path
stringlengths 13
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sequencelengths 1
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|---|---|---|---|
130008558/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.index.names = ['Month']
df.sort_index(inplace=True)
df.isnull().sum()
null_columns = df.columns[df.isnull().any()]
df[null_columns].isnull().sum()
df.dropna(inplace=True)
df.isnull().sum()
print('Min', np.min(df))
print('Max', np.max(df)) | code |
130008558/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.index.names = ['Month']
df.sort_index(inplace=True)
df.isnull().sum()
null_columns = df.columns[df.isnull().any()]
df[null_columns].isnull().sum()
df.dropna(inplace=True)
df.isnull().sum()
dataset = df.astype('float32')
scaler = MinMaxScaler(feature_range=(0, 1))
scaled = scaler.fit_transform(dataset)
def create_dataset(dataset, look_back=1):
dataX, dataY = ([], [])
for i in range(len(dataset) - look_back - 1):
a = dataset[i:i + look_back, 0]
dataset[i + look_back, 0]
dataX.append(a)
dataY.append(dataset[i + look_back, 0])
return (np.array(dataX), np.array(dataY))
look_back = 1
X_train, y_train = create_dataset(train, look_back)
X_test, y_test = create_dataset(test, look_back) | code |
130008558/cell_12 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.index.names = ['Month']
df.sort_index(inplace=True)
df.isnull().sum()
null_columns = df.columns[df.isnull().any()]
df[null_columns].isnull().sum()
df.dropna(inplace=True)
df.isnull().sum()
df.hist(bins=10) | code |
130008558/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/international-airline-passengers/international-airline-passengers.csv', index_col='Month')
columns_to_keep = ['Passengers']
df = df[columns_to_keep]
df['Passengers'] = df['Passengers'].apply(lambda x: x * 1000)
df.index.names = ['Month']
df.sort_index(inplace=True)
df.describe() | code |
18127716/cell_25 | [
"text_html_output_1.png"
] | import seaborn as sns
import seaborn as sns
import seaborn as sns
import seaborn as sns
import seaborn as sns
cal.dtypes
cal.drop(['day'], axis=1)
cal.dtypes
import seaborn as sns
import seaborn as sns
sns.countplot(x='month', data=cal, hue='Reason', palette='viridis') | code |
18127716/cell_4 | [
"text_plain_output_1.png"
] | cal['twp'].value_counts().head(5) | code |
18127716/cell_23 | [
"text_plain_output_1.png"
] | import seaborn as sns
import seaborn as sns
import seaborn as sns
import seaborn as sns
cal.dtypes
cal.drop(['day'], axis=1)
cal.dtypes
import seaborn as sns
sns.countplot(x='Day of Week', data=cal, hue='Reason', palette='viridis') | code |
18127716/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
import seaborn as sns
import seaborn as sns
import seaborn as sns
import seaborn as sns
cal.dtypes
cal.drop(['day'], axis=1)
cal.dtypes
import seaborn as sns
import seaborn as sns
byMonth = cal.groupby('month').count()
t = cal['timeStamp'].iloc[0]
t.date()
cal['date'] = cal['timeStamp'].apply(lambda t: t.date())
x = cal.groupby('date').count().head()
hr = cal.groupby(by=['Day of Week', 'hour']).count()['Reason'].unstack()
sns.heatmap(hr, cmap='viridis') | code |
18127716/cell_20 | [
"text_plain_output_1.png"
] | cal.dtypes
cal.drop(['day'], axis=1)
cal.dtypes | code |
18127716/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | cal['title'] | code |
18127716/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | cal.dtypes
cal.drop(['day'], axis=1)
cal.dtypes
byMonth = cal.groupby('month').count()
t = cal['timeStamp'].iloc[0]
t.date()
cal['date'] = cal['timeStamp'].apply(lambda t: t.date())
cal.head() | code |
18127716/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | cal.dtypes
cal.drop(['day'], axis=1)
cal.dtypes
byMonth = cal.groupby('month').count()
byMonth.head() | code |
18127716/cell_2 | [
"text_html_output_1.png"
] | cal.head(3) | code |
18127716/cell_11 | [
"text_plain_output_1.png"
] | cal.head(2) | code |
18127716/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
cal = pd.read_csv('../input/911.csv')
cal.info() | code |
18127716/cell_7 | [
"text_plain_output_1.png"
] | def ref_string(code):
if 'Fire' in code:
return 'Fire'
elif 'EMS' in code:
return 'EMS'
elif 'Traffic' in code:
return 'Traffic'
else:
return False
cal['Reason'] = cal['title'].apply(lambda x: ref_string(x))
cal['Reason'] | code |
18127716/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | cal.dtypes
cal.drop(['day'], axis=1) | code |
18127716/cell_32 | [
"text_html_output_1.png"
] | cal.dtypes
cal.drop(['day'], axis=1)
cal.dtypes
byMonth = cal.groupby('month').count()
t = cal['timeStamp'].iloc[0]
t.date()
cal['date'] = cal['timeStamp'].apply(lambda t: t.date())
x = cal.groupby('date').count().head()
hr = cal.groupby(by=['Day of Week', 'hour']).count()['Reason'].unstack()
print(hr) | code |
18127716/cell_28 | [
"text_plain_output_1.png"
] | cal.dtypes
cal.drop(['day'], axis=1)
cal.dtypes
byMonth = cal.groupby('month').count()
t = cal['timeStamp'].iloc[0]
t.date() | code |
18127716/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | cal['Reason'].max() | code |
18127716/cell_15 | [
"text_plain_output_1.png"
] | cal.dtypes
time = cal['timeStamp'].iloc[0]
time.dayofweek | code |
18127716/cell_16 | [
"text_plain_output_1.png"
] | cal.dtypes
time = cal['timeStamp'].iloc[0]
time.dayofweek
cal['hour'] = cal['timeStamp'].apply(lambda time: time.hour)
cal['month'] = cal['timeStamp'].apply(lambda time: time.month)
cal['Day of Week'] = cal['timeStamp'].apply(lambda time: time.dayofweek)
cal['month']
cal['hour']
cal['Day of Week'] | code |
18127716/cell_3 | [
"text_plain_output_1.png"
] | cal['zip'].value_counts().head(5) | code |
18127716/cell_17 | [
"text_plain_output_1.png"
] | cal.dtypes
cal.head(2) | code |
18127716/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | cal.dtypes
cal.drop(['day'], axis=1)
cal.dtypes
byMonth = cal.groupby('month').count()
t = cal['timeStamp'].iloc[0]
t.date()
cal['date'] = cal['timeStamp'].apply(lambda t: t.date())
x = cal.groupby('date').count().head()
x['lat'].plot() | code |
18127716/cell_24 | [
"text_plain_output_1.png"
] | cal.dtypes
cal.drop(['day'], axis=1)
cal.dtypes
cal['month'] | code |
18127716/cell_14 | [
"text_plain_output_1.png"
] | cal.dtypes
cal.info() | code |
18127716/cell_22 | [
"text_plain_output_1.png"
] | cal.dtypes
cal.drop(['day'], axis=1)
cal.dtypes
cal['Day of Week'] | code |
18127716/cell_10 | [
"text_html_output_1.png"
] | import seaborn as sns
import seaborn as sns
import seaborn as sns
sns.countplot(x='Reason', data=cal) | code |
18127716/cell_27 | [
"text_plain_output_1.png"
] | cal.dtypes
cal.drop(['day'], axis=1)
cal.dtypes
byMonth = cal.groupby('month').count()
byMonth['lat'].plot() | code |
18127716/cell_12 | [
"text_plain_output_1.png"
] | cal.dtypes | code |
18127716/cell_5 | [
"text_html_output_1.png"
] | cal['title'].nunique() | code |
128005859/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_train_pred = knn.predict(X_train)
y_test_pred = knn.predict(X_test)
train_acc_knn = accuracy_score(y_train, y_train_pred)
test_acc_knn = accuracy_score(y_test, y_test_pred)
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_train_pred = dt.predict(X_train)
y_test_pred = dt.predict(X_test)
train_acc_dt = accuracy_score(y_train, y_train_pred)
test_acc_dt = accuracy_score(y_test, y_test_pred)
svm = SVC()
svm.fit(X_train, y_train)
y_train_pred = svm.predict(X_train)
y_test_pred = svm.predict(X_test)
train_acc_svm = accuracy_score(y_train, y_train_pred)
test_acc_svm = accuracy_score(y_test, y_test_pred)
lr = LogisticRegression()
lr.fit(X_train, y_train)
y_train_pred = lr.predict(X_train)
y_test_pred = lr.predict(X_test)
train_acc_lr = accuracy_score(y_train, y_train_pred)
test_acc_lr = accuracy_score(y_test, y_test_pred)
print('Potential of overfitting for KNN: ', train_acc_knn - test_acc_knn)
print('Potential of overfitting for Decision Tree: ', train_acc_dt - test_acc_dt)
print('Potential of overfitting for SVM: ', train_acc_svm - test_acc_svm)
print('Potential of overfitting for Logistic Regression: ', train_acc_lr - test_acc_lr) | code |
128005859/cell_13 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
data | code |
128005859/cell_23 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_train_pred = knn.predict(X_train)
y_test_pred = knn.predict(X_test)
train_acc_knn = accuracy_score(y_train, y_train_pred)
test_acc_knn = accuracy_score(y_test, y_test_pred)
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_train_pred = dt.predict(X_train)
y_test_pred = dt.predict(X_test)
train_acc_dt = accuracy_score(y_train, y_train_pred)
test_acc_dt = accuracy_score(y_test, y_test_pred)
svm = SVC()
svm.fit(X_train, y_train)
y_train_pred = svm.predict(X_train)
y_test_pred = svm.predict(X_test)
train_acc_svm = accuracy_score(y_train, y_train_pred)
test_acc_svm = accuracy_score(y_test, y_test_pred)
lr = LogisticRegression()
lr.fit(X_train, y_train)
y_train_pred = lr.predict(X_train)
y_test_pred = lr.predict(X_test)
train_acc_lr = accuracy_score(y_train, y_train_pred)
test_acc_lr = accuracy_score(y_test, y_test_pred)
from sklearn.metrics import confusion_matrix
knn_cm = confusion_matrix(y_test, y_test_pred)
print('KNN confusion matrix:\n', knn_cm)
dt_cm = confusion_matrix(y_test, y_test_pred)
print('Decision Tree confusion matrix:\n', dt_cm)
svm_cm = confusion_matrix(y_test, y_test_pred)
print('SVM confusion matrix:\n', svm_cm)
lr_cm = confusion_matrix(y_test, y_test_pred)
print('Logistic Regression confusion matrix:\n', lr_cm) | code |
128005859/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
data | code |
128005859/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_train_pred = knn.predict(X_train)
y_test_pred = knn.predict(X_test)
train_acc_knn = accuracy_score(y_train, y_train_pred)
test_acc_knn = accuracy_score(y_test, y_test_pred)
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_train_pred = dt.predict(X_train)
y_test_pred = dt.predict(X_test)
train_acc_dt = accuracy_score(y_train, y_train_pred)
test_acc_dt = accuracy_score(y_test, y_test_pred)
svm = SVC()
svm.fit(X_train, y_train)
y_train_pred = svm.predict(X_train)
y_test_pred = svm.predict(X_test)
train_acc_svm = accuracy_score(y_train, y_train_pred)
test_acc_svm = accuracy_score(y_test, y_test_pred)
lr = LogisticRegression()
lr.fit(X_train, y_train)
y_train_pred = lr.predict(X_train)
y_test_pred = lr.predict(X_test)
train_acc_lr = accuracy_score(y_train, y_train_pred)
test_acc_lr = accuracy_score(y_test, y_test_pred)
print('Training accuracy of Logistic Regression: ', train_acc_lr)
print('Testing accuracy of Logistic Regression: ', test_acc_lr) | code |
128005859/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_train_pred = knn.predict(X_train)
y_test_pred = knn.predict(X_test)
train_acc_knn = accuracy_score(y_train, y_train_pred)
test_acc_knn = accuracy_score(y_test, y_test_pred)
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_train_pred = dt.predict(X_train)
y_test_pred = dt.predict(X_test)
train_acc_dt = accuracy_score(y_train, y_train_pred)
test_acc_dt = accuracy_score(y_test, y_test_pred)
svm = SVC()
svm.fit(X_train, y_train)
y_train_pred = svm.predict(X_train)
y_test_pred = svm.predict(X_test)
train_acc_svm = accuracy_score(y_train, y_train_pred)
test_acc_svm = accuracy_score(y_test, y_test_pred)
print('Training accuracy of SVM: ', train_acc_svm)
print('Testing accuracy of SVM: ', test_acc_svm) | code |
128005859/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape | code |
128005859/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_train_pred = knn.predict(X_train)
y_test_pred = knn.predict(X_test)
train_acc_knn = accuracy_score(y_train, y_train_pred)
test_acc_knn = accuracy_score(y_test, y_test_pred)
print('Training accuracy of KNN: ', train_acc_knn)
print('Testing accuracy of KNN: ', test_acc_knn) | code |
128005859/cell_17 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_train_pred = knn.predict(X_train)
y_test_pred = knn.predict(X_test)
train_acc_knn = accuracy_score(y_train, y_train_pred)
test_acc_knn = accuracy_score(y_test, y_test_pred)
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_train_pred = dt.predict(X_train)
y_test_pred = dt.predict(X_test)
train_acc_dt = accuracy_score(y_train, y_train_pred)
test_acc_dt = accuracy_score(y_test, y_test_pred)
print('Training accuracy of Decision Tree: ', train_acc_dt)
print('Testing accuracy of Decision Tree: ', test_acc_dt) | code |
128005859/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import recall_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_train_pred = knn.predict(X_train)
y_test_pred = knn.predict(X_test)
train_acc_knn = accuracy_score(y_train, y_train_pred)
test_acc_knn = accuracy_score(y_test, y_test_pred)
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_train_pred = dt.predict(X_train)
y_test_pred = dt.predict(X_test)
train_acc_dt = accuracy_score(y_train, y_train_pred)
test_acc_dt = accuracy_score(y_test, y_test_pred)
svm = SVC()
svm.fit(X_train, y_train)
y_train_pred = svm.predict(X_train)
y_test_pred = svm.predict(X_test)
train_acc_svm = accuracy_score(y_train, y_train_pred)
test_acc_svm = accuracy_score(y_test, y_test_pred)
lr = LogisticRegression()
lr.fit(X_train, y_train)
y_train_pred = lr.predict(X_train)
y_test_pred = lr.predict(X_test)
train_acc_lr = accuracy_score(y_train, y_train_pred)
test_acc_lr = accuracy_score(y_test, y_test_pred)
from sklearn.metrics import recall_score
knn_recall = recall_score(y_test, y_test_pred)
print('KNN recall score:', knn_recall)
dt_recall = recall_score(y_test, y_test_pred)
print('Decision Tree recall score:', dt_recall)
svm_recall = recall_score(y_test, y_test_pred)
print('SVM recall score:', svm_recall)
lr_recall = recall_score(y_test, y_test_pred)
print('Logistic Regression recall score:', lr_recall) | code |
128005859/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
le = LabelEncoder()
data['Gender'] = le.fit_transform(data['Gender'])
data['Married'] = le.fit_transform(data['Married'])
data['Dependents'] = le.fit_transform(data['Dependents'])
data['Education'] = le.fit_transform(data['Education'])
data['Self_Employed'] = le.fit_transform(data['Self_Employed'])
data['Property_Area'] = le.fit_transform(data['Property_Area'])
data['Loan_Status'] = le.fit_transform(data['Loan_Status'])
new_data = pd.read_csv('/path/to/new/data.csv')
new_data.drop('Loan_ID', axis=1, inplace=True)
new_data['Gender'] = le.transform(new_data['Gender'])
new_data['Married'] = le.transform(new_data['Married'])
new_data['Education'] = le.transform(new_data['Education'])
new_data['Self_Employed'] = le.transform(new_data['Self_Employed'])
new_data['Property_Area'] = le.transform(new_data['Property_Area']) | code |
128005859/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
print('Training set shape:', X_train.shape)
print('Testing set shape:', X_test.shape) | code |
128005859/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape | code |
105177694/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
data.head() | code |
105177694/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 |
105177694/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv')
import seaborn as sns
plt.figure(figsize=(10, 10))
sns.heatmap(data.isna()) | code |
104115351/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import datasets
from sklearn.multiclass import OutputCodeClassifier
from sklearn.svm import LinearSVC
from sklearn import datasets
from sklearn.multiclass import OutputCodeClassifier
from sklearn.svm import LinearSVC
X, y = datasets.load_iris(return_X_y=True)
clf = OutputCodeClassifier(LinearSVC(random_state=0), code_size=2, random_state=0)
clf.fit(X, y).predict(X) | code |
104115351/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from skmultilearn.problem_transform import BinaryRelevance
from skmultilearn.problem_transform import BinaryRelevance
from sklearn.naive_bayes import GaussianNB
classifier = BinaryRelevance(GaussianNB())
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
classifier.fit(X_train, y_train)
predictions = classifier.predict(X_test)
predictions | code |
2010421/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.describe(include='all') | code |
2010421/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import tensorflow as tf | code |
2010421/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
train.head() | code |
2010421/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.head() | code |
2010421/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
train['Name'] = train.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
test['Name'] = test.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
train.head() | code |
2010421/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test.describe(include='all') | code |
89132121/cell_42 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
num_missing = (RTA_Data.iloc[:, 0:31] == 0).sum()
num_missing
oginal_data = RTA_Data.copy()
RTA_EDA = oginal_data.copy()
plt.xticks(rotation=90)
RTA_Data_time = RTA_EDA.copy()
def catDistribution(col_name):
plt.xticks(rotation=90)
catDistribution('Day_of_week') | code |
89132121/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
RTA_Data.hist() | code |
89132121/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
plt.figure(figsize=(10, 10))
sns.heatmap(RTA_Data.isnull(), cmap='binary') | code |
89132121/cell_57 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
num_missing = (RTA_Data.iloc[:, 0:31] == 0).sum()
num_missing
oginal_data = RTA_Data.copy()
RTA_EDA = oginal_data.copy()
plt.xticks(rotation=90)
RTA_Data_time = RTA_EDA.copy()
def catDistribution(col_name):
plt.xticks(rotation=90)
catDistribution('Driving_experience') | code |
89132121/cell_34 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
num_missing = (RTA_Data.iloc[:, 0:31] == 0).sum()
num_missing
oginal_data = RTA_Data.copy()
RTA_EDA = oginal_data.copy()
def fetchtime(x):
return int(x.split(':')[0])
RTA_EDA['Time'] = RTA_EDA['Time'].apply(fetchtime)
RTA_EDA | code |
89132121/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
num_missing = (RTA_Data.iloc[:, 0:31] == 0).sum()
num_missing
sns.distplot(RTA_Data['Number_of_casualties']) | code |
89132121/cell_30 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
num_missing = (RTA_Data.iloc[:, 0:31] == 0).sum()
num_missing
oginal_data = RTA_Data.copy()
RTA_EDA = oginal_data.copy()
RTA_EDA['Time'].isnull().sum() | code |
89132121/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
RTA_Data.head(10) | code |
89132121/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | """for col in RTA_Data.columns:
if RTA_Data[col].dtype == 'object':
sns.countplot(y=col,data=RTA_Data)
plt.show()""" | code |
89132121/cell_65 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
num_missing = (RTA_Data.iloc[:, 0:31] == 0).sum()
num_missing
oginal_data = RTA_Data.copy()
RTA_EDA = oginal_data.copy()
plt.xticks(rotation=90)
RTA_Data_time = RTA_EDA.copy()
def catDistribution(col_name):
plt.xticks(rotation=90)
catDistribution('Service_year_of_vehicle') | code |
89132121/cell_48 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
num_missing = (RTA_Data.iloc[:, 0:31] == 0).sum()
num_missing
oginal_data = RTA_Data.copy()
RTA_EDA = oginal_data.copy()
plt.xticks(rotation=90)
RTA_Data_time = RTA_EDA.copy()
def catDistribution(col_name):
plt.xticks(rotation=90)
catDistribution('Sex_of_driver') | code |
89132121/cell_73 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
num_missing = (RTA_Data.iloc[:, 0:31] == 0).sum()
num_missing
oginal_data = RTA_Data.copy()
RTA_EDA = oginal_data.copy()
plt.xticks(rotation=90)
RTA_Data_time = RTA_EDA.copy()
def catDistribution(col_name):
plt.xticks(rotation=90)
catDistribution('Road_allignment') | code |
89132121/cell_54 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
num_missing = (RTA_Data.iloc[:, 0:31] == 0).sum()
num_missing
oginal_data = RTA_Data.copy()
RTA_EDA = oginal_data.copy()
plt.xticks(rotation=90)
RTA_Data_time = RTA_EDA.copy()
def catDistribution(col_name):
plt.xticks(rotation=90)
catDistribution('Vehicle_driver_relation') | code |
89132121/cell_67 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
num_missing = (RTA_Data.iloc[:, 0:31] == 0).sum()
num_missing
oginal_data = RTA_Data.copy()
RTA_EDA = oginal_data.copy()
plt.xticks(rotation=90)
RTA_Data_time = RTA_EDA.copy()
def catDistribution(col_name):
plt.xticks(rotation=90)
catDistribution('Defect_of_vehicle') | code |
89132121/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert | code |
89132121/cell_60 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
num_missing = (RTA_Data.iloc[:, 0:31] == 0).sum()
num_missing
oginal_data = RTA_Data.copy()
RTA_EDA = oginal_data.copy()
plt.xticks(rotation=90)
RTA_Data_time = RTA_EDA.copy()
def catDistribution(col_name):
plt.xticks(rotation=90)
catDistribution('Type_of_vehicle') | code |
89132121/cell_69 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
num_missing = (RTA_Data.iloc[:, 0:31] == 0).sum()
num_missing
oginal_data = RTA_Data.copy()
RTA_EDA = oginal_data.copy()
plt.xticks(rotation=90)
RTA_Data_time = RTA_EDA.copy()
def catDistribution(col_name):
plt.xticks(rotation=90)
catDistribution('Area_accident_occured') | code |
89132121/cell_1 | [
"text_plain_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
89132121/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
RTA_Data.info() | code |
89132121/cell_45 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
num_missing = (RTA_Data.iloc[:, 0:31] == 0).sum()
num_missing
oginal_data = RTA_Data.copy()
RTA_EDA = oginal_data.copy()
plt.xticks(rotation=90)
RTA_Data_time = RTA_EDA.copy()
def catDistribution(col_name):
plt.xticks(rotation=90)
catDistribution('Age_band_of_driver') | code |
89132121/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
RTA_Data.describe(include='object') | code |
89132121/cell_32 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
num_missing = (RTA_Data.iloc[:, 0:31] == 0).sum()
num_missing
oginal_data = RTA_Data.copy()
RTA_EDA = oginal_data.copy()
plt.figure(figsize=(30, 30))
sns.countplot(RTA_EDA['Time'])
plt.xticks(rotation=90) | code |
89132121/cell_51 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
num_missing = (RTA_Data.iloc[:, 0:31] == 0).sum()
num_missing
oginal_data = RTA_Data.copy()
RTA_EDA = oginal_data.copy()
plt.xticks(rotation=90)
RTA_Data_time = RTA_EDA.copy()
def catDistribution(col_name):
plt.xticks(rotation=90)
catDistribution('Educational_level') | code |
89132121/cell_62 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
num_missing = (RTA_Data.iloc[:, 0:31] == 0).sum()
num_missing
oginal_data = RTA_Data.copy()
RTA_EDA = oginal_data.copy()
plt.xticks(rotation=90)
RTA_Data_time = RTA_EDA.copy()
def catDistribution(col_name):
plt.xticks(rotation=90)
catDistribution('Owner_of_vehicle') | code |
89132121/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
RTA_Data.describe() | code |
89132121/cell_75 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
num_missing = (RTA_Data.iloc[:, 0:31] == 0).sum()
num_missing
oginal_data = RTA_Data.copy()
RTA_EDA = oginal_data.copy()
plt.xticks(rotation=90)
RTA_Data_time = RTA_EDA.copy()
def catDistribution(col_name):
plt.xticks(rotation=90)
catDistribution('Types_of_Junction') | code |
89132121/cell_35 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
num_missing = (RTA_Data.iloc[:, 0:31] == 0).sum()
num_missing
oginal_data = RTA_Data.copy()
RTA_EDA = oginal_data.copy()
def fetchtime(x):
return int(x.split(':')[0])
RTA_EDA['Time'] = RTA_EDA['Time'].apply(fetchtime)
RTA_EDA
def categorizetime(x):
if x >= 6 and x < 18:
return 'Day'
elif x < 6 or x >= 18:
return 'Night'
RTA_EDA['Time'] = RTA_EDA['Time'].apply(categorizetime)
RTA_EDA | code |
89132121/cell_77 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
num_missing = (RTA_Data.iloc[:, 0:31] == 0).sum()
num_missing
oginal_data = RTA_Data.copy()
RTA_EDA = oginal_data.copy()
plt.xticks(rotation=90)
RTA_Data_time = RTA_EDA.copy()
def catDistribution(col_name):
plt.xticks(rotation=90)
catDistribution('Road_surface_type') | code |
89132121/cell_31 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
num_missing = (RTA_Data.iloc[:, 0:31] == 0).sum()
num_missing
oginal_data = RTA_Data.copy()
RTA_EDA = oginal_data.copy()
RTA_EDA['Time'].unique() | code |
89132121/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
mask = df_null_pert['Percentage'] >= 20
df_null_pert[mask] | code |
89132121/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
sns.distplot(RTA_Data['Number_of_vehicles_involved'])
num_missing = (RTA_Data.iloc[:, 0:31] == 0).sum()
num_missing | code |
89132121/cell_37 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
num_missing = (RTA_Data.iloc[:, 0:31] == 0).sum()
num_missing
oginal_data = RTA_Data.copy()
RTA_EDA = oginal_data.copy()
plt.xticks(rotation=90)
plt.figure(figsize=(10, 10))
sns.countplot(x=RTA_EDA['Time']) | code |
89132121/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
miss_val_col | code |
89132121/cell_71 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
num_missing = (RTA_Data.iloc[:, 0:31] == 0).sum()
num_missing
oginal_data = RTA_Data.copy()
RTA_EDA = oginal_data.copy()
plt.xticks(rotation=90)
RTA_Data_time = RTA_EDA.copy()
def catDistribution(col_name):
plt.xticks(rotation=90)
catDistribution('Lanes_or_Medians') | code |
89132121/cell_36 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pd.set_option('display.max_columns', None)
RTA_Data = pd.read_csv('../input/road-traffic-accidents/RTA Dataset.csv')
cols = []
per = []
miss_val_col = []
for col in RTA_Data.columns:
cols.append(col)
pert = RTA_Data[col].isnull().sum() / RTA_Data.shape[0] * 100
per.append(pert)
if RTA_Data[col].isnull().sum() > 0:
miss_val_col.append(col)
df_null_pert = pd.DataFrame({'columns': cols, 'Percentage': per})
df_null_pert
num_missing = (RTA_Data.iloc[:, 0:31] == 0).sum()
num_missing
oginal_data = RTA_Data.copy()
RTA_EDA = oginal_data.copy()
RTA_EDA['Time'].unique() | code |
104127064/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df.isna().sum()
round(df.isna().sum() / len(df.index) * 100, 2)
round(df.isna().sum() / len(df.index) * 100, 2) | code |
104127064/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df | code |
104127064/cell_33 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df.isna().sum()
round(df.isna().sum() / len(df.index) * 100, 2)
round(df.isna().sum() / len(df.index) * 100, 2)
categrocal_col = df.select_dtypes(exclude=np.number)
categrocal_col
labels_ = ['child', 'young', 'teenage', 'adult', 'old']
bins_ = [0, 10, 18, 28, 45, 80]
df['Age'] = pd.cut(df['Age'], bins=bins_, labels=labels_)
age = pd.get_dummies(df['Age'])
data = pd.concat([df, age], axis=1)
data
data.drop(['Age'], axis=1, inplace=True)
from sklearn.utils import shuffle
shuffle_data = shuffle(data, random_state=42)
shuffle_data
x = shuffle_data.drop('Survived', axis=1)
y = shuffle_data['Survived']
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=42)
(x_train.shape, x_test.shape, y_train.shape, y_test.shape)
lo = LogisticRegression()
lo.fit(x_train, y_train) | code |
104127064/cell_39 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df.isna().sum()
round(df.isna().sum() / len(df.index) * 100, 2)
round(df.isna().sum() / len(df.index) * 100, 2)
categrocal_col = df.select_dtypes(exclude=np.number)
categrocal_col
labels_ = ['child', 'young', 'teenage', 'adult', 'old']
bins_ = [0, 10, 18, 28, 45, 80]
df['Age'] = pd.cut(df['Age'], bins=bins_, labels=labels_)
age = pd.get_dummies(df['Age'])
data = pd.concat([df, age], axis=1)
data
data.drop(['Age'], axis=1, inplace=True)
from sklearn.utils import shuffle
shuffle_data = shuffle(data, random_state=42)
shuffle_data
x = shuffle_data.drop('Survived', axis=1)
y = shuffle_data['Survived']
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=42)
(x_train.shape, x_test.shape, y_train.shape, y_test.shape)
lo = LogisticRegression()
lo.fit(x_train, y_train)
y_pred = lo.predict(x_test)
metrics.accuracy_score(y_test, y_pred) | code |
104127064/cell_26 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df.isna().sum()
round(df.isna().sum() / len(df.index) * 100, 2)
round(df.isna().sum() / len(df.index) * 100, 2)
categrocal_col = df.select_dtypes(exclude=np.number)
categrocal_col
labels_ = ['child', 'young', 'teenage', 'adult', 'old']
bins_ = [0, 10, 18, 28, 45, 80]
df['Age'] = pd.cut(df['Age'], bins=bins_, labels=labels_)
age = pd.get_dummies(df['Age'])
data = pd.concat([df, age], axis=1)
data
data.drop(['Age'], axis=1, inplace=True)
data | code |
104127064/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)
df = pd.read_csv('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df.isna().sum()
round(df.isna().sum() / len(df.index) * 100, 2) | code |
104127064/cell_19 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df.isna().sum()
round(df.isna().sum() / len(df.index) * 100, 2)
round(df.isna().sum() / len(df.index) * 100, 2)
categrocal_col = df.select_dtypes(exclude=np.number)
categrocal_col
df | code |
104127064/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 |
104127064/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df.isna().sum()
round(df.isna().sum() / len(df.index) * 100, 2)
round(df.isna().sum() / len(df.index) * 100, 2)
categrocal_col = df.select_dtypes(exclude=np.number)
categrocal_col | code |
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