path
stringlengths 13
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sequencelengths 1
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stringlengths 0
40.4k
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stringclasses 1
value |
|---|---|---|---|
32063375/cell_5 | [
"image_output_1.png"
] | import pandas as pd
hp = pd.read_csv('../input/london-house-prices/hpdemo.csv')
hp | code |
121148301/cell_13 | [
"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/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.duplicated().sum()
df.isnull().sum()
df.isnull().sum()
df | code |
121148301/cell_4 | [
"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/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.tail() | code |
121148301/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt # data visualization library
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # data visualization library
df = pd.read_csv('/kaggle/input/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.duplicated().sum()
df.isnull().sum()
df.isnull().sum()
df.nunique()
top_10_markets = df['Market'].value_counts().nlargest(10)
plt.xticks(rotation=60)
plt.figure(figsize=(15,6))
scatt = sns.scatterplot(data=df, x='Price', y='Change', hue='Pricing')
plt.ylabel('Price Change')
plt.title('Prices VS Prices Change')
plt.show()
top_10_unit = df['Unit'].value_counts().nlargest(10)
plt.figure(figsize=(15, 6))
sns.countplot(data=df, x='Unit', order=top_10_unit.index)
plt.xticks(rotation=60)
plt.title('Top 10 Units on Sudanese Agriculture Markets')
plt.show() | code |
121148301/cell_6 | [
"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/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.duplicated().sum() | code |
121148301/cell_11 | [
"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/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.duplicated().sum()
df.isnull().sum()
df.isnull().sum() | code |
121148301/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # data visualization library
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # data visualization library
df = pd.read_csv('/kaggle/input/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.duplicated().sum()
df.isnull().sum()
df.isnull().sum()
df.nunique()
top_10_markets = df['Market'].value_counts().nlargest(10)
plt.xticks(rotation=60)
plt.figure(figsize=(15, 6))
scatt = sns.scatterplot(data=df, x='Price', y='Change', hue='Pricing')
plt.ylabel('Price Change')
plt.title('Prices VS Prices Change')
plt.show() | code |
121148301/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import style
style.use('ggplot')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
121148301/cell_7 | [
"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/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.duplicated().sum()
df.isnull().sum() | code |
121148301/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # data visualization library
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # data visualization library
df = pd.read_csv('/kaggle/input/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.duplicated().sum()
df.isnull().sum()
df.isnull().sum()
df.nunique()
top_10_markets = df['Market'].value_counts().nlargest(10)
plt.xticks(rotation=60)
plt.figure(figsize=(15, 6))
sns.countplot(data=df, x='Pricing')
plt.title('Markets Quantities ')
plt.xlabel('Quantities')
plt.show() | code |
121148301/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.duplicated().sum()
df.isnull().sum()
df.isnull().sum()
df.nunique() | code |
121148301/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt # data visualization library
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # data visualization library
df = pd.read_csv('/kaggle/input/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.duplicated().sum()
df.isnull().sum()
df.isnull().sum()
df.nunique()
top_10_markets = df['Market'].value_counts().nlargest(10)
plt.figure(figsize=(15, 6))
sns.countplot(data=df, order=top_10_markets.index, y='Market')
plt.title('Top 10 Markets')
plt.show() | code |
121148301/cell_3 | [
"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/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.head() | code |
121148301/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt # data visualization library
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # data visualization library
df = pd.read_csv('/kaggle/input/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.duplicated().sum()
df.isnull().sum()
df.isnull().sum()
df.nunique()
top_10_markets = df['Market'].value_counts().nlargest(10)
plt.figure(figsize=(15, 6))
df['Product'].value_counts().nlargest(10).plot(kind='bar')
plt.xticks(rotation=60)
plt.xlabel('Products')
plt.ylabel('Transactions Counts')
plt.title('Counts of Top 10 Products')
plt.show() | code |
121148301/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.duplicated().sum()
df.isnull().sum()
df.isnull().sum()
df.describe() | code |
121148301/cell_5 | [
"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/sudan-food-and-agriculture/prices_oct_nov_2022.csv', index_col=0, parse_dates=['Date'])
df.info() | code |
32068527/cell_2 | [
"application_vnd.jupyter.stderr_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 |
32068527/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.preprocessing import PolynomialFeatures
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
X_train = train[['Id']]
test['Id'] = test['ForecastId']
X_test = test[['Id']]
y_train_cc = train[['ConfirmedCases']]
y_train_ft = train[['Fatalities']]
X_tr = np.array_split(X_train, 313)
y_cc = np.array_split(y_train_cc, 313)
y_ft = np.array_split(y_train_ft, 313)
X_te = np.array_split(X_test, 313)
a = np.max(X_tr[0]).values
b = a - 71
b = b[0]
from sklearn.linear_model import Lasso
from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(3)
y_pred_cc = []
for i in range(313):
X_tr[i] = poly.fit_transform(X_tr[i])
X_te[i] = poly.fit_transform(X_te[i])
model = Lasso(alpha=0.1)
model.fit(X_tr[i], y_cc[i])
y_pr_cc = model.predict(X_te[i])
y_cc[i] = y_cc[i][71:]
y_pr_cc = y_pr_cc[b:]
y_pr_cc = np.append(y_cc[i], y_pr_cc)
y_pred_cc.append(y_pr_cc)
y_pred_ft = []
for i in range(313):
model = Lasso()
model.fit(X_tr[i], y_ft[i])
y_pr_ft = model.predict(X_te[i])
y_ft[i] = y_ft[i][71:]
y_pr_ft = y_pr_ft[b:]
y_pr_ft = np.append(y_ft[i], y_pr_ft)
y_pred_ft.append(y_pr_ft)
y_pred_ft_1 = [item for sublist in y_pred_ft for item in sublist]
y_pred_cc_1 = [item for sublist in y_pred_cc for item in sublist]
result = pd.DataFrame({'ForecastId': submission.ForecastId, 'ConfirmedCases': y_pred_cc_1, 'Fatalities': y_pred_ft_1})
result.to_csv('/kaggle/working/submission.csv', index=False)
data = pd.read_csv('/kaggle/working/submission.csv')
data.head(50) | code |
32068527/cell_7 | [
"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)
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
plt.figure(figsize=(20, 10))
plt.plot(train.Id, train.ConfirmedCases)
plt.title('Confirmed Cases')
plt.show() | code |
32068527/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.preprocessing import PolynomialFeatures
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
X_train = train[['Id']]
test['Id'] = test['ForecastId']
X_test = test[['Id']]
y_train_cc = train[['ConfirmedCases']]
y_train_ft = train[['Fatalities']]
X_tr = np.array_split(X_train, 313)
y_cc = np.array_split(y_train_cc, 313)
y_ft = np.array_split(y_train_ft, 313)
X_te = np.array_split(X_test, 313)
a = np.max(X_tr[0]).values
b = a - 71
b = b[0]
from sklearn.linear_model import Lasso
from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(3)
y_pred_cc = []
for i in range(313):
X_tr[i] = poly.fit_transform(X_tr[i])
X_te[i] = poly.fit_transform(X_te[i])
model = Lasso(alpha=0.1)
model.fit(X_tr[i], y_cc[i])
y_pr_cc = model.predict(X_te[i])
y_cc[i] = y_cc[i][71:]
y_pr_cc = y_pr_cc[b:]
y_pr_cc = np.append(y_cc[i], y_pr_cc)
y_pred_cc.append(y_pr_cc) | code |
32068527/cell_16 | [
"image_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.preprocessing import PolynomialFeatures
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
X_train = train[['Id']]
test['Id'] = test['ForecastId']
X_test = test[['Id']]
y_train_cc = train[['ConfirmedCases']]
y_train_ft = train[['Fatalities']]
X_tr = np.array_split(X_train, 313)
y_cc = np.array_split(y_train_cc, 313)
y_ft = np.array_split(y_train_ft, 313)
X_te = np.array_split(X_test, 313)
a = np.max(X_tr[0]).values
b = a - 71
b = b[0]
from sklearn.linear_model import Lasso
from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(3)
y_pred_cc = []
for i in range(313):
X_tr[i] = poly.fit_transform(X_tr[i])
X_te[i] = poly.fit_transform(X_te[i])
model = Lasso(alpha=0.1)
model.fit(X_tr[i], y_cc[i])
y_pr_cc = model.predict(X_te[i])
y_cc[i] = y_cc[i][71:]
y_pr_cc = y_pr_cc[b:]
y_pr_cc = np.append(y_cc[i], y_pr_cc)
y_pred_cc.append(y_pr_cc)
y_pred_ft = []
for i in range(313):
model = Lasso()
model.fit(X_tr[i], y_ft[i])
y_pr_ft = model.predict(X_te[i])
y_ft[i] = y_ft[i][71:]
y_pr_ft = y_pr_ft[b:]
y_pr_ft = np.append(y_ft[i], y_pr_ft)
y_pred_ft.append(y_pr_ft) | code |
89127002/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix, classification_report, cohen_kappa_score
from tensorflow.keras.layers import Conv1D, MaxPooling1D, GlobalMaxPooling1D, GlobalAveragePooling1D
from tensorflow.keras.layers import Dense, Input, Activation, Embedding, Dropout
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
plt.rcParams['figure.figsize'] = [12, 5]
train = pd.read_csv('../input/drugsComTrain_raw.csv')
test = pd.read_csv('../input/drugsComTest_raw.csv')
data = pad_sequences(sequences, maxlen=sequence_length)
ratings = train['rating'].values
labels = 1.0 * (ratings >= 8) + 1.0 * (ratings >= 5)
hot_labels = to_categorical(labels)
hot_labels[:3]
VALIDATION_SPLIT = 0.25
N = int(VALIDATION_SPLIT * data.shape[0])
indices = np.arange(data.shape[0])
np.random.shuffle(indices)
data = data[indices]
hot_labels = hot_labels[indices]
train_data = data[:-N]
train_cat = hot_labels[:-N]
val_data = data[-N:]
val_cat = hot_labels[-N:]
embedding_dim = 100
model = Sequential([Embedding(max_features + 1, embedding_dim), Dropout(0.25), Conv1D(128, 7, padding='valid', activation='relu', strides=3), GlobalAveragePooling1D(), Dropout(0.25), Dense(128, activation='relu'), Dense(3, activation='softmax')])
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['acc'])
model.summary()
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
e = np.arange(len(acc)) + 1
pred_labels = np.argmax(model.predict(val_data), axis=1)
val_labels = np.argmax(val_cat, axis=1)
cr = classification_report(val_labels, pred_labels)
k = cohen_kappa_score(val_labels, pred_labels)
print(f"Cohen's kappa (linear) = {k:.4f}")
k2 = cohen_kappa_score(val_labels, pred_labels, weights='quadratic')
print(f"Cohen's kappa (quadratic) = {k2:.4f}") | code |
89127002/cell_6 | [
"text_plain_output_1.png"
] | import os
print(os.listdir('../input')) | code |
89127002/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix, classification_report, cohen_kappa_score
from tensorflow.keras.layers import Conv1D, MaxPooling1D, GlobalMaxPooling1D, GlobalAveragePooling1D
from tensorflow.keras.layers import Dense, Input, Activation, Embedding, Dropout
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
plt.rcParams['figure.figsize'] = [12, 5]
train = pd.read_csv('../input/drugsComTrain_raw.csv')
test = pd.read_csv('../input/drugsComTest_raw.csv')
data = pad_sequences(sequences, maxlen=sequence_length)
ratings = train['rating'].values
labels = 1.0 * (ratings >= 8) + 1.0 * (ratings >= 5)
hot_labels = to_categorical(labels)
hot_labels[:3]
VALIDATION_SPLIT = 0.25
N = int(VALIDATION_SPLIT * data.shape[0])
indices = np.arange(data.shape[0])
np.random.shuffle(indices)
data = data[indices]
hot_labels = hot_labels[indices]
train_data = data[:-N]
train_cat = hot_labels[:-N]
val_data = data[-N:]
val_cat = hot_labels[-N:]
embedding_dim = 100
model = Sequential([Embedding(max_features + 1, embedding_dim), Dropout(0.25), Conv1D(128, 7, padding='valid', activation='relu', strides=3), GlobalAveragePooling1D(), Dropout(0.25), Dense(128, activation='relu'), Dense(3, activation='softmax')])
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['acc'])
model.summary()
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
e = np.arange(len(acc)) + 1
pred_labels = np.argmax(model.predict(val_data), axis=1)
val_labels = np.argmax(val_cat, axis=1)
cr = classification_report(val_labels, pred_labels)
print(cr) | code |
89127002/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/drugsComTrain_raw.csv')
test = pd.read_csv('../input/drugsComTest_raw.csv')
ratings = train['rating'].values
labels = 1.0 * (ratings >= 8) + 1.0 * (ratings >= 5)
hot_labels = to_categorical(labels)
print('Shape of label tensor:', hot_labels.shape)
hot_labels[:3] | code |
89127002/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/drugsComTrain_raw.csv')
test = pd.read_csv('../input/drugsComTest_raw.csv')
train.head() | code |
89127002/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import tensorflow as tf
import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Model
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Input, Activation, Embedding, Dropout
from tensorflow.keras.layers import Conv1D, MaxPooling1D, GlobalMaxPooling1D, GlobalAveragePooling1D
from tensorflow.keras.initializers import Constant
from sklearn.metrics import confusion_matrix, classification_report, cohen_kappa_score
import os
print('tensorflow version:', tf.__version__) | code |
89127002/cell_31 | [
"image_output_2.png",
"image_output_1.png"
] | from sklearn.metrics import confusion_matrix, classification_report, cohen_kappa_score
from tensorflow.keras.layers import Conv1D, MaxPooling1D, GlobalMaxPooling1D, GlobalAveragePooling1D
from tensorflow.keras.layers import Dense, Input, Activation, Embedding, Dropout
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
plt.rcParams['figure.figsize'] = [12, 5]
train = pd.read_csv('../input/drugsComTrain_raw.csv')
test = pd.read_csv('../input/drugsComTest_raw.csv')
data = pad_sequences(sequences, maxlen=sequence_length)
ratings = train['rating'].values
labels = 1.0 * (ratings >= 8) + 1.0 * (ratings >= 5)
hot_labels = to_categorical(labels)
hot_labels[:3]
VALIDATION_SPLIT = 0.25
N = int(VALIDATION_SPLIT * data.shape[0])
indices = np.arange(data.shape[0])
np.random.shuffle(indices)
data = data[indices]
hot_labels = hot_labels[indices]
train_data = data[:-N]
train_cat = hot_labels[:-N]
val_data = data[-N:]
val_cat = hot_labels[-N:]
embedding_dim = 100
model = Sequential([Embedding(max_features + 1, embedding_dim), Dropout(0.25), Conv1D(128, 7, padding='valid', activation='relu', strides=3), GlobalAveragePooling1D(), Dropout(0.25), Dense(128, activation='relu'), Dense(3, activation='softmax')])
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['acc'])
model.summary()
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
e = np.arange(len(acc)) + 1
pred_labels = np.argmax(model.predict(val_data), axis=1)
val_labels = np.argmax(val_cat, axis=1)
cr = classification_report(val_labels, pred_labels)
cm = confusion_matrix(val_labels, pred_labels).T
print(cm) | code |
89127002/cell_24 | [
"text_plain_output_1.png"
] | history = model.fit(train_data, train_cat, batch_size=128, epochs=10, verbose=0, validation_data=(val_data, val_cat)) | code |
89127002/cell_14 | [
"text_plain_output_1.png"
] | data = pad_sequences(sequences, maxlen=sequence_length)
print('Shape of data tensor:', data.shape) | code |
89127002/cell_22 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Conv1D, MaxPooling1D, GlobalMaxPooling1D, GlobalAveragePooling1D
from tensorflow.keras.layers import Dense, Input, Activation, Embedding, Dropout
embedding_dim = 100
model = Sequential([Embedding(max_features + 1, embedding_dim), Dropout(0.25), Conv1D(128, 7, padding='valid', activation='relu', strides=3), GlobalAveragePooling1D(), Dropout(0.25), Dense(128, activation='relu'), Dense(3, activation='softmax')])
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['acc'])
model.summary() | code |
89127002/cell_27 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
plt.rcParams['figure.figsize'] = [12, 5]
train = pd.read_csv('../input/drugsComTrain_raw.csv')
test = pd.read_csv('../input/drugsComTest_raw.csv')
data = pad_sequences(sequences, maxlen=sequence_length)
ratings = train['rating'].values
labels = 1.0 * (ratings >= 8) + 1.0 * (ratings >= 5)
hot_labels = to_categorical(labels)
hot_labels[:3]
VALIDATION_SPLIT = 0.25
N = int(VALIDATION_SPLIT * data.shape[0])
indices = np.arange(data.shape[0])
np.random.shuffle(indices)
data = data[indices]
hot_labels = hot_labels[indices]
train_data = data[:-N]
train_cat = hot_labels[:-N]
val_data = data[-N:]
val_cat = hot_labels[-N:]
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
e = np.arange(len(acc)) + 1
plt.plot(e, acc, label='train')
plt.plot(e, val_acc, label='validation')
plt.title('Training and validation accuracy')
plt.xlabel('Epoch')
plt.grid()
plt.legend()
plt.figure()
plt.plot(e, loss, label='train')
plt.plot(e, val_loss, label='validation')
plt.title('Training and validation loss')
plt.xlabel('Epoch')
plt.grid()
plt.legend()
plt.show() | code |
89127002/cell_12 | [
"text_plain_output_1.png"
] | max_features = 5000
sequence_length = 200
samples = train['review']
tokenizer = Tokenizer(num_words=max_features)
tokenizer.fit_on_texts(samples)
sequences = tokenizer.texts_to_sequences(samples)
word_index = tokenizer.word_index
print(f'Found {len(word_index)} unique tokens.') | code |
2039737/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
train_df[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
2039737/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
test_df.describe() | code |
2039737/cell_30 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
pd.crosstab(train_df['Title'], train_df['Sex'])
for dataset in combine:
dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess', 'Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')
title_mapping = {'Mr': 1, 'Miss': 2, 'Mrs': 3, 'Master': 4, 'Rare': 5}
for dataset in combine:
dataset['Title'] = dataset['Title'].map(title_mapping)
dataset['Title'] = dataset['Title'].fillna(0)
train_df = train_df.drop(['Name', 'PassengerId', 'Ticket', 'Cabin'], axis=1)
test_df = test_df.drop(['Name', 'Ticket', 'Cabin'], axis=1)
combine = [train_df, test_df]
for dataset in combine:
dataset['Sex'] = dataset['Sex'].map({'female': 1, 'male': 0}).astype(int)
guess_ages = np.zeros((2, 3))
for dataset in combine:
for i in range(0, 2):
for j in range(0, 3):
guess_df = dataset[(dataset['Sex'] == i) & (dataset['Pclass'] == j + 1)]['Age'].dropna()
age_guess = guess_df.median()
guess_ages[i, j] = int(age_guess / 0.5 + 0.5) * 0.5
for i in range(0, 2):
for j in range(0, 3):
dataset.loc[dataset.Age.isnull() & (dataset.Sex == i) & (dataset.Pclass == j + 1), 'Age'] = guess_ages[i, j]
dataset['Age'] = dataset['Age'].astype(int)
for dataset in combine:
dataset.loc[dataset['Age'] <= 16, 'Age'] = 0
dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1
dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2
dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3
dataset.loc[dataset['Age'] > 64, 'Age']
train_df.head() | code |
2039737/cell_33 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
pd.crosstab(train_df['Title'], train_df['Sex'])
for dataset in combine:
dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess', 'Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')
title_mapping = {'Mr': 1, 'Miss': 2, 'Mrs': 3, 'Master': 4, 'Rare': 5}
for dataset in combine:
dataset['Title'] = dataset['Title'].map(title_mapping)
dataset['Title'] = dataset['Title'].fillna(0)
train_df = train_df.drop(['Name', 'PassengerId', 'Ticket', 'Cabin'], axis=1)
test_df = test_df.drop(['Name', 'Ticket', 'Cabin'], axis=1)
combine = [train_df, test_df]
for dataset in combine:
dataset['Sex'] = dataset['Sex'].map({'female': 1, 'male': 0}).astype(int)
guess_ages = np.zeros((2, 3))
for dataset in combine:
for i in range(0, 2):
for j in range(0, 3):
guess_df = dataset[(dataset['Sex'] == i) & (dataset['Pclass'] == j + 1)]['Age'].dropna()
age_guess = guess_df.median()
guess_ages[i, j] = int(age_guess / 0.5 + 0.5) * 0.5
for i in range(0, 2):
for j in range(0, 3):
dataset.loc[dataset.Age.isnull() & (dataset.Sex == i) & (dataset.Pclass == j + 1), 'Age'] = guess_ages[i, j]
dataset['Age'] = dataset['Age'].astype(int)
for dataset in combine:
dataset.loc[dataset['Age'] <= 16, 'Age'] = 0
dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1
dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2
dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3
dataset.loc[dataset['Age'] > 64, 'Age']
train_df = train_df.drop(['AgeBand'], axis=1)
combine = [train_df, test_df]
for dataset in combine:
dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1
train_df[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
2039737/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
train_df.head() | code |
2039737/cell_29 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
pd.crosstab(train_df['Title'], train_df['Sex'])
for dataset in combine:
dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess', 'Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')
title_mapping = {'Mr': 1, 'Miss': 2, 'Mrs': 3, 'Master': 4, 'Rare': 5}
for dataset in combine:
dataset['Title'] = dataset['Title'].map(title_mapping)
dataset['Title'] = dataset['Title'].fillna(0)
train_df = train_df.drop(['Name', 'PassengerId', 'Ticket', 'Cabin'], axis=1)
test_df = test_df.drop(['Name', 'Ticket', 'Cabin'], axis=1)
combine = [train_df, test_df]
for dataset in combine:
dataset['Sex'] = dataset['Sex'].map({'female': 1, 'male': 0}).astype(int)
guess_ages = np.zeros((2, 3))
for dataset in combine:
for i in range(0, 2):
for j in range(0, 3):
guess_df = dataset[(dataset['Sex'] == i) & (dataset['Pclass'] == j + 1)]['Age'].dropna()
age_guess = guess_df.median()
guess_ages[i, j] = int(age_guess / 0.5 + 0.5) * 0.5
for i in range(0, 2):
for j in range(0, 3):
dataset.loc[dataset.Age.isnull() & (dataset.Sex == i) & (dataset.Pclass == j + 1), 'Age'] = guess_ages[i, j]
dataset['Age'] = dataset['Age'].astype(int)
train_df['AgeBand'] = pd.cut(train_df['Age'], 5)
train_df[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True) | code |
2039737/cell_26 | [
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
pd.crosstab(train_df['Title'], train_df['Sex'])
for dataset in combine:
dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess', 'Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')
title_mapping = {'Mr': 1, 'Miss': 2, 'Mrs': 3, 'Master': 4, 'Rare': 5}
for dataset in combine:
dataset['Title'] = dataset['Title'].map(title_mapping)
dataset['Title'] = dataset['Title'].fillna(0)
train_df = train_df.drop(['Name', 'PassengerId', 'Ticket', 'Cabin'], axis=1)
test_df = test_df.drop(['Name', 'Ticket', 'Cabin'], axis=1)
combine = [train_df, test_df]
for dataset in combine:
dataset['Sex'] = dataset['Sex'].map({'female': 1, 'male': 0}).astype(int)
train_df.head() | code |
2039737/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
test_df.describe(include=['O']) | code |
2039737/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
grid = sns.FacetGrid(train_df, col='Survived')
grid.map(plt.hist, 'Age', bins=20)
grid = sns.FacetGrid(train_df, col='Survived', row='Pclass', size=2.2, aspect=1.6)
grid.map(plt.hist, 'Age', alpha=0.5, bins=20)
grid.add_legend()
grid = sns.FacetGrid(train_df, row='Embarked', size=2.2, aspect=1.6)
grid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')
grid.add_legend()
grid = sns.FacetGrid(train_df, row='Embarked', col='Survived', size=2.2, aspect=1.6)
grid.map(sns.barplot, 'Sex', 'Fare', alpha=0.5, ci=None)
grid.add_legend() | code |
2039737/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
test_df.head() | code |
2039737/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
grid = sns.FacetGrid(train_df, col='Survived')
grid.map(plt.hist, 'Age', bins=20)
grid = sns.FacetGrid(train_df, col='Survived', row='Pclass', size=2.2, aspect=1.6)
grid.map(plt.hist, 'Age', alpha=0.5, bins=20)
grid.add_legend()
grid = sns.FacetGrid(train_df, row='Embarked', size=2.2, aspect=1.6)
grid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')
grid.add_legend() | code |
2039737/cell_28 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
pd.crosstab(train_df['Title'], train_df['Sex'])
for dataset in combine:
dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess', 'Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')
title_mapping = {'Mr': 1, 'Miss': 2, 'Mrs': 3, 'Master': 4, 'Rare': 5}
for dataset in combine:
dataset['Title'] = dataset['Title'].map(title_mapping)
dataset['Title'] = dataset['Title'].fillna(0)
train_df = train_df.drop(['Name', 'PassengerId', 'Ticket', 'Cabin'], axis=1)
test_df = test_df.drop(['Name', 'Ticket', 'Cabin'], axis=1)
combine = [train_df, test_df]
for dataset in combine:
dataset['Sex'] = dataset['Sex'].map({'female': 1, 'male': 0}).astype(int)
guess_ages = np.zeros((2, 3))
for dataset in combine:
for i in range(0, 2):
for j in range(0, 3):
guess_df = dataset[(dataset['Sex'] == i) & (dataset['Pclass'] == j + 1)]['Age'].dropna()
age_guess = guess_df.median()
guess_ages[i, j] = int(age_guess / 0.5 + 0.5) * 0.5
for i in range(0, 2):
for j in range(0, 3):
dataset.loc[dataset.Age.isnull() & (dataset.Sex == i) & (dataset.Pclass == j + 1), 'Age'] = guess_ages[i, j]
dataset['Age'] = dataset['Age'].astype(int)
train_df.head() | code |
2039737/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
train_df.describe() | code |
2039737/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
train_df[['Parch', 'Survived']].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
2039737/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
grid = sns.FacetGrid(train_df, col='Survived')
grid.map(plt.hist, 'Age', bins=20) | code |
2039737/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from subprocess import check_output
import pandas as pd
import numpy as np
import random as rnd
import sklearn.linear_model
import sklearn.svm
import sklearn.ensemble
import sklearn.neighbors
import sklearn.naive_bayes
import sklearn.tree
import sklearn.neural_network
from subprocess import check_output
import seaborn as sns
import matplotlib.pyplot as plt
import os
train_set_size = 891
valid_set_size = 0
print(os.path.dirname(os.getcwd()) + ':', os.listdir(os.path.dirname(os.getcwd())))
print(os.getcwd() + ':', os.listdir(os.getcwd())) | code |
2039737/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
grid = sns.FacetGrid(train_df, col='Survived')
grid.map(plt.hist, 'Age', bins=20)
grid = sns.FacetGrid(train_df, col='Survived', row='Pclass', size=2.2, aspect=1.6)
grid.map(plt.hist, 'Age', alpha=0.5, bins=20)
grid.add_legend() | code |
2039737/cell_31 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
pd.crosstab(train_df['Title'], train_df['Sex'])
for dataset in combine:
dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess', 'Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')
title_mapping = {'Mr': 1, 'Miss': 2, 'Mrs': 3, 'Master': 4, 'Rare': 5}
for dataset in combine:
dataset['Title'] = dataset['Title'].map(title_mapping)
dataset['Title'] = dataset['Title'].fillna(0)
train_df = train_df.drop(['Name', 'PassengerId', 'Ticket', 'Cabin'], axis=1)
test_df = test_df.drop(['Name', 'Ticket', 'Cabin'], axis=1)
combine = [train_df, test_df]
for dataset in combine:
dataset['Sex'] = dataset['Sex'].map({'female': 1, 'male': 0}).astype(int)
guess_ages = np.zeros((2, 3))
for dataset in combine:
for i in range(0, 2):
for j in range(0, 3):
guess_df = dataset[(dataset['Sex'] == i) & (dataset['Pclass'] == j + 1)]['Age'].dropna()
age_guess = guess_df.median()
guess_ages[i, j] = int(age_guess / 0.5 + 0.5) * 0.5
for i in range(0, 2):
for j in range(0, 3):
dataset.loc[dataset.Age.isnull() & (dataset.Sex == i) & (dataset.Pclass == j + 1), 'Age'] = guess_ages[i, j]
dataset['Age'] = dataset['Age'].astype(int)
for dataset in combine:
dataset.loc[dataset['Age'] <= 16, 'Age'] = 0
dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1
dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2
dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3
dataset.loc[dataset['Age'] > 64, 'Age']
train_df = train_df.drop(['AgeBand'], axis=1)
combine = [train_df, test_df]
train_df.head() | code |
2039737/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
pd.crosstab(train_df['Title'], train_df['Sex'])
for dataset in combine:
dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess', 'Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')
title_mapping = {'Mr': 1, 'Miss': 2, 'Mrs': 3, 'Master': 4, 'Rare': 5}
for dataset in combine:
dataset['Title'] = dataset['Title'].map(title_mapping)
dataset['Title'] = dataset['Title'].fillna(0)
train_df = train_df.drop(['Name', 'PassengerId', 'Ticket', 'Cabin'], axis=1)
test_df = test_df.drop(['Name', 'Ticket', 'Cabin'], axis=1)
combine = [train_df, test_df]
print('train_df = ', train_df.shape)
print('test_df = ', test_df.shape) | code |
2039737/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
train_df[['SibSp', 'Survived']].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
2039737/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
pd.crosstab(train_df['Title'], train_df['Sex'])
for dataset in combine:
dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess', 'Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')
title_mapping = {'Mr': 1, 'Miss': 2, 'Mrs': 3, 'Master': 4, 'Rare': 5}
for dataset in combine:
dataset['Title'] = dataset['Title'].map(title_mapping)
dataset['Title'] = dataset['Title'].fillna(0)
train_df.head() | code |
2039737/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
train_df.describe(include=['O']) | code |
2039737/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
train_df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
2039737/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
train_df.info()
print('_' * 40)
test_df.info() | code |
72116842/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
path = '/kaggle/input/sentiment140/training.1600000.processed.noemoticon.csv'
df = pd.read_csv(path, header=None)
df.columns = ['target', 'ids', 'date', 'flag', 'user', 'text']
df.sample(10)
df.shape
sns.countplot(df['target']) | code |
72116842/cell_30 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import SnowballStemmer
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
path = '/kaggle/input/sentiment140/training.1600000.processed.noemoticon.csv'
df = pd.read_csv(path, header=None)
df.columns = ['target', 'ids', 'date', 'flag', 'user', 'text']
df.sample(10)
df.shape
stop_words = stopwords.words('english')
stemmer = SnowballStemmer('english')
cleaning = '@\\S+|https?:\\S+|http?:\\S|[^A-Za-z0-9]+'
def preprocess(text, stem=False):
text = re.sub(cleaning, ' ', str(text).lower()).strip()
tokens = []
for token in text.split():
if token not in stop_words:
if stem:
tokens.append(stemmer.stem(token))
else:
tokens.append(token)
return ' '.join(tokens)
df.text = df.text.apply(lambda x: preprocess(x, stem=True))
x = df.text
y = df.target
x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=42)
df | code |
72116842/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/sentiment140/training.1600000.processed.noemoticon.csv'
df = pd.read_csv(path, header=None)
df.columns = ['target', 'ids', 'date', 'flag', 'user', 'text']
df.info() | code |
72116842/cell_29 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import SnowballStemmer
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
path = '/kaggle/input/sentiment140/training.1600000.processed.noemoticon.csv'
df = pd.read_csv(path, header=None)
df.columns = ['target', 'ids', 'date', 'flag', 'user', 'text']
df.sample(10)
df.shape
stop_words = stopwords.words('english')
stemmer = SnowballStemmer('english')
cleaning = '@\\S+|https?:\\S+|http?:\\S|[^A-Za-z0-9]+'
def preprocess(text, stem=False):
text = re.sub(cleaning, ' ', str(text).lower()).strip()
tokens = []
for token in text.split():
if token not in stop_words:
if stem:
tokens.append(stemmer.stem(token))
else:
tokens.append(token)
return ' '.join(tokens)
df.text = df.text.apply(lambda x: preprocess(x, stem=True))
x = df.text
y = df.target
print(x.shape, y.shape)
x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=42) | code |
72116842/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/sentiment140/training.1600000.processed.noemoticon.csv'
df = pd.read_csv(path, header=None)
df.columns = ['target', 'ids', 'date', 'flag', 'user', 'text']
df.sample(10) | code |
72116842/cell_16 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/sentiment140/training.1600000.processed.noemoticon.csv'
df = pd.read_csv(path, header=None)
df.columns = ['target', 'ids', 'date', 'flag', 'user', 'text']
df.sample(10)
df.shape
df['text'].iloc[0] | code |
72116842/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/sentiment140/training.1600000.processed.noemoticon.csv'
df = pd.read_csv(path, header=None)
df.columns = ['target', 'ids', 'date', 'flag', 'user', 'text']
df.sample(10)
df.shape
df['text'].iloc[1] | code |
72116842/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/sentiment140/training.1600000.processed.noemoticon.csv'
df = pd.read_csv(path, header=None)
df.columns = ['target', 'ids', 'date', 'flag', 'user', 'text']
df.head() | code |
72116842/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/sentiment140/training.1600000.processed.noemoticon.csv'
df = pd.read_csv(path, header=None)
df.columns = ['target', 'ids', 'date', 'flag', 'user', 'text']
df.sample(10)
df.shape | code |
2003059/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test (1).csv')
def simplify_ages(df):
df.Age = df.Age.fillna(-0.5)
bins = (-1, 0, 5, 12, 18, 25, 35, 60, 120)
group_names = ['Unknown', 'Baby', 'Child', 'Teenager', 'Student', 'Young Adult', 'Adult', 'Senior']
categories = pd.cut(df.Age, bins, labels=group_names)
df.Age = categories
return df
def simplify_cabins(df):
df.Cabin = df.Cabin.fillna('N')
df.Cabin = df.Cabin.apply(lambda x: x[0])
return df
def simplify_fares(df):
df.Fare = df.Fare.fillna('-0.5')
bins = (-1, 0, 8, 15, 32, 600)
data_train.Fare.describe()
group_names = ['Unknown', '1', '2', '3', '4', '5']
categories = pd.cut(df.Fare, bins, labels=group_names)
df.Fare = categories
return df
def format_names(df):
df['Lastname'] = df.Name.apply(lambda x: x.split('')[0])
df['Prefix'] = df.Name.apply(lambda x: x.split('')[1])
return df
def drop_features(df):
return df.drop(['Ticket', 'Name', 'Embarked'], axis=1)
def transform_features(df):
df = simplify_ages(df)
df = simplify_cabins(df)
df = simplify_fares(df)
df = format_names(df)
df = drop_features(df)
return df
data_train = transform_features(data_train)
data_test = transform_features(data_test)
data_train.head() | code |
2003059/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test (1).csv')
sns.barplot(x='Embarked', y='Survived', data=data_train, hue='Pclass') | code |
2003059/cell_1 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test (1).csv')
data_train.head(10) | code |
2003059/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test (1).csv')
sns.barplot(x='Pclass', y='Survived', data=data_train, hue='Sex') | code |
90147643/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath + 'SBAnational.csv', converters={'DisbursementGross': fixvals, 'SBA_Appv': fixvals, 'GrAppv': fixvals, 'ChgOffPrinGr': fixvals}, parse_dates=['DisbursementDate'], low_memory=False)
sba = sba.astype({'DisbursementGross': np.float64, 'SBA_Appv': np.float64, 'GrAppv': np.float64, 'ChgOffPrinGr': np.float64, 'NAICS': np.str_})
sba.to_csv(savepath + 'sba_save1.csv', index=False)
cols_to_drop = ['LoanNr_ChkDgt', 'Zip', 'Bank', 'BankState', 'ApprovalDate', 'ApprovalFY', 'ChgOffDate', 'BalanceGross']
sba.drop(columns=cols_to_drop, inplace=True)
x = sba[sba['DisbursementDate'].isna()]
print(x.shape) | code |
90147643/cell_25 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath + 'SBAnational.csv', converters={'DisbursementGross': fixvals, 'SBA_Appv': fixvals, 'GrAppv': fixvals, 'ChgOffPrinGr': fixvals}, parse_dates=['DisbursementDate'], low_memory=False)
sba = sba.astype({'DisbursementGross': np.float64, 'SBA_Appv': np.float64, 'GrAppv': np.float64, 'ChgOffPrinGr': np.float64, 'NAICS': np.str_})
sba.to_csv(savepath + 'sba_save1.csv', index=False)
cols_to_drop = ['LoanNr_ChkDgt', 'Zip', 'Bank', 'BankState', 'ApprovalDate', 'ApprovalFY', 'ChgOffDate', 'BalanceGross']
sba.drop(columns=cols_to_drop, inplace=True)
x = sba[sba['DisbursementDate'].isna()]
sba.dropna(subset=['DisbursementDate'], how='all', inplace=True)
x = sba[sba['DisbursementDate'].isna()]
sba.drop(columns=['RevLineCr'], inplace=True)
'RevLineCR' in sba.columns | code |
90147643/cell_34 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath + 'SBAnational.csv', converters={'DisbursementGross': fixvals, 'SBA_Appv': fixvals, 'GrAppv': fixvals, 'ChgOffPrinGr': fixvals}, parse_dates=['DisbursementDate'], low_memory=False)
sba = sba.astype({'DisbursementGross': np.float64, 'SBA_Appv': np.float64, 'GrAppv': np.float64, 'ChgOffPrinGr': np.float64, 'NAICS': np.str_})
sba.to_csv(savepath + 'sba_save1.csv', index=False)
cols_to_drop = ['LoanNr_ChkDgt', 'Zip', 'Bank', 'BankState', 'ApprovalDate', 'ApprovalFY', 'ChgOffDate', 'BalanceGross']
sba.drop(columns=cols_to_drop, inplace=True)
x = sba[sba['DisbursementDate'].isna()]
sba.dropna(subset=['DisbursementDate'], how='all', inplace=True)
x = sba[sba['DisbursementDate'].isna()]
sba.drop(columns=['RevLineCr'], inplace=True)
'RevLineCR' in sba.columns
sba = sba[(sba['LowDoc'] == 'Y') | (sba['LowDoc'] == 'N')]
len(sba[(sba['LowDoc'] != 'Y') & (sba['LowDoc'] != 'N')])
len(sba[(sba['NewExist'] != 1) & (sba['NewExist'] != 2)]) | code |
90147643/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
sba.head(2) | code |
90147643/cell_29 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath + 'SBAnational.csv', converters={'DisbursementGross': fixvals, 'SBA_Appv': fixvals, 'GrAppv': fixvals, 'ChgOffPrinGr': fixvals}, parse_dates=['DisbursementDate'], low_memory=False)
sba = sba.astype({'DisbursementGross': np.float64, 'SBA_Appv': np.float64, 'GrAppv': np.float64, 'ChgOffPrinGr': np.float64, 'NAICS': np.str_})
def check_cols_with_nulls(df):
cols_with_missing = [col for col in df.columns if df[col].isnull().any()]
check_cols_with_nulls(sba)
sba.to_csv(savepath + 'sba_save1.csv', index=False)
cols_to_drop = ['LoanNr_ChkDgt', 'Zip', 'Bank', 'BankState', 'ApprovalDate', 'ApprovalFY', 'ChgOffDate', 'BalanceGross']
sba.drop(columns=cols_to_drop, inplace=True)
x = sba[sba['DisbursementDate'].isna()]
sba.dropna(subset=['DisbursementDate'], how='all', inplace=True)
x = sba[sba['DisbursementDate'].isna()]
sba.drop(columns=['RevLineCr'], inplace=True)
'RevLineCR' in sba.columns
sns.countplot(x='LowDoc', data=sba) | code |
90147643/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath + 'SBAnational.csv', converters={'DisbursementGross': fixvals, 'SBA_Appv': fixvals, 'GrAppv': fixvals, 'ChgOffPrinGr': fixvals}, parse_dates=['DisbursementDate'], low_memory=False)
sba = sba.astype({'DisbursementGross': np.float64, 'SBA_Appv': np.float64, 'GrAppv': np.float64, 'ChgOffPrinGr': np.float64, 'NAICS': np.str_})
sba.to_csv(savepath + 'sba_save1.csv', index=False)
cols_to_drop = ['LoanNr_ChkDgt', 'Zip', 'Bank', 'BankState', 'ApprovalDate', 'ApprovalFY', 'ChgOffDate', 'BalanceGross']
sba.drop(columns=cols_to_drop, inplace=True)
sba.head(2) | code |
90147643/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath + 'SBAnational.csv', converters={'DisbursementGross': fixvals, 'SBA_Appv': fixvals, 'GrAppv': fixvals, 'ChgOffPrinGr': fixvals}, parse_dates=['DisbursementDate'], low_memory=False)
sba = sba.astype({'DisbursementGross': np.float64, 'SBA_Appv': np.float64, 'GrAppv': np.float64, 'ChgOffPrinGr': np.float64, 'NAICS': np.str_})
sba.to_csv(savepath + 'sba_save1.csv', index=False)
cols_to_drop = ['LoanNr_ChkDgt', 'Zip', 'Bank', 'BankState', 'ApprovalDate', 'ApprovalFY', 'ChgOffDate', 'BalanceGross']
sba.drop(columns=cols_to_drop, inplace=True)
x = sba[sba['DisbursementDate'].isna()]
sba.dropna(subset=['DisbursementDate'], how='all', inplace=True)
x = sba[sba['DisbursementDate'].isna()]
sba.drop(columns=['RevLineCr'], inplace=True)
'RevLineCR' in sba.columns
sba = sba[(sba['LowDoc'] == 'Y') | (sba['LowDoc'] == 'N')]
len(sba[(sba['LowDoc'] != 'Y') & (sba['LowDoc'] != 'N')])
len(sba[(sba['LowDoc'] == 'Y') | (sba['LowDoc'] == 'N')]) | code |
90147643/cell_28 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath + 'SBAnational.csv', converters={'DisbursementGross': fixvals, 'SBA_Appv': fixvals, 'GrAppv': fixvals, 'ChgOffPrinGr': fixvals}, parse_dates=['DisbursementDate'], low_memory=False)
sba = sba.astype({'DisbursementGross': np.float64, 'SBA_Appv': np.float64, 'GrAppv': np.float64, 'ChgOffPrinGr': np.float64, 'NAICS': np.str_})
sba.to_csv(savepath + 'sba_save1.csv', index=False)
cols_to_drop = ['LoanNr_ChkDgt', 'Zip', 'Bank', 'BankState', 'ApprovalDate', 'ApprovalFY', 'ChgOffDate', 'BalanceGross']
sba.drop(columns=cols_to_drop, inplace=True)
x = sba[sba['DisbursementDate'].isna()]
sba.dropna(subset=['DisbursementDate'], how='all', inplace=True)
x = sba[sba['DisbursementDate'].isna()]
sba.drop(columns=['RevLineCr'], inplace=True)
'RevLineCR' in sba.columns
len(sba[(sba['LowDoc'] != 'Y') & (sba['LowDoc'] != 'N')]) | code |
90147643/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath + 'SBAnational.csv', converters={'DisbursementGross': fixvals, 'SBA_Appv': fixvals, 'GrAppv': fixvals, 'ChgOffPrinGr': fixvals}, parse_dates=['DisbursementDate'], low_memory=False)
print('Shape of SBA : ', sba.shape)
sba[['DisbursementGross', 'SBA_Appv', 'GrAppv', 'ChgOffPrinGr', 'DisbursementDate']].head(2) | code |
90147643/cell_15 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath + 'SBAnational.csv', converters={'DisbursementGross': fixvals, 'SBA_Appv': fixvals, 'GrAppv': fixvals, 'ChgOffPrinGr': fixvals}, parse_dates=['DisbursementDate'], low_memory=False)
sba = sba.astype({'DisbursementGross': np.float64, 'SBA_Appv': np.float64, 'GrAppv': np.float64, 'ChgOffPrinGr': np.float64, 'NAICS': np.str_})
def check_cols_with_nulls(df):
cols_with_missing = [col for col in df.columns if df[col].isnull().any()]
if len(cols_with_missing) == 0:
print('No Missing Values')
else:
print(cols_with_missing)
sns.heatmap(df.isnull(), yticklabels=False, cbar=False, cmap='viridis')
check_cols_with_nulls(sba) | code |
90147643/cell_35 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath + 'SBAnational.csv', converters={'DisbursementGross': fixvals, 'SBA_Appv': fixvals, 'GrAppv': fixvals, 'ChgOffPrinGr': fixvals}, parse_dates=['DisbursementDate'], low_memory=False)
sba = sba.astype({'DisbursementGross': np.float64, 'SBA_Appv': np.float64, 'GrAppv': np.float64, 'ChgOffPrinGr': np.float64, 'NAICS': np.str_})
def check_cols_with_nulls(df):
cols_with_missing = [col for col in df.columns if df[col].isnull().any()]
check_cols_with_nulls(sba)
sba.to_csv(savepath + 'sba_save1.csv', index=False)
cols_to_drop = ['LoanNr_ChkDgt', 'Zip', 'Bank', 'BankState', 'ApprovalDate', 'ApprovalFY', 'ChgOffDate', 'BalanceGross']
sba.drop(columns=cols_to_drop, inplace=True)
x = sba[sba['DisbursementDate'].isna()]
sba.dropna(subset=['DisbursementDate'], how='all', inplace=True)
x = sba[sba['DisbursementDate'].isna()]
sba.drop(columns=['RevLineCr'], inplace=True)
'RevLineCR' in sba.columns
sba = sba[(sba['LowDoc'] == 'Y') | (sba['LowDoc'] == 'N')]
len(sba[(sba['LowDoc'] != 'Y') & (sba['LowDoc'] != 'N')])
sns.countplot(x='NewExist', data=sba) | code |
90147643/cell_31 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath + 'SBAnational.csv', converters={'DisbursementGross': fixvals, 'SBA_Appv': fixvals, 'GrAppv': fixvals, 'ChgOffPrinGr': fixvals}, parse_dates=['DisbursementDate'], low_memory=False)
sba = sba.astype({'DisbursementGross': np.float64, 'SBA_Appv': np.float64, 'GrAppv': np.float64, 'ChgOffPrinGr': np.float64, 'NAICS': np.str_})
sba.to_csv(savepath + 'sba_save1.csv', index=False)
cols_to_drop = ['LoanNr_ChkDgt', 'Zip', 'Bank', 'BankState', 'ApprovalDate', 'ApprovalFY', 'ChgOffDate', 'BalanceGross']
sba.drop(columns=cols_to_drop, inplace=True)
x = sba[sba['DisbursementDate'].isna()]
sba.dropna(subset=['DisbursementDate'], how='all', inplace=True)
x = sba[sba['DisbursementDate'].isna()]
sba.drop(columns=['RevLineCr'], inplace=True)
'RevLineCR' in sba.columns
sba = sba[(sba['LowDoc'] == 'Y') | (sba['LowDoc'] == 'N')]
len(sba[(sba['LowDoc'] != 'Y') & (sba['LowDoc'] != 'N')]) | code |
90147643/cell_24 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath + 'SBAnational.csv', converters={'DisbursementGross': fixvals, 'SBA_Appv': fixvals, 'GrAppv': fixvals, 'ChgOffPrinGr': fixvals}, parse_dates=['DisbursementDate'], low_memory=False)
sba = sba.astype({'DisbursementGross': np.float64, 'SBA_Appv': np.float64, 'GrAppv': np.float64, 'ChgOffPrinGr': np.float64, 'NAICS': np.str_})
sba.to_csv(savepath + 'sba_save1.csv', index=False)
cols_to_drop = ['LoanNr_ChkDgt', 'Zip', 'Bank', 'BankState', 'ApprovalDate', 'ApprovalFY', 'ChgOffDate', 'BalanceGross']
sba.drop(columns=cols_to_drop, inplace=True)
x = sba[sba['DisbursementDate'].isna()]
sba.dropna(subset=['DisbursementDate'], how='all', inplace=True)
x = sba[sba['DisbursementDate'].isna()]
len(sba[(sba['RevLineCr'] != 'Y') & (sba['RevLineCr'] != 'N')]) | code |
90147643/cell_22 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath + 'SBAnational.csv', converters={'DisbursementGross': fixvals, 'SBA_Appv': fixvals, 'GrAppv': fixvals, 'ChgOffPrinGr': fixvals}, parse_dates=['DisbursementDate'], low_memory=False)
sba = sba.astype({'DisbursementGross': np.float64, 'SBA_Appv': np.float64, 'GrAppv': np.float64, 'ChgOffPrinGr': np.float64, 'NAICS': np.str_})
sba.to_csv(savepath + 'sba_save1.csv', index=False)
cols_to_drop = ['LoanNr_ChkDgt', 'Zip', 'Bank', 'BankState', 'ApprovalDate', 'ApprovalFY', 'ChgOffDate', 'BalanceGross']
sba.drop(columns=cols_to_drop, inplace=True)
x = sba[sba['DisbursementDate'].isna()]
sba.dropna(subset=['DisbursementDate'], how='all', inplace=True)
x = sba[sba['DisbursementDate'].isna()]
print(x.shape) | code |
90147643/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath + 'SBAnational.csv', converters={'DisbursementGross': fixvals, 'SBA_Appv': fixvals, 'GrAppv': fixvals, 'ChgOffPrinGr': fixvals}, parse_dates=['DisbursementDate'], low_memory=False)
print(sba.columns)
print()
print(sba.info()) | code |
90147643/cell_27 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath + 'SBAnational.csv', converters={'DisbursementGross': fixvals, 'SBA_Appv': fixvals, 'GrAppv': fixvals, 'ChgOffPrinGr': fixvals}, parse_dates=['DisbursementDate'], low_memory=False)
sba = sba.astype({'DisbursementGross': np.float64, 'SBA_Appv': np.float64, 'GrAppv': np.float64, 'ChgOffPrinGr': np.float64, 'NAICS': np.str_})
sba.to_csv(savepath + 'sba_save1.csv', index=False)
cols_to_drop = ['LoanNr_ChkDgt', 'Zip', 'Bank', 'BankState', 'ApprovalDate', 'ApprovalFY', 'ChgOffDate', 'BalanceGross']
sba.drop(columns=cols_to_drop, inplace=True)
x = sba[sba['DisbursementDate'].isna()]
sba.dropna(subset=['DisbursementDate'], how='all', inplace=True)
x = sba[sba['DisbursementDate'].isna()]
sba.drop(columns=['RevLineCr'], inplace=True)
'RevLineCR' in sba.columns
sba['LowDoc'].isna().sum() | code |
90147643/cell_36 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
filepath = '../input/should-this-loan-be-approved-or-denied/'
savepath = './'
sba = pd.read_csv(filepath + 'SBAnational.csv', low_memory=False)
def fixvals(val):
retval = val.replace('$', '')
retval = retval.replace(',', '')
return retval
sba = pd.read_csv(filepath + 'SBAnational.csv', converters={'DisbursementGross': fixvals, 'SBA_Appv': fixvals, 'GrAppv': fixvals, 'ChgOffPrinGr': fixvals}, parse_dates=['DisbursementDate'], low_memory=False)
sba = sba.astype({'DisbursementGross': np.float64, 'SBA_Appv': np.float64, 'GrAppv': np.float64, 'ChgOffPrinGr': np.float64, 'NAICS': np.str_})
sba.to_csv(savepath + 'sba_save1.csv', index=False)
cols_to_drop = ['LoanNr_ChkDgt', 'Zip', 'Bank', 'BankState', 'ApprovalDate', 'ApprovalFY', 'ChgOffDate', 'BalanceGross']
sba.drop(columns=cols_to_drop, inplace=True)
x = sba[sba['DisbursementDate'].isna()]
sba.dropna(subset=['DisbursementDate'], how='all', inplace=True)
x = sba[sba['DisbursementDate'].isna()]
sba.drop(columns=['RevLineCr'], inplace=True)
'RevLineCR' in sba.columns
sba = sba[(sba['LowDoc'] == 'Y') | (sba['LowDoc'] == 'N')]
len(sba[(sba['LowDoc'] != 'Y') & (sba['LowDoc'] != 'N')])
sba = sba[(sba['NewExist'] == 1) | (sba['NewExist'] == 2)]
len(sba[(sba['NewExist'] != 1) & (sba['NewExist'] != 2)]) | code |
90153696/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
df = df.drop(columns=['Neck', 'Chest', 'Hip'])
X = df[['BodyFat', 'Age']]
y = df['Density']
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_
model.coef_
model.predict([[6000, 3]])
model.predict([[10000, 3]])
model.predict([[6000, 4]]) | code |
90153696/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
df = df.drop(columns=['Neck', 'Chest', 'Hip'])
X = df[['BodyFat', 'Age']]
y = df['Density']
X.head() | code |
90153696/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
df = df.drop(columns=['Neck', 'Chest', 'Hip'])
df.head() | code |
90153696/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
sns.lmplot(x='BodyFat', y='Age', data=df, ci=None) | code |
90153696/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
df = df.drop(columns=['Neck', 'Chest', 'Hip'])
X = df[['BodyFat', 'Age']]
y = df['Density']
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_
model.coef_
model.predict([[6000, 3]])
model.predict([[10000, 3]])
model.predict([[6000, 4]])
y_hat = model.predict(X)
y_hat
dc = pd.concat([df[0:].reset_index(), pd.Series(y_hat, name='predicted')], axis='columns')
dc | code |
90153696/cell_20 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
df = df.drop(columns=['Neck', 'Chest', 'Hip'])
X = df[['BodyFat', 'Age']]
y = df['Density']
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_
model.coef_
model.predict([[6000, 3]])
model.predict([[10000, 3]]) | code |
90153696/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
sns.kdeplot(x='Weight', data=df) | code |
90153696/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
df = df.drop(columns=['Neck', 'Chest', 'Hip'])
len(df) | code |
90153696/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
df = df.drop(columns=['Neck', 'Chest', 'Hip'])
X = df[['BodyFat', 'Age']]
y = df['Density']
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_
model.coef_
model.predict([[6000, 3]]) | code |
90153696/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
sns.kdeplot(x='BodyFat', data=df) | code |
90153696/cell_18 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
df = df.drop(columns=['Neck', 'Chest', 'Hip'])
X = df[['BodyFat', 'Age']]
y = df['Density']
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_
model.coef_ | code |
90153696/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
df.info() | code |
90153696/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
df = df.drop(columns=['Neck', 'Chest', 'Hip'])
X = df[['BodyFat', 'Age']]
y = df['Density']
model = LinearRegression()
model.fit(X, y) | code |
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