path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
129011222/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
FOLDS = 5
STRA... | code |
129011222/cell_5 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import plotly.express as px
import matplotlib.pyplot as plt
import plotly.graph_objects as go
from plotly.subplots import make_subplots | code |
129011222/cell_36 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
fig = make_subplots(rows=1, cols=2,
column_titles = ["Train Data", "Test Data",],
x_title = "Missing Values")
fig.show() | code |
72120326/cell_13 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from xgboost import XGBRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_filepath = '../input/30-days-of-ml/train.csv'
test_filepath = '../input/30-days-of-ml/train.csv'
submission = '../input/30-days-of-ml/sample_submission.csv'
t... | code |
72120326/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_filepath = '../input/30-days-of-ml/train.csv'
test_filepath = '../input/30-days-of-ml/train.csv'
submission = '../input/30-days-of-ml/sample_submission.csv'
train = pd.read_csv(train_filepath)
test = pd.read_csv(test_filepath)
categorical_c... | code |
72120326/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_filepath = '../input/30-days-of-ml/train.csv'
test_filepath = '../input/30-days-of-ml/train.csv'
submission = '../input/30-days-of-ml/sample_submission.csv'
train = pd.read_csv(train_filepath)
test = pd.read_csv(test_filepath)
train.columns | code |
72120326/cell_11 | [
"text_html_output_1.png"
] | from xgboost import XGBRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_filepath = '../input/30-days-of-ml/train.csv'
test_filepath = '../input/30-days-of-ml/train.csv'
submission = '../input/30-days-of-ml/sample_submission.csv'
train = pd.read_csv(train_filepath)
test = pd.read... | code |
72120326/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 |
72120326/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from xgboost import XGBRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_filepath = '../input/30-days-of-ml/train.csv'
test_filepath = '../input/30-days-of-ml/train.csv'
submission = '../input/30-days-of-ml/sample_submission.csv'
t... | code |
72120326/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from xgboost import XGBRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_filepath = '../input/30-days-of-ml/train.csv'
test_filepath = '../input/30-days-of-ml/train.csv'
submission = '../input/30-days-of-ml/sample_submission.csv'
t... | code |
72120326/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_filepath = '../input/30-days-of-ml/train.csv'
test_filepath = '../input/30-days-of-ml/train.csv'
submission = '../input/30-days-of-ml/sample_submission.csv'
train = pd.read_csv(train_filepath)
test = pd.read_csv(test_filepath)
train.head() | code |
122258112/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airquality2022/Konvertirani_vrednosti_novi1.csv')
df = df.drop(['Unnamed: 0'], axis=1)
df
df['day-night'] = df['hour'].apply(lambda x: 'day' if x in range(8, 20) else 'night')
df['gr... | code |
122258112/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airquality2022/Konvertirani_vrednosti_novi1.csv')
df = df.drop(['Unnamed: 0'], axis=1)
df
df['day-night'] = df['hour'].apply(lambda x: 'day' if x in range(8, 20) else 'night')
df['grejna-negrejna'] = df.apply(lambda... | code |
122258112/cell_2 | [
"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/airquality2022/Konvertirani_vrednosti_novi1.csv')
df = df.drop(['Unnamed: 0'], axis=1)
df | code |
122258112/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
122258112/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airquality2022/Konvertirani_vrednosti_novi1.csv')
df = df.drop(['Unnamed: 0'], axis=1)
df
df['day-night'] = df['hour'].apply(lambda x: 'day' if x in range(8, 20) else 'night')
df['gr... | code |
122258112/cell_16 | [
"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)
df = pd.read_csv('/kaggle/input/airquality2022/Konvertirani_vrednosti_novi1.csv')
df = df.drop(['Unnamed: 0'], axis=1)
df
df['day-night'] = df['hour'].apply(lambda x: 'day' if x in range(8, 20) else 'night')
df['gr... | code |
122258112/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airquality2022/Konvertirani_vrednosti_novi1.csv')
df = df.drop(['Unnamed: 0'], axis=1)
df
df['UTC1'] = pd.to_datetime(df['UTC'], format='%m/%d/%Y %H:%M')
df['year'] = df['UTC1'].dt.year
df | code |
122258112/cell_14 | [
"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)
df = pd.read_csv('/kaggle/input/airquality2022/Konvertirani_vrednosti_novi1.csv')
df = df.drop(['Unnamed: 0'], axis=1)
df
df['day-night'] = df['hour'].apply(lambda x: 'day' if x in range(8, 20) else 'night')
df['gr... | code |
122258112/cell_10 | [
"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)
df = pd.read_csv('/kaggle/input/airquality2022/Konvertirani_vrednosti_novi1.csv')
df = df.drop(['Unnamed: 0'], axis=1)
df
df['day-night'] = df['hour'].apply(lambda x: 'day' if x in range(8, 20) else 'night')
df['gr... | code |
122258112/cell_12 | [
"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)
df = pd.read_csv('/kaggle/input/airquality2022/Konvertirani_vrednosti_novi1.csv')
df = df.drop(['Unnamed: 0'], axis=1)
df
df['day-night'] = df['hour'].apply(lambda x: 'day' if x in range(8, 20) else 'night')
df['gr... | code |
122258112/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airquality2022/Konvertirani_vrednosti_novi1.csv')
df = df.drop(['Unnamed: 0'], axis=1)
df
df['day-night'] = df['hour'].apply(lambda x: 'day' if x in range(8, 20) else 'night')
df['grejna-negrejna'] = df.apply(lambda... | code |
74052794/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/cars-moldova/cars.csv')
data.duplicated().sum()
data = data.drop_duplicates()
data.info() | code |
74052794/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/cars-moldova/cars.csv')
data.describe() | code |
74052794/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/cars-moldova/cars.csv')
data.duplicated().sum()
data = data.drop_duplicates()
data = data.reset_index(drop=True)
question_dist = data[(data.Year < 2021) & (data.Distance < 1000)]
data = data.drop(question_dist.inde... | code |
74052794/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/cars-moldova/cars.csv')
data.duplicated().sum() | code |
74052794/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/cars-moldova/cars.csv')
data.duplicated().sum()
data = data.drop_duplicates()
data = data.reset_index(drop=True)
question_dist = data[(data.Year < 2021) & (data.Distance < 1000)]
data = data.drop(question_dist.inde... | code |
74052794/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/cars-moldova/cars.csv')
data.duplicated().sum()
data = data.drop_duplicates()
data = data.reset_index(drop=True)
data.tail() | code |
74052794/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 |
74052794/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/cars-moldova/cars.csv')
data.duplicated().sum()
data = data.drop_duplicates()
data = data.reset_index(drop=True)
question_dist = data[(data.Year < 2021) & (data.Distance < 1000)]
data = data.drop(question_dist.inde... | code |
74052794/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/cars-moldova/cars.csv')
data.duplicated().sum()
data = data.drop_duplicates()
data = data.reset_index(drop=True)
question_dist = data[(data.Year < 2021) & (data.Distance < 1000)]
data = data.drop(question_dist.inde... | code |
74052794/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/cars-moldova/cars.csv')
data.duplicated().sum()
data = data.drop_duplicates()
data.tail() | code |
74052794/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('../input/cars-moldova/cars.csv')
data.head() | code |
74052794/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/cars-moldova/cars.csv')
data.duplicated().sum()
data = data.drop_duplicates()
data = data.reset_index(drop=True)
question_dist = data[(data.Year < 2021) & (data.Distance < 1000)]
data = data.drop(question_dist.inde... | code |
74052794/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/cars-moldova/cars.csv')
data.duplicated().sum()
data = data.drop_duplicates()
data = data.reset_index(drop=True)
question_dist = data[(data.Year < 2021) & (data.Distance < 1000)]
question_dist.describe() | code |
74052794/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/cars-moldova/cars.csv')
data.duplicated().sum()
data = data.drop_duplicates()
data = data.reset_index(drop=True)
question_dist = data[(data.Year < 2021) & (data.Distance < 1000)]
data = data.drop(question_dist.inde... | code |
128027676/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df = df.drop('url', axis=1)
df.isnull().sum()
df = df.dropna()
df['sale'] = df['sale'].str.rstrip('%').astype('float') / 100.0 | code |
128027676/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df = df.drop('url', axis=1)
df.isnull().sum() | code |
128027676/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df | code |
128027676/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df = df.drop('url', axis=1)
df.isnull().sum()
df = df.dropna()
corr = df.corr()
furniture_type = df['type'].value_counts()
plt.figure(figsize=(10, 15))
ax = sns.barplot(y=furnitu... | code |
128027676/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df.info() | code |
128027676/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df = df.drop('url', axis=1)
df.isnull().sum()
df = df.dropna()
corr = df.corr()
furniture_type = df['type'].value_counts()
print(furniture_type)
print(len(furniture_type)) | code |
128027676/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df = df.drop('url', axis=1)
df.isnull().sum()
df = df.dropna()
corr = df.corr()
sns.heatmap(corr, annot=True, linewidth=0.5) | code |
128027676/cell_8 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df = df.drop('url', axis=1)
df | code |
128027676/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df = df.drop('url', axis=1)
df.isnull().sum()
df = df.dropna()
for i in df.columns:
print(f'{i:15}: {df[i].nunique()} unique values') | code |
128027676/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df = df.drop('url', axis=1)
df.isnull().sum()
df = df.dropna()
plt.figure(figsize=(12, 5))
sns.displot(df['rate'])
plt.suptitle('Distribution of the rate')
plt.show() | code |
128027676/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df = df.drop('url', axis=1)
df.isnull().sum()
df = df.dropna()
plt.figure(figsize=(12, 5))
sns.kdeplot(df['delivery'], color='b', shade=True)
plt.suptitle('Distribution of the deli... | code |
128027676/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df = df.drop('url', axis=1)
df.isnull().sum()
df = df.dropna()
df.describe() | code |
128027676/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/furniture-price-prediction/Furniture Price Prediction.csv')
df = df.drop('url', axis=1)
df.isnull().sum()
df = df.dropna()
corr = df.corr()
furniture_type = df['type'].value_counts()
plt.figure(figsize=(10,15))
ax = sns.barplot(y = furnit... | code |
72091432/cell_13 | [
"text_plain_output_1.png"
] | from pyspark.ml import Pipeline
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.feature import StringIndexer, VectorIndexer, StringIndexerModel, IndexToString
from pyspark.ml.feature import VectorAssembler
from pyspark.sql import SparkSession
import pandas as pd
import pyspark
from pys... | code |
72091432/cell_9 | [
"text_plain_output_1.png"
] | from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.feature import StringIndexer, VectorIndexer, StringIndexerModel, IndexToString
from pyspark.ml.feature import VectorAssembler
from pyspark.sql import SparkSession
import pandas as pd
import pyspark
from pyspark.ml.classification import Dec... | code |
72091432/cell_6 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
import pandas as pd
import pyspark
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.linalg import Vectors
from pyspark.ml.feature import VectorAssembler
from pyspark.ml import Pipeline
from pyspark.sql import SparkSession
from pyspark.ml.feature import S... | code |
72091432/cell_2 | [
"text_plain_output_1.png"
] | !pip install pyspark | code |
72091432/cell_11 | [
"text_plain_output_1.png"
] | from pyspark.ml import Pipeline
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.feature import StringIndexer, VectorIndexer, StringIndexerModel, IndexToString
from pyspark.ml.feature import VectorAssembler
from pyspark.sql import SparkSession
import pandas as pd
import pyspark
from pys... | code |
72091432/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 |
72091432/cell_7 | [
"text_plain_output_1.png"
] | from pyspark.ml.feature import StringIndexer, VectorIndexer, StringIndexerModel, IndexToString
from pyspark.sql import SparkSession
import pandas as pd
import pyspark
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.linalg import Vectors
from pyspark.ml.feature import VectorAssembler
from ... | code |
72091432/cell_8 | [
"text_plain_output_1.png"
] | from pyspark.ml.feature import StringIndexer, VectorIndexer, StringIndexerModel, IndexToString
from pyspark.ml.feature import VectorAssembler
from pyspark.sql import SparkSession
import pandas as pd
import pyspark
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.linalg import Vectors
from... | code |
72091432/cell_14 | [
"text_plain_output_1.png"
] | from pyspark.ml import Pipeline
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.feature import StringIndexer, VectorIndexer, StringIndexerModel, IndexToString
from pyspark.ml.feature import VectorAssembler
from pyspark.sql import SparkSession
import pandas as pd
import pyspark
from pys... | code |
72091432/cell_5 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
import pandas as pd
import pyspark
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.linalg import Vectors
from pyspark.ml.feature import VectorAssembler
from pyspark.ml import Pipeline
from pyspark.sql import SparkSession
from pyspark.ml.feature import S... | code |
1006176/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
train_df = train_df[pd.isnull(train_df['Age']) == False]
features = train_df.drop(['PassengerId', 'Survived', 'Name', 'Ticket'], axis=1)
labels = train_df['Survived']
n_samples = len(train_df)
n_features... | code |
1006176/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../... | code |
1006176/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1006176/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../... | code |
1006176/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_... | code |
1006176/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
train_df = train_df[pd.isnull(train_df['Age']) == False]
features = train_df.drop(['PassengerId', 'Survived', 'Name', 'Ticket'], axis=1)
labels = train_df['Survived']
n_samples = len(train_df)
n_features... | code |
1006176/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
train_df = train_df[pd.isnull(train_df['Age']) == False]
features = train_df.drop(['PassengerId', 'Survived', 'Name', 'Ticket'], axis=1)
labels = train_df['Survived']
n_samples = len(train_df)
n_features... | code |
17103363/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
df_train1 = df_train.drop... | code |
17103363/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
df_train1 = df_train.drop(['belongs_to_collection'], axis=1)
df_test = df_test.dr... | code |
17103363/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
df_train['revenue'].min() | code |
17103363/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.head() | code |
17103363/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
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
df_train1 = df_train.drop... | code |
17103363/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns) | code |
17103363/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
for j, k in enumerate(df_train['belongs_to_collection'][:5]):
print(j, k) | code |
17103363/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
df_train1 = df_train.drop... | code |
17103363/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
print(os.listdir('../input')) | code |
17103363/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum() | code |
17103363/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
df_train1 = df_train.drop... | code |
17103363/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any() | code |
17103363/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
df_train1 = df_train.drop... | code |
17103363/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
df_train1 = df_train.drop... | code |
17103363/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.info() | code |
17103363/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
df_train1 = df_train.drop... | code |
17103363/cell_24 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().a... | code |
17103363/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
df_train1 = df_train.drop(['belongs_to_collection'], axis=1)
df_test = df_test.dr... | code |
17103363/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().a... | code |
17103363/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
list(df_train.columns)
df_train.isna().sum()
df_train.isnull().any().any()
df_train['revenue'].max() | code |
17103363/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)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
print(df_train.shape, df_test.shape) | code |
18111717/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
drugTree = DecisionTreeClassifier(criterion='entropy', max_depth=4)
drugTree.fit(x_train, y_train) | code |
18111717/cell_6 | [
"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/drug200.csv', delimiter=',')
df.dropna
df.head() | code |
18111717/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18111717/cell_18 | [
"text_html_output_1.png"
] | print(x_train.shape)
print(y_train.shape)
print(x_test.shape)
print(y_test.shape) | code |
18111717/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('../input/drug200.csv', delimiter=',')
df.dropna
from sklearn.tree import DecisionTreeClassifier
X = df[['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K']].values
X | code |
18111717/cell_24 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.tree import DecisionTreeClassifier
drugTree = DecisionTreeClassifier(criterion='entropy', max_depth=4)
drugTree.fit(x_train, y_train)
y_predict = drugTree.predict(x_test)
y_predict
from sklearn import metrics
import matplotlib.pyplot as plt
metrics.accuracy_score(y_test, y_... | code |
18111717/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
drugTree = DecisionTreeClassifier(criterion='entropy', max_depth=4)
drugTree.fit(x_train, y_train)
y_predict = drugTree.predict(x_test)
y_predict | code |
18111717/cell_12 | [
"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/drug200.csv', delimiter=',')
df.dropna
from sklearn.tree import DecisionTreeClassifier
X = df[['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K']].values
X
X[:5] | code |
18111717/cell_5 | [
"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/drug200.csv', delimiter=',')
df.dropna | code |
105193696/cell_21 | [
"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_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/ny... | code |
105193696/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_race_table.csv')
d... | code |
105193696/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_race_table.csv')
d... | code |
105193696/cell_25 | [
"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_tracking = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')
df_start = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df_race = pd.read_csv('/kaggle/input/big-data-derby-2022/ny... | code |
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