.features = self._make_layers(cfg)
self.classifier = nn.Linear(4608, 2)
def forward(self, x):
out = self.features(x)
out = out.view(out.size(0), -1)
out = self.classifier(out)
return out
def _make_layers(self, cfg):
"""
cfg: a list define layers this layer contains
'M': MaxPool, number: Conv2d(out_channels=number) -> BN -> ReLU
"""
layers = []
in_channels = 3
for x in cfg:
if x == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=1), nn.BatchNorm2d(x), nn.ReLU(inplace=True)]
in_channels = x
layers += [nn.AvgPool2d(kernel_size=1, stride=1)]
return nn.Sequential(*layers)
vgg_cfg = {'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], 'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], 'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'], 'VGG19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M']}
def train(model, train_loader, loss_func, optimizer, device):
total_loss = 0
for i, (images, labels) in enumerate(train_loader):
images = images.to(device)
labels = labels.to(device)
outputs = model(images)
loss = loss_func(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.item()
return total_loss / len(train_loader)
def save_model(model, save_path):
torch.save(model.state_dict(), save_path)
def evaluate(model, val_loader, device):
model.eval()
with torch.no_grad():
correct = 0
total = 0
for i, (images, labels) in enumerate(val_loader):
images = images.to(device)
labels = labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, dim=1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
accuracy = correct / total
return accuracy
def show_curve(ys, title):
"""
plot curlve for Loss and Accuacy
Args:
ys: loss or acc list
title: loss or accuracy
"""
x = np.array(range(len(ys)))
y = np.array(ys)
plt.axis()
def fit(model, num_epochs, optimizer, device):
loss_func = nn.CrossEntropyLoss()
model.to(device)
loss_func.to(device)
losses = []
accs = []
for epoch in range(num_epochs):
loss = train(model, loader_train, loss_func, optimizer, device)
losses.append(loss)
accuracy = evaluate(model, loader_valid, device)
accs.append(accuracy)
num_epochs = 30
num_classes = 2
batch_size = 128
learning_rate = 0.001
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model = VGG(vgg_cfg['VGG16'])
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
fit(model, num_epochs, optimizer, device) | code |
17138453/cell_5 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
labels = pd.read_csv('../input/train.csv')
sub = pd.read_csv('../input/sample_submission.csv')
train_path = '../input/train/train/'
test_path = '../input/test/test/'
dtrain, dval = train_test_split(labels, stratify=labels.has_cactus, test_size=0.1)
(dtrain.shape, dval.shape) | code |
88091034/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv', sep=',')
data['Overstrain [minNm]'] = data['Torque [Nm]'] * data['Tool wear [min]']
data['Required Power [W]'] = data['Torque [Nm]'] * data['Rotational speed [rpm]']
data['Heat Dissipation [K]'] = data['Process temperature [K]'] - data['Air temperature [K]']
cols = data.columns.tolist()
cols = cols[-3:] + cols[:-3]
data = data[cols]
data['Type'].value_counts(ascending=True)
corr = data.drop('UDI', axis=1).corr()
cmap = sns.diverging_palette(230, 20, as_cmap=True)
mask = np.triu(np.ones_like(corr, dtype=bool))
plt.figure(figsize=(20, 15))
plt.subplot(3, 3, 1)
sns.boxplot(x='Air temperature [K]', data=data)
plt.subplot(3, 3, 2)
sns.boxplot(x='Process temperature [K]', data=data)
plt.subplot(3, 3, 3)
sns.boxplot(x='Rotational speed [rpm]', data=data)
plt.subplot(3, 3, 4)
sns.boxplot(x='Torque [Nm]', data=data)
plt.subplot(3, 3, 5)
sns.boxplot(x='Tool wear [min]', data=data)
plt.subplot(3, 3, 6)
sns.boxplot(x='Overstrain [minNm]', data=data)
plt.subplot(3, 3, 7)
sns.boxplot(x='Required Power [W]', data=data)
plt.subplot(3, 3, 8)
sns.boxplot(x='Heat Dissipation [K]', data=data) | code |
88091034/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv', sep=',')
data['Overstrain [minNm]'] = data['Torque [Nm]'] * data['Tool wear [min]']
data['Required Power [W]'] = data['Torque [Nm]'] * data['Rotational speed [rpm]']
data['Heat Dissipation [K]'] = data['Process temperature [K]'] - data['Air temperature [K]']
cols = data.columns.tolist()
cols = cols[-3:] + cols[:-3]
data = data[cols]
sns.countplot(x='Type', data=data, hue='Target')
data['Type'].value_counts(ascending=True) | code |
88091034/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv', sep=',')
data['Overstrain [minNm]'] = data['Torque [Nm]'] * data['Tool wear [min]']
data['Required Power [W]'] = data['Torque [Nm]'] * data['Rotational speed [rpm]']
data['Heat Dissipation [K]'] = data['Process temperature [K]'] - data['Air temperature [K]']
cols = data.columns.tolist()
cols = cols[-3:] + cols[:-3]
data = data[cols]
data.head() | code |
88091034/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv', sep=',')
data['Overstrain [minNm]'] = data['Torque [Nm]'] * data['Tool wear [min]']
data['Required Power [W]'] = data['Torque [Nm]'] * data['Rotational speed [rpm]']
data['Heat Dissipation [K]'] = data['Process temperature [K]'] - data['Air temperature [K]']
cols = data.columns.tolist()
cols = cols[-3:] + cols[:-3]
data = data[cols]
data.info() | code |
88091034/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv', sep=',')
data['Overstrain [minNm]'] = data['Torque [Nm]'] * data['Tool wear [min]']
data['Required Power [W]'] = data['Torque [Nm]'] * data['Rotational speed [rpm]']
data['Heat Dissipation [K]'] = data['Process temperature [K]'] - data['Air temperature [K]']
cols = data.columns.tolist()
cols = cols[-3:] + cols[:-3]
data = data[cols]
data['Type'].value_counts(ascending=True)
corr = data.drop('UDI', axis=1).corr()
plt.figure(figsize=(12, 10))
cmap = sns.diverging_palette(230, 20, as_cmap=True)
mask = np.triu(np.ones_like(corr, dtype=bool))
sns.heatmap(corr, annot=True, cmap=cmap, mask=mask, center=0) | code |
88091034/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv', sep=',')
data['Overstrain [minNm]'] = data['Torque [Nm]'] * data['Tool wear [min]']
data['Required Power [W]'] = data['Torque [Nm]'] * data['Rotational speed [rpm]']
data['Heat Dissipation [K]'] = data['Process temperature [K]'] - data['Air temperature [K]']
cols = data.columns.tolist()
cols = cols[-3:] + cols[:-3]
data = data[cols]
data.describe() | code |
88091034/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv', sep=',')
data['Overstrain [minNm]'] = data['Torque [Nm]'] * data['Tool wear [min]']
data['Required Power [W]'] = data['Torque [Nm]'] * data['Rotational speed [rpm]']
data['Heat Dissipation [K]'] = data['Process temperature [K]'] - data['Air temperature [K]']
cols = data.columns.tolist()
cols = cols[-3:] + cols[:-3]
data = data[cols]
print(f"Unique Product IDs: {len(data['Product ID'].value_counts())}")
print('Test/Validation columns with null values: \n', data.isnull().sum()) | code |
88091034/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv', sep=',')
data['Overstrain [minNm]'] = data['Torque [Nm]'] * data['Tool wear [min]']
data['Required Power [W]'] = data['Torque [Nm]'] * data['Rotational speed [rpm]']
data['Heat Dissipation [K]'] = data['Process temperature [K]'] - data['Air temperature [K]']
cols = data.columns.tolist()
cols = cols[-3:] + cols[:-3]
data = data[cols]
data['Type'].value_counts(ascending=True)
corr = data.drop('UDI', axis=1).corr()
cmap = sns.diverging_palette(230, 20, as_cmap=True)
mask = np.triu(np.ones_like(corr, dtype=bool))
clean_data = pd.get_dummies(data, prefix=['Type'], columns=['Type'], drop_first=False)
clean_data = clean_data.drop(['Product ID', 'UDI'], axis=1)
cols = clean_data.columns.tolist()
cols = cols[-3:] + cols[:-3]
clean_data = clean_data[cols]
failures = clean_data.copy()
clean_data = clean_data.drop('Failure Type', axis=1)
failures.head() | code |
88091034/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv', sep=',')
data.head() | code |
88091034/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv', sep=',')
data['Overstrain [minNm]'] = data['Torque [Nm]'] * data['Tool wear [min]']
data['Required Power [W]'] = data['Torque [Nm]'] * data['Rotational speed [rpm]']
data['Heat Dissipation [K]'] = data['Process temperature [K]'] - data['Air temperature [K]']
cols = data.columns.tolist()
cols = cols[-3:] + cols[:-3]
data = data[cols]
data['Type'].value_counts(ascending=True)
plt.figure(figsize=(15, 5))
sns.countplot(x='Failure Type', data=data[data['Failure Type'] != 'No Failure']) | code |
88091034/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv', sep=',')
data['Overstrain [minNm]'] = data['Torque [Nm]'] * data['Tool wear [min]']
data['Required Power [W]'] = data['Torque [Nm]'] * data['Rotational speed [rpm]']
data['Heat Dissipation [K]'] = data['Process temperature [K]'] - data['Air temperature [K]']
cols = data.columns.tolist()
cols = cols[-3:] + cols[:-3]
data = data[cols]
data['Type'].value_counts(ascending=True)
corr = data.drop('UDI', axis=1).corr()
cmap = sns.diverging_palette(230, 20, as_cmap=True)
mask = np.triu(np.ones_like(corr, dtype=bool))
plt.figure(figsize=(15, 5))
sns.pairplot(data.select_dtypes(exclude=object).drop(['UDI'], axis=1), corner=True, hue='Target') | code |
50240953/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
train_data.shape
train_data['Embarked'].unique() | code |
50240953/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
train_data.shape
train_data.isna().sum()
test_data.isna().sum()
train_data.sample(10)
Ticket1 = []
TicketNumber1 = []
TicketNumber2 = []
for i in list(train_data.Ticket):
if not i.isdigit():
Ticket1.append(i.replace('.', '').replace('/', '').strip().split(' ')[0])
else:
Ticket1.append('X')
TicketNumber1.append(0)
train_data['Ticket'] = Ticket1
Ticket2 = []
for j in list(test_data.Ticket):
if not j.isdigit():
Ticket2.append(j.replace('.', '').replace('/', '').strip().split(' ')[0])
else:
Ticket2.append('X')
test_data['Ticket'] = Ticket2
print(train_data['Ticket'].unique())
print(test_data['Ticket'].unique()) | code |
50240953/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
train_data.shape
train_data.isna().sum()
test_data.isna().sum()
train_data.sample(10)
Ticket1 = []
TicketNumber1 = []
TicketNumber2 = []
for i in list(train_data.Ticket):
if not i.isdigit():
Ticket1.append(i.replace('.', '').replace('/', '').strip().split(' ')[0])
else:
Ticket1.append('X')
TicketNumber1.append(0)
train_data['Ticket'] = Ticket1
Ticket2 = []
for j in list(test_data.Ticket):
if not j.isdigit():
Ticket2.append(j.replace('.', '').replace('/', '').strip().split(' ')[0])
else:
Ticket2.append('X')
test_data['Ticket'] = Ticket2
unique1 = []
unique2 = []
for i in train_data['Ticket'].unique():
if i not in test_data['Ticket'].unique():
unique1.append(i)
else:
pass
for j in test_data['Ticket'].unique():
if j not in train_data['Ticket'].unique():
unique2.append(j)
else:
pass
unique1 = ['Ticket_' + s for s in unique1]
unique2 = ['Ticket_' + s for s in unique2]
train_data = pd.get_dummies(train_data, columns=['Ticket'])
test_data = pd.get_dummies(test_data, columns=['Ticket'])
train_data = train_data.drop(labels=unique1, axis='columns')
test_data = test_data.drop(labels=unique2, axis='columns')
train_data.head() | code |
50240953/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
test_data.isna().sum() | code |
50240953/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
train_data.shape
train_data.isna().sum() | code |
50240953/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.head() | code |
50240953/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum() | code |
50240953/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
train_data.shape
train_data | code |
50240953/cell_38 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
train_data.shape
train_data.isna().sum()
test_data.isna().sum()
train_data.sample(10)
Ticket1 = []
TicketNumber1 = []
TicketNumber2 = []
for i in list(train_data.Ticket):
if not i.isdigit():
Ticket1.append(i.replace('.', '').replace('/', '').strip().split(' ')[0])
else:
Ticket1.append('X')
TicketNumber1.append(0)
train_data['Ticket'] = Ticket1
Ticket2 = []
for j in list(test_data.Ticket):
if not j.isdigit():
Ticket2.append(j.replace('.', '').replace('/', '').strip().split(' ')[0])
else:
Ticket2.append('X')
test_data['Ticket'] = Ticket2
unique1 = []
unique2 = []
for i in train_data['Ticket'].unique():
if i not in test_data['Ticket'].unique():
unique1.append(i)
else:
pass
for j in test_data['Ticket'].unique():
if j not in train_data['Ticket'].unique():
unique2.append(j)
else:
pass
unique1 = ['Ticket_' + s for s in unique1]
unique2 = ['Ticket_' + s for s in unique2]
train_data = pd.get_dummies(train_data, columns=['Ticket'])
test_data = pd.get_dummies(test_data, columns=['Ticket'])
train_data = train_data.drop(labels=unique1, axis='columns')
test_data = test_data.drop(labels=unique2, axis='columns')
train_data = train_data.drop(labels='Name', axis='columns')
test_data = test_data.drop(labels='Name', axis='columns')
train_data = train_data.drop(labels='Survived', axis=1)
train_data.columns | code |
50240953/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
test_data.describe() | code |
50240953/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
train_data.shape
train_data.isna().sum()
test_data.isna().sum()
train_data.sample(10)
Ticket1 = []
TicketNumber1 = []
TicketNumber2 = []
for i in list(train_data.Ticket):
if not i.isdigit():
Ticket1.append(i.replace('.', '').replace('/', '').strip().split(' ')[0])
else:
Ticket1.append('X')
TicketNumber1.append(0)
train_data['Ticket'] = Ticket1
Ticket2 = []
for j in list(test_data.Ticket):
if not j.isdigit():
Ticket2.append(j.replace('.', '').replace('/', '').strip().split(' ')[0])
else:
Ticket2.append('X')
test_data['Ticket'] = Ticket2
unique1 = []
unique2 = []
for i in train_data['Ticket'].unique():
if i not in test_data['Ticket'].unique():
unique1.append(i)
else:
pass
for j in test_data['Ticket'].unique():
if j not in train_data['Ticket'].unique():
unique2.append(j)
else:
pass
unique1 = ['Ticket_' + s for s in unique1]
unique2 = ['Ticket_' + s for s in unique2]
train_data = pd.get_dummies(train_data, columns=['Ticket'])
test_data = pd.get_dummies(test_data, columns=['Ticket'])
train_data = train_data.drop(labels=unique1, axis='columns')
test_data = test_data.drop(labels=unique2, axis='columns')
print(train_data.columns, test_data.columns, sep='\n') | code |
50240953/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
train_data.shape
train_data.isna().sum()
test_data.isna().sum()
train_data.sample(10)
Ticket1 = []
TicketNumber1 = []
TicketNumber2 = []
for i in list(train_data.Ticket):
if not i.isdigit():
Ticket1.append(i.replace('.', '').replace('/', '').strip().split(' ')[0])
else:
Ticket1.append('X')
TicketNumber1.append(0)
train_data['Ticket'] = Ticket1
Ticket2 = []
for j in list(test_data.Ticket):
if not j.isdigit():
Ticket2.append(j.replace('.', '').replace('/', '').strip().split(' ')[0])
else:
Ticket2.append('X')
test_data['Ticket'] = Ticket2
train_data | code |
50240953/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
test_data['Embarked'].unique() | code |
50240953/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
train_data.shape
train_data.isna().sum()
train_data.sample(10) | code |
50240953/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
train_data.shape | code |
50240953/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd # data processing
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isna().sum()
train_data = train_data.drop(labels=['Cabin'], axis='columns')
test_data = test_data.drop(labels=['Cabin'], axis='columns')
train_data.shape
train_data.isna().sum()
test_data.isna().sum()
train_data.sample(10)
Ticket1 = []
TicketNumber1 = []
TicketNumber2 = []
for i in list(train_data.Ticket):
if not i.isdigit():
Ticket1.append(i.replace('.', '').replace('/', '').strip().split(' ')[0])
else:
Ticket1.append('X')
TicketNumber1.append(0)
train_data['Ticket'] = Ticket1
Ticket2 = []
for j in list(test_data.Ticket):
if not j.isdigit():
Ticket2.append(j.replace('.', '').replace('/', '').strip().split(' ')[0])
else:
Ticket2.append('X')
test_data['Ticket'] = Ticket2
unique1 = []
unique2 = []
for i in train_data['Ticket'].unique():
if i not in test_data['Ticket'].unique():
unique1.append(i)
else:
pass
for j in test_data['Ticket'].unique():
if j not in train_data['Ticket'].unique():
unique2.append(j)
else:
pass
print('Unique in train data: {} \nUnique in test data: {}'.format(unique1, unique2)) | code |
50240953/cell_5 | [
"text_html_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import neighbors
from sklearn.preprocessing import LabelEncoder
from sklearn.impute import SimpleImputer
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
print('import complete') | code |
128029205/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier, plot_tree
gini_classifier = DecisionTreeClassifier(criterion='gini')
gini_classifier.fit(X_train, y_train)
y_pred = gini_classifier.predict(X_test)
features = list(X_train.columns)
target = list(y_train.unique())
fig, ax = plt.subplots(figsize=(12, 8))
plot_tree(gini_classifier, filled=True, feature_names=features, class_names=target, ax=ax)
plt.show() | code |
128029205/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/drugs-a-b-c-x-y-for-decision-trees/drug200.csv')
data.shape | code |
128029205/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/drugs-a-b-c-x-y-for-decision-trees/drug200.csv')
import pandas as pd
iris = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=None)
iris.columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'class']
class_counts = iris['class'].value_counts()
class_proportions = class_counts / len(iris)
iris.head() | code |
128029205/cell_30 | [
"image_output_1.png"
] | from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.tree import DecisionTreeClassifier, plot_tree
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/drugs-a-b-c-x-y-for-decision-trees/drug200.csv')
gini_classifier = DecisionTreeClassifier(criterion='gini')
gini_classifier.fit(X_train, y_train)
y_pred = gini_classifier.predict(X_test)
features = list(X_train.columns)
target = list(y_train.unique())
fig, ax = plt.subplots(figsize=(12, 8))
plot_tree(gini_classifier, filled=True, feature_names=features, class_names=target, ax=ax)
plt.show()
import pandas as pd
iris = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=None)
iris.columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'class']
class_counts = iris['class'].value_counts()
class_proportions = class_counts / len(iris)
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier, plot_tree
import matplotlib.pyplot as plt
# Load the iris dataset
iris = load_iris()
# Train a decision tree classifier
clf = DecisionTreeClassifier()
clf.fit(iris.data, iris.target)
# Visualize the decision tree using Matplotlib
fig, ax = plt.subplots(figsize=(12, 8))
plot_tree(clf, filled=True, feature_names=iris.feature_names, class_names=iris.target_names, ax=ax)
plt.show()
iris = load_iris()
iris.target | code |
128029205/cell_20 | [
"text_plain_output_1.png"
] | features = list(X_train.columns)
target = list(y_train.unique())
y_train.unique() | code |
128029205/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.tree import DecisionTreeClassifier, plot_tree
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/drugs-a-b-c-x-y-for-decision-trees/drug200.csv')
gini_classifier = DecisionTreeClassifier(criterion='gini')
gini_classifier.fit(X_train, y_train)
y_pred = gini_classifier.predict(X_test)
features = list(X_train.columns)
target = list(y_train.unique())
fig, ax = plt.subplots(figsize=(12, 8))
plot_tree(gini_classifier, filled=True, feature_names=features, class_names=target, ax=ax)
plt.show()
import pandas as pd
iris = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=None)
iris.columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'class']
class_counts = iris['class'].value_counts()
class_proportions = class_counts / len(iris)
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier, plot_tree
import matplotlib.pyplot as plt
iris = load_iris()
clf = DecisionTreeClassifier()
clf.fit(iris.data, iris.target)
fig, ax = plt.subplots(figsize=(12, 8))
plot_tree(clf, filled=True, feature_names=iris.feature_names, class_names=iris.target_names, ax=ax)
plt.show() | code |
128029205/cell_26 | [
"text_html_output_1.png"
] | import math
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/drugs-a-b-c-x-y-for-decision-trees/drug200.csv')
import pandas as pd
iris = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=None)
iris.columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'class']
class_counts = iris['class'].value_counts()
class_proportions = class_counts / len(iris)
import math
entropy = 0
for proportion in class_proportions:
entropy -= proportion * math.log2(proportion)
print('Entropy:', entropy) | code |
128029205/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/drugs-a-b-c-x-y-for-decision-trees/drug200.csv')
data.shape
X = data.drop(['Drug'], axis=1)
y = data['Drug']
data.describe() | code |
128029205/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix
from sklearn.tree import DecisionTreeClassifier, plot_tree
gini_classifier = DecisionTreeClassifier(criterion='gini')
gini_classifier.fit(X_train, y_train)
y_pred = gini_classifier.predict(X_test)
confusion_matrix(y_test, y_pred) | code |
128029205/cell_28 | [
"text_plain_output_1.png"
] | import math
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/drugs-a-b-c-x-y-for-decision-trees/drug200.csv')
import pandas as pd
iris = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=None)
iris.columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'class']
class_counts = iris['class'].value_counts()
class_proportions = class_counts / len(iris)
import math
entropy = 0
for proportion in class_proportions:
entropy -= proportion * math.log2(proportion)
proportions = [0.25, 0.25, 0.1667, 0.1667, 0.1667]
e = 0
for proportion in proportions:
e -= proportion + math.log2(proportion)
e | code |
128029205/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier, plot_tree
gini_classifier = DecisionTreeClassifier(criterion='gini')
gini_classifier.fit(X_train, y_train)
y_pred = gini_classifier.predict(X_test)
print(f'Predicted Values : {y_pred}') | code |
128029205/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/drugs-a-b-c-x-y-for-decision-trees/drug200.csv')
data.head() | code |
128029205/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier, plot_tree
gini_classifier = DecisionTreeClassifier(criterion='gini')
gini_classifier.fit(X_train, y_train)
y_pred = gini_classifier.predict(X_test)
accuracy = accuracy_score(y_test, y_pred) * 100
print(f'accuracy : {accuracy}') | code |
128029205/cell_24 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/drugs-a-b-c-x-y-for-decision-trees/drug200.csv')
import pandas as pd
iris = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=None)
iris.columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'class']
class_counts = iris['class'].value_counts()
class_proportions = class_counts / len(iris)
print(class_counts)
print()
print(class_proportions) | code |
128029205/cell_14 | [
"text_html_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier, plot_tree
gini_classifier = DecisionTreeClassifier(criterion='gini')
gini_classifier.fit(X_train, y_train) | code |
128029205/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/drugs-a-b-c-x-y-for-decision-trees/drug200.csv')
data.shape
X = data.drop(['Drug'], axis=1)
y = data['Drug']
len(data['Drug'].unique()) | code |
17096225/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
X = X.reshape(X.shape[0], 28, 28)
X = X / 255.0
import matplotlib.pyplot as plt
fig = plt.gcf()
fig.set_size_inches(9, 9)
for i, img in enumerate(X):
if i + 1 > 3 * 3:
break
plt.subplot(3, 3, i + 1)
plt.imshow(img)
plt.show() | code |
17096225/cell_4 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
train = df.drop(['label'], axis=1)
train.head() | code |
17096225/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
train = df.drop(['label'], axis=1)
labels = df['label']
labels.head() | code |
17096225/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df.head() | code |
17096225/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17096225/cell_18 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_test.head() | code |
17096225/cell_15 | [
"text_html_output_1.png"
] | import tensorflow as tf
model = tf.keras.Sequential([tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(64, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10, activation='softmax')])
model.compile(metrics=['acc'], optimizer='adam', loss='sparse_categorical_crossentropy')
model.summary()
history = model.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=100) | code |
17096225/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import tensorflow as tf
X = X.reshape(X.shape[0], 28, 28)
X = X / 255.0
import matplotlib.pyplot as plt
fig = plt.gcf()
fig.set_size_inches(9, 9)
for i, img in enumerate(X):
if i + 1 > 3 * 3:
break
model = tf.keras.Sequential([tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(64, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10, activation='softmax')])
model.compile(metrics=['acc'], optimizer='adam', loss='sparse_categorical_crossentropy')
model.summary()
history = model.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=100)
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc)
plt.plot(epochs, val_acc)
plt.title('Training and validation accuracy')
plt.figure()
plt.plot(epochs, loss)
plt.plot(epochs, val_loss)
plt.title('Training and validation loss') | code |
17096225/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
df = pd.read_csv('../input/train.csv')
model = tf.keras.Sequential([tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(64, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10, activation='softmax')])
model.compile(metrics=['acc'], optimizer='adam', loss='sparse_categorical_crossentropy')
model.summary()
history = model.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=100)
df_test = pd.read_csv('../input/test.csv')
X_testing = df_test.values
X_testing = X_testing.reshape(X_testing.shape[0], 28, 28)
X_testing = X_testing / 255.0
pred = model.predict(X_testing)
pred = pred.argmax(axis=1)
pred[0:19] | code |
17096225/cell_12 | [
"text_html_output_1.png"
] | import tensorflow as tf
model = tf.keras.Sequential([tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(64, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10, activation='softmax')])
model.compile(metrics=['acc'], optimizer='adam', loss='sparse_categorical_crossentropy')
model.summary() | code |
130020397/cell_4 | [
"text_plain_output_1.png"
] | import requests
url = 'https://www.trendyol.com/cep-telefonu-x-c103498?pi=6?'
header = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36 OPR/98.0.0.0'}
page = requests.get(url, headers=header)
print(page) | code |
130020397/cell_5 | [
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import requests
url = 'https://www.trendyol.com/cep-telefonu-x-c103498?pi=6?'
header = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36 OPR/98.0.0.0'}
page = requests.get(url, headers=header)
soup = BeautifulSoup(page.content, 'html.parser')
product_cards = soup.find_all('div', class_='p-card-wrppr')
dataset = []
for card in product_cards:
product_down_div = card.find('div', class_='product-down')
product_name_div = product_down_div.find('div', class_='prdct-desc-cntnr-ttl-w two-line-text')
product_name_span_1 = product_name_div.find('span', class_='prdct-desc-cntnr-ttl')
product_name_span_2 = product_name_div.find('span', class_='prdct-desc-cntnr-name hasRatings')
product_name_1 = product_name_span_1.text.strip() if product_name_span_1 else None
product_name_2 = product_name_span_2.text.strip() if product_name_span_2 else None
star_ratings_container = product_down_div.find('div', class_='ratings')
filled_star_count = None
if star_ratings_container:
star_w_divs = star_ratings_container.find_all('div', class_='star-w')
for star_w_div in star_w_divs:
star_div = star_w_div.find('div', class_='full')
if star_div and star_div.get('style'):
width_match = re.search('width:\\s*([\\d.]+)%', star_div['style'])
if width_match:
width = float(width_match.group(1))
if width > 0:
filled_star_count = round(width / 20)
rating_count_span = product_down_div.find('span', class_='ratingCount')
rating_count = int(rating_count_span.text.strip('()')) if rating_count_span else None
price_promotion_container = product_down_div.find('div', class_='prc-box-dscntd')
original_price_div = price_promotion_container.text.strip()
product_data = {'Brand': product_name_1, 'Product': product_name_2, 'Filled Star Percentages': filled_star_count, 'Original Price': original_price_div, 'Rating Count': rating_count}
dataset.append(product_data)
df = pd.DataFrame(dataset)
print(df)
df.to_csv('trendyol_data.csv') | code |
73072707/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain
total = dtrain.isnull().sum().sort_values(ascending=False)
percent = (dtrain.isnull().sum() / dtrain.isnull().count()).sort_values(ascending=False)
missing_values = pd.concat([total, percent], axis=1, keys=['total', 'percent'])
dtrain = dtrain.drop(missing_values[missing_values['percent'] > 0.8].index, 1)
dtest = dtest.drop(missing_values[missing_values['percent'] > 0.8].index, 1)
dtrain.isnull().sum().sort_values(ascending=False).head(13)
random_sample = dtrain['GarageCond'].dropna().sample(dtrain['GarageCond'].isnull().sum(), random_state=0)
random_sample.isnull().sum() | code |
73072707/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain
total = dtrain.isnull().sum().sort_values(ascending=False)
percent = (dtrain.isnull().sum() / dtrain.isnull().count()).sort_values(ascending=False)
missing_values = pd.concat([total, percent], axis=1, keys=['total', 'percent'])
dtrain = dtrain.drop(missing_values[missing_values['percent'] > 0.8].index, 1)
dtest = dtest.drop(missing_values[missing_values['percent'] > 0.8].index, 1)
dtrain['MasVnrArea'] | code |
73072707/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain
total = dtrain.isnull().sum().sort_values(ascending=False)
percent = (dtrain.isnull().sum() / dtrain.isnull().count()).sort_values(ascending=False)
missing_values = pd.concat([total, percent], axis=1, keys=['total', 'percent'])
missing_values[missing_values['percent'] > 0.8] | code |
73072707/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain
total = dtrain.isnull().sum().sort_values(ascending=False)
percent = (dtrain.isnull().sum() / dtrain.isnull().count()).sort_values(ascending=False)
missing_values = pd.concat([total, percent], axis=1, keys=['total', 'percent'])
dtrain = dtrain.drop(missing_values[missing_values['percent'] > 0.8].index, 1)
dtest = dtest.drop(missing_values[missing_values['percent'] > 0.8].index, 1)
dtrain['LotFrontage'] | code |
73072707/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain | code |
73072707/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain
total = dtrain.isnull().sum().sort_values(ascending=False)
percent = (dtrain.isnull().sum() / dtrain.isnull().count()).sort_values(ascending=False)
missing_values = pd.concat([total, percent], axis=1, keys=['total', 'percent'])
dtrain = dtrain.drop(missing_values[missing_values['percent'] > 0.8].index, 1)
dtest = dtest.drop(missing_values[missing_values['percent'] > 0.8].index, 1)
dtrain.isnull().sum().sort_values(ascending=False).head(13) | code |
73072707/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 |
73072707/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain
total = dtrain.isnull().sum().sort_values(ascending=False)
percent = (dtrain.isnull().sum() / dtrain.isnull().count()).sort_values(ascending=False)
missing_values = pd.concat([total, percent], axis=1, keys=['total', 'percent'])
dtrain = dtrain.drop(missing_values[missing_values['percent'] > 0.8].index, 1)
dtest = dtest.drop(missing_values[missing_values['percent'] > 0.8].index, 1)
median = dtrain['LotFrontage'].median()
dtrain['LotFrontage'] = dtrain['LotFrontage'].fillna(median)
dtrain['LotFrontage'].isnull().sum() | code |
73072707/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain
total = dtrain.isnull().sum().sort_values(ascending=False)
percent = (dtrain.isnull().sum() / dtrain.isnull().count()).sort_values(ascending=False)
missing_values = pd.concat([total, percent], axis=1, keys=['total', 'percent'])
dtrain = dtrain.drop(missing_values[missing_values['percent'] > 0.8].index, 1)
dtest = dtest.drop(missing_values[missing_values['percent'] > 0.8].index, 1)
dtrain['GarageYrBlt'] = np.where(dtrain['GarageYrBlt'], 1, 0)
dtrain['GarageYrBlt'].isnull().sum() | code |
73072707/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain
total = dtrain.isnull().sum().sort_values(ascending=False)
percent = (dtrain.isnull().sum() / dtrain.isnull().count()).sort_values(ascending=False)
missing_values = pd.concat([total, percent], axis=1, keys=['total', 'percent'])
dtrain = dtrain.drop(missing_values[missing_values['percent'] > 0.8].index, 1)
dtest = dtest.drop(missing_values[missing_values['percent'] > 0.8].index, 1)
dtrain.isnull().sum().sort_values(ascending=False).head(13)
dtrain = dtrain.fillna(dtrain.mode())
dtrain.isnull().sum()
dtrain = pd.concat((dtrain.select_dtypes(include=object), dtrain.select_dtypes(exclude=object)), axis=1)
dtrain.dtypes.head(50) | code |
73072707/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain
total = dtrain.isnull().sum().sort_values(ascending=False)
percent = (dtrain.isnull().sum() / dtrain.isnull().count()).sort_values(ascending=False)
missing_values = pd.concat([total, percent], axis=1, keys=['total', 'percent'])
dtrain = dtrain.drop(missing_values[missing_values['percent'] > 0.8].index, 1)
dtest = dtest.drop(missing_values[missing_values['percent'] > 0.8].index, 1)
dtrain.isnull().sum().sort_values(ascending=False).head(13)
dtrain = dtrain.fillna(dtrain.mode())
dtrain.isnull().sum()
dtrain = pd.concat((dtrain.select_dtypes(include=object), dtrain.select_dtypes(exclude=object)), axis=1)
dtrain.columns | code |
73072707/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain
total = dtrain.isnull().sum().sort_values(ascending=False)
percent = (dtrain.isnull().sum() / dtrain.isnull().count()).sort_values(ascending=False)
missing_values = pd.concat([total, percent], axis=1, keys=['total', 'percent'])
missing_values.head(30) | code |
73072707/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain
total = dtrain.isnull().sum().sort_values(ascending=False)
percent = (dtrain.isnull().sum() / dtrain.isnull().count()).sort_values(ascending=False)
missing_values = pd.concat([total, percent], axis=1, keys=['total', 'percent'])
dtrain = dtrain.drop(missing_values[missing_values['percent'] > 0.8].index, 1)
dtest = dtest.drop(missing_values[missing_values['percent'] > 0.8].index, 1)
dtrain.isnull().sum().sort_values(ascending=False).head(13)
dtrain = dtrain.fillna(dtrain.mode())
dtrain.isnull().sum() | code |
73072707/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dtrain = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dtest = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dtrain
total = dtrain.isnull().sum().sort_values(ascending=False)
percent = (dtrain.isnull().sum() / dtrain.isnull().count()).sort_values(ascending=False)
missing_values = pd.concat([total, percent], axis=1, keys=['total', 'percent'])
dtrain = dtrain.drop(missing_values[missing_values['percent'] > 0.8].index, 1)
dtest = dtest.drop(missing_values[missing_values['percent'] > 0.8].index, 1)
extreme = dtrain['MasVnrArea'].mean() + 3 * dtrain['MasVnrArea'].std()
dtrain['MasVnrArea'] = dtrain['MasVnrArea'].fillna(extreme)
dtrain['MasVnrArea'].isnull().mean() | code |
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