path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
128023684/cell_26 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-s... | code |
128023684/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-study-data/Transactions.csv')
md = df... | code |
128023684/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-s... | code |
128023684/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 |
128023684/cell_32 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-s... | code |
128023684/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-s... | code |
128023684/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-study-data/Transactions.csv')
md = df... | code |
128023684/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_second.keys()
df_second.sample(10) | code |
128023684/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-study-data/Transactions.csv')
md = df... | code |
128023684/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-study-data/Transactions.csv')
md = df... | code |
128023684/cell_27 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-s... | code |
128023684/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-study-data/Transactions.csv')
md = df... | code |
128023684/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_first = pd.read_csv('/kaggle/input/retail-case-study-data/prod_cat_info.csv')
df_second = pd.read_csv('/kaggle/input/retail-case-study-data/Customer.csv')
df_third = pd.read_csv('/kaggle/input/retail-case-study-data/Transactions.csv')
df_thir... | code |
129012029/cell_21 | [
"text_html_output_1.png"
] | from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
import matplotlib.pyplot as plt
import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker)
from matplotlib.pyplot import figure
df = yf.download(ticker, start='2020-05-10', end='2021-05-10')
from matplotlib.pyplot import figure
fig... | code |
129012029/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker)
tesla.head() | code |
129012029/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
import matplotlib.pyplot as plt
import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker)
from matplotlib.pyplot impor... | code |
129012029/cell_23 | [
"text_plain_output_1.png"
] | from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
import matplotlib.pyplot as plt
import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker)
from matplotlib.pyplot import figure
df = yf.download(ticker, start='2020-05-10', end='2021-05-10')
fr... | code |
129012029/cell_20 | [
"text_plain_output_1.png"
] | import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker)
df = yf.download(ticker, start='2020-05-10', end='2021-05-10')
df.info() | code |
129012029/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
import matplotli... | code |
129012029/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
import matplotlib.pyplot as plt
import yfinance as yf
ticker = 'tsla'
tesla = yf.download... | code |
129012029/cell_2 | [
"image_output_1.png"
] | !pip install yfinance | code |
129012029/cell_11 | [
"text_plain_output_1.png"
] | import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker)
tesla.describe() | code |
129012029/cell_19 | [
"text_html_output_1.png"
] | import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker)
df = yf.download(ticker, start='2020-05-10', end='2021-05-10')
df | code |
129012029/cell_18 | [
"text_plain_output_1.png"
] | import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker)
df = yf.download(ticker, start='2020-05-10', end='2021-05-10') | code |
129012029/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker) | code |
129012029/cell_24 | [
"image_output_1.png"
] | from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
import matplotlib.pyplot as plt
import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker)
from matplotlib.pyplot import figure
df = yf.download(ticker, sta... | code |
129012029/cell_14 | [
"text_html_output_1.png"
] | from matplotlib.pyplot import figure
import matplotlib.pyplot as plt
import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker)
from matplotlib.pyplot import figure
figure(figsize=(15, 7), dpi=80)
plt.plot(tesla.Close)
plt.show() | code |
129012029/cell_10 | [
"text_plain_output_1.png"
] | import yfinance as yf
ticker = 'tsla'
tesla = yf.download(ticker)
tesla.info() | code |
129012029/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
from matplotlib.pyplot import figure
import matplotlib.pyplot as plt
import yfinance as yf... | code |
2017020/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from pandas import DataFrame
from pandas import Series
import matplotlib.pyplot as plt
data = pd.read_csv('../input/Top_hashtag.csv')
data.shape | code |
2017020/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2017020/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
left = [1, 2, 3, 4, 5]
height = [4967, 6833, 893, 813, 3473]
tick_label = ['love', 'freind', 'beachfamily', 'yellow']
plt.bar(left, height, tick_label=tick_label, width=0.8, color=['blue', 'blue'])
plt.xlabel('x - axis')
p... | code |
105187552/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/penguins/penguins.csv')
'In order to take a closer look at our dataset, we will use head() to print\nthe first five observations of our dataset and tail() to print the last five observations.'
"""Now... | code |
105187552/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/penguins/penguins.csv')
'In order to take a closer look at our dataset, we will use head() to print\nthe first five observations of our dataset and tail() to print the last five observations.'
df.tai... | code |
105187552/cell_6 | [
"text_plain_output_1.png"
] | """Here, we conclude that our dataset comprises of
344 observations and 9 characteristics.""" | code |
105187552/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 |
105187552/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)
df = pd.read_csv('../input/penguins/penguins.csv')
'In order to take a closer look at our dataset, we will use head() to print\nthe first five observations of our dataset and tail() to print the last five observations.'
"""Now... | code |
105187552/cell_8 | [
"text_html_output_1.png"
] | """So the conclusion is that our data contains float, integer and string values(object).
Also,there is no null/missing values. """ | code |
105187552/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/penguins/penguins.csv')
'In order to take a closer look at our dataset, we will use head() to print\nthe first five observations of our dataset and tail() to print the last five observations.'
df.head... | code |
105187552/cell_10 | [
"text_html_output_1.png"
] | import seaborn as sns
data = sns.load_dataset('penguins') | code |
105187552/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)
df = pd.read_csv('../input/penguins/penguins.csv')
'In order to take a closer look at our dataset, we will use head() to print\nthe first five observations of our dataset and tail() to print the last five observations.'
"""Now... | code |
89130324/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/nytfug/train.csv')
test_data = pd.read_csv('/kaggle/input/nytfug/test.csv')
train_data.shape
train_data.columns | code |
89130324/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
clf = clf.fit(X_train, y_train)
clf.score(X_train, y_train) | code |
89130324/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/nytfug/train.csv')
test_data = pd.read_csv('/kaggle/input/nytfug/test.csv')
train_data.shape
test_data.shape
train_data.columns
train_data.isnull().sum()
test_... | code |
89130324/cell_11 | [
"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_data = pd.read_csv('/kaggle/input/nytfug/train.csv')
test_data = pd.read_csv('/kaggle/input/nytfug/test.csv')
train_data.shape
train_data.columns
train_data.isnull().sum() | code |
89130324/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 |
89130324/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_data = pd.read_csv('/kaggle/input/nytfug/train.csv')
test_data = pd.read_csv('/kaggle/input/nytfug/test.csv')
train_data.shape | code |
89130324/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/nytfug/train.csv')
test_data = pd.read_csv('/kaggle/input/nytfug/test.csv')
train_data.shape
test_data.shape... | code |
89130324/cell_8 | [
"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_data = pd.read_csv('/kaggle/input/nytfug/train.csv')
test_data = pd.read_csv('/kaggle/input/nytfug/test.csv')
test_data.shape | code |
89130324/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/nytfug/train.csv')
test_data = pd.read_csv('/kaggle/input/nytfug/test.csv')
train_data.shape
test_data.shape
train_data.columns
train_data.isnull().sum()
test_... | code |
89130324/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
clf = clf.fit(X_train, y_train)
clf.score(X_train, y_train)
clf.score(X_test, y_test) | code |
89130324/cell_10 | [
"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_data = pd.read_csv('/kaggle/input/nytfug/train.csv')
test_data = pd.read_csv('/kaggle/input/nytfug/test.csv')
train_data.shape
train_data.columns
train_data.head() | code |
89130324/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_data = pd.read_csv('/kaggle/input/nytfug/train.csv')
test_data = pd.read_csv('/kaggle/input/nytfug/test.csv')
test_data.shape
test_data.isnull().sum() | code |
128017503/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import random
import seaborn as sns
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
seed_everything(42)
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age']... | code |
128017503/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
exercise = pd.read_csv('/kaggle/input/fmendesdat263xdemos/exercise.csv')
calories = pd.read_csv('/kaggle/input/fmendesdat263xdemos/calories.csv')
exercise['Calories_Burned'] = calories['Calories']
exercise = exercise.drop(['User_ID'], axis=1)
exercise | code |
128017503/cell_2 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import warnings
import pandas as pd
import numpy as np
import random
import os
import gc
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression, Ridge
import matplotlib.py... | code |
128017503/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
fig, axes = plt.subplots(2, 3, figsize=(10, 10))
sns.boxplot(y=train['Age'], ax=axes[0][0])
sns.boxplot(y=train['Height'], ax=axes[0][1])
sns.boxplot(y=train['Weight'], ax=axes[0][2])
sns.boxplot(y=train['Duration'], ax=axes[1][0])
sns.boxplot(y=train['Heart_Rate'... | code |
128017503/cell_19 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import random
import seaborn as sns
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
seed_everything(42)
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age']... | code |
128017503/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import random
import seaborn as sns
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
seed_everything(42)
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age']... | code |
128017503/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import random
import seaborn as sns
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
seed_everything(42)
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age']... | code |
128017503/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import random
import seaborn as sns
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
seed_everything(42)
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age']... | code |
105196788/cell_21 | [
"text_html_output_1.png"
] | from sklearn.linear_model import Ridge, RidgeCV
import numpy as np
import numpy as np # linear algebra
from sklearn.linear_model import Ridge, RidgeCV
alphas = np.random.uniform(0, 10, 50)
ridge_cv = RidgeCV(alphas=alphas, cv=10, normalize=True)
ridge_cv.fit(X_train, y_train)
alpha = ridge_cv.alpha_
alpha
ridge = ... | code |
105196788/cell_13 | [
"image_output_1.png"
] | from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler
from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.datasets import load_boston
data = load_boston... | code |
105196788/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
from sklearn.linear_model import Ridge, RidgeCV
from sklearn.preprocessing import StandardScaler
from statsmodels.stats.outliers_influence import variance_inflation_factor
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # dat... | code |
105196788/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.datasets import load_boston
data = load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
print(df) | code |
105196788/cell_23 | [
"text_html_output_1.png"
] | from sklearn.linear_model import Ridge, RidgeCV
import numpy as np
import numpy as np # linear algebra
from sklearn.linear_model import Ridge, RidgeCV
alphas = np.random.uniform(0, 10, 50)
ridge_cv = RidgeCV(alphas=alphas, cv=10, normalize=True)
ridge_cv.fit(X_train, y_train)
alpha = ridge_cv.alpha_
alpha
ridge = ... | code |
105196788/cell_20 | [
"image_output_1.png"
] | from sklearn.linear_model import Ridge, RidgeCV
import numpy as np
import numpy as np # linear algebra
from sklearn.linear_model import Ridge, RidgeCV
alphas = np.random.uniform(0, 10, 50)
ridge_cv = RidgeCV(alphas=alphas, cv=10, normalize=True)
ridge_cv.fit(X_train, y_train)
alpha = ridge_cv.alpha_
alpha | code |
105196788/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn.datasets import load_boston
data = load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['MEDV... | code |
105196788/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
from sklearn.linear_model import Ridge, RidgeCV
from sklearn.preprocessing import StandardScaler
from statsmodels.stats.outliers_influence import variance_inflation_factor
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # dat... | code |
105196788/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.datasets import load_boston
data = load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['MEDV'] = d... | code |
105196788/cell_19 | [
"text_html_output_1.png"
] | from sklearn.linear_model import Ridge, RidgeCV
import numpy as np
import numpy as np # linear algebra
from sklearn.linear_model import Ridge, RidgeCV
alphas = np.random.uniform(0, 10, 50)
ridge_cv = RidgeCV(alphas=alphas, cv=10, normalize=True)
ridge_cv.fit(X_train, y_train) | code |
105196788/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 |
105196788/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn.datasets import load_boston
data = load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['MEDV... | code |
105196788/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn.datasets import load_boston
data = load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['MEDV... | code |
105196788/cell_15 | [
"image_output_1.png"
] | from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import statsmodels.formula.api as smf
from sklearn.datasets import load_boston
data = load_boston()
df = pd.DataFrame(data.data... | code |
105196788/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import statsmodels.formula.api as smf
from sklearn.datasets import load_boston
data = load_boston()
df = pd.DataFrame(data.data... | code |
105196788/cell_17 | [
"text_html_output_1.png"
] | from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn.datasets import load_boston
data = load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['MEDV... | code |
105196788/cell_14 | [
"image_output_1.png"
] | from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn.datasets import load_boston
data = load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['MEDV... | code |
105196788/cell_22 | [
"text_html_output_1.png"
] | from sklearn.linear_model import Ridge, RidgeCV
import numpy as np
import numpy as np # linear algebra
from sklearn.linear_model import Ridge, RidgeCV
alphas = np.random.uniform(0, 10, 50)
ridge_cv = RidgeCV(alphas=alphas, cv=10, normalize=True)
ridge_cv.fit(X_train, y_train)
alpha = ridge_cv.alpha_
alpha
ridge = ... | code |
105196788/cell_12 | [
"text_html_output_1.png"
] | from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler
from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.datasets import load_boston
data = load_boston... | code |
105196788/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.datasets import load_boston
data = load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['MEDV'] = data.target
df.head() | code |
105210042/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
df_v = pd.DataFrame(data['symboling'].value_counts()).reset_index().rename(columns={'index': 'symboling', 'symboling': 'count'})
plt.figure(figs... | code |
105210042/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
data.head() | code |
105210042/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.info() | code |
105210042/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_absolute_error , r2_score , mean_squared_error
import numpy as np
from sklearn.linear_model import LinearRegression
Lr = LinearRegression()
Lr.fit(X_train... | code |
105210042/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
df_v = pd.DataFrame(data['symboling'].value_counts()).reset_index().rename(columns={'index': 'symboling', 'symboling': 'count'})
data.duplicated... | code |
105210042/cell_30 | [
"image_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.linear_model import Lasso
LO = Lasso()
LO.fit(X_train, y_train)
LO.score(X_test, y_test) | code |
105210042/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import Ridge
from sklearn.linear_model import Ridge
RD = Ridge()
RD.fit(X_train, y_train)
RD.score(X_test, y_test) | code |
105210042/cell_20 | [
"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/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
df_v = pd.DataFrame(data['symboling'].value_counts()).reset_index().rename(columns={'index': 'symboling', 'symboling': 'count'})
plt.figure(figs... | code |
105210042/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
data.describe() | code |
105210042/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
df_v = pd.DataFrame(data['symboling'].value_counts()).reset_index().rename(columns={'index': 'symboling', 'symboling': 'count'})
sns.pairplot(data[['horsepower', 'price', 'symbo... | code |
105210042/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
data.describe(include=['O']) | code |
105210042/cell_18 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
df_v = pd.DataFrame(data['symboling'].value_counts()).reset_index().rename(columns={'index': 'symboling', 'symboling': 'count'})
sns.scatterplot(x='horsepower', y='price', data=... | code |
105210042/cell_28 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error , r2_score , mean_squared_error
import numpy as np
from sklearn.linear_model import LinearRegression
Lr = LinearRegression()
Lr.fit(X_train, y_train)
Lr.score(X_test, y_test)
print('Test RMSE', np.sqrt(mean_squared_err... | code |
105210042/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
df_v = pd.DataFrame(data['symboling'].value_counts()).reset_index().rename(columns={'index': 'symboling', 'symboling': 'count'})
sns.barplot(x='symboling', y='count', data=df_v) | code |
105210042/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
df_v = pd.DataFrame(data['symboling'].value_counts()).reset_index().rename(columns={'index': 'symboling', 'symboling': 'count'})
sns.boxplot(x='symboling', y='price', data=data,... | code |
105210042/cell_3 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.head(5) | code |
105210042/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
df_v = pd.DataFrame(data['symboling'].value_counts()).reset_index().rename(columns={'index': 'symboling', 'symboling': 'count'})
sns.scatterplot(x='wheelbase', y='price', data=d... | code |
105210042/cell_31 | [
"image_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error , r2_score , mean_squared_error
import numpy as np
from sklearn.linear_model import LinearRegression
Lr = LinearRegression()
Lr.fit(X_train, y_train)
Lr.score(X_test, y_test)
fro... | code |
105210042/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
sns.boxplot(x='symboling', y='price', data=data, palette='winter_r') | code |
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