path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
90107080/cell_31 | [
"text_plain_output_1.png"
] | from joblib import dump, load
from sklearn.linear_model import LinearRegression , Ridge , LogisticRegression
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.... | code |
90107080/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression , Ridge , LogisticRegression
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv(... | code |
90107080/cell_27 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import warnings
import pandas as pd
impo... | code |
90107080/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv', index_col=None)
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv', index_col=None)
train = train.drop(... | code |
18152915/cell_13 | [
"text_html_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/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('../input/structures.csv')
def map_atom_info(df, atom_idx):
df ... | code |
18152915/cell_6 | [
"text_html_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/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('../input/structures.csv')
test.head() | code |
18152915/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('../input/structures.csv')
def map_atom_info(df, atom_idx):
df ... | code |
18152915/cell_19 | [
"text_html_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/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('../input/structures.csv')
def map_atom_info(df, atom_idx):
df ... | code |
18152915/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18152915/cell_7 | [
"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/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('../input/structures.csv')
print(f'There are {train.shape[0]} rows ... | code |
18152915/cell_8 | [
"text_html_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/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('../input/structures.csv')
len(structures) | code |
18152915/cell_17 | [
"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/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('../input/structures.csv')
def map_atom_info(df, atom_idx):
df ... | code |
18152915/cell_24 | [
"text_html_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/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('../input/structures.csv')
def map_atom_info(df, atom_idx):
df ... | code |
18152915/cell_14 | [
"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/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('../input/structures.csv')
def map_atom_info(df, atom_idx):
df ... | code |
18152915/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)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('../input/structures.csv')
train.head() | code |
105179548/cell_23 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
num_classes = 10
f, ax = plt.subplots(1, num_classes, figsize = (20,20))
for i in range(0, num_classes):
sample = x_train[y_train == i][... | code |
105179548/cell_20 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as plt
import tensorflow as tf
num_classes = 10
f, ax = plt.subplots(1, num_classes, figsize = (20,20))
for i in range(0, num_classes):
sample = x_train[y_train == i][0]
ax[i].imshow(... | code |
105179548/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import tensorflow as tf
num_classes = 10
f, ax = plt.subplots(1, num_classes, figsize = (20,20))... | code |
105179548/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from tensorflow.keras.datasets import mnist
from tensorflow.keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data() | code |
105179548/cell_18 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import Sequential
model = Sequential()
model.add(Dense(units=128, input_shape=(784,), activation='relu'))
model.add(Dense(units=128, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(units=10, activation='softmax'))
model.compi... | code |
105179548/cell_8 | [
"image_output_1.png"
] | print(x_train.shape, y_train.shape)
print(x_test.shape, y_test.shape) | code |
105179548/cell_16 | [
"text_plain_output_1.png"
] | x_train = x_train / 255.0
x_test = x_test / 255.0
x_train = x_train.reshape(x_train.shape[0], -1)
x_test = x_test.reshape(x_test.shape[0], -1)
print(x_train.shape) | code |
105179548/cell_24 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
num_classes = 10
f, ax = plt.subplots(1, num_classes, figsize = (20,20))
for i in range(0, num_classes):
sample = x_train[y_train == i][... | code |
105179548/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import tensorflow as tf
num_classes = 10
f, ax = plt.subplots(1, num_classes, figsize = (20,20))
for i in range(0, num_classes):
sample = x_train[y_train == i][0]
ax[i].imshow(sample, cmap='gray')
ax[i].set_title(f'Label: {i}', fontsize=16)
y_train = tf.keras.utils.to_ca... | code |
105179548/cell_22 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as plt
import tensorflow as tf
num_classes = 10
f, ax = plt.subplots(1, num_classes, figsize = (20,20))
for i in range(0, num_classes):
sample = x_train[y_train == i][0]
ax[i].imshow(... | code |
105179548/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
num_classes = 10
f, ax = plt.subplots(1, num_classes, figsize=(20, 20))
for i in range(0, num_classes):
sample = x_train[y_train == i][0]
ax[i].imshow(sample, cmap='gray')
ax[i].set_title(f'Label: {i}', fontsize=16) | code |
105179548/cell_12 | [
"text_plain_output_1.png"
] | for i in range(10):
print(y_train[i]) | code |
2002001/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
plt.figure(figsize=(10, 5))
sns.countplot(data=data, x='year') | code |
2002001/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
a = data[['StartupName', 'IndustryVertical']].groupby('IndustryVertical').count().sort_values('StartupName', ascending=False).head(8)
a.reset_index(inplace=True)
plt.pie(a['StartupName'], labels=a['IndustryVertical'])
plt.show() | code |
2002001/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | b = data.SubVertical.value_counts().sort_values(ascending=False).head(10)
location = data.CityLocation.value_counts().head(5)
location.plot(kind='barh',figsize=(10, 5))
InvestorsName = data.InvestorsName.value_counts().head(10)
InvestorsName.plot(kind='barh',figsize=(15, 10))
InvestmentType = data.InvestmentType.va... | code |
2002001/cell_8 | [
"text_html_output_1.png"
] | b = data.SubVertical.value_counts().sort_values(ascending=False).head(10)
b.plot(kind='barh', figsize=(15, 10)) | code |
2002001/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | b = data.SubVertical.value_counts().sort_values(ascending=False).head(10)
location = data.CityLocation.value_counts().head(5)
location.plot(kind='barh',figsize=(10, 5))
InvestorsName = data.InvestorsName.value_counts().head(10)
InvestorsName.plot(kind='barh',figsize=(15, 10))
InvestmentType = data.InvestmentType.va... | code |
2002001/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | b = data.SubVertical.value_counts().sort_values(ascending=False).head(10)
location = data.CityLocation.value_counts().head(5)
location.plot(kind='barh',figsize=(10, 5))
InvestorsName = data.InvestorsName.value_counts().head(10)
InvestorsName.plot(kind='barh',figsize=(15, 10))
InvestmentType = data.InvestmentType.val... | code |
2002001/cell_10 | [
"text_plain_output_1.png",
"image_output_1.png"
] | b = data.SubVertical.value_counts().sort_values(ascending=False).head(10)
location = data.CityLocation.value_counts().head(5)
location.plot(kind='barh', figsize=(10, 5)) | code |
2002001/cell_12 | [
"image_output_1.png"
] | b = data.SubVertical.value_counts().sort_values(ascending=False).head(10)
location = data.CityLocation.value_counts().head(5)
location.plot(kind='barh',figsize=(10, 5))
InvestorsName = data.InvestorsName.value_counts().head(10)
InvestorsName.plot(kind='barh', figsize=(15, 10)) | code |
105179600/cell_4 | [
"text_plain_output_1.png"
] | a = open('../input/poetry/Kanye_West.txt')
a.read() | code |
105179600/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | a = open('../input/poetry/Kanye_West.txt')
a.read()
a.close()
print(a.read()) | code |
333675/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
events = pd.read_csv('../input/events.csv', parse_dates=['timestamp'])
test = pd.... | code |
333675/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
events = pd.read_csv('../input/events.csv', parse_dates=['timestamp'])
test = pd.read_csv('../input... | code |
130026736/cell_13 | [
"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
df = pd.read_csv('/kaggle/input/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df
top_5_popular_movies = df[['title', 'popularity']]
top_5_popular_movies = top_5_popul... | code |
130026736/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)
df = pd.read_csv('/kaggle/input/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df
top_5_popular_movies = df[['title', 'popularity']]
top_5_popular_movies.head(5) | code |
130026736/cell_23 | [
"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/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df
top_5_popular_movies = df[['title', 'popularity']]
most_popularity_lng = df.groupby('... | code |
130026736/cell_20 | [
"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
df = pd.read_csv('/kaggle/input/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df
top_5_popular_movies = df[['title', 'popularity']]
most_popularity_lng = df.groupby('... | code |
130026736/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/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df
most_popularity_lng = df.groupby('original_language')['popularity'].mean()
most_popularity_lng | code |
130026736/cell_26 | [
"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
df = pd.read_csv('/kaggle/input/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df
top_5_popular_movies = df[['title', 'popularity']]
most_popularity_lng = df.groupby('... | code |
130026736/cell_2 | [
"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 = pd.read_csv('/kaggle/input/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df | code |
130026736/cell_19 | [
"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/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df
df_2 = pd.read_csv('/kaggle/input/anime-recommendations-database/anime.csv')
df_2
Top_10_anime_members = df_2[['name', 'members']]
Top_10_ani... | code |
130026736/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 |
130026736/cell_15 | [
"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/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df
top_5_popular_movies = df[['title', 'popularity']]
most_popularity_lng = df.groupby('... | code |
130026736/cell_17 | [
"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/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df
df_2 = pd.read_csv('/kaggle/input/anime-recommendations-database/anime.csv')
df_2 | code |
130026736/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
df = pd.read_csv('/kaggle/input/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df
top_5_popular_movies = df[['title', 'popularity']]
most_popularity_lng = df.groupby('... | code |
130026736/cell_14 | [
"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 = pd.read_csv('/kaggle/input/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df
top_5_popular_movies = df[['title', 'popularity']]
most_popularity_lng = df.groupby('... | code |
130026736/cell_22 | [
"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/top-10000-popular-movies-tmdb-05-2023/popular_10000_movies_tmdb.csv')
df
df_2 = pd.read_csv('/kaggle/input/anime-recommendations-database/anime.csv')
df_2
top_10_anime_rating = df_2[['name', 'rating']]
top_10_anime... | code |
130026736/cell_12 | [
"text_html_output_1.png"
] | import seaborn as sns
import matplotlib.pyplot as plt | code |
34142220/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
d = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
te = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
sns.set_style('dark')
plt.figure(figsize=(10, 10))
sns.lineplot(d['OverallCon... | code |
34142220/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
d = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
te = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
sns.set_style('dark')
plt.figure(figsize=(19, 13))
sns.barplot(x=d['MSSubClas... | code |
34142220/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
d = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
te = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
d.info() | code |
34142220/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
d = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
te = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
sns.set_style('dark')
plt.figure(figsize=(10, 15))
sns.barplot(x=d['SaleType'... | code |
34142220/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
d = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
te = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
plt.figure(figsize=(15, 10))
sns.set_style('dark')
sns.lineplot(x=d['YearBuilt... | code |
34142220/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
d = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
te = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
d.describe() | code |
34142220/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
d = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
te = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
sns.set_style('dark')
plt.figure(figsize=(15, 10))
sns.lineplot(x=d['YrSold']... | code |
34142220/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
d = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
te = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
sns.set_style('dark')
plt.figure(figsize=(20, 10))
sns.barplot(d['MSZoning'],... | code |
34142220/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
d = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
te = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
d.head() | code |
105201240/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data = data.drop('Posted On', axis=True)
data
data.dtypes
data = data.drop(['Floor', 'Area Type', 'Area Locality'], axis=1)
data
... | code |
105201240/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data = data.drop('Posted On', axis=True)
data | code |
105201240/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.info() | code |
105201240/cell_23 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data['Posted_On'] = pd.to_datetime(data['Posted_On'])
data['Posted_On'].dtypes
data = data.drop(... | code |
105201240/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data = data.drop('Posted On', axis=True)
data
data.dtypes
data = data.drop(['Floor', 'Area Type', 'Area Locality'], axis=1)
data
... | code |
105201240/cell_6 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
plt.figure(figsize=(10, 6))
sns.heatmap(data.corr(), annot=True) | code |
105201240/cell_29 | [
"text_html_output_1.png"
] | from lazypredict.Supervised import LazyRegressor
import lazypredict
from lazypredict.Supervised import LazyRegressor
lazy_model = LazyRegressor()
model, predict = lazy_model.fit(x_train, x_test, y_train, y_test)
model | code |
105201240/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data['Posted_On'].dt.day_name() | code |
105201240/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data = data.drop('Posted On', axis=True)
data
data.dtypes
data = data.drop(['Floor', 'Area Type', 'Area Locality'], axis=1)
data | code |
105201240/cell_7 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data | code |
105201240/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data = data.drop('Posted On', axis=True)
data
data.dtypes
data | code |
105201240/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum() | code |
105201240/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data = data.drop('Posted On', axis=True)
data
data.dtypes
data['Floor'] | code |
105201240/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data = data.drop('Posted On', axis=True)
data
data.dtypes
data | code |
105201240/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data | code |
105201240/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data = data.drop('Posted On', axis=True)
data
data.dtypes
data['House Floor'] = 0
list0 = data['Floor'].str.split(pat=' ', n=2, ex... | code |
105201240/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data = data.drop('Posted On', axis=True)
data
data.dtypes | code |
105201240/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data['Posted_On'] = pd.to_datetime(data['Posted_On'])
data['Posted_On'].dtypes | code |
105201240/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import preprocessing
from sklearn import preprocessing
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data['Posted_On'] = pd.to_datetime(data['Posted_On'])
data['Po... | code |
105201240/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.isnull().sum()
data | code |
105201240/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
data
data.describe() | code |
130005860/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
train_df.shape | code |
130005860/cell_25 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.ensemble import RandomForestRegressor
reg = RandomForestRegressor(random_state=1)
reg.fit(X_train, y_train)
pred = reg.predict(X_val)
from sklearn.metrics import mean_absolute_error
mae = mean_absolute_e... | code |
130005860/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
reg = RandomForestRegressor(random_state=1)
reg.fit(X_train, y_train) | code |
130005860/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
train_df.head() | code |
130005860/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
train_df.shape
train_df.columns
train_df.isnull().sum()
train_df.head(10) | code |
130005860/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
test_df.head() | code |
130005860/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
train_df.shape
train_df.columns
train_df.isnull().sum()
train_df.describe() | code |
130005860/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
train_df.shape
train_df.columns
train_df.isnull().sum()
train_df.info() | code |
130005860/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
train_df.shape
train_df.columns | code |
130005860/cell_27 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
from sklearn.ensemble import RandomForestRegressor
reg = RandomForestRegressor(random_state=1)
reg.fi... | code |
130005860/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
train_df.shape
train_df.columns
train_df.isnull().sum() | code |
88087414/cell_9 | [
"image_output_1.png"
] | import pandas as pd
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
data_train = data_train[['id', 'text', 'target']]
data_test = data_test[['id', 'text']]
print('shape train dataframe:', data_train.shape)
print('shape test dataframe... | code |
88087414/cell_34 | [
"image_output_1.png"
] | from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.text import Tokenizer
from sklearn.model_selection import train_test_split
import pandas as pd
import re
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/trai... | code |
88087414/cell_33 | [
"image_output_1.png"
] | from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.text import Tokenizer
from sklearn.model_selection import train_test_split
import pandas as pd
import re
import seaborn as sns
data_train = pd.read_csv('../input/nlp-getting-started/trai... | code |
88087414/cell_44 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import LSTM,Embedding, Conv1D, Dense, Flatten, MaxPooling1D, Dropout , Bidirectional , Dropout , Flatten , GlobalMaxPooling1D
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.text impo... | code |
88087414/cell_6 | [
"image_output_1.png"
] | import pandas as pd
data_train = pd.read_csv('../input/nlp-getting-started/train.csv')
data_test = pd.read_csv('../input/nlp-getting-started/test.csv')
len(data_test) | code |
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