path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
2007618/cell_14 | [
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
def priceOverTime(data, label):
"""Plot price over time"""
priceOverTime(newdf3, 'California')
priceOverTime(newdf4, 'Colorado')
priceOverTime(newdf5, 'Michigan')
def priceOverTime2(data, label):
pass
priceOverTime2(newdf6, 'San Francisco')
priceOverTime2(ne... | code |
2007618/cell_5 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import seaborn as sns
def plotDistribution(data, metric):
""" Plot distributions """
sns.set_style('whitegrid')
distributionTwo = sns.FacetGrid(data, hue='RegionName', aspect=2.5)
distributionTwo.map(sns.kdeplot, metric, shade=True)
distributionTwo.set(xlim=(100000, 550000))
distributionTwo.add... | code |
74050915/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/water-potability/water_potability.csv')
fig, ax = plt.subplots(figsize=(12,6))
sns.heatmap(df.isnull(), ax=ax)
ax.set_title('Null values')
df.loc[df_notnull_c... | code |
74050915/cell_4 | [
"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/water-potability/water_potability.csv')
df.describe() | code |
74050915/cell_2 | [
"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/water-potability/water_potability.csv')
df.head(5) | code |
74050915/cell_1 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
plt.style.use('ggplot')
import numpy as np
import pandas as pd
import os
import seaborn as sns
from scipy import stats
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
74050915/cell_7 | [
"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
df = pd.read_csv('/kaggle/input/water-potability/water_potability.csv')
fig, ax = plt.subplots(figsize=(12,6))
sns.heatmap(df.isnull(), ax=ax)
ax.set_title('Null values')
df['Potability'].va... | code |
74050915/cell_3 | [
"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/water-potability/water_potability.csv')
df.info() | code |
74050915/cell_5 | [
"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
df = pd.read_csv('/kaggle/input/water-potability/water_potability.csv')
fig, ax = plt.subplots(figsize=(12, 6))
sns.heatmap(df.isnull(), ax=ax)
ax.set_title('Null values') | code |
105207156/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
sns.set(rc={'figure.figsize': (11.7, 8.27)}... | code |
105207156/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
sns.set(rc={'figure.figsize': (11.7, 8.27)}... | code |
105207156/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
plt.figure(figsize=(10, 6))
data_pie = [73452, 56428]
labels = ['1', '0']
explode = [0.1, 0]
plt.pie(data_pie, labels=labels, explode=explode, autopct='%1.2f%%', shadow=True, colors=['#256D85', '#3BACB6'])
plt.legend()
plt.show() | code |
105207156/cell_25 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
sns.set(rc={'figure.figsize': (11.7, 8.27)}... | code |
105207156/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
sns.set(rc={'figure.figsize': (11.7, 8.27)})
from sklearn.neighbors import K... | code |
105207156/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
sns.set(rc={'figure.figsize': (11.7, 8.27)}... | code |
105207156/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
sns.set(rc={'figure.figsize': (11.7, 8.27)})
from sklearn.neighbors import K... | code |
105207156/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
sns.set(rc={'figure.figsize': (11.7, 8.27)}... | code |
105207156/cell_2 | [
"image_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
sns.set(rc={'figure.figsize': (11.7, 8.27)})
from sklearn.neighbors import K... | code |
105207156/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
sns.set(rc={'figure.figsize': (11.7, 8.27)}... | code |
105207156/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
sns.set(rc={'figure.figsize': (11.7, 8.27)}... | code |
105207156/cell_1 | [
"text_plain_output_1.png"
] | import os
import seaborn as sns
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
sns.set(rc={'figure.figsize': (11.7, 8.27)})
from sklearn.neighbors import KNeighborsClassifier
for dirname, _, filenames in os.walk('/kaggle/input'... | code |
105207156/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
sns.set(rc={'figure.figsize': (11.7, 8.27)}... | code |
105207156/cell_32 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
sns.set(rc={'figure.figsize': (11.7, 8.27)}... | code |
105207156/cell_8 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
sns.set(rc={'figure.figsize': (11.7, 8.27)})
from sklearn.neighbors import K... | code |
105207156/cell_3 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
sns.set(rc={'figure.figsize': (11.7, 8.27)})
from sklearn.neighbors import K... | code |
105207156/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
sns.set(rc={'figure.figsize': (11.7, 8.27)}... | code |
105207156/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
sns.set(rc={'figure.figsize': (11.7, 8.27)}... | code |
105207156/cell_24 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
sns.set(rc={'figure.figsize': (11.7, 8.27)}... | code |
105207156/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
sns.set(rc={'figure.figsize': (11.7, 8.27)}... | code |
105207156/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
sns.set(rc={'figure.figsize': (11.7, 8.27)}... | code |
105207156/cell_10 | [
"text_html_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
sns.set(rc={'figure.figsize': (11.7, 8.27)})
from sklearn.neighbors import K... | code |
105207156/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
sns.set(rc={'figure.figsize': (11.7, 8.27)}... | code |
105207156/cell_5 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
sns.set(rc={'figure.figsize': (11.7, 8.27)})
from sklearn.neighbors import K... | code |
73080128/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras import layers
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import pandas as pd
import te... | code |
73080128/cell_9 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
df = pd.read_json('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', lines=True)
dfv2 = pd.read_json('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset_v2.json', lines=... | code |
73080128/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras import layers
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import matplotlib.pyplot as pl... | code |
73080128/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_json('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', lines=True)
dfv2 = pd.read_json('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset_v2.json', lines=True)
df = pd.concat([df, dfv2])
df.head() | code |
73080128/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras import layers
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import pandas as pd
import te... | code |
73080128/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_json('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', lines=True)
dfv2 = pd.read_json('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset_v2.json', lines=True)
df = pd.concat([df, dfv2])
df['is_sarcastic'].v... | code |
73080128/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras import layers
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import matplotlib.pyplot as pl... | code |
73080128/cell_7 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_json('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', lines=True)
dfv2 = pd.read_json('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset_v2.json', lines=True)
df = pd.concat([df, dfv2]... | code |
73080128/cell_17 | [
"text_plain_output_1.png"
] | from tensorflow.keras import layers
import tensorflow as tf
import tensorflow as tf
vocab_size = 10000
max_length = 32
embedding_dim = 16
oov_token = '<oov>'
padding_type = 'post'
trunc_type = 'post'
model = tf.keras.models.Sequential([layers.Embedding(vocab_size, embedding_dim, input_length=max_length), layers.Fla... | code |
73080128/cell_12 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import pandas as pd
df = pd.read_json('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', lines=Tr... | code |
73080128/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_json('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', lines=True)
dfv2 = pd.read_json('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset_v2.json', lines=True)
df = pd.concat([df, dfv2])
df.info() | code |
128019578/cell_1 | [
"text_plain_output_1.png"
] | !pip install torchsummary | code |
34136064/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, plot_confusion_matrix
from sklearn.metrics import precision_recall_curve, plot_precision_recall_curve
import matplotlib.pyplot as plt
import matplotlib.pyplo... | code |
34136064/cell_40 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, plot_confusion_matrix
from sklearn.metrics import precision_recall_curve, plot_precision_recall_curve
from sklearn.preprocessing import LabelEncoder
import m... | code |
34136064/cell_39 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, plot_confusion_matrix
from sklearn.metrics import precision_recall_curve, plot_precision_recall_curve
from sklearn.preprocessing import LabelEncoder
import m... | code |
34136064/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/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
data
data.isna().sum()
data.drop(['salary', 'sl_no'], axis=1, inplace=True)
data.isna().sum() | code |
34136064/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
best_score = -1
best_estimators = 0
for i in range(10, 250):
model = RandomForestClassifier(n_estimators=i, random_state=0)
model.fit(train_x, train_y)
pred = model.predict(test_x)
score = accuracy_score(pre... | code |
34136064/cell_8 | [
"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/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
data
data.isna().sum()
data.drop(['salary', 'sl_no'], axis=1, inplace=True)
data.isna().sum()
data.nunique() | code |
34136064/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
data
data.isna().sum()
data.drop(['salary', 'sl_no'], axis=1, inplace=True)
data.isna().sum()
da... | code |
34136064/cell_38 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, plot_confusion_matrix
from sklearn.metrics import precision_recall_curve, plot_precision_recall_curve
from sklearn.preprocessing import LabelEncoder
import m... | code |
34136064/cell_3 | [
"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/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
data | code |
34136064/cell_17 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
data
data.isna().sum()
data.drop(['salary', 'sl_no'], axis=1, inplace=True)
data.isna().sum()
da... | code |
34136064/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
data
data.isna().sum()
data.drop(['salary', 'sl_no'], axis=1, inplace=True)
data.isna().sum()
da... | code |
34136064/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, precision_score, plot_confusion_matrix
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
best_score = -1
best_estimators = 0
for i in range(10, 250):
model ... | code |
34136064/cell_24 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
best_score = -1
best_estimators = 0
for i in range(10, 250):
model = RandomForestClassifier(n_estimators=i, random_state=0)
model.fit(train_x, train_y)
pred = model.predict(test_x)
score = accuracy_score(pre... | code |
34136064/cell_14 | [
"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/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
data
data.isna().sum()
data.drop(['salary', 'sl_no'], axis=1, inplace=True)
data.isna().sum()
data.nunique()
corr = data.corr()
corr.style.back... | code |
34136064/cell_10 | [
"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/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
data
data.isna().sum()
data.drop(['salary', 'sl_no'], axis=1, inplace=True)
data.isna().sum()
data.nunique()
corr = data.corr()
corr.style.back... | code |
34136064/cell_27 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
best_score = -1
best_estimators = 0
for i in range(10, 250):
model = RandomForestClassifier(n_estimators=i, random_state=0)
model.fit(train_x, train_y)
pred = model.predict(test_x)
score = accuracy_score(pre... | code |
34136064/cell_37 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
data
data.isna().sum()
data.drop(['salary', 'sl_no'], axis=1, inplace=True)
data.isna().sum()
da... | code |
34136064/cell_12 | [
"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/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
data
data.isna().sum()
data.drop(['salary', 'sl_no'], axis=1, inplace=True)
data.isna().sum()
data.nunique()
corr = data.corr()
corr.style.back... | code |
34136064/cell_5 | [
"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/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
data
data.isna().sum() | code |
50236508/cell_4 | [
"text_plain_output_1.png"
] | def binary_search_recursive(array, element, start, end):
if start > end:
return -1
mid = (start + end) // 2
if element == array[mid]:
return mid
if element < array[mid]:
return binary_search_recursive(array, element, start, mid - 1)
else:
return binary_search_recursiv... | code |
50236508/cell_6 | [
"text_plain_output_1.png"
] | def binary_search_recursive(array, element, start, end):
if start > end:
return -1
mid = (start + end) // 2
if element == array[mid]:
return mid
if element < array[mid]:
return binary_search_recursive(array, element, start, mid - 1)
else:
return binary_search_recursiv... | code |
50236508/cell_2 | [
"text_plain_output_1.png"
] | for num in range(1, 1001):
if num > 0:
for i in range(1000, num):
if num % i == 0:
break
else:
print(num) | code |
50236508/cell_7 | [
"text_plain_output_1.png"
] | def binary_search_recursive(array, element, start, end):
if start > end:
return -1
mid = (start + end) // 2
if element == array[mid]:
return mid
if element < array[mid]:
return binary_search_recursive(array, element, start, mid - 1)
else:
return binary_search_recursiv... | code |
50236508/cell_8 | [
"text_plain_output_1.png"
] | def binary_search_recursive(array, element, start, end):
if start > end:
return -1
mid = (start + end) // 2
if element == array[mid]:
return mid
if element < array[mid]:
return binary_search_recursive(array, element, start, mid - 1)
else:
return binary_search_recursiv... | code |
50236508/cell_5 | [
"text_plain_output_1.png"
] | def sequentialSearch(x, array):
position = 0
global iterations
iterations = 0
while position < len(List):
iterations += 1
if Target == List[position]:
return position
position += 1
return -1
if __name__ == '__main__':
List = ['10', '20', '30', '40', '50', '60'... | code |
74052566/cell_42 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_validate
from sklearn.pipeline import Pipe... | code |
74052566/cell_21 | [
"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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
(df_train.shape, df_test.shape)
numerical_features = [cols f... | code |
74052566/cell_9 | [
"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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
(df_train.shape, df_test.shape)
numerical_features = [cols f... | code |
74052566/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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
(df_train.shape, df_test.shape)
df_train.info() | code |
74052566/cell_23 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
(df_train.shape, df_test.shape)
numerical_features = [cols f... | code |
74052566/cell_20 | [
"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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
(df_train.shape, df_test.shape)
numerical_features = [cols f... | code |
74052566/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
(df_train.shape, df_test.shape)
numerical_features = [cols f... | code |
74052566/cell_29 | [
"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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
(df_train.shape, df_test.shape)
df_test.isnull().sum() | code |
74052566/cell_26 | [
"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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
(df_train.shape, df_test.shape)
numerical_features = [cols f... | code |
74052566/cell_48 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import mutual_info_regression
from sklearn.impute import SimpleImputer
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)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-tec... | code |
74052566/cell_11 | [
"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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
(df_train.shape, df_test.shape)
numerical_features = [cols f... | code |
74052566/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 |
74052566/cell_49 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import mutual_info_regression
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
... | code |
74052566/cell_18 | [
"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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
(df_train.shape, df_test.shape)
numerical_features = [cols f... | code |
74052566/cell_28 | [
"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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
(df_train.shape, df_test.shape)
df_test['KitchenQual'].value... | code |
74052566/cell_8 | [
"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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
(df_train.shape, df_test.shape)
numerical_features = [cols f... | code |
74052566/cell_47 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import mutual_info_regression
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
... | code |
74052566/cell_3 | [
"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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
(df_train.shape, df_test.shape) | code |
74052566/cell_24 | [
"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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
(df_train.shape, df_test.shape)
numerical_features = [cols f... | code |
74052566/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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
(df_train.shape, df_test.shape)
numerical_features = [cols f... | code |
74052566/cell_27 | [
"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('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
(df_train.shape, df_test.shape)
numerical_features = [cols f... | code |
74052566/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)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
(df_train.shape, df_test.sha... | code |
104130018/cell_21 | [
"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/black-friday-sales-prediction/train_oSwQCTC (1)/train.csv')
data
data.shape
data.describe().T
data.isnull().sum() / data.shape[0] * 100
data.nunique()
data.Gender.value_counts()
data['Purchase'].skew() | code |
104130018/cell_13 | [
"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/black-friday-sales-prediction/train_oSwQCTC (1)/train.csv')
data
data.shape
data.describe().T
data.isnull().sum() / data.shape[0] * 100
data.nunique()
data.Gender.value_counts() | code |
104130018/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/black-friday-sales-prediction/train_oSwQCTC (1)/train.csv')
data
data.shape
data.describe().T
data.isnull().sum() / data.shape[0] * 100 | code |
104130018/cell_25 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/black-friday-sales-prediction/train_oSwQCTC (1)/train.csv')
data
data.shape
data.describe().T
data.isnull().sum() / data.shape[0] * 100
data.nunique()
data.Gender.value_counts()
data.groupby('Gender')['Purchase'].... | code |
104130018/cell_4 | [
"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/black-friday-sales-prediction/train_oSwQCTC (1)/train.csv')
data
data.shape | code |
104130018/cell_34 | [
"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('../input/black-friday-sales-prediction/train_oSwQCTC (1)/train.csv')
data
data.shape
data.describe().T
data.isnull().sum() / data.shape[0] * 100
data.nunique()
data.Ge... | code |
104130018/cell_23 | [
"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 # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/black-friday-sales-prediction/train_oSwQCTC (1)/train.csv')
data
data.shape
data.describe().T
data.isnull().sum() / data.shape[0] * 100
data.nunique()
data.Ge... | code |
104130018/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('../input/black-friday-sales-prediction/train_oSwQCTC (1)/train.csv')
data
data.shape
data.describe().T
data.isnull().sum() / data.shape[0] * 100
data.nunique()
data.Ge... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.