path
stringlengths 13
17
| screenshot_names
listlengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
130012258/cell_8
|
[
"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)
import pandas as pd
df = pd.read_csv('/kaggle/input/midjourney-v5-prompts-and-links/MJ_Part3.csv')
df
df = df.drop(['AuthorID', 'Author', 'Date', 'Reactions', 'Attachments'], axis=1)
df
df = df.dropna()
df
|
code
|
130012258/cell_10
|
[
"text_html_output_1.png"
] |
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import pandas as pd
df = pd.read_csv('/kaggle/input/midjourney-v5-prompts-and-links/MJ_Part3.csv')
df
df = df.drop(['AuthorID', 'Author', 'Date', 'Reactions', 'Attachments'], axis=1)
df
df = df.dropna()
df
import re
pattern = '(?:\\*\\*)?(?:<.*?>)?(?:<.*?>)?(.*?)(?=\\s--)'
df['Content'] = df['Content'].apply(lambda text: re.findall(pattern, text)[0].strip() if re.findall(pattern, text) else 'No Match')
df
|
code
|
130012258/cell_27
|
[
"text_html_output_1.png"
] |
from sklearn.cluster import KMeans
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('/kaggle/input/midjourney-v5-prompts-and-links/MJ_Part3.csv')
df
df = df.drop(['AuthorID', 'Author', 'Date', 'Reactions', 'Attachments'], axis=1)
df
df = df.dropna()
df
df = df[df['Content'] != 'No Match']
df
from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer(max_features=5000, stop_words='english')
vectors = cv.fit_transform(df['Content']).toarray()
vectors
from sklearn.cluster import KMeans
num_clusters = 100
kmeans = KMeans(n_clusters=num_clusters)
kmeans.fit(vectors)
cluster_labels = kmeans.labels_
df['Cluster'] = cluster_labels
for cluster in range(num_clusters):
prompts_in_cluster = df[df['Cluster'] == cluster]['Content'].values
print(f'Cluster {cluster}:')
for prompt in prompts_in_cluster:
print(prompt)
print()
|
code
|
130012258/cell_12
|
[
"text_html_output_1.png"
] |
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('/kaggle/input/midjourney-v5-prompts-and-links/MJ_Part3.csv')
df
df = df.drop(['AuthorID', 'Author', 'Date', 'Reactions', 'Attachments'], axis=1)
df
df = df.dropna()
df
df = df[df['Content'] != 'No Match']
df
|
code
|
122264951/cell_4
|
[
"text_html_output_1.png"
] |
import pandas as pd
import pandas as pd
df = pd.read_csv('/kaggle/input/nbagameinfo/nba_games (1).csv', index_col=0)
df
df = df.sort_values('date')
df
df = df.reset_index(drop=True)
df.drop(['mp.1', 'mp_opp.1', 'index_opp'], axis=1, inplace=True)
def add_target(team):
team['target'] = team['won'].shift(-1)
return team
df = df.groupby('team', group_keys=False).apply(add_target)
df[df['team'] == 'LAL']
|
code
|
122264951/cell_6
|
[
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] |
pip install scikit-learn
|
code
|
122264951/cell_2
|
[
"text_html_output_1.png"
] |
import pandas as pd
import pandas as pd
df = pd.read_csv('/kaggle/input/nbagameinfo/nba_games (1).csv', index_col=0)
df
df = df.sort_values('date')
df
|
code
|
122264951/cell_1
|
[
"text_html_output_1.png"
] |
import pandas as pd
import pandas as pd
df = pd.read_csv('/kaggle/input/nbagameinfo/nba_games (1).csv', index_col=0)
df
|
code
|
122264951/cell_5
|
[
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] |
import pandas as pd
import pandas as pd
df = pd.read_csv('/kaggle/input/nbagameinfo/nba_games (1).csv', index_col=0)
df
df = df.sort_values('date')
df
df = df.reset_index(drop=True)
df.drop(['mp.1', 'mp_opp.1', 'index_opp'], axis=1, inplace=True)
def add_target(team):
team['target'] = team['won'].shift(-1)
return team
df = df.groupby('team', group_keys=False).apply(add_target)
df[df['team'] == 'LAL']
df['target'][pd.isnull(df['target'])] = 2
df['target'] = df['target'].astype(int, errors='ignore')
nulls = pd.isnull(df)
nulls = nulls.sum()
nulls = nulls[nulls > 0]
valid_columns = df.columns[~df.columns.isin(nulls.index)]
df = df[valid_columns].copy()
df
|
code
|
105207802/cell_63
|
[
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] |
from sklearn.metrics import accuracy_score,plot_confusion_matrix
from sklearn.svm import SVC
svc = SVC()
svc.fit(X_train, y_train)
y_pred = svc.predict(X_test)
train_acc = round(svc.score(X_train, y_train) * 100, 1)
val_acc = round(accuracy_score(y_pred, y_test) * 100, 2)
plot_confusion_matrix(svc, X_test, y_test)
|
code
|
105207802/cell_25
|
[
"image_output_1.png"
] |
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No Failure']
df_fail = df[df['Target'] == 1]
df_fail = df_fail[df_fail['Failure Type'] != 'No Failure']
df1 = df[df['Failure Type'] == 'Heat Dissipation Failure']
df2 = df[df['Failure Type'] == 'Power Failure']
df3 = df[df['Failure Type'] == 'Overstrain Failure']
df4 = df[df['Failure Type'] == 'Tool Wear Failure']
df5 = df[df['Failure Type'] == 'Random Failures ']
x = df_pass['Type'].value_counts().plot.pie(explode=[0.5, 0.5, 0.5], autopct='%1.1f%%')
|
code
|
105207802/cell_57
|
[
"text_html_output_1.png"
] |
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No Failure']
df_fail = df[df['Target'] == 1]
df_fail = df_fail[df_fail['Failure Type'] != 'No Failure']
df1 = df[df['Failure Type'] == 'Heat Dissipation Failure']
df2 = df[df['Failure Type'] == 'Power Failure']
df3 = df[df['Failure Type'] == 'Overstrain Failure']
df4 = df[df['Failure Type'] == 'Tool Wear Failure']
df5 = df[df['Failure Type'] == 'Random Failures ']
df_fail = df_fail.drop(columns=['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]', 'Product ID', 'UDI', 'Temp_diff', 'td', 'Target'])
df_fail = pd.get_dummies(data=df_fail, columns=['Type'], drop_first=True)
scaler = LabelEncoder()
df_fail['Failure Type'] = scaler.fit_transform(df_fail['Failure Type'])
df_fail_targ = df_fail_train = df_fail.iloc[:, 0]
df_fail_train = df_fail.iloc[:, 1:]
df_fail
|
code
|
105207802/cell_34
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No Failure']
df_fail = df[df['Target'] == 1]
df_fail = df_fail[df_fail['Failure Type'] != 'No Failure']
df1 = df[df['Failure Type'] == 'Heat Dissipation Failure']
df2 = df[df['Failure Type'] == 'Power Failure']
df3 = df[df['Failure Type'] == 'Overstrain Failure']
df4 = df[df['Failure Type'] == 'Tool Wear Failure']
df5 = df[df['Failure Type'] == 'Random Failures ']
plt.figure(figsize=(18, 10))
sns.scatterplot(data=df_fail, x='Rotational speed [rpm]', y='Torque [Nm]', hue='Failure Type')
|
code
|
105207802/cell_23
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
x = df['Type'].value_counts().plot.pie(explode=[0.5, 0.5, 0.5], autopct='%1.1f%%')
|
code
|
105207802/cell_30
|
[
"image_output_1.png"
] |
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No Failure']
df_fail = df[df['Target'] == 1]
df_fail = df_fail[df_fail['Failure Type'] != 'No Failure']
df1 = df[df['Failure Type'] == 'Heat Dissipation Failure']
df2 = df[df['Failure Type'] == 'Power Failure']
df3 = df[df['Failure Type'] == 'Overstrain Failure']
df4 = df[df['Failure Type'] == 'Tool Wear Failure']
df5 = df[df['Failure Type'] == 'Random Failures ']
sns.displot(data=df_fail, x='Rotational speed [rpm]', hue='Failure Type', kde=True, bins=30, height=8, aspect=1.5)
|
code
|
105207802/cell_20
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No Failure']
df_fail = df[df['Target'] == 1]
df_fail = df_fail[df_fail['Failure Type'] != 'No Failure']
df1 = df[df['Failure Type'] == 'Heat Dissipation Failure']
df2 = df[df['Failure Type'] == 'Power Failure']
df3 = df[df['Failure Type'] == 'Overstrain Failure']
df4 = df[df['Failure Type'] == 'Tool Wear Failure']
df5 = df[df['Failure Type'] == 'Random Failures ']
sns.displot(data=df_fail, x='Process temperature [K]', hue='Failure Type', kde=True, bins=30, height=8, aspect=1.5)
|
code
|
105207802/cell_74
|
[
"text_html_output_1.png"
] |
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No Failure']
df_fail = df[df['Target'] == 1]
df_fail = df_fail[df_fail['Failure Type'] != 'No Failure']
df1 = df[df['Failure Type'] == 'Heat Dissipation Failure']
df2 = df[df['Failure Type'] == 'Power Failure']
df3 = df[df['Failure Type'] == 'Overstrain Failure']
df4 = df[df['Failure Type'] == 'Tool Wear Failure']
df5 = df[df['Failure Type'] == 'Random Failures ']
df.columns
df.columns
df_fail = df_fail.drop(columns=['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]', 'Product ID', 'UDI', 'Temp_diff', 'td', 'Target'])
df_fail = pd.get_dummies(data=df_fail, columns=['Type'], drop_first=True)
scaler = LabelEncoder()
df_fail['Failure Type'] = scaler.fit_transform(df_fail['Failure Type'])
df2 = df.drop(columns=['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]', 'Product ID', 'UDI', 'Temp_diff', 'td', 'Failure Type'])
df2 = pd.get_dummies(data=df2, columns=['Type'], drop_first=True)
train = df2.iloc[:, 1:]
tar = df2.iloc[:, 0]
df2
|
code
|
105207802/cell_76
|
[
"text_plain_output_1.png"
] |
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No Failure']
df_fail = df[df['Target'] == 1]
df_fail = df_fail[df_fail['Failure Type'] != 'No Failure']
df1 = df[df['Failure Type'] == 'Heat Dissipation Failure']
df2 = df[df['Failure Type'] == 'Power Failure']
df3 = df[df['Failure Type'] == 'Overstrain Failure']
df4 = df[df['Failure Type'] == 'Tool Wear Failure']
df5 = df[df['Failure Type'] == 'Random Failures ']
df.columns
df.columns
df_fail = df_fail.drop(columns=['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]', 'Product ID', 'UDI', 'Temp_diff', 'td', 'Target'])
df_fail = pd.get_dummies(data=df_fail, columns=['Type'], drop_first=True)
scaler = LabelEncoder()
df_fail['Failure Type'] = scaler.fit_transform(df_fail['Failure Type'])
df2 = df.drop(columns=['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]', 'Product ID', 'UDI', 'Temp_diff', 'td', 'Failure Type'])
df2 = pd.get_dummies(data=df2, columns=['Type'], drop_first=True)
train = df2.iloc[:, 1:]
tar = df2.iloc[:, 0]
tar
|
code
|
105207802/cell_40
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No Failure']
df_fail = df[df['Target'] == 1]
df_fail = df_fail[df_fail['Failure Type'] != 'No Failure']
df1 = df[df['Failure Type'] == 'Heat Dissipation Failure']
df2 = df[df['Failure Type'] == 'Power Failure']
df3 = df[df['Failure Type'] == 'Overstrain Failure']
df4 = df[df['Failure Type'] == 'Tool Wear Failure']
df5 = df[df['Failure Type'] == 'Random Failures ']
plt.figure(figsize=(18, 10))
sns.scatterplot(data=df_fail, x='Tool wear [min]', y='Rotational speed [rpm]', hue='Failure Type')
|
code
|
105207802/cell_48
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df.columns
df.columns
|
code
|
105207802/cell_61
|
[
"text_html_output_1.png"
] |
X_train
|
code
|
105207802/cell_11
|
[
"image_output_1.png"
] |
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df.head()
|
code
|
105207802/cell_69
|
[
"text_html_output_1.png"
] |
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No Failure']
df_fail = df[df['Target'] == 1]
df_fail = df_fail[df_fail['Failure Type'] != 'No Failure']
df1 = df[df['Failure Type'] == 'Heat Dissipation Failure']
df2 = df[df['Failure Type'] == 'Power Failure']
df3 = df[df['Failure Type'] == 'Overstrain Failure']
df4 = df[df['Failure Type'] == 'Tool Wear Failure']
df5 = df[df['Failure Type'] == 'Random Failures ']
df.columns
df.columns
df2 = df.drop(columns=['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]', 'Product ID', 'UDI', 'Temp_diff', 'td', 'Failure Type'])
df2
|
code
|
105207802/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 sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, plot_confusion_matrix
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
|
code
|
105207802/cell_45
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No Failure']
df_fail = df[df['Target'] == 1]
df_fail = df_fail[df_fail['Failure Type'] != 'No Failure']
df1 = df[df['Failure Type'] == 'Heat Dissipation Failure']
df2 = df[df['Failure Type'] == 'Power Failure']
df3 = df[df['Failure Type'] == 'Overstrain Failure']
df4 = df[df['Failure Type'] == 'Tool Wear Failure']
df5 = df[df['Failure Type'] == 'Random Failures ']
plt.figure(figsize=(18, 10))
sns.scatterplot(data=df_fail, x='td', y='Rotational speed [rpm]', hue='Failure Type')
|
code
|
105207802/cell_18
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No Failure']
df_fail = df[df['Target'] == 1]
df_fail = df_fail[df_fail['Failure Type'] != 'No Failure']
df1 = df[df['Failure Type'] == 'Heat Dissipation Failure']
df2 = df[df['Failure Type'] == 'Power Failure']
df3 = df[df['Failure Type'] == 'Overstrain Failure']
df4 = df[df['Failure Type'] == 'Tool Wear Failure']
df5 = df[df['Failure Type'] == 'Random Failures ']
sns.displot(data=df_fail, x='Air temperature [K]', hue='Failure Type', kde=True, bins=30, height=8, aspect=1.5)
|
code
|
105207802/cell_32
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No Failure']
df_fail = df[df['Target'] == 1]
df_fail = df_fail[df_fail['Failure Type'] != 'No Failure']
df1 = df[df['Failure Type'] == 'Heat Dissipation Failure']
df2 = df[df['Failure Type'] == 'Power Failure']
df3 = df[df['Failure Type'] == 'Overstrain Failure']
df4 = df[df['Failure Type'] == 'Tool Wear Failure']
df5 = df[df['Failure Type'] == 'Random Failures ']
sns.displot(data=df_fail, x='Rotational speed [rpm]', hue='Failure Type', kde=True, bins=30, height=8, aspect=1.5)
|
code
|
105207802/cell_62
|
[
"text_plain_output_1.png"
] |
from sklearn.metrics import accuracy_score,plot_confusion_matrix
from sklearn.svm import SVC
svc = SVC()
svc.fit(X_train, y_train)
y_pred = svc.predict(X_test)
train_acc = round(svc.score(X_train, y_train) * 100, 1)
val_acc = round(accuracy_score(y_pred, y_test) * 100, 2)
print('Training Accuracy :', train_acc, '%')
print('Model Accuracy Score :', val_acc, '%')
|
code
|
105207802/cell_59
|
[
"text_plain_output_1.png"
] |
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No Failure']
df_fail = df[df['Target'] == 1]
df_fail = df_fail[df_fail['Failure Type'] != 'No Failure']
df1 = df[df['Failure Type'] == 'Heat Dissipation Failure']
df2 = df[df['Failure Type'] == 'Power Failure']
df3 = df[df['Failure Type'] == 'Overstrain Failure']
df4 = df[df['Failure Type'] == 'Tool Wear Failure']
df5 = df[df['Failure Type'] == 'Random Failures ']
df_fail = df_fail.drop(columns=['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]', 'Product ID', 'UDI', 'Temp_diff', 'td', 'Target'])
df_fail = pd.get_dummies(data=df_fail, columns=['Type'], drop_first=True)
scaler = LabelEncoder()
df_fail['Failure Type'] = scaler.fit_transform(df_fail['Failure Type'])
df_fail_targ = df_fail_train = df_fail.iloc[:, 0]
df_fail_train = df_fail.iloc[:, 1:]
df_fail_targ
|
code
|
105207802/cell_58
|
[
"text_html_output_1.png"
] |
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No Failure']
df_fail = df[df['Target'] == 1]
df_fail = df_fail[df_fail['Failure Type'] != 'No Failure']
df1 = df[df['Failure Type'] == 'Heat Dissipation Failure']
df2 = df[df['Failure Type'] == 'Power Failure']
df3 = df[df['Failure Type'] == 'Overstrain Failure']
df4 = df[df['Failure Type'] == 'Tool Wear Failure']
df5 = df[df['Failure Type'] == 'Random Failures ']
df_fail = df_fail.drop(columns=['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]', 'Product ID', 'UDI', 'Temp_diff', 'td', 'Target'])
df_fail = pd.get_dummies(data=df_fail, columns=['Type'], drop_first=True)
scaler = LabelEncoder()
df_fail['Failure Type'] = scaler.fit_transform(df_fail['Failure Type'])
df_fail_targ = df_fail_train = df_fail.iloc[:, 0]
df_fail_train = df_fail.iloc[:, 1:]
df_fail_train
|
code
|
105207802/cell_78
|
[
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png",
"image_output_1.png"
] |
from sklearn.metrics import accuracy_score,plot_confusion_matrix
from sklearn.svm import SVC
svc = SVC()
svc.fit(X_train, y_train)
y_pred = svc.predict(X_test)
train_acc = round(svc.score(X_train, y_train) * 100, 1)
val_acc = round(accuracy_score(y_pred, y_test) * 100, 2)
svc = SVC()
svc.fit(X_train2, y_train2)
y_pred2 = svc.predict(X_test2)
train_acc2 = round(svc.score(X_train2, y_train2) * 100, 1)
val_acc2 = round(accuracy_score(y_pred2, y_test2) * 100, 2)
print('Training Accuracy:', train_acc2, '%')
print('Model Accuracy Score :', val_acc2, '%')
plot_confusion_matrix(svc, X_test, y_test)
|
code
|
105207802/cell_16
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No Failure']
df_fail = df[df['Target'] == 1]
df_fail = df_fail[df_fail['Failure Type'] != 'No Failure']
df1 = df[df['Failure Type'] == 'Heat Dissipation Failure']
df2 = df[df['Failure Type'] == 'Power Failure']
df3 = df[df['Failure Type'] == 'Overstrain Failure']
df4 = df[df['Failure Type'] == 'Tool Wear Failure']
df5 = df[df['Failure Type'] == 'Random Failures ']
sns.displot(data=df_fail, x='td', hue='Failure Type', kde=True, bins=30, height=8, aspect=1.5)
|
code
|
105207802/cell_75
|
[
"text_html_output_1.png"
] |
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No Failure']
df_fail = df[df['Target'] == 1]
df_fail = df_fail[df_fail['Failure Type'] != 'No Failure']
df1 = df[df['Failure Type'] == 'Heat Dissipation Failure']
df2 = df[df['Failure Type'] == 'Power Failure']
df3 = df[df['Failure Type'] == 'Overstrain Failure']
df4 = df[df['Failure Type'] == 'Tool Wear Failure']
df5 = df[df['Failure Type'] == 'Random Failures ']
df.columns
df.columns
df_fail = df_fail.drop(columns=['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]', 'Product ID', 'UDI', 'Temp_diff', 'td', 'Target'])
df_fail = pd.get_dummies(data=df_fail, columns=['Type'], drop_first=True)
scaler = LabelEncoder()
df_fail['Failure Type'] = scaler.fit_transform(df_fail['Failure Type'])
df2 = df.drop(columns=['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]', 'Product ID', 'UDI', 'Temp_diff', 'td', 'Failure Type'])
df2 = pd.get_dummies(data=df2, columns=['Type'], drop_first=True)
train = df2.iloc[:, 1:]
train
|
code
|
105207802/cell_47
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No Failure']
df_fail = df[df['Target'] == 1]
df_fail = df_fail[df_fail['Failure Type'] != 'No Failure']
df1 = df[df['Failure Type'] == 'Heat Dissipation Failure']
df2 = df[df['Failure Type'] == 'Power Failure']
df3 = df[df['Failure Type'] == 'Overstrain Failure']
df4 = df[df['Failure Type'] == 'Tool Wear Failure']
df5 = df[df['Failure Type'] == 'Random Failures ']
plt.figure(figsize=(18, 10))
sns.scatterplot(data=df_fail, x='td', y='Torque [Nm]', hue='Failure Type')
|
code
|
105207802/cell_66
|
[
"text_html_output_1.png"
] |
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df.columns
df.columns
df
|
code
|
105207802/cell_35
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df.columns
|
code
|
105207802/cell_43
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No Failure']
df_fail = df[df['Target'] == 1]
df_fail = df_fail[df_fail['Failure Type'] != 'No Failure']
df1 = df[df['Failure Type'] == 'Heat Dissipation Failure']
df2 = df[df['Failure Type'] == 'Power Failure']
df3 = df[df['Failure Type'] == 'Overstrain Failure']
df4 = df[df['Failure Type'] == 'Tool Wear Failure']
df5 = df[df['Failure Type'] == 'Random Failures ']
plt.figure(figsize=(18, 10))
sns.scatterplot(data=df_fail, x='Tool wear [min]', y='Torque [Nm]', hue='Failure Type')
|
code
|
105207802/cell_14
|
[
"text_html_output_1.png"
] |
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No Failure']
df_fail = df[df['Target'] == 1]
df_fail = df_fail[df_fail['Failure Type'] != 'No Failure']
df1 = df[df['Failure Type'] == 'Heat Dissipation Failure']
df2 = df[df['Failure Type'] == 'Power Failure']
df3 = df[df['Failure Type'] == 'Overstrain Failure']
df4 = df[df['Failure Type'] == 'Tool Wear Failure']
df5 = df[df['Failure Type'] == 'Random Failures ']
sns.displot(data=df_pass, x='td', hue='Failure Type', kde=True, bins=50, height=8, aspect=1.5)
|
code
|
105207802/cell_27
|
[
"image_output_1.png"
] |
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No Failure']
df_fail = df[df['Target'] == 1]
df_fail = df_fail[df_fail['Failure Type'] != 'No Failure']
df1 = df[df['Failure Type'] == 'Heat Dissipation Failure']
df2 = df[df['Failure Type'] == 'Power Failure']
df3 = df[df['Failure Type'] == 'Overstrain Failure']
df4 = df[df['Failure Type'] == 'Tool Wear Failure']
df5 = df[df['Failure Type'] == 'Random Failures ']
x = df_fail['Type'].value_counts().plot.pie(explode=[0.5, 0.5, 0.5], autopct='%1.1f%%')
|
code
|
105207802/cell_37
|
[
"text_plain_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No Failure']
df_fail = df[df['Target'] == 1]
df_fail = df_fail[df_fail['Failure Type'] != 'No Failure']
df1 = df[df['Failure Type'] == 'Heat Dissipation Failure']
df2 = df[df['Failure Type'] == 'Power Failure']
df3 = df[df['Failure Type'] == 'Overstrain Failure']
df4 = df[df['Failure Type'] == 'Tool Wear Failure']
df5 = df[df['Failure Type'] == 'Random Failures ']
sns.displot(data=df_fail, x='Tool wear [min]', hue='Failure Type', kde=True, bins=30, height=8, aspect=1.5)
|
code
|
105207802/cell_71
|
[
"text_html_output_1.png"
] |
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
df_pass = df[df['Target'] == 0]
df_pass = df_pass[df_pass['Failure Type'] == 'No Failure']
df_fail = df[df['Target'] == 1]
df_fail = df_fail[df_fail['Failure Type'] != 'No Failure']
df1 = df[df['Failure Type'] == 'Heat Dissipation Failure']
df2 = df[df['Failure Type'] == 'Power Failure']
df3 = df[df['Failure Type'] == 'Overstrain Failure']
df4 = df[df['Failure Type'] == 'Tool Wear Failure']
df5 = df[df['Failure Type'] == 'Random Failures ']
df.columns
df.columns
df_fail = df_fail.drop(columns=['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]', 'Product ID', 'UDI', 'Temp_diff', 'td', 'Target'])
df_fail = pd.get_dummies(data=df_fail, columns=['Type'], drop_first=True)
scaler = LabelEncoder()
df_fail['Failure Type'] = scaler.fit_transform(df_fail['Failure Type'])
df2 = df.drop(columns=['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]', 'Product ID', 'UDI', 'Temp_diff', 'td', 'Failure Type'])
df2 = pd.get_dummies(data=df2, columns=['Type'], drop_first=True)
df2
|
code
|
105207802/cell_5
|
[
"text_plain_output_1.png"
] |
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/machine-predictive-maintenance-classification/predictive_maintenance.csv')
x = df['Target'].value_counts().plot.pie(explode=[0.5, 0.5], autopct='%1.1f%%')
|
code
|
17112996/cell_4
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ipldf = pd.read_csv('../input/matches.csv')
print(ipldf.columns)
print(ipldf.values)
|
code
|
17112996/cell_2
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ipldf = pd.read_csv('../input/matches.csv')
print(ipldf.head(5))
|
code
|
17112996/cell_1
|
[
"text_plain_output_1.png"
] |
import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input'))
|
code
|
17112996/cell_7
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ipldf = pd.read_csv('../input/matches.csv')
idf = pd.DataFrame(ipldf.groupby('toss_winner').size())
print(idf.plot(kind='bar'))
|
code
|
17112996/cell_3
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ipldf = pd.read_csv('../input/matches.csv')
print(ipldf.describe())
|
code
|
17112996/cell_10
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ipldf = pd.read_csv('../input/matches.csv')
idf = pd.DataFrame(ipldf.groupby('toss_winner').size())
idf2 = ipldf.groupby('winner').size()
print(idf2)
|
code
|
17112996/cell_5
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ipldf = pd.read_csv('../input/matches.csv')
print(ipldf.groupby('season').size())
print(ipldf.groupby('season').size().plot(kind='bar'))
|
code
|
33112093/cell_25
|
[
"text_html_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
data.isnull().sum()
x = sns.PairGrid(data)
x = x.map(plt.scatter)
corr = data.corr()
data1 = data
data1.corr()
simple = data1[['carat', 'x', 'y', 'z', 'price']]
simple.shape
simple.corr()
|
code
|
33112093/cell_23
|
[
"text_html_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
data.isnull().sum()
x = sns.PairGrid(data)
x = x.map(plt.scatter)
corr = data.corr()
data1 = data
data1.corr()
simple = data1[['carat', 'x', 'y', 'z', 'price']]
simple.head()
|
code
|
33112093/cell_20
|
[
"text_plain_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
data.isnull().sum()
x = sns.PairGrid(data)
x = x.map(plt.scatter)
corr = data.corr()
data1 = data
data1.corr()
|
code
|
33112093/cell_11
|
[
"text_html_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
data.isnull().sum()
x = sns.PairGrid(data)
x = x.map(plt.scatter)
|
code
|
33112093/cell_19
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
data.isnull().sum()
x = sns.PairGrid(data)
x = x.map(plt.scatter)
corr = data.corr()
data1 = data
data1.head()
|
code
|
33112093/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
|
33112093/cell_7
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
data.isnull().sum()
|
code
|
33112093/cell_32
|
[
"text_plain_output_1.png"
] |
from sklearn.linear_model import *
lr = LinearRegression()
lr.fit(X_train, y_train)
print(lr.score(X_test, y_test))
|
code
|
33112093/cell_15
|
[
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
data.isnull().sum()
x = sns.PairGrid(data)
x = x.map(plt.scatter)
corr = data.corr()
print(data.cut.unique())
print(data.color.unique())
print(data.clarity.unique())
|
code
|
33112093/cell_3
|
[
"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/diamonds/diamonds.csv')
data.head(3)
|
code
|
33112093/cell_31
|
[
"text_plain_output_1.png"
] |
from sklearn.linear_model import *
lr = LinearRegression()
lr.fit(X_train, y_train)
|
code
|
33112093/cell_24
|
[
"text_html_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
data.isnull().sum()
x = sns.PairGrid(data)
x = x.map(plt.scatter)
corr = data.corr()
data1 = data
data1.corr()
simple = data1[['carat', 'x', 'y', 'z', 'price']]
simple.shape
|
code
|
33112093/cell_27
|
[
"text_plain_output_1.png"
] |
print(X_train.shape)
print(X_test.shape)
|
code
|
33112093/cell_12
|
[
"text_plain_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')
data.isnull().sum()
x = sns.PairGrid(data)
x = x.map(plt.scatter)
corr = data.corr()
sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns)
|
code
|
90142006/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
|
90142006/cell_5
|
[
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] |
from imblearn.over_sampling import SMOTE
from sklearn import preprocessing
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from sklearn.naive_bayes import BernoulliNB
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/review/reviews.csv')
label_encoder = preprocessing.LabelEncoder()
data['label'] = label_encoder.fit_transform(data['label'])
tfidf_vectorizer = TfidfVectorizer(max_features=5000, ngram_range=(2, 2))
X = tfidf_vectorizer.fit_transform(data['body'])
y = data['label']
smote = SMOTE(random_state=42)
X_res, y_res = smote.fit_resample(X, y)
logreg = LogisticRegression(solver='lbfgs', max_iter=1000, random_state=0)
naiveBayes = BernoulliNB()
logreg.fit(X_train, y_train)
clf1 = logreg.predict(X_test)
naiveBayes.fit(X_train, y_train)
clf2 = naiveBayes.predict(X_test)
len(clf1)
data['prediction'] = logreg.predict(X)
data.to_csv('logisticRegression.csv')
data['prediction'] = naiveBayes.predict(X)
data.to_csv('naiveBayes.csv')
print('Classification Report of Logistic Regression:\n', classification_report(y_test, clf1))
cm = confusion_matrix(y_test, clf1)
disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Negative', 'Neutral', 'Positive'])
disp.plot()
plt.show()
print('\n')
print('Classification Report of Naive Bayes:\n', classification_report(y_test, clf2))
cm = confusion_matrix(y_test, clf2)
disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Negative', 'Neutral', 'Positive'])
disp.plot()
plt.show()
|
code
|
18102611/cell_9
|
[
"text_plain_output_1.png"
] |
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=label)
pd.Series(data=arr, index=label)
pd.Series(d)
pd.Series(label)
ser1 = pd.Series(['TN', 'KL', 'AN'], label)
ser1
|
code
|
18102611/cell_4
|
[
"text_plain_output_1.png"
] |
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data)
|
code
|
18102611/cell_6
|
[
"text_plain_output_1.png"
] |
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=label)
pd.Series(data=arr, index=label)
|
code
|
18102611/cell_11
|
[
"text_plain_output_1.png"
] |
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=label)
pd.Series(data=arr, index=label)
pd.Series(d)
pd.Series(label)
ser1 = pd.Series(['TN', 'KL', 'AN'], label)
ser1
serr1 = pd.Series([1, 2, 3], ['USA', 'USSR', 'JAPAN'])
serr1
serr2 = pd.Series([1, 2, 3], ['SA', 'USSR', 'JAPAN'])
serr2
|
code
|
18102611/cell_7
|
[
"text_plain_output_1.png"
] |
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=label)
pd.Series(data=arr, index=label)
pd.Series(d)
|
code
|
18102611/cell_8
|
[
"text_plain_output_1.png"
] |
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=label)
pd.Series(data=arr, index=label)
pd.Series(d)
pd.Series(label)
|
code
|
18102611/cell_10
|
[
"text_plain_output_1.png"
] |
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=label)
pd.Series(data=arr, index=label)
pd.Series(d)
pd.Series(label)
ser1 = pd.Series(['TN', 'KL', 'AN'], label)
ser1
serr1 = pd.Series([1, 2, 3], ['USA', 'USSR', 'JAPAN'])
serr1
|
code
|
18102611/cell_12
|
[
"text_plain_output_1.png"
] |
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=label)
pd.Series(data=arr, index=label)
pd.Series(d)
pd.Series(label)
ser1 = pd.Series(['TN', 'KL', 'AN'], label)
ser1
serr1 = pd.Series([1, 2, 3], ['USA', 'USSR', 'JAPAN'])
serr1
serr2 = pd.Series([1, 2, 3], ['SA', 'USSR', 'JAPAN'])
serr2
serr1 + serr2
|
code
|
18102611/cell_5
|
[
"text_plain_output_1.png"
] |
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
label = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'ax': 10, 'by': 20, 'cz': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=label)
|
code
|
104129687/cell_9
|
[
"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/big-data-derby-2022/nyra_2019_complete.csv', delimiter=',', encoding='utf8')
pd.set_option('display.max_columns', None)
df.head().style.set_properties(**{'background-color': 'black', 'color': '#03e8fc'})
df['track_id'].value_counts()
|
code
|
104129687/cell_6
|
[
"text_html_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_2019_complete.csv', delimiter=',', encoding='utf8')
pd.set_option('display.max_columns', None)
df.head().style.set_properties(**{'background-color': 'black', 'color': '#03e8fc'})
df1 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv', delimiter=',', encoding='utf8')
pd.set_option('display.max_columns', None)
df1.head()
|
code
|
104129687/cell_1
|
[
"text_plain_output_1.png"
] |
import os
import plotly
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objs as go
import plotly
plotly.offline.init_notebook_mode(connected=True)
import warnings
warnings.filterwarnings('ignore')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
|
code
|
104129687/cell_7
|
[
"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/big-data-derby-2022/nyra_2019_complete.csv', delimiter=',', encoding='utf8')
pd.set_option('display.max_columns', None)
df.head().style.set_properties(**{'background-color': 'black', 'color': '#03e8fc'})
df1 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv', delimiter=',', encoding='utf8')
pd.set_option('display.max_columns', None)
df2 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv', delimiter=',', encoding='utf8')
pd.set_option('display.max_columns', None)
df2.head(3)
|
code
|
104129687/cell_8
|
[
"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/big-data-derby-2022/nyra_2019_complete.csv', delimiter=',', encoding='utf8')
pd.set_option('display.max_columns', None)
df.head().style.set_properties(**{'background-color': 'black', 'color': '#03e8fc'})
df1 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv', delimiter=',', encoding='utf8')
pd.set_option('display.max_columns', None)
df2 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv', delimiter=',', encoding='utf8')
pd.set_option('display.max_columns', None)
df3 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_race_table.csv', delimiter=',', encoding='utf8')
pd.set_option('display.max_columns', None)
df3.head()
|
code
|
104129687/cell_5
|
[
"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/big-data-derby-2022/nyra_2019_complete.csv', delimiter=',', encoding='utf8')
pd.set_option('display.max_columns', None)
df.head().style.set_properties(**{'background-color': 'black', 'color': '#03e8fc'})
|
code
|
17118428/cell_4
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/scmp2k19.csv')
df.info()
|
code
|
17118428/cell_1
|
[
"text_html_output_1.png",
"text_plain_output_1.png"
] |
from bokeh.io import output_file,show,output_notebook,push_notebook
import os
import numpy as np
import pandas as pd
import seaborn as sns
from ipywidgets import interact
from bokeh.io import output_file, show, output_notebook, push_notebook
from bokeh.plotting import *
from bokeh.models import ColumnDataSource, HoverTool, CategoricalColorMapper
from bokeh.layouts import row, column, gridplot, widgetbox
from bokeh.layouts import layout
from bokeh.embed import file_html
from bokeh.models import Text
from bokeh.models import Plot
from bokeh.models import Slider
from bokeh.models import Circle
from bokeh.models import Range1d
from bokeh.models import CustomJS
from bokeh.models import LinearAxis
from bokeh.models import SingleIntervalTicker
from bokeh.palettes import Spectral6
output_notebook()
import os
print(os.listdir('../input'))
|
code
|
18147081/cell_13
|
[
"text_html_output_1.png"
] |
import pandas as pd
train = pd.read_excel('../input/Data_Train.xlsx')
test = pd.read_excel('../input/Data_Test.xlsx')
(train.shape, test.shape)
train.profile_report()
train.sample(5)
categorical_feature = ['Name', 'Location', 'Fuel_Type', 'Transmission', 'Owner_Type']
train[categorical_feature].nunique()
|
code
|
18147081/cell_4
|
[
"text_plain_output_1.png"
] |
import pandas as pd
train = pd.read_excel('../input/Data_Train.xlsx')
test = pd.read_excel('../input/Data_Test.xlsx')
(train.shape, test.shape)
|
code
|
18147081/cell_20
|
[
"text_html_output_1.png",
"text_plain_output_1.png"
] |
import pandas as pd
train = pd.read_excel('../input/Data_Train.xlsx')
test = pd.read_excel('../input/Data_Test.xlsx')
(train.shape, test.shape)
train.profile_report()
train.sample(5)
test.sample(5)
list(train.Fuel_Type[~train.Fuel_Type.isin(test.Fuel_Type)].unique())
train = train[train.Fuel_Type != 'Electric']
train['Full_name'] = train.Name.copy()
test['Full_name'] = test.Name.copy()
train.Name.sample(20)
|
code
|
18147081/cell_6
|
[
"text_plain_output_1.png"
] |
import pandas as pd
train = pd.read_excel('../input/Data_Train.xlsx')
test = pd.read_excel('../input/Data_Test.xlsx')
(train.shape, test.shape)
train.profile_report()
|
code
|
18147081/cell_11
|
[
"text_plain_output_1.png"
] |
import pandas as pd
train = pd.read_excel('../input/Data_Train.xlsx')
test = pd.read_excel('../input/Data_Test.xlsx')
(train.shape, test.shape)
train.profile_report()
train.sample(5)
def missing_values_table(df):
mis_val = df.isnull().sum()
mis_val_percent = 100 * df.isnull().sum() / len(df)
mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)
mis_val_table_ren_columns = mis_val_table.rename(columns={0: 'Missing Values', 1: '% of Total Values'})
mis_val_table_ren_columns = mis_val_table_ren_columns[mis_val_table_ren_columns.iloc[:, 1] != 0].sort_values('% of Total Values', ascending=False).round(1)
return mis_val_table_ren_columns
missing_values_table(train)
|
code
|
18147081/cell_7
|
[
"text_plain_output_1.png"
] |
import pandas as pd
train = pd.read_excel('../input/Data_Train.xlsx')
test = pd.read_excel('../input/Data_Test.xlsx')
(train.shape, test.shape)
train.profile_report()
train.sample(5)
|
code
|
18147081/cell_8
|
[
"text_plain_output_1.png"
] |
import pandas as pd
train = pd.read_excel('../input/Data_Train.xlsx')
test = pd.read_excel('../input/Data_Test.xlsx')
(train.shape, test.shape)
test.sample(5)
|
code
|
18147081/cell_16
|
[
"text_html_output_1.png",
"text_plain_output_1.png"
] |
import pandas as pd
train = pd.read_excel('../input/Data_Train.xlsx')
test = pd.read_excel('../input/Data_Test.xlsx')
(train.shape, test.shape)
train.profile_report()
train.sample(5)
test.sample(5)
list(train.Fuel_Type[~train.Fuel_Type.isin(test.Fuel_Type)].unique())
|
code
|
18147081/cell_14
|
[
"text_html_output_1.png"
] |
import pandas as pd
train = pd.read_excel('../input/Data_Train.xlsx')
test = pd.read_excel('../input/Data_Test.xlsx')
(train.shape, test.shape)
test.sample(5)
categorical_feature = ['Name', 'Location', 'Fuel_Type', 'Transmission', 'Owner_Type']
test[categorical_feature].nunique()
|
code
|
18147081/cell_22
|
[
"text_plain_output_1.png"
] |
import pandas as pd
train = pd.read_excel('../input/Data_Train.xlsx')
test = pd.read_excel('../input/Data_Test.xlsx')
(train.shape, test.shape)
train.profile_report()
train.sample(5)
test.sample(5)
list(train.Fuel_Type[~train.Fuel_Type.isin(test.Fuel_Type)].unique())
train = train[train.Fuel_Type != 'Electric']
train['Full_name'] = train.Name.copy()
test['Full_name'] = test.Name.copy()
train.Name.sample(20)
temp1 = list(train.Name.str.split(' ').str[0].unique())
temp2 = list(test.Name.str.split(' ').str[0].unique())
temp3 = [item for item in temp1 if item not in temp2]
temp3
|
code
|
18147081/cell_12
|
[
"text_html_output_1.png",
"text_plain_output_1.png"
] |
import pandas as pd
train = pd.read_excel('../input/Data_Train.xlsx')
test = pd.read_excel('../input/Data_Test.xlsx')
(train.shape, test.shape)
test.sample(5)
def missing_values_table(df):
mis_val = df.isnull().sum()
mis_val_percent = 100 * df.isnull().sum() / len(df)
mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)
mis_val_table_ren_columns = mis_val_table.rename(columns={0: 'Missing Values', 1: '% of Total Values'})
mis_val_table_ren_columns = mis_val_table_ren_columns[mis_val_table_ren_columns.iloc[:, 1] != 0].sort_values('% of Total Values', ascending=False).round(1)
return mis_val_table_ren_columns
missing_values_table(test)
|
code
|
104124186/cell_21
|
[
"text_plain_output_1.png"
] |
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/salary/Salary_Data.csv')
data
df = pd.DataFrame(data)
df
x = df.YearsExperience.values.reshape(-1, 1)
y = df.Salary.values.reshape(-1, 1)
regressor = LinearRegression()
regressor.fit(x_train, y_train)
y_pred = regressor.predict(x_test)
y_pred
a = x_test
b = y_test
c = x_test
d = y_pred
a = x_test
b = y_test
c = x_test
d = y_pred
compare = pd.DataFrame({'Actual': y_test.flatten(), 'Prediction': y_pred.flatten()})
compare
a = x_train
b = y_train
c = x_test
d = y_pred
df2 = pd.DataFrame({'YearsExperience': [1.5, 2.5, 3.5, 4.5, 5], 'Salary': [1, 4, 8, 9, 10]})
df3 = df.append(df2)
df3
train = df3.iloc[:25]
test = df3.iloc[25:]
x_train = df3['YearsExperience'][:30].values.reshape(-1, 1)
y_train = df3['Salary'][:30].values.reshape(-1, 1)
x_test = df3['YearsExperience'][30:].values.reshape(-1, 1)
regressor.fit(x_train, y_train)
y_pred = regressor.predict(x_test)
plt.scatter(x_test, y_pred)
plt.grid()
plt.show()
|
code
|
104124186/cell_13
|
[
"text_plain_output_1.png"
] |
from sklearn.linear_model import LinearRegression
import pandas as pd
data = pd.read_csv('../input/salary/Salary_Data.csv')
data
df = pd.DataFrame(data)
df
regressor = LinearRegression()
regressor.fit(x_train, y_train)
y_pred = regressor.predict(x_test)
y_pred
compare = pd.DataFrame({'Actual': y_test.flatten(), 'Prediction': y_pred.flatten()})
compare
|
code
|
104124186/cell_9
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
regressor = LinearRegression()
regressor.fit(x_train, y_train)
y_pred = regressor.predict(x_test)
y_pred
a = x_test
b = y_test
c = x_test
d = y_pred
a = x_test
b = y_test
c = x_test
d = y_pred
plt.scatter(a, b)
plt.plot(c, d)
plt.grid()
plt.show()
|
code
|
104124186/cell_6
|
[
"text_html_output_1.png"
] |
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(x_train, y_train)
|
code
|
104124186/cell_2
|
[
"text_plain_output_1.png"
] |
import pandas as pd
data = pd.read_csv('../input/salary/Salary_Data.csv')
data
|
code
|
104124186/cell_11
|
[
"text_html_output_1.png"
] |
from sklearn import metrics
from sklearn.linear_model import LinearRegression
import numpy as np
regressor = LinearRegression()
regressor.fit(x_train, y_train)
y_pred = regressor.predict(x_test)
y_pred
print('mean absolute error: ', metrics.mean_absolute_error(y_test, y_pred))
print('mean squared_error: ', metrics.mean_squared_error(y_test, y_pred))
print('root mean absolte error: ', np.sqrt(metrics.mean_absolute_error(y_test, y_pred)))
print('r2score:', metrics.r2_score(y_test, y_pred))
|
code
|
104124186/cell_7
|
[
"image_output_1.png"
] |
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(x_train, y_train)
y_pred = regressor.predict(x_test)
y_pred
|
code
|
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