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90107080/cell_31 | [
"text_plain_output_1.png"
] | from joblib import dump, load
from sklearn.linear_model import LinearRegression , Ridge , LogisticRegression
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv', index_col=None)
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv', index_col=None)
train = train.drop('Unnamed: 0', axis=1)
train = train.fillna(train.mean())
train.columns
X = train.drop(['satisfaction'], axis=1)
y = train['satisfaction']
from sklearn.linear_model import LogisticRegression
clf_lr = LogisticRegression(solver='liblinear')
clf_lr.fit(X, y)
dump(clf_lr, 'filename.joblib') | code |
90107080/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression , Ridge , LogisticRegression
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv', index_col=None)
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv', index_col=None)
train = train.drop('Unnamed: 0', axis=1)
train = train.fillna(train.mean())
train.columns
X = train.drop(['satisfaction'], axis=1)
y = train['satisfaction']
from sklearn.linear_model import LogisticRegression
clf_lr = LogisticRegression(solver='liblinear')
clf_lr.fit(X, y) | code |
90107080/cell_27 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import statsmodels.api as sm
from sklearn.linear_model import LinearRegression, Ridge, LogisticRegression
from sklearn.preprocessing import LabelEncoder, PolynomialFeatures
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score, mean_squared_error
from scipy import stats
from sklearn.pipeline import make_pipeline
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
from sklearn.ensemble import RandomForestClassifier
from imblearn.over_sampling import SMOTE
from graphviz import Source
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import confusion_matrix, classification_report
from mlxtend.plotting import plot_confusion_matrix
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
import warnings
warnings.filterwarnings('ignore')
sns.set(style='darkgrid')
plt.style.use('fivethirtyeight')
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv', index_col=None)
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv', index_col=None)
def dataset_overview(data, col):
pass
test = test.drop('Unnamed: 0', axis=1)
test = test.fillna(test.mean())
def correlation_matrix(data):
corr = data.corr().round(2)
# Mask for the upper triangle
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
# Set figure size
f, ax = plt.subplots(figsize=(20, 20))
# Define custom colormap
cmap = sns.diverging_palette(220, 10, as_cmap=True)
# Draw the heatmap
d=sns.heatmap(corr, mask=mask, cmap=cmap, vmin=-1, vmax=1, center=0,
square=True, linewidths=.5, cbar_kws={"shrink": .5}, annot=True)
plt.tight_layout()
return d
def label_encoding(data, col):
label_encoder = preprocessing.LabelEncoder()
data[col] = label_encoder.fit_transform(data[col])
return
label_encoding(test, 'Gender')
label_encoding(test, 'Customer Type')
label_encoding(test, 'Type of Travel')
label_encoding(test, 'satisfaction')
label_encoding(test, 'Class') | code |
90107080/cell_12 | [
"text_plain_output_1.png",
"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)
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv', index_col=None)
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv', index_col=None)
train = train.drop('Unnamed: 0', axis=1)
train = train.fillna(train.mean())
for _ in train.columns:
print('The number of null values in:{} == {}'.format(_, train[_].isnull().sum())) | code |
18152915/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('../input/structures.csv')
def map_atom_info(df, atom_idx):
df = pd.merge(df, structures, how = 'left',
left_on = ['molecule_name', f'atom_index_{atom_idx}'],
right_on = ['molecule_name', 'atom_index'])
df = df.drop('atom_index', axis=1)
df = df.rename(columns={'atom': f'atom_{atom_idx}',
'x': f'x_{atom_idx}',
'y': f'y_{atom_idx}',
'z': f'z_{atom_idx}'})
return df
train = map_atom_info(train, 0)
train = map_atom_info(train, 1)
test = map_atom_info(test, 0)
test = map_atom_info(test, 1)
train.describe() | code |
18152915/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('../input/structures.csv')
test.head() | code |
18152915/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('../input/structures.csv')
def map_atom_info(df, atom_idx):
df = pd.merge(df, structures, how = 'left',
left_on = ['molecule_name', f'atom_index_{atom_idx}'],
right_on = ['molecule_name', 'atom_index'])
df = df.drop('atom_index', axis=1)
df = df.rename(columns={'atom': f'atom_{atom_idx}',
'x': f'x_{atom_idx}',
'y': f'y_{atom_idx}',
'z': f'z_{atom_idx}'})
return df
train = map_atom_info(train, 0)
train = map_atom_info(train, 1)
test = map_atom_info(test, 0)
test = map_atom_info(test, 1)
train.head() | code |
18152915/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('../input/structures.csv')
def map_atom_info(df, atom_idx):
df = pd.merge(df, structures, how = 'left',
left_on = ['molecule_name', f'atom_index_{atom_idx}'],
right_on = ['molecule_name', 'atom_index'])
df = df.drop('atom_index', axis=1)
df = df.rename(columns={'atom': f'atom_{atom_idx}',
'x': f'x_{atom_idx}',
'y': f'y_{atom_idx}',
'z': f'z_{atom_idx}'})
return df
train = map_atom_info(train, 0)
train = map_atom_info(train, 1)
test = map_atom_info(test, 0)
test = map_atom_info(test, 1)
train.isnull().sum()
train['molecule_name_unique'] = train['molecule_name'].map(train.groupby(train['molecule_name'])['molecule_name'].nunique())
test['molecule_name_unique'] = test['molecule_name'].map(test.groupby(test['molecule_name'])['molecule_name'].nunique())
train['molecule_name_type'] = train['molecule_name'].map(train.groupby(train['molecule_name'])['type'].nunique())
test['molecule_name_type'] = test['molecule_name'].map(test.groupby(test['molecule_name'])['type'].nunique())
train['molecule_dist_mean'] = train['molecule_name'].map(train.groupby(train['molecule_name'])['dist'].mean())
test['molecule_dist_mean'] = test['molecule_name'].map(test.groupby(test['molecule_name'])['dist'].mean())
train['molecule_dist_sum'] = train['molecule_name'].map(train.groupby(train['molecule_name'])['dist'].sum())
test['molecule_dist_sum'] = test['molecule_name'].map(test.groupby(test['molecule_name'])['dist'].sum())
train['molecule_dist_min'] = train['molecule_name'].map(train.groupby(train['molecule_name'])['dist'].min())
test['molecule_dist_min'] = test['molecule_name'].map(test.groupby(test['molecule_name'])['dist'].min())
train['molecule_atom_count'] = train['molecule_name'].map(train.groupby(train['molecule_name'])['atom_1'].count())
test['molecule_atom_count'] = test['molecule_name'].map(test.groupby(test['molecule_name'])['atom_1'].count())
train['molecule_atom_u'] = train['molecule_name'].map(train.groupby(train['molecule_name'])['atom_1'].nunique())
test['molecule_atom_u'] = test['molecule_name'].map(test.groupby(test['molecule_name'])['atom_1'].nunique())
train['type_unique'] = train['type'].map(train.groupby(train['type'])['type'].nunique())
test['type_unique'] = test['type'].map(test.groupby(test['type'])['type'].nunique())
train['type_dist_mean'] = train['type'].map(train.groupby(train['type'])['dist'].mean())
test['type_dist_mean'] = test['type'].map(test.groupby(test['type'])['dist'].mean())
train['type_dist_sum'] = train['type'].map(train.groupby(train['type'])['dist'].sum())
test['type_dist_sum'] = test['type'].map(test.groupby(test['type'])['dist'].sum())
train['type_dist_min'] = train['type'].map(train.groupby(train['type'])['dist'].min())
test['type_dist_min'] = test['type'].map(test.groupby(test['type'])['dist'].min())
train['type_atom_count'] = train['type'].map(train.groupby(train['type'])['atom_1'].count())
test['type_atom_count'] = test['type'].map(test.groupby(test['type'])['atom_1'].count())
train['type_atom_u'] = train['type'].map(train.groupby(train['type'])['atom_1'].nunique())
test['type_atom_u'] = test['type'].map(test.groupby(test['type'])['atom_1'].nunique())
train.head() | code |
18152915/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18152915/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)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('../input/structures.csv')
print(f'There are {train.shape[0]} rows in train data.')
print(f'There are {test.shape[0]} rows in test data.')
print(f"There are {train['molecule_name'].nunique()} distinct molecules in train data.")
print(f"There are {test['molecule_name'].nunique()} distinct molecules in test data.")
print(f"There are {train['atom_index_0'].nunique()} unique atoms.")
print(f"There are {train['type'].nunique()} unique types.") | code |
18152915/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('../input/structures.csv')
len(structures) | code |
18152915/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('../input/structures.csv')
def map_atom_info(df, atom_idx):
df = pd.merge(df, structures, how = 'left',
left_on = ['molecule_name', f'atom_index_{atom_idx}'],
right_on = ['molecule_name', 'atom_index'])
df = df.drop('atom_index', axis=1)
df = df.rename(columns={'atom': f'atom_{atom_idx}',
'x': f'x_{atom_idx}',
'y': f'y_{atom_idx}',
'z': f'z_{atom_idx}'})
return df
train = map_atom_info(train, 0)
train = map_atom_info(train, 1)
test = map_atom_info(test, 0)
test = map_atom_info(test, 1)
train.isnull().sum() | code |
18152915/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('../input/structures.csv')
def map_atom_info(df, atom_idx):
df = pd.merge(df, structures, how = 'left',
left_on = ['molecule_name', f'atom_index_{atom_idx}'],
right_on = ['molecule_name', 'atom_index'])
df = df.drop('atom_index', axis=1)
df = df.rename(columns={'atom': f'atom_{atom_idx}',
'x': f'x_{atom_idx}',
'y': f'y_{atom_idx}',
'z': f'z_{atom_idx}'})
return df
train = map_atom_info(train, 0)
train = map_atom_info(train, 1)
test = map_atom_info(test, 0)
test = map_atom_info(test, 1)
train.isnull().sum()
train['molecule_name_unique'] = train['molecule_name'].map(train.groupby(train['molecule_name'])['molecule_name'].nunique())
test['molecule_name_unique'] = test['molecule_name'].map(test.groupby(test['molecule_name'])['molecule_name'].nunique())
train['molecule_name_type'] = train['molecule_name'].map(train.groupby(train['molecule_name'])['type'].nunique())
test['molecule_name_type'] = test['molecule_name'].map(test.groupby(test['molecule_name'])['type'].nunique())
train['molecule_dist_mean'] = train['molecule_name'].map(train.groupby(train['molecule_name'])['dist'].mean())
test['molecule_dist_mean'] = test['molecule_name'].map(test.groupby(test['molecule_name'])['dist'].mean())
train['molecule_dist_sum'] = train['molecule_name'].map(train.groupby(train['molecule_name'])['dist'].sum())
test['molecule_dist_sum'] = test['molecule_name'].map(test.groupby(test['molecule_name'])['dist'].sum())
train['molecule_dist_min'] = train['molecule_name'].map(train.groupby(train['molecule_name'])['dist'].min())
test['molecule_dist_min'] = test['molecule_name'].map(test.groupby(test['molecule_name'])['dist'].min())
train['molecule_atom_count'] = train['molecule_name'].map(train.groupby(train['molecule_name'])['atom_1'].count())
test['molecule_atom_count'] = test['molecule_name'].map(test.groupby(test['molecule_name'])['atom_1'].count())
train['molecule_atom_u'] = train['molecule_name'].map(train.groupby(train['molecule_name'])['atom_1'].nunique())
test['molecule_atom_u'] = test['molecule_name'].map(test.groupby(test['molecule_name'])['atom_1'].nunique())
train['type_unique'] = train['type'].map(train.groupby(train['type'])['type'].nunique())
test['type_unique'] = test['type'].map(test.groupby(test['type'])['type'].nunique())
train['type_dist_mean'] = train['type'].map(train.groupby(train['type'])['dist'].mean())
test['type_dist_mean'] = test['type'].map(test.groupby(test['type'])['dist'].mean())
train['type_dist_sum'] = train['type'].map(train.groupby(train['type'])['dist'].sum())
test['type_dist_sum'] = test['type'].map(test.groupby(test['type'])['dist'].sum())
train['type_dist_min'] = train['type'].map(train.groupby(train['type'])['dist'].min())
test['type_dist_min'] = test['type'].map(test.groupby(test['type'])['dist'].min())
train['type_atom_count'] = train['type'].map(train.groupby(train['type'])['atom_1'].count())
test['type_atom_count'] = test['type'].map(test.groupby(test['type'])['atom_1'].count())
train['type_atom_u'] = train['type'].map(train.groupby(train['type'])['atom_1'].nunique())
test['type_atom_u'] = test['type'].map(test.groupby(test['type'])['atom_1'].nunique())
object_data = train.dtypes[train.dtypes == 'object'].index
object_data | code |
18152915/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('../input/structures.csv')
def map_atom_info(df, atom_idx):
df = pd.merge(df, structures, how = 'left',
left_on = ['molecule_name', f'atom_index_{atom_idx}'],
right_on = ['molecule_name', 'atom_index'])
df = df.drop('atom_index', axis=1)
df = df.rename(columns={'atom': f'atom_{atom_idx}',
'x': f'x_{atom_idx}',
'y': f'y_{atom_idx}',
'z': f'z_{atom_idx}'})
return df
train = map_atom_info(train, 0)
train = map_atom_info(train, 1)
test = map_atom_info(test, 0)
test = map_atom_info(test, 1)
print(train['atom_index_0'].nunique())
print(train['atom_index_1'].nunique())
print(train['id'].nunique()) | code |
18152915/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)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('../input/structures.csv')
train.head() | code |
105179548/cell_23 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
num_classes = 10
f, ax = plt.subplots(1, num_classes, figsize = (20,20))
for i in range(0, num_classes):
sample = x_train[y_train == i][0]
ax[i].imshow(sample, cmap='gray')
ax[i].set_title(f'Label: {i}', fontsize=16)
y_train = tf.keras.utils.to_categorical(y_train, num_classes)
y_test = tf.keras.utils.to_categorical(y_test, num_classes)
x_train = x_train / 255.0
x_test = x_test / 255.0
x_train = x_train.reshape(x_train.shape[0], -1)
x_test = x_test.reshape(x_test.shape[0], -1)
model = Sequential()
model.add(Dense(units=128, input_shape=(784,), activation='relu'))
model.add(Dense(units=128, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(units=10, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
model.fit(x=x_train, y=y_train, batch_size=512, epochs=10)
test_loss, test_acc = model.evaluate(x_test, y_test)
y_pred = model.predict(x_test)
y_pred_classes = np.argmax(y_pred, axis=1)
print(y_pred)
print(y_pred_classes) | code |
105179548/cell_20 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as plt
import tensorflow as tf
num_classes = 10
f, ax = plt.subplots(1, num_classes, figsize = (20,20))
for i in range(0, num_classes):
sample = x_train[y_train == i][0]
ax[i].imshow(sample, cmap='gray')
ax[i].set_title(f'Label: {i}', fontsize=16)
y_train = tf.keras.utils.to_categorical(y_train, num_classes)
y_test = tf.keras.utils.to_categorical(y_test, num_classes)
x_train = x_train / 255.0
x_test = x_test / 255.0
x_train = x_train.reshape(x_train.shape[0], -1)
x_test = x_test.reshape(x_test.shape[0], -1)
model = Sequential()
model.add(Dense(units=128, input_shape=(784,), activation='relu'))
model.add(Dense(units=128, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(units=10, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
model.fit(x=x_train, y=y_train, batch_size=512, epochs=10) | code |
105179548/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import tensorflow as tf
num_classes = 10
f, ax = plt.subplots(1, num_classes, figsize = (20,20))
for i in range(0, num_classes):
sample = x_train[y_train == i][0]
ax[i].imshow(sample, cmap='gray')
ax[i].set_title(f'Label: {i}', fontsize=16)
y_train = tf.keras.utils.to_categorical(y_train, num_classes)
y_test = tf.keras.utils.to_categorical(y_test, num_classes)
x_train = x_train / 255.0
x_test = x_test / 255.0
x_train = x_train.reshape(x_train.shape[0], -1)
x_test = x_test.reshape(x_test.shape[0], -1)
model = Sequential()
model.add(Dense(units=128, input_shape=(784,), activation='relu'))
model.add(Dense(units=128, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(units=10, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
model.fit(x=x_train, y=y_train, batch_size=512, epochs=10)
test_loss, test_acc = model.evaluate(x_test, y_test)
y_pred = model.predict(x_test)
y_pred_classes = np.argmax(y_pred, axis=1)
random_idx = np.random.choice(len(x_test))
x_sample = x_test[random_idx]
y_true = np.argmax(y_test, axis=1)
y_sample_true = y_true[random_idx]
y_sample_pred_class = y_pred_classes[random_idx]
confusion_mtx = confusion_matrix(y_true, y_pred_classes)
fig, ax = plt.subplots(figsize=(15, 10))
ax = sns.heatmap(confusion_mtx, annot=True, fmt='d', ax=ax, cmap='Blues')
ax.set_xlabel('Predicted Label')
ax.set_ylabel('True Label')
ax.set_title('Confusion Matrix') | code |
105179548/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from tensorflow.keras.datasets import mnist
from tensorflow.keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data() | code |
105179548/cell_18 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import Sequential
model = Sequential()
model.add(Dense(units=128, input_shape=(784,), activation='relu'))
model.add(Dense(units=128, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(units=10, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary() | code |
105179548/cell_8 | [
"image_output_1.png"
] | print(x_train.shape, y_train.shape)
print(x_test.shape, y_test.shape) | code |
105179548/cell_16 | [
"text_plain_output_1.png"
] | x_train = x_train / 255.0
x_test = x_test / 255.0
x_train = x_train.reshape(x_train.shape[0], -1)
x_test = x_test.reshape(x_test.shape[0], -1)
print(x_train.shape) | code |
105179548/cell_24 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
num_classes = 10
f, ax = plt.subplots(1, num_classes, figsize = (20,20))
for i in range(0, num_classes):
sample = x_train[y_train == i][0]
ax[i].imshow(sample, cmap='gray')
ax[i].set_title(f'Label: {i}', fontsize=16)
y_train = tf.keras.utils.to_categorical(y_train, num_classes)
y_test = tf.keras.utils.to_categorical(y_test, num_classes)
x_train = x_train / 255.0
x_test = x_test / 255.0
x_train = x_train.reshape(x_train.shape[0], -1)
x_test = x_test.reshape(x_test.shape[0], -1)
model = Sequential()
model.add(Dense(units=128, input_shape=(784,), activation='relu'))
model.add(Dense(units=128, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(units=10, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
model.fit(x=x_train, y=y_train, batch_size=512, epochs=10)
test_loss, test_acc = model.evaluate(x_test, y_test)
y_pred = model.predict(x_test)
y_pred_classes = np.argmax(y_pred, axis=1)
random_idx = np.random.choice(len(x_test))
x_sample = x_test[random_idx]
y_true = np.argmax(y_test, axis=1)
y_sample_true = y_true[random_idx]
y_sample_pred_class = y_pred_classes[random_idx]
plt.title(f'Predicted: {y_sample_pred_class}, \nTrue: {y_sample_true}', fontsize=16)
plt.imshow(x_sample.reshape(28, 28), cmap='gray') | code |
105179548/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import tensorflow as tf
num_classes = 10
f, ax = plt.subplots(1, num_classes, figsize = (20,20))
for i in range(0, num_classes):
sample = x_train[y_train == i][0]
ax[i].imshow(sample, cmap='gray')
ax[i].set_title(f'Label: {i}', fontsize=16)
y_train = tf.keras.utils.to_categorical(y_train, num_classes)
y_test = tf.keras.utils.to_categorical(y_test, num_classes)
for i in range(10):
print(y_train[i]) | code |
105179548/cell_22 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as plt
import tensorflow as tf
num_classes = 10
f, ax = plt.subplots(1, num_classes, figsize = (20,20))
for i in range(0, num_classes):
sample = x_train[y_train == i][0]
ax[i].imshow(sample, cmap='gray')
ax[i].set_title(f'Label: {i}', fontsize=16)
y_train = tf.keras.utils.to_categorical(y_train, num_classes)
y_test = tf.keras.utils.to_categorical(y_test, num_classes)
x_train = x_train / 255.0
x_test = x_test / 255.0
x_train = x_train.reshape(x_train.shape[0], -1)
x_test = x_test.reshape(x_test.shape[0], -1)
model = Sequential()
model.add(Dense(units=128, input_shape=(784,), activation='relu'))
model.add(Dense(units=128, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(units=10, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
model.fit(x=x_train, y=y_train, batch_size=512, epochs=10)
test_loss, test_acc = model.evaluate(x_test, y_test)
print(f'Test Loss: {test_loss}, \nTess Accuracy: {test_acc}') | code |
105179548/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
num_classes = 10
f, ax = plt.subplots(1, num_classes, figsize=(20, 20))
for i in range(0, num_classes):
sample = x_train[y_train == i][0]
ax[i].imshow(sample, cmap='gray')
ax[i].set_title(f'Label: {i}', fontsize=16) | code |
105179548/cell_12 | [
"text_plain_output_1.png"
] | for i in range(10):
print(y_train[i]) | code |
2002001/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
plt.figure(figsize=(10, 5))
sns.countplot(data=data, x='year') | code |
2002001/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
a = data[['StartupName', 'IndustryVertical']].groupby('IndustryVertical').count().sort_values('StartupName', ascending=False).head(8)
a.reset_index(inplace=True)
plt.pie(a['StartupName'], labels=a['IndustryVertical'])
plt.show() | code |
2002001/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | b = data.SubVertical.value_counts().sort_values(ascending=False).head(10)
location = data.CityLocation.value_counts().head(5)
location.plot(kind='barh',figsize=(10, 5))
InvestorsName = data.InvestorsName.value_counts().head(10)
InvestorsName.plot(kind='barh',figsize=(15, 10))
InvestmentType = data.InvestmentType.value_counts().head(5)
InvestmentType.plot(kind='barh',figsize=(15, 5))
AmountInUSD = data.AmountInUSD.value_counts().head(5)
AmountInUSD.plot(kind='barh',figsize=(15, 5))
amount = data[['StartupName', 'AmountInUSD']].groupby('AmountInUSD').count().sort_values('StartupName', ascending=False).head(10)
amount | code |
2002001/cell_8 | [
"text_html_output_1.png"
] | b = data.SubVertical.value_counts().sort_values(ascending=False).head(10)
b.plot(kind='barh', figsize=(15, 10)) | code |
2002001/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | b = data.SubVertical.value_counts().sort_values(ascending=False).head(10)
location = data.CityLocation.value_counts().head(5)
location.plot(kind='barh',figsize=(10, 5))
InvestorsName = data.InvestorsName.value_counts().head(10)
InvestorsName.plot(kind='barh',figsize=(15, 10))
InvestmentType = data.InvestmentType.value_counts().head(5)
InvestmentType.plot(kind='barh',figsize=(15, 5))
AmountInUSD = data.AmountInUSD.value_counts().head(5)
AmountInUSD.plot(kind='barh', figsize=(15, 5)) | code |
2002001/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | b = data.SubVertical.value_counts().sort_values(ascending=False).head(10)
location = data.CityLocation.value_counts().head(5)
location.plot(kind='barh',figsize=(10, 5))
InvestorsName = data.InvestorsName.value_counts().head(10)
InvestorsName.plot(kind='barh',figsize=(15, 10))
InvestmentType = data.InvestmentType.value_counts().head(5)
InvestmentType.plot(kind='barh', figsize=(15, 5)) | code |
2002001/cell_10 | [
"text_plain_output_1.png",
"image_output_1.png"
] | b = data.SubVertical.value_counts().sort_values(ascending=False).head(10)
location = data.CityLocation.value_counts().head(5)
location.plot(kind='barh', figsize=(10, 5)) | code |
2002001/cell_12 | [
"image_output_1.png"
] | b = data.SubVertical.value_counts().sort_values(ascending=False).head(10)
location = data.CityLocation.value_counts().head(5)
location.plot(kind='barh',figsize=(10, 5))
InvestorsName = data.InvestorsName.value_counts().head(10)
InvestorsName.plot(kind='barh', figsize=(15, 10)) | code |
105179600/cell_4 | [
"text_plain_output_1.png"
] | a = open('../input/poetry/Kanye_West.txt')
a.read() | code |
105179600/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | a = open('../input/poetry/Kanye_West.txt')
a.read()
a.close()
print(a.read()) | code |
333675/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
events = pd.read_csv('../input/events.csv', parse_dates=['timestamp'])
test = pd.read_csv('../input/gender_age_test.csv')
train = pd.read_csv('../input/gender_age_train.csv') | code |
333675/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
events = pd.read_csv('../input/events.csv', parse_dates=['timestamp'])
test = pd.read_csv('../input/gender_age_test.csv')
train = pd.read_csv('../input/gender_age_train.csv')
events['timestamp'].loc[(events.longitude != 0.0) & (events.latitude != 0.0)] += events['longitude'].apply(lambda x: pd.Timedelta(seconds=240 * (x - 116.407)))
events['hourly'] = events.timestamp.dt.hour
events['hourly'].loc[(events.longitude != 0.0) & (events.latitude != 0.0)] = np.nan
hourly = events.groupby('device_id')['hourly'].apply(lambda x: ' '.join((str(s) for s in x)))
train['hourly'] = 'Hourly:' + train['device_id'].map(hourly).astype(str)
test['hourly'] = 'Hourly:' + test['device_id'].map(hourly).astype(str)
print(train.loc[['device_id', 'hourly']]) | code |
130026736/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df
top_5_popular_movies = df[['title', 'popularity']]
top_5_popular_movies = top_5_popular_movies.head(5)
plt.figure()
sns.barplot(x='popularity', y='title', data=top_5_popular_movies, palette='viridis')
plt.title('top 5 popular movie')
plt.xlabel('popularity')
plt.ylabel('title')
plt.show() | code |
130026736/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df
top_5_popular_movies = df[['title', 'popularity']]
top_5_popular_movies.head(5) | code |
130026736/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df
top_5_popular_movies = df[['title', 'popularity']]
most_popularity_lng = df.groupby('original_language')['popularity'].mean()
most_popularity_lng
top_10_selling_movie = df[["title","revenue"]]
top_10_selling_movie = top_10_selling_movie.sort_values(["revenue"],ascending=False)
top_10_selling_movie=top_10_selling_movie.head(10)
top_5_popular_movies=top_5_popular_movies.head(5)
plt.figure()
sns.barplot(x='popularity', y='title', data = top_5_popular_movies, palette='viridis')
plt.title('top 5 popular movie')
plt.xlabel('popularity')
plt.ylabel('title')
plt.show()
df_2 = pd.read_csv('/kaggle/input/anime-recommendations-database/anime.csv')
df_2
Top_10_anime_members = df_2[['name', 'members']]
Top_10_anime_members = Top_10_anime_members.sort_values(['members'], ascending=False)
Top_10_anime_members=Top_10_anime_members.head(10)
plt.figure()
sns.barplot(x='members', y='name', data=Top_10_anime_members, palette='viridis')
plt.title('top 10 anime members')
plt.xlabel('members')
plt.ylabel('name')
plt.show()
top_10_anime_rating = df_2[['name', 'rating']]
top_10_anime_rating = top_10_anime_rating.sort_values(['rating'], ascending=False)
plt.figure(figsize=(40, 10))
sns.barplot(x='rating', y='name', data=top_10_anime_rating)
plt.xlabel('Rating')
plt.ylabel('Anime Name')
plt.title('Top 10 Anime Ratings')
plt.show() | code |
130026736/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df
top_5_popular_movies = df[['title', 'popularity']]
most_popularity_lng = df.groupby('original_language')['popularity'].mean()
most_popularity_lng
top_10_selling_movie = df[["title","revenue"]]
top_10_selling_movie = top_10_selling_movie.sort_values(["revenue"],ascending=False)
top_10_selling_movie=top_10_selling_movie.head(10)
top_5_popular_movies=top_5_popular_movies.head(5)
plt.figure()
sns.barplot(x='popularity', y='title', data = top_5_popular_movies, palette='viridis')
plt.title('top 5 popular movie')
plt.xlabel('popularity')
plt.ylabel('title')
plt.show()
df_2 = pd.read_csv('/kaggle/input/anime-recommendations-database/anime.csv')
df_2
Top_10_anime_members = df_2[['name', 'members']]
Top_10_anime_members = Top_10_anime_members.sort_values(['members'], ascending=False)
Top_10_anime_members = Top_10_anime_members.head(10)
plt.figure()
sns.barplot(x='members', y='name', data=Top_10_anime_members, palette='viridis')
plt.title('top 10 anime members')
plt.xlabel('members')
plt.ylabel('name')
plt.show() | code |
130026736/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('/kaggle/input/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df
most_popularity_lng = df.groupby('original_language')['popularity'].mean()
most_popularity_lng | code |
130026736/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df
top_5_popular_movies = df[['title', 'popularity']]
most_popularity_lng = df.groupby('original_language')['popularity'].mean()
most_popularity_lng
top_10_selling_movie = df[["title","revenue"]]
top_10_selling_movie = top_10_selling_movie.sort_values(["revenue"],ascending=False)
top_10_selling_movie=top_10_selling_movie.head(10)
top_5_popular_movies=top_5_popular_movies.head(5)
plt.figure()
sns.barplot(x='popularity', y='title', data = top_5_popular_movies, palette='viridis')
plt.title('top 5 popular movie')
plt.xlabel('popularity')
plt.ylabel('title')
plt.show()
df_2 = pd.read_csv('/kaggle/input/anime-recommendations-database/anime.csv')
df_2
Top_10_anime_members = df_2[['name', 'members']]
Top_10_anime_members = Top_10_anime_members.sort_values(['members'], ascending=False)
Top_10_anime_members=Top_10_anime_members.head(10)
plt.figure()
sns.barplot(x='members', y='name', data=Top_10_anime_members, palette='viridis')
plt.title('top 10 anime members')
plt.xlabel('members')
plt.ylabel('name')
plt.show()
top_10_anime_rating = df_2[['name', 'rating']]
top_10_anime_rating = top_10_anime_rating.sort_values(['rating'], ascending=False)
df2_20 = df_2.head(20)
plt.figure(figsize=(20, 10))
sns.lineplot(x='members', y='rating',data=df2_20, color='red')
# plt.title('the relation between episodes and name')
plt.xlabel('members')
plt.ylabel('rating')
top_5_anime_episodes = df_2[['name', 'episodes']]
top_5_anime_episodes.head(10) | code |
130026736/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df | code |
130026736/cell_19 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df
df_2 = pd.read_csv('/kaggle/input/anime-recommendations-database/anime.csv')
df_2
Top_10_anime_members = df_2[['name', 'members']]
Top_10_anime_members = Top_10_anime_members.sort_values(['members'], ascending=False)
Top_10_anime_members.head(10) | code |
130026736/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 |
130026736/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df
top_5_popular_movies = df[['title', 'popularity']]
most_popularity_lng = df.groupby('original_language')['popularity'].mean()
most_popularity_lng
top_10_selling_movie = df[["title","revenue"]]
top_10_selling_movie = top_10_selling_movie.sort_values(["revenue"],ascending=False)
top_10_selling_movie=top_10_selling_movie.head(10)
top_5_popular_movies=top_5_popular_movies.head(5)
plt.figure()
sns.barplot(x='popularity', y='title', data = top_5_popular_movies, palette='viridis')
plt.title('top 5 popular movie')
plt.xlabel('popularity')
plt.ylabel('title')
plt.show()
plt.figure(figsize=(20, 10))
sns.barplot(x='revenue', y='title', data=top_10_selling_movie) | code |
130026736/cell_17 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df
df_2 = pd.read_csv('/kaggle/input/anime-recommendations-database/anime.csv')
df_2 | code |
130026736/cell_24 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df
top_5_popular_movies = df[['title', 'popularity']]
most_popularity_lng = df.groupby('original_language')['popularity'].mean()
most_popularity_lng
top_10_selling_movie = df[["title","revenue"]]
top_10_selling_movie = top_10_selling_movie.sort_values(["revenue"],ascending=False)
top_10_selling_movie=top_10_selling_movie.head(10)
top_5_popular_movies=top_5_popular_movies.head(5)
plt.figure()
sns.barplot(x='popularity', y='title', data = top_5_popular_movies, palette='viridis')
plt.title('top 5 popular movie')
plt.xlabel('popularity')
plt.ylabel('title')
plt.show()
df_2 = pd.read_csv('/kaggle/input/anime-recommendations-database/anime.csv')
df_2
Top_10_anime_members = df_2[['name', 'members']]
Top_10_anime_members = Top_10_anime_members.sort_values(['members'], ascending=False)
Top_10_anime_members=Top_10_anime_members.head(10)
plt.figure()
sns.barplot(x='members', y='name', data=Top_10_anime_members, palette='viridis')
plt.title('top 10 anime members')
plt.xlabel('members')
plt.ylabel('name')
plt.show()
top_10_anime_rating = df_2[['name', 'rating']]
top_10_anime_rating = top_10_anime_rating.sort_values(['rating'], ascending=False)
df2_20 = df_2.head(20)
plt.figure(figsize=(20, 10))
sns.lineplot(x='members', y='rating', data=df2_20, color='red')
plt.xlabel('members')
plt.ylabel('rating') | code |
130026736/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df
top_5_popular_movies = df[['title', 'popularity']]
most_popularity_lng = df.groupby('original_language')['popularity'].mean()
most_popularity_lng
top_5_popular_movies=top_5_popular_movies.head(5)
plt.figure()
sns.barplot(x='popularity', y='title', data = top_5_popular_movies, palette='viridis')
plt.title('top 5 popular movie')
plt.xlabel('popularity')
plt.ylabel('title')
plt.show()
plt.figure(figsize=(20, 30))
sns.barplot(x=most_popularity_lng.values, y=most_popularity_lng.index, data=top_5_popular_movies)
plt.title('The Average of popularity for each language')
plt.xlabel('Avg')
plt.ylabel('Language')
plt.show() | code |
130026736/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df
df_2 = pd.read_csv('/kaggle/input/anime-recommendations-database/anime.csv')
df_2
top_10_anime_rating = df_2[['name', 'rating']]
top_10_anime_rating = top_10_anime_rating.sort_values(['rating'], ascending=False)
top_10_anime_rating.head(10) | code |
130026736/cell_12 | [
"text_html_output_1.png"
] | import seaborn as sns
import matplotlib.pyplot as plt | code |
34142220/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
d = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
te = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
sns.set_style('dark')
plt.figure(figsize=(10, 10))
sns.lineplot(d['OverallCond'], d['SalePrice']) | code |
34142220/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
d = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
te = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
sns.set_style('dark')
plt.figure(figsize=(19, 13))
sns.barplot(x=d['MSSubClass'], y=d['SalePrice'])
plt.title('saleprice for different building classes') | code |
34142220/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
d = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
te = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
d.info() | code |
34142220/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
d = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
te = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
sns.set_style('dark')
plt.figure(figsize=(10, 15))
sns.barplot(x=d['SaleType'], y=d['SalePrice']) | code |
34142220/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
d = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
te = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
plt.figure(figsize=(15, 10))
sns.set_style('dark')
sns.lineplot(x=d['YearBuilt'], y=d['SalePrice'])
plt.title('saleprice difference on each year ') | code |
34142220/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
d = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
te = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
d.describe() | code |
34142220/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
d = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
te = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
sns.set_style('dark')
plt.figure(figsize=(15, 10))
sns.lineplot(x=d['YrSold'], y=d['SalePrice'])
plt.title('saleprice vs year it sold') | code |
34142220/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
d = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
te = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
sns.set_style('dark')
plt.figure(figsize=(20, 10))
sns.barplot(d['MSZoning'], d['SalePrice']) | code |
34142220/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
d = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
te = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
d.head() | code |
105201240/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data = data.drop('Posted On', axis=True)
data
data.dtypes
data = data.drop(['Floor', 'Area Type', 'Area Locality'], axis=1)
data
data['Posted_On'] = data['Posted_On'].astype(int) | code |
105201240/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data = data.drop('Posted On', axis=True)
data | code |
105201240/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.info() | code |
105201240/cell_23 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data['Posted_On'] = pd.to_datetime(data['Posted_On'])
data['Posted_On'].dtypes
data = data.drop('Posted On', axis=True)
data
data.dtypes
data = data.drop(['Floor', 'Area Type', 'Area Locality'], axis=1)
data
from sklearn import preprocessing
lb = preprocessing.LabelEncoder()
data = data.apply(lb.fit_transform)
data = pd.get_dummies(data, columns=['City'], drop_first=True)
data.head() | code |
105201240/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data = data.drop('Posted On', axis=True)
data
data.dtypes
data = data.drop(['Floor', 'Area Type', 'Area Locality'], axis=1)
data
data['City'].unique() | code |
105201240/cell_6 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
plt.figure(figsize=(10, 6))
sns.heatmap(data.corr(), annot=True) | code |
105201240/cell_29 | [
"text_html_output_1.png"
] | from lazypredict.Supervised import LazyRegressor
import lazypredict
from lazypredict.Supervised import LazyRegressor
lazy_model = LazyRegressor()
model, predict = lazy_model.fit(x_train, x_test, y_train, y_test)
model | code |
105201240/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data['Posted_On'].dt.day_name() | code |
105201240/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data = data.drop('Posted On', axis=True)
data
data.dtypes
data = data.drop(['Floor', 'Area Type', 'Area Locality'], axis=1)
data | code |
105201240/cell_7 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data | code |
105201240/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data = data.drop('Posted On', axis=True)
data
data.dtypes
data | code |
105201240/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum() | code |
105201240/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data = data.drop('Posted On', axis=True)
data
data.dtypes
data['Floor'] | code |
105201240/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data = data.drop('Posted On', axis=True)
data
data.dtypes
data | code |
105201240/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data | code |
105201240/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data = data.drop('Posted On', axis=True)
data
data.dtypes
data['House Floor'] = 0
list0 = data['Floor'].str.split(pat=' ', n=2, expand=True).iloc[:, 0]
for i, v in enumerate(data['Floor']):
if list0.iloc[i] == 'Ground':
data['House Floor'][i] = 0
elif list0.iloc[i] == 'Upper':
data['House Floor'][i] = -1
elif list0.iloc[i] == 'Lower':
data['House Floor'][i] = -2
else:
data['House Floor'][i] = list0[i] | code |
105201240/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data = data.drop('Posted On', axis=True)
data
data.dtypes | code |
105201240/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/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data['Posted_On'] = pd.to_datetime(data['Posted_On'])
data['Posted_On'].dtypes | code |
105201240/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import preprocessing
from sklearn import preprocessing
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data['Posted_On'] = pd.to_datetime(data['Posted_On'])
data['Posted_On'].dtypes
data = data.drop('Posted On', axis=True)
data
data.dtypes
data = data.drop(['Floor', 'Area Type', 'Area Locality'], axis=1)
data
from sklearn import preprocessing
lb = preprocessing.LabelEncoder()
data = data.apply(lb.fit_transform)
data = pd.get_dummies(data, columns=['City'], drop_first=True)
x = data.drop('Rent', axis=1)
y = data['Rent']
from sklearn import preprocessing
scaler = preprocessing.StandardScaler()
x = scaler.fit_transform(x)
x = pd.DataFrame(x)
x | code |
105201240/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data | code |
105201240/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.describe() | code |
130005860/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
train_df.shape | code |
130005860/cell_25 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.ensemble import RandomForestRegressor
reg = RandomForestRegressor(random_state=1)
reg.fit(X_train, y_train)
pred = reg.predict(X_val)
from sklearn.metrics import mean_absolute_error
mae = mean_absolute_error(y_val, pred)
mae | code |
130005860/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
reg = RandomForestRegressor(random_state=1)
reg.fit(X_train, y_train) | code |
130005860/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
train_df.head() | code |
130005860/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
train_df.shape
train_df.columns
train_df.isnull().sum()
train_df.head(10) | code |
130005860/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
test_df.head() | code |
130005860/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
train_df.shape
train_df.columns
train_df.isnull().sum()
train_df.describe() | code |
130005860/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
train_df.shape
train_df.columns
train_df.isnull().sum()
train_df.info() | code |
130005860/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
train_df.shape
train_df.columns | code |
130005860/cell_27 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
from sklearn.ensemble import RandomForestRegressor
reg = RandomForestRegressor(random_state=1)
reg.fit(X_train, y_train)
pred = reg.predict(X_val)
prediction = reg.predict(test_df)
submission = pd.DataFrame({'id': test_df.id, 'MedHouseVal': prediction})
submission.head() | code |
130005860/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
train_df.shape
train_df.columns
train_df.isnull().sum() | code |
88087414/cell_9 | [
"image_output_1.png"
] | import pandas as pd
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_test[['id', 'text']]
print('shape train dataframe:', data_train.shape)
print('shape test dataframe:', data_test.shape) | code |
88087414/cell_34 | [
"image_output_1.png"
] | from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.text import Tokenizer
from sklearn.model_selection import train_test_split
import pandas as pd
import re
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_test[['id', 'text']]
s = data_train.target.value_counts()
def clean_text(data):
data['clean_text'] = data['text'].str.lower()
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('http\\S+', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('[^\\w\\s]', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('/n', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\d+', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\s+', ' ', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\s+[a-zA-Z]\\s+', ' ', elem))
return data
data_train = clean_text(data_train)
data_test = clean_text(data_test)
max_fatures = 5000
tokenizer = Tokenizer(num_words=max_fatures, split=' ')
tokenizer.fit_on_texts(data_train['clean_text'].values)
X = tokenizer.texts_to_sequences(data_train['clean_text'].values)
X = pad_sequences(X, maxlen=31, padding='post')
Y = data_train['target'].values
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, random_state=0)
print(X_train[20])
print(' '.join([tokenizer.index_word[i] for i in X_train[20] if i != 0])) | code |
88087414/cell_33 | [
"image_output_1.png"
] | from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.text import Tokenizer
from sklearn.model_selection import train_test_split
import pandas as pd
import re
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_test[['id', 'text']]
s = data_train.target.value_counts()
def clean_text(data):
data['clean_text'] = data['text'].str.lower()
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('http\\S+', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('[^\\w\\s]', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('/n', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\d+', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\s+', ' ', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\s+[a-zA-Z]\\s+', ' ', elem))
return data
data_train = clean_text(data_train)
data_test = clean_text(data_test)
max_fatures = 5000
tokenizer = Tokenizer(num_words=max_fatures, split=' ')
tokenizer.fit_on_texts(data_train['clean_text'].values)
X = tokenizer.texts_to_sequences(data_train['clean_text'].values)
X = pad_sequences(X, maxlen=31, padding='post')
Y = data_train['target'].values
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, random_state=0)
print(X_train.shape, Y_train.shape)
print(X_test.shape, Y_test.shape) | code |
88087414/cell_44 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import LSTM,Embedding, Conv1D, Dense, Flatten, MaxPooling1D, Dropout , Bidirectional , Dropout , Flatten , GlobalMaxPooling1D
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.text import Tokenizer
from sklearn.metrics import classification_report,confusion_matrix
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import plot_model
import keras
import pandas as pd
import re
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_test[['id', 'text']]
s = data_train.target.value_counts()
def clean_text(data):
data['clean_text'] = data['text'].str.lower()
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('http\\S+', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('[^\\w\\s]', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('/n', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\d+', '', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\s+', ' ', elem))
data['clean_text'] = data['clean_text'].apply(lambda elem: re.sub('\\s+[a-zA-Z]\\s+', ' ', elem))
return data
data_train = clean_text(data_train)
data_test = clean_text(data_test)
max_fatures = 5000
tokenizer = Tokenizer(num_words=max_fatures, split=' ')
tokenizer.fit_on_texts(data_train['clean_text'].values)
X = tokenizer.texts_to_sequences(data_train['clean_text'].values)
X = pad_sequences(X, maxlen=31, padding='post')
Y = data_train['target'].values
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, random_state=0)
len(tokenizer.index_word)
embed_dim = 50
vocab_size = len(tokenizer.index_word) + 1
model1 = Sequential()
model1.add(Embedding(input_dim=vocab_size, input_length=31, output_dim=embed_dim))
model1.add(LSTM(30))
model1.add(Dropout(0.2))
model1.add(Flatten())
model1.add(Dense(1, activation='sigmoid'))
model1.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
from tensorflow.keras.utils import plot_model
keras.backend.clear_session()
batch_size = 32
history1 = model1.fit(X_train, Y_train, epochs=10, batch_size=batch_size, validation_data=(X_test, Y_test), verbose=1)
scores = model1.evaluate(X_test, Y_test, verbose=0)
predict = model1.predict(X_test)
predict1 = [1 if i > 0.5 else 0 for i in predict]
print(classification_report(Y_test, predict1)) | code |
88087414/cell_6 | [
"image_output_1.png"
] | import pandas as pd
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
len(data_test) | code |
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