path
stringlengths 13
17
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sequencelengths 1
873
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stringlengths 0
40.4k
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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(newdf7, 'Denver')
priceOverTime2(newdf8, 'Detroit')
State_raw_house = State_house.groupby(['RegionName', State_house.Date.dt.year])['ZHVI_SingleFamilyResidence'].mean().unstack()
State_raw_house.columns.name = None
State_raw_house = State_raw_house.reset_index()
State_raw_house = State_raw_house[['RegionName', 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017]]
State_raw_house = State_raw_house.dropna()
Feature = State_raw_house['RegionName']
weightage = State_raw_house[2010]
total = State_raw_house[2017]
percent = (State_raw_house[2017] - State_raw_house[2010]) / State_raw_house[2010] * 100
mid_pos = (State_raw_house[2010] + State_raw_house[2017]) / 2
weightage = np.array(weightage)
Feature = np.array(Feature)
total = np.array(total)
percent = np.array(percent)
mid_pos = np.array(mid_pos)
idx = percent.argsort()
Feature, total, percent, mid_pos, weightage = [np.take(x, idx) for x in [Feature, total, percent, mid_pos, weightage]]
s = 1
size = []
for i, cn in enumerate(weightage):
s = s + 1
size.append(s)
fig, ax = plt.subplots(figsize=(13, 13))
ax.scatter(total, size, marker='o', color='lightBlue', s=size, linewidths=10)
ax.scatter(weightage, size, marker='o', color='LightGreen', s=size, linewidths=10)
ax.set_xlabel('Home Value')
ax.set_ylabel('States')
ax.spines['right'].set_visible(False)
ax.grid()
for i, txt in enumerate(Feature):
ax.annotate(txt, (720000, size[i]), fontsize=12, rotation=0, color='Red')
ax.annotate('.', xy=(total[i], size[i]), xytext=(weightage[i], size[i]), arrowprops=dict(facecolor='LightGreen', shrink=0.06))
for i, pct in enumerate(percent):
ax.annotate(str(pct)[0:4], (mid_pos[i], size[i]), fontsize=12, rotation=0, color='Brown')
ax.annotate('2010 Home Value', (300000, 26), fontsize=14, rotation=0, color='Green')
ax.annotate('2017 Home Value', (300000, 25), fontsize=14, rotation=0, color='Blue')
ax.annotate('w/ Percent Change', (300000, 24), fontsize=14, rotation=0, color='Brown') | 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_legend()
distributionTwo.set_axis_labels(str(metric), 'Proportion')
distributionTwo.fig.suptitle(str(metric) + ' vs Region (2016)')
plotDistribution(newdf2, 'MedianListingPrice_SingleFamilyResidence')
plotDistribution(newdf2, 'MedianSoldPrice_AllHomes') | 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_col.index, col].unique() | 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'].value_counts().plot(kind='pie', autopct='%1.1f%%', radius=1.5, textprops={'fontsize': 16}) | 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)})
from sklearn.neighbors import KNeighborsClassifier
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
file = os.path.join(dirname, filename)
data = pd.read_csv(file)
data.columns
data.shape
data_pie = [73452, 56428]
labels = ['1', '0']
explode = [0.1, 0]
fig, ax = plt.subplots(1,2)
sns.histplot(x="Gender", data=data, stat="density", shrink=0.9, color="steelblue", ax=ax[0])
sns.histplot(x="Customer Type", data=data,stat="density", shrink=0.9, color="steelblue",ax=ax[1])
fig.show()
fig, ax = plt.subplots(1, 2)
plt.figure(figsize= (20, 20))
sns.histplot(x="Type of Travel", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[0]);
sns.histplot(x="Class", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[1]);
fig.show()
g = sns.countplot(x="Flight Distance", data=data, lw=0, color="red")
_xticklabels = g.get_xticklabels()
for ind, label in enumerate(_xticklabels):
if int(label.get_text()) % 200 == 0:
label.set_visible(True)
else:
label.set_visible(False)
g.set_xticklabels(_xticklabels, rotation=45)
g.set(ylim=(0, 550))
x_predictor_col = ['Baggage Handling', 'Departure and Arrival Time Convenience', 'In-flight Wifi Service', 'Ease of Online Booking', 'In-flight Entertainment', 'Check-in Service', 'Online Boarding', 'Gate Location']
def create_plot_pivot(df, x_column):
_df_plot = df.groupby([x_column, 'Satisfaction']).size().reset_index().pivot(columns='Satisfaction', index=x_column, values=0)
return _df_plot
fig, ax = plt.subplots(2, 2, figsize=(20,12))
axe = ax.ravel()
for i in range(4):
create_plot_pivot(data, x_predictor_col[i]).plot(kind='bar',stacked=True, ax=axe[i])
plt.xlabel(x_predictor_col[i])
axe[i].set_ylabel('Count of Respondants')
fig.show()
fig, ax = plt.subplots(2, 2, figsize=(20,12))
axe = ax.ravel()
for i in range(4, 8):
j = i - 4
create_plot_pivot(data, x_predictor_col[i]).plot(kind='bar',stacked=True, ax=axe[j])
plt.xlabel(x_predictor_col[i])
axe[j].set_ylabel('Count of Respondants')
fig.show()
data.isnull().sum()
plt.figure(figsize=(20, 15))
sns.heatmap(data.corr(), annot=True, cmap='YlGnBu') | 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)})
from sklearn.neighbors import KNeighborsClassifier
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
file = os.path.join(dirname, filename)
data = pd.read_csv(file)
data.columns
data.shape
data_pie = [73452, 56428]
labels = ['1', '0']
explode = [0.1, 0]
fig, ax = plt.subplots(1,2)
sns.histplot(x="Gender", data=data, stat="density", shrink=0.9, color="steelblue", ax=ax[0])
sns.histplot(x="Customer Type", data=data,stat="density", shrink=0.9, color="steelblue",ax=ax[1])
fig.show()
fig, ax = plt.subplots(1, 2)
plt.figure(figsize= (20, 20))
sns.histplot(x="Type of Travel", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[0]);
sns.histplot(x="Class", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[1]);
fig.show()
g = sns.countplot(x='Flight Distance', data=data, lw=0, color='red')
_xticklabels = g.get_xticklabels()
for ind, label in enumerate(_xticklabels):
if int(label.get_text()) % 200 == 0:
label.set_visible(True)
else:
label.set_visible(False)
g.set_xticklabels(_xticklabels, rotation=45)
g.set(ylim=(0, 550)) | 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)})
from sklearn.neighbors import KNeighborsClassifier
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
file = os.path.join(dirname, filename)
data = pd.read_csv(file)
data.columns
data.shape
data_pie = [73452, 56428]
labels = ['1', '0']
explode = [0.1, 0]
fig, ax = plt.subplots(1,2)
sns.histplot(x="Gender", data=data, stat="density", shrink=0.9, color="steelblue", ax=ax[0])
sns.histplot(x="Customer Type", data=data,stat="density", shrink=0.9, color="steelblue",ax=ax[1])
fig.show()
fig, ax = plt.subplots(1, 2)
plt.figure(figsize= (20, 20))
sns.histplot(x="Type of Travel", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[0]);
sns.histplot(x="Class", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[1]);
fig.show()
g = sns.countplot(x="Flight Distance", data=data, lw=0, color="red")
_xticklabels = g.get_xticklabels()
for ind, label in enumerate(_xticklabels):
if int(label.get_text()) % 200 == 0:
label.set_visible(True)
else:
label.set_visible(False)
g.set_xticklabels(_xticklabels, rotation=45)
g.set(ylim=(0, 550))
x_predictor_col = ['Baggage Handling', 'Departure and Arrival Time Convenience', 'In-flight Wifi Service', 'Ease of Online Booking', 'In-flight Entertainment', 'Check-in Service', 'Online Boarding', 'Gate Location']
def create_plot_pivot(df, x_column):
_df_plot = df.groupby([x_column, 'Satisfaction']).size().reset_index().pivot(columns='Satisfaction', index=x_column, values=0)
return _df_plot
fig, ax = plt.subplots(2, 2, figsize=(20,12))
axe = ax.ravel()
for i in range(4):
create_plot_pivot(data, x_predictor_col[i]).plot(kind='bar',stacked=True, ax=axe[i])
plt.xlabel(x_predictor_col[i])
axe[i].set_ylabel('Count of Respondants')
fig.show()
fig, ax = plt.subplots(2, 2, figsize=(20,12))
axe = ax.ravel()
for i in range(4, 8):
j = i - 4
create_plot_pivot(data, x_predictor_col[i]).plot(kind='bar',stacked=True, ax=axe[j])
plt.xlabel(x_predictor_col[i])
axe[j].set_ylabel('Count of Respondants')
fig.show()
data.isnull().sum()
data = data.dropna(subset=['Arrival Delay'])
sns.heatmap(data.isnull()) | 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 KNeighborsClassifier
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
file = os.path.join(dirname, filename)
data = pd.read_csv(file)
data.columns | 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)})
from sklearn.neighbors import KNeighborsClassifier
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
file = os.path.join(dirname, filename)
data = pd.read_csv(file)
data.columns
data.shape
data_pie = [73452, 56428]
labels = ['1', '0']
explode = [0.1, 0]
fig, ax = plt.subplots(1,2)
sns.histplot(x="Gender", data=data, stat="density", shrink=0.9, color="steelblue", ax=ax[0])
sns.histplot(x="Customer Type", data=data,stat="density", shrink=0.9, color="steelblue",ax=ax[1])
fig.show()
fig, ax = plt.subplots(1, 2)
plt.figure(figsize= (20, 20))
sns.histplot(x="Type of Travel", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[0]);
sns.histplot(x="Class", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[1]);
fig.show()
g = sns.countplot(x="Flight Distance", data=data, lw=0, color="red")
_xticklabels = g.get_xticklabels()
for ind, label in enumerate(_xticklabels):
if int(label.get_text()) % 200 == 0:
label.set_visible(True)
else:
label.set_visible(False)
g.set_xticklabels(_xticklabels, rotation=45)
g.set(ylim=(0, 550))
data.isnull().sum() | 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 KNeighborsClassifier
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
file = os.path.join(dirname, filename)
data = pd.read_csv(file)
data.columns
data.shape
data['ID'].duplicated().sum() | 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)})
from sklearn.neighbors import KNeighborsClassifier
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
file = os.path.join(dirname, filename)
data = pd.read_csv(file)
data.columns
data.shape
data_pie = [73452, 56428]
labels = ['1', '0']
explode = [0.1, 0]
fig, ax = plt.subplots(1,2)
sns.histplot(x="Gender", data=data, stat="density", shrink=0.9, color="steelblue", ax=ax[0])
sns.histplot(x="Customer Type", data=data,stat="density", shrink=0.9, color="steelblue",ax=ax[1])
fig.show()
fig, ax = plt.subplots(1, 2)
plt.figure(figsize= (20, 20))
sns.histplot(x="Type of Travel", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[0]);
sns.histplot(x="Class", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[1]);
fig.show()
g = sns.countplot(x="Flight Distance", data=data, lw=0, color="red")
_xticklabels = g.get_xticklabels()
for ind, label in enumerate(_xticklabels):
if int(label.get_text()) % 200 == 0:
label.set_visible(True)
else:
label.set_visible(False)
g.set_xticklabels(_xticklabels, rotation=45)
g.set(ylim=(0, 550))
x_predictor_col = ['Baggage Handling', 'Departure and Arrival Time Convenience', 'In-flight Wifi Service', 'Ease of Online Booking', 'In-flight Entertainment', 'Check-in Service', 'Online Boarding', 'Gate Location']
def create_plot_pivot(df, x_column):
_df_plot = df.groupby([x_column, 'Satisfaction']).size().reset_index().pivot(columns='Satisfaction', index=x_column, values=0)
return _df_plot
fig, ax = plt.subplots(2, 2, figsize=(20,12))
axe = ax.ravel()
for i in range(4):
create_plot_pivot(data, x_predictor_col[i]).plot(kind='bar',stacked=True, ax=axe[i])
plt.xlabel(x_predictor_col[i])
axe[i].set_ylabel('Count of Respondants')
fig.show()
fig, ax = plt.subplots(2, 2, figsize=(20,12))
axe = ax.ravel()
for i in range(4, 8):
j = i - 4
create_plot_pivot(data, x_predictor_col[i]).plot(kind='bar',stacked=True, ax=axe[j])
plt.xlabel(x_predictor_col[i])
axe[j].set_ylabel('Count of Respondants')
fig.show()
data.isnull().sum()
data = data.dropna(subset=['Arrival Delay'])
data.info() | 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 KNeighborsClassifier
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
file = os.path.join(dirname, filename)
data = pd.read_csv(file)
data.head(10) | 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)})
from sklearn.neighbors import KNeighborsClassifier
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
file = os.path.join(dirname, filename)
data = pd.read_csv(file)
data.columns
data.shape
data_pie = [73452, 56428]
labels = ['1', '0']
explode = [0.1, 0]
fig, ax = plt.subplots(1, 2)
sns.histplot(x='Gender', data=data, stat='density', shrink=0.9, color='steelblue', ax=ax[0])
sns.histplot(x='Customer Type', data=data, stat='density', shrink=0.9, color='steelblue', ax=ax[1])
fig.show() | 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)})
from sklearn.neighbors import KNeighborsClassifier
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
file = os.path.join(dirname, filename)
data = pd.read_csv(file)
data.columns
data.shape
data_pie = [73452, 56428]
labels = ['1', '0']
explode = [0.1, 0]
fig, ax = plt.subplots(1,2)
sns.histplot(x="Gender", data=data, stat="density", shrink=0.9, color="steelblue", ax=ax[0])
sns.histplot(x="Customer Type", data=data,stat="density", shrink=0.9, color="steelblue",ax=ax[1])
fig.show()
fig, ax = plt.subplots(1, 2)
plt.figure(figsize= (20, 20))
sns.histplot(x="Type of Travel", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[0]);
sns.histplot(x="Class", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[1]);
fig.show()
g = sns.countplot(x="Flight Distance", data=data, lw=0, color="red")
_xticklabels = g.get_xticklabels()
for ind, label in enumerate(_xticklabels):
if int(label.get_text()) % 200 == 0:
label.set_visible(True)
else:
label.set_visible(False)
g.set_xticklabels(_xticklabels, rotation=45)
g.set(ylim=(0, 550))
sns.heatmap(data.isnull()) | 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'):
for filename in filenames:
print(os.path.join(dirname, filename))
file = os.path.join(dirname, filename) | 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)})
from sklearn.neighbors import KNeighborsClassifier
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
file = os.path.join(dirname, filename)
data = pd.read_csv(file)
data.columns
data.shape
data_pie = [73452, 56428]
labels = ['1', '0']
explode = [0.1, 0]
fig, ax = plt.subplots(1,2)
sns.histplot(x="Gender", data=data, stat="density", shrink=0.9, color="steelblue", ax=ax[0])
sns.histplot(x="Customer Type", data=data,stat="density", shrink=0.9, color="steelblue",ax=ax[1])
fig.show()
fig, ax = plt.subplots(1, 2)
plt.figure(figsize= (20, 20))
sns.histplot(x="Type of Travel", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[0]);
sns.histplot(x="Class", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[1]);
fig.show()
x_predictor_col = ['Baggage Handling', 'Departure and Arrival Time Convenience', 'In-flight Wifi Service', 'Ease of Online Booking', 'In-flight Entertainment', 'Check-in Service', 'Online Boarding', 'Gate Location']
def create_plot_pivot(df, x_column):
_df_plot = df.groupby([x_column, 'Satisfaction']).size().reset_index().pivot(columns='Satisfaction', index=x_column, values=0)
return _df_plot
fig, ax = plt.subplots(2, 2, figsize=(20,12))
axe = ax.ravel()
for i in range(4):
create_plot_pivot(data, x_predictor_col[i]).plot(kind='bar',stacked=True, ax=axe[i])
plt.xlabel(x_predictor_col[i])
axe[i].set_ylabel('Count of Respondants')
fig.show()
fig, ax = plt.subplots(2, 2, figsize=(20, 12))
axe = ax.ravel()
for i in range(4, 8):
j = i - 4
create_plot_pivot(data, x_predictor_col[i]).plot(kind='bar', stacked=True, ax=axe[j])
plt.xlabel(x_predictor_col[i])
axe[j].set_ylabel('Count of Respondants')
fig.show() | 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)})
from sklearn.neighbors import KNeighborsClassifier
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
file = os.path.join(dirname, filename)
data = pd.read_csv(file)
data.columns
data.shape
data_pie = [73452, 56428]
labels = ['1', '0']
explode = [0.1, 0]
fig, ax = plt.subplots(1,2)
sns.histplot(x="Gender", data=data, stat="density", shrink=0.9, color="steelblue", ax=ax[0])
sns.histplot(x="Customer Type", data=data,stat="density", shrink=0.9, color="steelblue",ax=ax[1])
fig.show()
fig, ax = plt.subplots(1, 2)
plt.figure(figsize= (20, 20))
sns.histplot(x="Type of Travel", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[0]);
sns.histplot(x="Class", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[1]);
fig.show()
g = sns.countplot(x="Flight Distance", data=data, lw=0, color="red")
_xticklabels = g.get_xticklabels()
for ind, label in enumerate(_xticklabels):
if int(label.get_text()) % 200 == 0:
label.set_visible(True)
else:
label.set_visible(False)
g.set_xticklabels(_xticklabels, rotation=45)
g.set(ylim=(0, 550))
x_predictor_col = ['Baggage Handling', 'Departure and Arrival Time Convenience', 'In-flight Wifi Service', 'Ease of Online Booking', 'In-flight Entertainment', 'Check-in Service', 'Online Boarding', 'Gate Location']
def create_plot_pivot(df, x_column):
_df_plot = df.groupby([x_column, 'Satisfaction']).size().reset_index().pivot(columns='Satisfaction', index=x_column, values=0)
return _df_plot
fig, ax = plt.subplots(2, 2, figsize=(20,12))
axe = ax.ravel()
for i in range(4):
create_plot_pivot(data, x_predictor_col[i]).plot(kind='bar',stacked=True, ax=axe[i])
plt.xlabel(x_predictor_col[i])
axe[i].set_ylabel('Count of Respondants')
fig.show()
fig, ax = plt.subplots(2, 2, figsize=(20,12))
axe = ax.ravel()
for i in range(4, 8):
j = i - 4
create_plot_pivot(data, x_predictor_col[i]).plot(kind='bar',stacked=True, ax=axe[j])
plt.xlabel(x_predictor_col[i])
axe[j].set_ylabel('Count of Respondants')
fig.show()
data.isnull().sum()
data = data.dropna(subset=['Arrival Delay'])
data.shape | 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 KNeighborsClassifier
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
file = os.path.join(dirname, filename)
data = pd.read_csv(file)
data.columns
data.shape
data['Satisfaction'].value_counts() | 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 KNeighborsClassifier
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
file = os.path.join(dirname, filename)
data = pd.read_csv(file)
data.tail() | 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)})
from sklearn.neighbors import KNeighborsClassifier
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
file = os.path.join(dirname, filename)
data = pd.read_csv(file)
data.columns
data.shape
data_pie = [73452, 56428]
labels = ['1', '0']
explode = [0.1, 0]
fig, ax = plt.subplots(1,2)
sns.histplot(x="Gender", data=data, stat="density", shrink=0.9, color="steelblue", ax=ax[0])
sns.histplot(x="Customer Type", data=data,stat="density", shrink=0.9, color="steelblue",ax=ax[1])
fig.show()
fig, ax = plt.subplots(1, 2)
plt.figure(figsize= (20, 20))
sns.histplot(x="Type of Travel", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[0]);
sns.histplot(x="Class", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[1]);
fig.show()
x_predictor_col = ['Baggage Handling', 'Departure and Arrival Time Convenience', 'In-flight Wifi Service', 'Ease of Online Booking', 'In-flight Entertainment', 'Check-in Service', 'Online Boarding', 'Gate Location']
def create_plot_pivot(df, x_column):
_df_plot = df.groupby([x_column, 'Satisfaction']).size().reset_index().pivot(columns='Satisfaction', index=x_column, values=0)
return _df_plot
fig, ax = plt.subplots(2, 2, figsize=(20, 12))
axe = ax.ravel()
for i in range(4):
create_plot_pivot(data, x_predictor_col[i]).plot(kind='bar', stacked=True, ax=axe[i])
plt.xlabel(x_predictor_col[i])
axe[i].set_ylabel('Count of Respondants')
fig.show() | 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)})
from sklearn.neighbors import KNeighborsClassifier
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
file = os.path.join(dirname, filename)
data = pd.read_csv(file)
data.columns
data.shape
data_pie = [73452, 56428]
labels = ['1', '0']
explode = [0.1, 0]
fig, ax = plt.subplots(1,2)
sns.histplot(x="Gender", data=data, stat="density", shrink=0.9, color="steelblue", ax=ax[0])
sns.histplot(x="Customer Type", data=data,stat="density", shrink=0.9, color="steelblue",ax=ax[1])
fig.show()
fig, ax = plt.subplots(1, 2)
plt.figure(figsize= (20, 20))
sns.histplot(x="Type of Travel", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[0]);
sns.histplot(x="Class", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[1]);
fig.show()
g = sns.countplot(x="Flight Distance", data=data, lw=0, color="red")
_xticklabels = g.get_xticklabels()
for ind, label in enumerate(_xticklabels):
if int(label.get_text()) % 200 == 0:
label.set_visible(True)
else:
label.set_visible(False)
g.set_xticklabels(_xticklabels, rotation=45)
g.set(ylim=(0, 550))
x_predictor_col = ['Baggage Handling', 'Departure and Arrival Time Convenience', 'In-flight Wifi Service', 'Ease of Online Booking', 'In-flight Entertainment', 'Check-in Service', 'Online Boarding', 'Gate Location']
def create_plot_pivot(df, x_column):
_df_plot = df.groupby([x_column, 'Satisfaction']).size().reset_index().pivot(columns='Satisfaction', index=x_column, values=0)
return _df_plot
fig, ax = plt.subplots(2, 2, figsize=(20,12))
axe = ax.ravel()
for i in range(4):
create_plot_pivot(data, x_predictor_col[i]).plot(kind='bar',stacked=True, ax=axe[i])
plt.xlabel(x_predictor_col[i])
axe[i].set_ylabel('Count of Respondants')
fig.show()
fig, ax = plt.subplots(2, 2, figsize=(20,12))
axe = ax.ravel()
for i in range(4, 8):
j = i - 4
create_plot_pivot(data, x_predictor_col[i]).plot(kind='bar',stacked=True, ax=axe[j])
plt.xlabel(x_predictor_col[i])
axe[j].set_ylabel('Count of Respondants')
fig.show()
data.isnull().sum()
data = data.dropna(subset=['Arrival Delay'])
sns.catplot(x='Satisfaction', y='Departure Delay', data=data, palette='cubehelix') | 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)})
from sklearn.neighbors import KNeighborsClassifier
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
file = os.path.join(dirname, filename)
data = pd.read_csv(file)
data.columns
data.shape
data_pie = [73452, 56428]
labels = ['1', '0']
explode = [0.1, 0]
fig, ax = plt.subplots(1,2)
sns.histplot(x="Gender", data=data, stat="density", shrink=0.9, color="steelblue", ax=ax[0])
sns.histplot(x="Customer Type", data=data,stat="density", shrink=0.9, color="steelblue",ax=ax[1])
fig.show()
fig, ax = plt.subplots(1, 2)
plt.figure(figsize= (20, 20))
sns.histplot(x="Type of Travel", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[0]);
sns.histplot(x="Class", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[1]);
fig.show()
g = sns.countplot(x="Flight Distance", data=data, lw=0, color="red")
_xticklabels = g.get_xticklabels()
for ind, label in enumerate(_xticklabels):
if int(label.get_text()) % 200 == 0:
label.set_visible(True)
else:
label.set_visible(False)
g.set_xticklabels(_xticklabels, rotation=45)
g.set(ylim=(0, 550))
x_predictor_col = ['Baggage Handling', 'Departure and Arrival Time Convenience', 'In-flight Wifi Service', 'Ease of Online Booking', 'In-flight Entertainment', 'Check-in Service', 'Online Boarding', 'Gate Location']
def create_plot_pivot(df, x_column):
_df_plot = df.groupby([x_column, 'Satisfaction']).size().reset_index().pivot(columns='Satisfaction', index=x_column, values=0)
return _df_plot
fig, ax = plt.subplots(2, 2, figsize=(20,12))
axe = ax.ravel()
for i in range(4):
create_plot_pivot(data, x_predictor_col[i]).plot(kind='bar',stacked=True, ax=axe[i])
plt.xlabel(x_predictor_col[i])
axe[i].set_ylabel('Count of Respondants')
fig.show()
fig, ax = plt.subplots(2, 2, figsize=(20,12))
axe = ax.ravel()
for i in range(4, 8):
j = i - 4
create_plot_pivot(data, x_predictor_col[i]).plot(kind='bar',stacked=True, ax=axe[j])
plt.xlabel(x_predictor_col[i])
axe[j].set_ylabel('Count of Respondants')
fig.show()
data.isnull().sum()
data = data.dropna(subset=['Arrival Delay'])
data['Arrival Delay'].isnull().sum() | 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)})
from sklearn.neighbors import KNeighborsClassifier
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
file = os.path.join(dirname, filename)
data = pd.read_csv(file)
data.columns
data.shape
data_pie = [73452, 56428]
labels = ['1', '0']
explode = [0.1, 0]
fig, ax = plt.subplots(1,2)
sns.histplot(x="Gender", data=data, stat="density", shrink=0.9, color="steelblue", ax=ax[0])
sns.histplot(x="Customer Type", data=data,stat="density", shrink=0.9, color="steelblue",ax=ax[1])
fig.show()
fig, ax = plt.subplots(1, 2)
plt.figure(figsize= (20, 20))
sns.histplot(x="Type of Travel", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[0]);
sns.histplot(x="Class", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[1]);
fig.show()
g = sns.countplot(x="Flight Distance", data=data, lw=0, color="red")
_xticklabels = g.get_xticklabels()
for ind, label in enumerate(_xticklabels):
if int(label.get_text()) % 200 == 0:
label.set_visible(True)
else:
label.set_visible(False)
g.set_xticklabels(_xticklabels, rotation=45)
g.set(ylim=(0, 550))
sns.kdeplot(data[data['Satisfaction'] == 0]['Age'], shade=True, color='b')
sns.kdeplot(data[data['Satisfaction'] == 1]['Age'], shade=True, color='r') | 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)})
from sklearn.neighbors import KNeighborsClassifier
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
file = os.path.join(dirname, filename)
data = pd.read_csv(file)
data.columns
data.shape
data_pie = [73452, 56428]
labels = ['1', '0']
explode = [0.1, 0]
fig, ax = plt.subplots(1,2)
sns.histplot(x="Gender", data=data, stat="density", shrink=0.9, color="steelblue", ax=ax[0])
sns.histplot(x="Customer Type", data=data,stat="density", shrink=0.9, color="steelblue",ax=ax[1])
fig.show()
fig, ax = plt.subplots(1, 2)
plt.figure(figsize= (20, 20))
sns.histplot(x="Type of Travel", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[0]);
sns.histplot(x="Class", data=data, stat="density", shrink=0.8, color="steelblue",ax=ax[1]);
fig.show()
g = sns.countplot(x="Flight Distance", data=data, lw=0, color="red")
_xticklabels = g.get_xticklabels()
for ind, label in enumerate(_xticklabels):
if int(label.get_text()) % 200 == 0:
label.set_visible(True)
else:
label.set_visible(False)
g.set_xticklabels(_xticklabels, rotation=45)
g.set(ylim=(0, 550))
x_predictor_col = ['Baggage Handling', 'Departure and Arrival Time Convenience', 'In-flight Wifi Service', 'Ease of Online Booking', 'In-flight Entertainment', 'Check-in Service', 'Online Boarding', 'Gate Location']
def create_plot_pivot(df, x_column):
_df_plot = df.groupby([x_column, 'Satisfaction']).size().reset_index().pivot(columns='Satisfaction', index=x_column, values=0)
return _df_plot
fig, ax = plt.subplots(2, 2, figsize=(20,12))
axe = ax.ravel()
for i in range(4):
create_plot_pivot(data, x_predictor_col[i]).plot(kind='bar',stacked=True, ax=axe[i])
plt.xlabel(x_predictor_col[i])
axe[i].set_ylabel('Count of Respondants')
fig.show()
fig, ax = plt.subplots(2, 2, figsize=(20,12))
axe = ax.ravel()
for i in range(4, 8):
j = i - 4
create_plot_pivot(data, x_predictor_col[i]).plot(kind='bar',stacked=True, ax=axe[j])
plt.xlabel(x_predictor_col[i])
axe[j].set_ylabel('Count of Respondants')
fig.show()
data.isnull().sum()
data['Arrival Delay'].describe() | 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 KNeighborsClassifier
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
file = os.path.join(dirname, filename)
data = pd.read_csv(file)
data.columns
data.shape
data['Gender'].value_counts() | 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)})
from sklearn.neighbors import KNeighborsClassifier
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
file = os.path.join(dirname, filename)
data = pd.read_csv(file)
data.columns
data.shape
data_pie = [73452, 56428]
labels = ['1', '0']
explode = [0.1, 0]
fig, ax = plt.subplots(1,2)
sns.histplot(x="Gender", data=data, stat="density", shrink=0.9, color="steelblue", ax=ax[0])
sns.histplot(x="Customer Type", data=data,stat="density", shrink=0.9, color="steelblue",ax=ax[1])
fig.show()
fig, ax = plt.subplots(1, 2)
plt.figure(figsize=(20, 20))
sns.histplot(x='Type of Travel', data=data, stat='density', shrink=0.8, color='steelblue', ax=ax[0])
sns.histplot(x='Class', data=data, stat='density', shrink=0.8, color='steelblue', ax=ax[1])
fig.show() | 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 KNeighborsClassifier
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
file = os.path.join(dirname, filename)
data = pd.read_csv(file)
data.columns
data.shape | 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 tensorflow as tf
import tensorflow as tf
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])
from sklearn.model_selection import train_test_split
X = df.headline.values
y = df.is_sarcastic.values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
vocab_size = 10000
max_length = 32
embedding_dim = 16
oov_token = '<oov>'
padding_type = 'post'
trunc_type = 'post'
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_token)
tokenizer.fit_on_texts(X_train)
word_index = tokenizer.word_index
training_sequences = tokenizer.texts_to_sequences(X_train)
training_padded = pad_sequences(training_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)
testing_sequences = tokenizer.texts_to_sequences(X_test)
testing_padded = pad_sequences(testing_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)
(training_padded.shape, y_train.shape, testing_padded.shape, y_test.shape)
model = tf.keras.models.Sequential([layers.Embedding(vocab_size, embedding_dim, input_length=max_length), layers.Flatten(), layers.Dense(32, activation='relu'), layers.Dropout(0.5), layers.Dense(10, activation='relu'), layers.Dropout(0.5), layers.Dense(1, activation='sigmoid')], name='sarcasm-detection-model')
model.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001), metrics=['accuracy'])
model.summary()
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
callbacks = [EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10, restore_best_weights=True)]
history = model.fit(training_padded, y_train, batch_size=256, epochs=1000, validation_split=0.1, callbacks=callbacks) | 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=True)
df = pd.concat([df, dfv2])
from sklearn.model_selection import train_test_split
X = df.headline.values
y = df.is_sarcastic.values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
(X_train.shape, y_train.shape, X_test.shape, y_test.shape) | 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 plt
import pandas as pd
import tensorflow as tf
import tensorflow as tf
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])
from sklearn.model_selection import train_test_split
X = df.headline.values
y = df.is_sarcastic.values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
vocab_size = 10000
max_length = 32
embedding_dim = 16
oov_token = '<oov>'
padding_type = 'post'
trunc_type = 'post'
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_token)
tokenizer.fit_on_texts(X_train)
word_index = tokenizer.word_index
training_sequences = tokenizer.texts_to_sequences(X_train)
training_padded = pad_sequences(training_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)
testing_sequences = tokenizer.texts_to_sequences(X_test)
testing_padded = pad_sequences(testing_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)
(training_padded.shape, y_train.shape, testing_padded.shape, y_test.shape)
model = tf.keras.models.Sequential([layers.Embedding(vocab_size, embedding_dim, input_length=max_length), layers.Flatten(), layers.Dense(32, activation='relu'), layers.Dropout(0.5), layers.Dense(10, activation='relu'), layers.Dropout(0.5), layers.Dense(1, activation='sigmoid')], name='sarcasm-detection-model')
model.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001), metrics=['accuracy'])
model.summary()
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
callbacks = [EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10, restore_best_weights=True)]
history = model.fit(training_padded, y_train, batch_size=256, epochs=1000, validation_split=0.1, callbacks=callbacks)
epochs = history.epoch
plt.plot(epochs, history.history['accuracy'], 'g', label='Training Accuracy')
plt.plot(epochs, history.history['val_accuracy'], 'b', label='Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()
plt.show() | 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 tensorflow as tf
import tensorflow as tf
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])
from sklearn.model_selection import train_test_split
X = df.headline.values
y = df.is_sarcastic.values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
vocab_size = 10000
max_length = 32
embedding_dim = 16
oov_token = '<oov>'
padding_type = 'post'
trunc_type = 'post'
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_token)
tokenizer.fit_on_texts(X_train)
word_index = tokenizer.word_index
training_sequences = tokenizer.texts_to_sequences(X_train)
training_padded = pad_sequences(training_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)
testing_sequences = tokenizer.texts_to_sequences(X_test)
testing_padded = pad_sequences(testing_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)
(training_padded.shape, y_train.shape, testing_padded.shape, y_test.shape)
model = tf.keras.models.Sequential([layers.Embedding(vocab_size, embedding_dim, input_length=max_length), layers.Flatten(), layers.Dense(32, activation='relu'), layers.Dropout(0.5), layers.Dense(10, activation='relu'), layers.Dropout(0.5), layers.Dense(1, activation='sigmoid')], name='sarcasm-detection-model')
model.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001), metrics=['accuracy'])
model.summary()
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
callbacks = [EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10, restore_best_weights=True)]
history = model.fit(training_padded, y_train, batch_size=256, epochs=1000, validation_split=0.1, callbacks=callbacks)
model.evaluate(testing_padded, y_test) | 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'].value_counts() | 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 plt
import pandas as pd
import tensorflow as tf
import tensorflow as tf
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])
from sklearn.model_selection import train_test_split
X = df.headline.values
y = df.is_sarcastic.values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
vocab_size = 10000
max_length = 32
embedding_dim = 16
oov_token = '<oov>'
padding_type = 'post'
trunc_type = 'post'
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_token)
tokenizer.fit_on_texts(X_train)
word_index = tokenizer.word_index
training_sequences = tokenizer.texts_to_sequences(X_train)
training_padded = pad_sequences(training_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)
testing_sequences = tokenizer.texts_to_sequences(X_test)
testing_padded = pad_sequences(testing_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)
(training_padded.shape, y_train.shape, testing_padded.shape, y_test.shape)
model = tf.keras.models.Sequential([layers.Embedding(vocab_size, embedding_dim, input_length=max_length), layers.Flatten(), layers.Dense(32, activation='relu'), layers.Dropout(0.5), layers.Dense(10, activation='relu'), layers.Dropout(0.5), layers.Dense(1, activation='sigmoid')], name='sarcasm-detection-model')
model.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001), metrics=['accuracy'])
model.summary()
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
callbacks = [EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10, restore_best_weights=True)]
history = model.fit(training_padded, y_train, batch_size=256, epochs=1000, validation_split=0.1, callbacks=callbacks)
epochs = history.epoch
epochs = history.epoch
plt.plot(epochs, history.history['loss'], 'g', label='Training Loss')
plt.plot(epochs, history.history['val_loss'], 'b', label='Validation Loss')
plt.title('Training and Validation Loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show() | 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])
sns.set_style('whitegrid')
sns.countplot(x='is_sarcastic', data=df) | 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.Flatten(), layers.Dense(32, activation='relu'), layers.Dropout(0.5), layers.Dense(10, activation='relu'), layers.Dropout(0.5), layers.Dense(1, activation='sigmoid')], name='sarcasm-detection-model')
model.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001), metrics=['accuracy'])
model.summary() | 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=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])
from sklearn.model_selection import train_test_split
X = df.headline.values
y = df.is_sarcastic.values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
vocab_size = 10000
max_length = 32
embedding_dim = 16
oov_token = '<oov>'
padding_type = 'post'
trunc_type = 'post'
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_token)
tokenizer.fit_on_texts(X_train)
word_index = tokenizer.word_index
training_sequences = tokenizer.texts_to_sequences(X_train)
training_padded = pad_sequences(training_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)
testing_sequences = tokenizer.texts_to_sequences(X_test)
testing_padded = pad_sequences(testing_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)
(training_padded.shape, y_train.shape, testing_padded.shape, y_test.shape) | 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.pyplot as plt
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(pred, test_y)
if score > best_score:
best_score = score
best_estimators = i
model = RandomForestClassifier(n_estimators=best_estimators, random_state=0)
model.fit(train_x, train_y)
pred = model.predict(test_x)
score = accuracy_score(pred, test_y)
from sklearn.metrics import confusion_matrix, precision_score, plot_confusion_matrix
import matplotlib.pyplot as plt
from sklearn.metrics import precision_recall_curve, plot_precision_recall_curve
y_pred_prob = model.predict_proba(val_x)[:, 1]
precision, recall, thresholds = precision_recall_curve(val_y, y_pred_prob)
plt.plot(recall, precision, label='Random Forest')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.plot([0, 1], [0.68837209, 0.68837209], label='Baseline')
plt.legend()
plt.show() | 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 matplotlib.pyplot as plt
import matplotlib.pyplot as plt
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.background_gradient(cmap='coolwarm').set_precision(2)
unique_vals = data.nunique()
col_log = data.columns
for i in range(0, len(unique_vals)):
coln = str(col_log[i])
if int(unique_vals[i]) < 5 and coln != 'status':
data = pd.concat([data.drop(coln, axis=1), pd.get_dummies(data[coln], prefix=coln)], axis=1)
data_y = pd.DataFrame(data['status'])
data_x = data.drop('status', axis=1)
status_encoder = LabelEncoder()
data_y = status_encoder.fit_transform(data_y)
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(pred, test_y)
if score > best_score:
best_score = score
best_estimators = i
model = RandomForestClassifier(n_estimators=best_estimators, random_state=0)
model.fit(train_x, train_y)
pred = model.predict(test_x)
score = accuracy_score(pred, test_y)
from sklearn.metrics import confusion_matrix, precision_score, plot_confusion_matrix
import matplotlib.pyplot as plt
from sklearn.metrics import precision_recall_curve, plot_precision_recall_curve
y_pred_prob = model.predict_proba(val_x)[:, 1]
precision, recall, thresholds = precision_recall_curve(val_y, y_pred_prob)
df = pd.DataFrame(data={'Precision': precision[:-1], 'Recall': recall[:-1], 'Thresholds': thresholds})
df
targets = df.loc[(df['Precision'] >= 1) & (df['Thresholds'] != 1)]
targets
best = -1
thresh_best = -1
y_test_prob = model.predict_proba(test_x)[:, 1]
for target in targets.to_numpy():
true_prediction = (y_test_prob > target[2]).astype(int)
score = precision_score(test_y, true_prediction)
if score > best:
best = score
thresh_best = target[2]
ypred = (model.predict_proba(test_x)[:, 1] > thresh_best).astype(int)
score = accuracy_score(ypred, test_y)
ypred = (model.predict_proba(val_x)[:, 1] > thresh_best).astype(int)
score = accuracy_score(ypred, val_y)
print('Test accuracy with threshold: %f' % (score * 100))
print('True Negatives: %d, False Positives: %d, False Negatives: %d, True Positives: %d' % tuple(confusion_matrix(val_y, ypred).ravel())) | 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 matplotlib.pyplot as plt
import matplotlib.pyplot as plt
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.background_gradient(cmap='coolwarm').set_precision(2)
unique_vals = data.nunique()
col_log = data.columns
for i in range(0, len(unique_vals)):
coln = str(col_log[i])
if int(unique_vals[i]) < 5 and coln != 'status':
data = pd.concat([data.drop(coln, axis=1), pd.get_dummies(data[coln], prefix=coln)], axis=1)
data_y = pd.DataFrame(data['status'])
data_x = data.drop('status', axis=1)
status_encoder = LabelEncoder()
data_y = status_encoder.fit_transform(data_y)
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(pred, test_y)
if score > best_score:
best_score = score
best_estimators = i
model = RandomForestClassifier(n_estimators=best_estimators, random_state=0)
model.fit(train_x, train_y)
pred = model.predict(test_x)
score = accuracy_score(pred, test_y)
from sklearn.metrics import confusion_matrix, precision_score, plot_confusion_matrix
import matplotlib.pyplot as plt
from sklearn.metrics import precision_recall_curve, plot_precision_recall_curve
y_pred_prob = model.predict_proba(val_x)[:, 1]
precision, recall, thresholds = precision_recall_curve(val_y, y_pred_prob)
df = pd.DataFrame(data={'Precision': precision[:-1], 'Recall': recall[:-1], 'Thresholds': thresholds})
df
targets = df.loc[(df['Precision'] >= 1) & (df['Thresholds'] != 1)]
targets
best = -1
thresh_best = -1
y_test_prob = model.predict_proba(test_x)[:, 1]
for target in targets.to_numpy():
true_prediction = (y_test_prob > target[2]).astype(int)
score = precision_score(test_y, true_prediction)
if score > best:
best = score
thresh_best = target[2]
ypred = (model.predict_proba(test_x)[:, 1] > thresh_best).astype(int)
score = accuracy_score(ypred, test_y)
print('Test accuracy with threshold: %f' % (score * 100))
print('True Negatives: %d, False Positives: %d, False Negatives: %d, True Positives: %d' % tuple(confusion_matrix(test_y, ypred).ravel())) | 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(pred, test_y)
if score > best_score:
best_score = score
best_estimators = i
model = RandomForestClassifier(n_estimators=best_estimators, random_state=0)
model.fit(train_x, train_y)
pred = model.predict(test_x)
score = accuracy_score(pred, test_y)
print('Test Accuracy: %f' % (score * 100)) | 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()
data.nunique()
corr = data.corr()
corr.style.background_gradient(cmap='coolwarm').set_precision(2)
unique_vals = data.nunique()
col_log = data.columns
for i in range(0, len(unique_vals)):
coln = str(col_log[i])
if int(unique_vals[i]) < 5 and coln != 'status':
data = pd.concat([data.drop(coln, axis=1), pd.get_dummies(data[coln], prefix=coln)], axis=1)
data_y = pd.DataFrame(data['status'])
data_x = data.drop('status', axis=1)
status_encoder = LabelEncoder()
data_y = status_encoder.fit_transform(data_y) | 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 matplotlib.pyplot as plt
import matplotlib.pyplot as plt
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.background_gradient(cmap='coolwarm').set_precision(2)
unique_vals = data.nunique()
col_log = data.columns
for i in range(0, len(unique_vals)):
coln = str(col_log[i])
if int(unique_vals[i]) < 5 and coln != 'status':
data = pd.concat([data.drop(coln, axis=1), pd.get_dummies(data[coln], prefix=coln)], axis=1)
data_y = pd.DataFrame(data['status'])
data_x = data.drop('status', axis=1)
status_encoder = LabelEncoder()
data_y = status_encoder.fit_transform(data_y)
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(pred, test_y)
if score > best_score:
best_score = score
best_estimators = i
model = RandomForestClassifier(n_estimators=best_estimators, random_state=0)
model.fit(train_x, train_y)
pred = model.predict(test_x)
score = accuracy_score(pred, test_y)
from sklearn.metrics import confusion_matrix, precision_score, plot_confusion_matrix
import matplotlib.pyplot as plt
from sklearn.metrics import precision_recall_curve, plot_precision_recall_curve
y_pred_prob = model.predict_proba(val_x)[:, 1]
precision, recall, thresholds = precision_recall_curve(val_y, y_pred_prob)
df = pd.DataFrame(data={'Precision': precision[:-1], 'Recall': recall[:-1], 'Thresholds': thresholds})
df
targets = df.loc[(df['Precision'] >= 1) & (df['Thresholds'] != 1)]
targets
best = -1
thresh_best = -1
y_test_prob = model.predict_proba(test_x)[:, 1]
for target in targets.to_numpy():
true_prediction = (y_test_prob > target[2]).astype(int)
score = precision_score(test_y, true_prediction)
if score > best:
best = score
thresh_best = target[2]
print('Score for threshold %f: %f' % (target[2], score * 100))
print('Best precision score of %f achieved with threshold %f.' % (best, thresh_best)) | 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()
data.nunique()
corr = data.corr()
corr.style.background_gradient(cmap='coolwarm').set_precision(2)
unique_vals = data.nunique()
col_log = data.columns
for i in range(0, len(unique_vals)):
coln = str(col_log[i])
if int(unique_vals[i]) < 5 and coln != 'status':
data = pd.concat([data.drop(coln, axis=1), pd.get_dummies(data[coln], prefix=coln)], axis=1)
data_y = pd.DataFrame(data['status'])
data_x = data.drop('status', axis=1)
status_encoder = LabelEncoder()
data_y = status_encoder.fit_transform(data_y)
print('Guessing always placed accuracy: %f' % ((data['status'] == 'Placed').sum() / data['status'].count() * 100)) | 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()
data.nunique()
corr = data.corr()
corr.style.background_gradient(cmap='coolwarm').set_precision(2)
unique_vals = data.nunique()
col_log = data.columns
for i in range(0, len(unique_vals)):
coln = str(col_log[i])
if int(unique_vals[i]) < 5 and coln != 'status':
data = pd.concat([data.drop(coln, axis=1), pd.get_dummies(data[coln], prefix=coln)], axis=1)
data_y = pd.DataFrame(data['status'])
data_x = data.drop('status', axis=1)
status_encoder = LabelEncoder()
data_y = status_encoder.fit_transform(data_y)
df = pd.DataFrame(data={'Precision': precision[:-1], 'Recall': recall[:-1], 'Thresholds': thresholds})
df | 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 = RandomForestClassifier(n_estimators=i, random_state=0)
model.fit(train_x, train_y)
pred = model.predict(test_x)
score = accuracy_score(pred, test_y)
if score > best_score:
best_score = score
best_estimators = i
model = RandomForestClassifier(n_estimators=best_estimators, random_state=0)
model.fit(train_x, train_y)
pred = model.predict(test_x)
score = accuracy_score(pred, test_y)
from sklearn.metrics import confusion_matrix, precision_score, plot_confusion_matrix
import matplotlib.pyplot as plt
print('True Negatives: %d, False Positives: %d, False Negatives: %d, True Positives: %d' % tuple(confusion_matrix(test_y, pred).ravel()))
print('Precision Score: %f' % (precision_score(test_y, pred) * 100))
plot_confusion_matrix(model, test_x, test_y, cmap=plt.cm.Reds)
plt.title('Confusion Matrix')
plt.show() | 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(pred, test_y)
if score > best_score:
best_score = score
best_estimators = i
print('The best number of estiamtors was %d with accuracy score %f' % (best_estimators, best_score * 100)) | 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.background_gradient(cmap='coolwarm').set_precision(2)
unique_vals = data.nunique()
col_log = data.columns
for i in range(0, len(unique_vals)):
coln = str(col_log[i])
if int(unique_vals[i]) < 5 and coln != 'status':
data = pd.concat([data.drop(coln, axis=1), pd.get_dummies(data[coln], prefix=coln)], axis=1)
data | 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.background_gradient(cmap='coolwarm').set_precision(2) | 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(pred, test_y)
if score > best_score:
best_score = score
best_estimators = i
model = RandomForestClassifier(n_estimators=best_estimators, random_state=0)
model.fit(train_x, train_y) | 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()
data.nunique()
corr = data.corr()
corr.style.background_gradient(cmap='coolwarm').set_precision(2)
unique_vals = data.nunique()
col_log = data.columns
for i in range(0, len(unique_vals)):
coln = str(col_log[i])
if int(unique_vals[i]) < 5 and coln != 'status':
data = pd.concat([data.drop(coln, axis=1), pd.get_dummies(data[coln], prefix=coln)], axis=1)
data_y = pd.DataFrame(data['status'])
data_x = data.drop('status', axis=1)
status_encoder = LabelEncoder()
data_y = status_encoder.fit_transform(data_y)
df = pd.DataFrame(data={'Precision': precision[:-1], 'Recall': recall[:-1], 'Thresholds': thresholds})
df
targets = df.loc[(df['Precision'] >= 1) & (df['Thresholds'] != 1)]
targets | 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.background_gradient(cmap='coolwarm').set_precision(2)
data | 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_recursive(array, element, mid + 1, end)
element = 35
array = list(range(1, 1000))
n = 1000
print('Searching for {}'.format(element))
print('Index of {}: {}'.format(element, binary_search_recursive(array, element, 0, len(array)))) | 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_recursive(array, element, mid + 1, end)
element = 35
array = list(range(1, 1000))
n = 1000
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_recursive(array, element, mid + 1, end)
element = 50
array = [10, 20, 30, 40, 50, 60, 70]
print('Angka yang dicari : {}'.format(element))
print('Index ke {}: {}'.format(element, binary_search_recursive(array, element, 0, len(array)))) | 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_recursive(array, element, mid + 1, end)
element = 35
array = list(range(1, 1000))
n = 1000
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_recursive(array, element, mid + 1, end)
element = 50
array = [10, 20, 30, 40, 50, 60, 70]
def selectionSort(array, size):
for step in range(size):
min_idx = step
for i in range(step + 1, size):
if array[i] < array[min_idx]:
min_idx = i
array[step], array[min_idx] = (array[min_idx], array[step])
data = [10, 5, 30, 15, 50, 6, 25]
size = len(data)
selectionSort(data, size)
print('Menurut Selection Sort:')
print(data) | 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_recursive(array, element, mid + 1, end)
element = 35
array = list(range(1, 1000))
n = 1000
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_recursive(array, element, mid + 1, end)
element = 50
array = [10, 20, 30, 40, 50, 60, 70]
def selectionSort(array, size):
for step in range(size):
min_idx = step
for i in range(step + 1, size):
if array[i] < array[min_idx]:
min_idx = i
array[step], array[min_idx] = (array[min_idx], array[step])
data = [10, 5, 30, 15, 50, 6, 25]
size = len(data)
selectionSort(data, size)
def insertionSort(array):
for step in range(1, len(array)):
key = array[step]
j = step - 1
while j >= 0 and key < array[j]:
array[j + 1] = array[j]
j = j - 1
array[j + 1] = key
data = [10, 5, 30, 15, 50, 6, 25]
insertionSort(data)
print('Menurut Insertion Sort:')
print(data) | 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', '70']
Target = '50'
answer = sequentialSearch(Target, List)
if answer != -1:
print('Target ditemukan diindex :', answer, 'melalui', iterations, 'iterasi')
else:
print('Target tidak ditemukan') | 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 Pipeline
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import OrdinalEncoder
from xgboost import XGBRegressor
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-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 for cols in df_train if df_train[cols].dtype in ['int', 'float']]
categorical_features = [cols for cols in df_train if df_train[cols].dtype == 'object']
numerical_missing_columns = list(df_train[numerical_features].columns[df_train[numerical_features].isna().any()])
(df_train[numerical_missing_columns].isnull().sum() / df_train.shape[0] * 100).sort_values(ascending=False)
data_frames = [df_train, df_test]
for df in data_frames:
df.drop('GarageYrBlt', axis=1, inplace=True)
numerical_features.remove('GarageYrBlt')
from sklearn.impute import SimpleImputer
imput_mean = SimpleImputer(missing_values=np.NaN, strategy='mean')
imput_with_zero = SimpleImputer(strategy='constant', fill_value=0)
df_train['LotFrontage'] = imput_mean.fit_transform(df_train[['LotFrontage']]).astype('int')
df_test['LotFrontage'] = imput_mean.transform(df_test[['LotFrontage']]).astype('int')
df_train['MasVnrArea'] = imput_with_zero.fit_transform(df_train[['MasVnrArea']]).astype('int')
df_test['MasVnrArea'] = imput_mean.transform(df_test[['MasVnrArea']]).astype('int')
categorical_features_to_investigate = [col for col in categorical_features if df_train[col].nunique() <= 6]
categorical_features_to_delete = [col for col in categorical_features if col not in categorical_features_to_investigate]
data_frames = [df_train, df_test]
for df in data_frames:
df.drop(categorical_features_to_delete, axis=1, inplace=True)
categorical_missing_columns = list(df_train[categorical_features_to_investigate].columns[df_train[categorical_features_to_investigate].isnull().any()])
(df_train[categorical_missing_columns].isnull().sum() / df_train.shape[0] * 100).sort_values(ascending=False)
ordinal_categories = ['Street', 'LotShape', 'LandContour', 'LandSlope', 'ExterQual', 'CentralAir', 'PavedDrive']
nominal_categories = ['MSZoning', 'Utilities', 'LotConfig', 'BldgType', 'RoofStyle', 'KitchenQual', 'Foundation', 'Heating', 'SaleCondition', 'HeatingQC', 'ExterCond']
X = df_train.drop(['SalePrice'], axis=1)
y = df_train['SalePrice']
ordinal_encoder = OrdinalEncoder()
one_hot = OneHotEncoder(handle_unknown='ignore')
imput_mean = SimpleImputer(strategy='mean')
imput_with_zero = SimpleImputer(strategy='constant', fill_value=0)
preprocessor = ColumnTransformer(transformers=[('ordinal', ordinal_encoder, ordinal_categories), ('one hot', one_hot, nominal_categories), ('mean', imput_mean, ['LotFrontage']), ('zero', imput_with_zero, ['MasVnrArea'])])
from sklearn.ensemble import RandomForestRegressor
from xgboost import XGBRegressor
from sklearn.linear_model import LinearRegression
model_random_regressor = Pipeline(steps=[('preprocessor', preprocessor), ('model', RandomForestRegressor(random_state=0))])
model_xgb = Pipeline(steps=[('preprocessor', preprocessor), ('model', XGBRegressor(random_state=0))])
model_linear = Pipeline(steps=[('preprocessor', preprocessor), ('model', LinearRegression())])
kfold = KFold(n_splits=5, shuffle=True, random_state=0)
rf_model_result = cross_validate(model_random_regressor, X, y, cv=kfold, scoring='neg_root_mean_squared_error')
xgb_result = cross_validate(model_xgb, X, y, cv=kfold, scoring='neg_root_mean_squared_error')
linear_result = cross_validate(model_linear, X, y, cv=kfold, scoring='neg_root_mean_squared_error')
print(f"The RMSE of Random Forest model was: {-rf_model_result['test_score'].mean()}")
print(f"The RMSE of XGB model was: {-xgb_result['test_score'].mean()}")
print(f"The RMSE of linear model was: {-linear_result['test_score'].mean()}") | 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 for cols in df_train if df_train[cols].dtype in ['int', 'float']]
categorical_features = [cols for cols in df_train if df_train[cols].dtype == 'object']
numerical_missing_columns = list(df_train[numerical_features].columns[df_train[numerical_features].isna().any()])
(df_train[numerical_missing_columns].isnull().sum() / df_train.shape[0] * 100).sort_values(ascending=False)
data_frames = [df_train, df_test]
for df in data_frames:
df.drop('GarageYrBlt', axis=1, inplace=True)
numerical_features.remove('GarageYrBlt')
categorical_features_to_investigate = [col for col in categorical_features if df_train[col].nunique() <= 6]
categorical_features_to_delete = [col for col in categorical_features if col not in categorical_features_to_investigate]
data_frames = [df_train, df_test]
for df in data_frames:
df.drop(categorical_features_to_delete, axis=1, inplace=True)
categorical_missing_columns = list(df_train[categorical_features_to_investigate].columns[df_train[categorical_features_to_investigate].isnull().any()])
(df_train[categorical_missing_columns].isnull().sum() / df_train.shape[0] * 100).sort_values(ascending=False) | 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 for cols in df_train if df_train[cols].dtype in ['int', 'float']]
categorical_features = [cols for cols in df_train if df_train[cols].dtype == 'object']
numerical_missing_columns = list(df_train[numerical_features].columns[df_train[numerical_features].isna().any()])
(df_train[numerical_missing_columns].isnull().sum() / df_train.shape[0] * 100).sort_values(ascending=False) | 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 for cols in df_train if df_train[cols].dtype in ['int', 'float']]
categorical_features = [cols for cols in df_train if df_train[cols].dtype == 'object']
numerical_missing_columns = list(df_train[numerical_features].columns[df_train[numerical_features].isna().any()])
(df_train[numerical_missing_columns].isnull().sum() / df_train.shape[0] * 100).sort_values(ascending=False)
data_frames = [df_train, df_test]
for df in data_frames:
df.drop('GarageYrBlt', axis=1, inplace=True)
numerical_features.remove('GarageYrBlt')
categorical_features_to_investigate = [col for col in categorical_features if df_train[col].nunique() <= 6]
categorical_features_to_delete = [col for col in categorical_features if col not in categorical_features_to_investigate]
data_frames = [df_train, df_test]
for df in data_frames:
df.drop(categorical_features_to_delete, axis=1, inplace=True)
categorical_missing_columns = list(df_train[categorical_features_to_investigate].columns[df_train[categorical_features_to_investigate].isnull().any()])
(df_train[categorical_missing_columns].isnull().sum() / df_train.shape[0] * 100).sort_values(ascending=False)
for df in data_frames:
df.drop(categorical_missing_columns, axis=1, inplace=True)
print(df.shape) | 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 for cols in df_train if df_train[cols].dtype in ['int', 'float']]
categorical_features = [cols for cols in df_train if df_train[cols].dtype == 'object']
numerical_missing_columns = list(df_train[numerical_features].columns[df_train[numerical_features].isna().any()])
(df_train[numerical_missing_columns].isnull().sum() / df_train.shape[0] * 100).sort_values(ascending=False)
data_frames = [df_train, df_test]
for df in data_frames:
df.drop('GarageYrBlt', axis=1, inplace=True)
numerical_features.remove('GarageYrBlt')
categorical_features_to_investigate = [col for col in categorical_features if df_train[col].nunique() <= 6]
categorical_features_to_delete = [col for col in categorical_features if col not in categorical_features_to_investigate]
data_frames = [df_train, df_test]
for df in data_frames:
df.drop(categorical_features_to_delete, axis=1, inplace=True)
print(f'Number of features that remains: {len(categorical_features_to_investigate)}') | 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 for cols in df_train if df_train[cols].dtype in ['int', 'float']]
categorical_features = [cols for cols in df_train if df_train[cols].dtype == 'object']
print(f'len of numerical features: {len(numerical_features)}')
print(f'len of categorical features: {len(categorical_features)}') | 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 for cols in df_train if df_train[cols].dtype in ['int', 'float']]
categorical_features = [cols for cols in df_train if df_train[cols].dtype == 'object']
numerical_missing_columns = list(df_train[numerical_features].columns[df_train[numerical_features].isna().any()])
(df_train[numerical_missing_columns].isnull().sum() / df_train.shape[0] * 100).sort_values(ascending=False)
data_frames = [df_train, df_test]
for df in data_frames:
df.drop('GarageYrBlt', axis=1, inplace=True)
numerical_features.remove('GarageYrBlt')
categorical_features_to_investigate = [col for col in categorical_features if df_train[col].nunique() <= 6]
categorical_features_to_delete = [col for col in categorical_features if col not in categorical_features_to_investigate]
data_frames = [df_train, df_test]
for df in data_frames:
df.drop(categorical_features_to_delete, axis=1, inplace=True)
categorical_missing_columns = list(df_train[categorical_features_to_investigate].columns[df_train[categorical_features_to_investigate].isnull().any()])
(df_train[categorical_missing_columns].isnull().sum() / df_train.shape[0] * 100).sort_values(ascending=False)
categorical_features_to_investigate = [col for col in categorical_features_to_investigate if col not in categorical_missing_columns]
len(categorical_features)
for col in categorical_features_to_investigate:
print(f'{col} \n{df_train[col].unique()}') | 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-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 for cols in df_train if df_train[cols].dtype in ['int', 'float']]
categorical_features = [cols for cols in df_train if df_train[cols].dtype == 'object']
numerical_missing_columns = list(df_train[numerical_features].columns[df_train[numerical_features].isna().any()])
(df_train[numerical_missing_columns].isnull().sum() / df_train.shape[0] * 100).sort_values(ascending=False)
import matplotlib.pyplot as plt
import seaborn as sns
def plot_count(feature):
pass
data_frames = [df_train, df_test]
for df in data_frames:
df.drop('GarageYrBlt', axis=1, inplace=True)
numerical_features.remove('GarageYrBlt')
from sklearn.impute import SimpleImputer
imput_mean = SimpleImputer(missing_values=np.NaN, strategy='mean')
imput_with_zero = SimpleImputer(strategy='constant', fill_value=0)
df_train['LotFrontage'] = imput_mean.fit_transform(df_train[['LotFrontage']]).astype('int')
df_test['LotFrontage'] = imput_mean.transform(df_test[['LotFrontage']]).astype('int')
df_train['MasVnrArea'] = imput_with_zero.fit_transform(df_train[['MasVnrArea']]).astype('int')
df_test['MasVnrArea'] = imput_mean.transform(df_test[['MasVnrArea']]).astype('int')
categorical_features_to_investigate = [col for col in categorical_features if df_train[col].nunique() <= 6]
categorical_features_to_delete = [col for col in categorical_features if col not in categorical_features_to_investigate]
data_frames = [df_train, df_test]
for df in data_frames:
df.drop(categorical_features_to_delete, axis=1, inplace=True)
categorical_missing_columns = list(df_train[categorical_features_to_investigate].columns[df_train[categorical_features_to_investigate].isnull().any()])
(df_train[categorical_missing_columns].isnull().sum() / df_train.shape[0] * 100).sort_values(ascending=False)
df_test.isnull().sum()
ordinal_var_dict = {'Street': ['Grvl', 'Pave'], 'LotShape': ['IR3', 'IR2', 'IR1', 'Reg'], 'LandContour': ['Low', 'HLS', 'Bnk', 'Lvl'], 'LandSlope': ['Sev', 'Mod', 'Gtl'], 'ExterQual': ['Fa', 'TA', 'Gd', 'Ex'], 'CentralAir': ['N', 'Y'], 'PavedDrive': ['N', 'P', 'Y']}
for var in ordinal_var_dict:
ordered_var = pd.api.types.CategoricalDtype(categories=ordinal_var_dict[var], ordered=True)
df_train[var] = df_train[var].astype(ordered_var)
df_test[var] = df_test[var].astype(ordered_var)
X = df_train.drop(['SalePrice'], axis=1)
y = df_train['SalePrice']
for colname in X.select_dtypes(include=['object', 'category']):
X[colname], _ = X[colname].factorize()
discrete_features = X.dtypes == int
for colname in df_test.select_dtypes(include=['object', 'category']):
df_test[colname], _ = df_test[colname].factorize()
from sklearn.feature_selection import mutual_info_regression
mi_scores = mutual_info_regression(X, y)
mi_scores = pd.Series(mi_scores, name='Mi scores', index=X.columns)
mi_scores = mi_scores.sort_values(ascending=False)
def plot_mi_scores(scores):
scores = scores.sort_values(ascending=True)
width = np.arange(len(scores))
ticks = list(scores.index)
plt.barh(width, scores)
plt.yticks(width, ticks)
plt.title('Mutual Information Scores')
plt.figure(dpi=100, figsize=(12, 5))
plot_mi_scores(mi_scores[:20]) | 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 for cols in df_train if df_train[cols].dtype in ['int', 'float']]
categorical_features = [cols for cols in df_train if df_train[cols].dtype == 'object']
numerical_missing_columns = list(df_train[numerical_features].columns[df_train[numerical_features].isna().any()])
(df_train[numerical_missing_columns].isnull().sum() / df_train.shape[0] * 100).sort_values(ascending=False)
df_train[numerical_missing_columns].describe() | 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')
(df_train.shape, df_test.shape)
numerical_features = [cols for cols in df_train if df_train[cols].dtype in ['int', 'float']]
categorical_features = [cols for cols in df_train if df_train[cols].dtype == 'object']
numerical_missing_columns = list(df_train[numerical_features].columns[df_train[numerical_features].isna().any()])
(df_train[numerical_missing_columns].isnull().sum() / df_train.shape[0] * 100).sort_values(ascending=False)
data_frames = [df_train, df_test]
for df in data_frames:
df.drop('GarageYrBlt', axis=1, inplace=True)
numerical_features.remove('GarageYrBlt')
categorical_features_to_investigate = [col for col in categorical_features if df_train[col].nunique() <= 6]
categorical_features_to_delete = [col for col in categorical_features if col not in categorical_features_to_investigate]
data_frames = [df_train, df_test]
for df in data_frames:
df.drop(categorical_features_to_delete, axis=1, inplace=True)
categorical_missing_columns = list(df_train[categorical_features_to_investigate].columns[df_train[categorical_features_to_investigate].isnull().any()])
(df_train[categorical_missing_columns].isnull().sum() / df_train.shape[0] * 100).sort_values(ascending=False)
df_test.isnull().sum()
ordinal_var_dict = {'Street': ['Grvl', 'Pave'], 'LotShape': ['IR3', 'IR2', 'IR1', 'Reg'], 'LandContour': ['Low', 'HLS', 'Bnk', 'Lvl'], 'LandSlope': ['Sev', 'Mod', 'Gtl'], 'ExterQual': ['Fa', 'TA', 'Gd', 'Ex'], 'CentralAir': ['N', 'Y'], 'PavedDrive': ['N', 'P', 'Y']}
for var in ordinal_var_dict:
ordered_var = pd.api.types.CategoricalDtype(categories=ordinal_var_dict[var], ordered=True)
df_train[var] = df_train[var].astype(ordered_var)
df_test[var] = df_test[var].astype(ordered_var)
X = df_train.drop(['SalePrice'], axis=1)
y = df_train['SalePrice']
for colname in X.select_dtypes(include=['object', 'category']):
X[colname], _ = X[colname].factorize()
discrete_features = X.dtypes == int
for colname in df_test.select_dtypes(include=['object', 'category']):
df_test[colname], _ = df_test[colname].factorize()
from sklearn.feature_selection import mutual_info_regression
mi_scores = mutual_info_regression(X, y)
mi_scores = pd.Series(mi_scores, name='Mi scores', index=X.columns)
mi_scores = mi_scores.sort_values(ascending=False)
selected_mi = mi_scores[mi_scores >= 0.2].index
X = X[selected_mi]
X | 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 for cols in df_train if df_train[cols].dtype in ['int', 'float']]
categorical_features = [cols for cols in df_train if df_train[cols].dtype == 'object']
numerical_missing_columns = list(df_train[numerical_features].columns[df_train[numerical_features].isna().any()])
(df_train[numerical_missing_columns].isnull().sum() / df_train.shape[0] * 100).sort_values(ascending=False)
df_test[['LotFrontage', 'MasVnrArea']].isnull().sum()
df_train[['LotFrontage', 'MasVnrArea']].isnull().sum() | 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_counts() | 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 for cols in df_train if df_train[cols].dtype in ['int', 'float']]
categorical_features = [cols for cols in df_train if df_train[cols].dtype == 'object']
numerical_missing_columns = list(df_train[numerical_features].columns[df_train[numerical_features].isna().any()])
print(numerical_missing_columns) | 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')
(df_train.shape, df_test.shape)
numerical_features = [cols for cols in df_train if df_train[cols].dtype in ['int', 'float']]
categorical_features = [cols for cols in df_train if df_train[cols].dtype == 'object']
numerical_missing_columns = list(df_train[numerical_features].columns[df_train[numerical_features].isna().any()])
(df_train[numerical_missing_columns].isnull().sum() / df_train.shape[0] * 100).sort_values(ascending=False)
data_frames = [df_train, df_test]
for df in data_frames:
df.drop('GarageYrBlt', axis=1, inplace=True)
numerical_features.remove('GarageYrBlt')
categorical_features_to_investigate = [col for col in categorical_features if df_train[col].nunique() <= 6]
categorical_features_to_delete = [col for col in categorical_features if col not in categorical_features_to_investigate]
data_frames = [df_train, df_test]
for df in data_frames:
df.drop(categorical_features_to_delete, axis=1, inplace=True)
categorical_missing_columns = list(df_train[categorical_features_to_investigate].columns[df_train[categorical_features_to_investigate].isnull().any()])
(df_train[categorical_missing_columns].isnull().sum() / df_train.shape[0] * 100).sort_values(ascending=False)
df_test.isnull().sum()
ordinal_var_dict = {'Street': ['Grvl', 'Pave'], 'LotShape': ['IR3', 'IR2', 'IR1', 'Reg'], 'LandContour': ['Low', 'HLS', 'Bnk', 'Lvl'], 'LandSlope': ['Sev', 'Mod', 'Gtl'], 'ExterQual': ['Fa', 'TA', 'Gd', 'Ex'], 'CentralAir': ['N', 'Y'], 'PavedDrive': ['N', 'P', 'Y']}
for var in ordinal_var_dict:
ordered_var = pd.api.types.CategoricalDtype(categories=ordinal_var_dict[var], ordered=True)
df_train[var] = df_train[var].astype(ordered_var)
df_test[var] = df_test[var].astype(ordered_var)
X = df_train.drop(['SalePrice'], axis=1)
y = df_train['SalePrice']
for colname in X.select_dtypes(include=['object', 'category']):
X[colname], _ = X[colname].factorize()
discrete_features = X.dtypes == int
for colname in df_test.select_dtypes(include=['object', 'category']):
df_test[colname], _ = df_test[colname].factorize()
from sklearn.feature_selection import mutual_info_regression
mi_scores = mutual_info_regression(X, y)
mi_scores = pd.Series(mi_scores, name='Mi scores', index=X.columns)
mi_scores = mi_scores.sort_values(ascending=False)
mi_scores[:15] | 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 for cols in df_train if df_train[cols].dtype in ['int', 'float']]
categorical_features = [cols for cols in df_train if df_train[cols].dtype == 'object']
numerical_missing_columns = list(df_train[numerical_features].columns[df_train[numerical_features].isna().any()])
(df_train[numerical_missing_columns].isnull().sum() / df_train.shape[0] * 100).sort_values(ascending=False)
data_frames = [df_train, df_test]
for df in data_frames:
df.drop('GarageYrBlt', axis=1, inplace=True)
numerical_features.remove('GarageYrBlt')
categorical_features_to_investigate = [col for col in categorical_features if df_train[col].nunique() <= 6]
categorical_features_to_delete = [col for col in categorical_features if col not in categorical_features_to_investigate]
data_frames = [df_train, df_test]
for df in data_frames:
df.drop(categorical_features_to_delete, axis=1, inplace=True)
categorical_missing_columns = list(df_train[categorical_features_to_investigate].columns[df_train[categorical_features_to_investigate].isnull().any()])
(df_train[categorical_missing_columns].isnull().sum() / df_train.shape[0] * 100).sort_values(ascending=False)
categorical_features_to_investigate = [col for col in categorical_features_to_investigate if col not in categorical_missing_columns]
len(categorical_features) | 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 for cols in df_train if df_train[cols].dtype in ['int', 'float']]
categorical_features = [cols for cols in df_train if df_train[cols].dtype == 'object']
numerical_missing_columns = list(df_train[numerical_features].columns[df_train[numerical_features].isna().any()])
(df_train[numerical_missing_columns].isnull().sum() / df_train.shape[0] * 100).sort_values(ascending=False)
garage_features = df_train[['YearBuilt', 'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual', 'GarageCond']]
garage_features[garage_features['GarageYrBlt'].isnull()].describe() | 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 for cols in df_train if df_train[cols].dtype in ['int', 'float']]
categorical_features = [cols for cols in df_train if df_train[cols].dtype == 'object']
numerical_missing_columns = list(df_train[numerical_features].columns[df_train[numerical_features].isna().any()])
(df_train[numerical_missing_columns].isnull().sum() / df_train.shape[0] * 100).sort_values(ascending=False)
data_frames = [df_train, df_test]
for df in data_frames:
df.drop('GarageYrBlt', axis=1, inplace=True)
numerical_features.remove('GarageYrBlt')
categorical_features_to_investigate = [col for col in categorical_features if df_train[col].nunique() <= 6]
categorical_features_to_delete = [col for col in categorical_features if col not in categorical_features_to_investigate]
data_frames = [df_train, df_test]
for df in data_frames:
df.drop(categorical_features_to_delete, axis=1, inplace=True)
categorical_missing_columns = list(df_train[categorical_features_to_investigate].columns[df_train[categorical_features_to_investigate].isnull().any()])
(df_train[categorical_missing_columns].isnull().sum() / df_train.shape[0] * 100).sort_values(ascending=False)
list_of_suspicious = ['ExterQual', 'ExterCond', 'HeatingQC', 'KitchenQual']
for col in list_of_suspicious:
print(f'{col} \n{df_train[col].unique()}')
print(f'{col} \n{df_test[col].unique()}')
print('\n') | 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.shape)
numerical_features = [cols for cols in df_train if df_train[cols].dtype in ['int', 'float']]
categorical_features = [cols for cols in df_train if df_train[cols].dtype == 'object']
numerical_missing_columns = list(df_train[numerical_features].columns[df_train[numerical_features].isna().any()])
(df_train[numerical_missing_columns].isnull().sum() / df_train.shape[0] * 100).sort_values(ascending=False)
import matplotlib.pyplot as plt
import seaborn as sns
def plot_count(feature):
plt.hist(feature, color='g')
plt.figure(figsize=(16, 4))
for count, value in enumerate(numerical_missing_columns):
plt.subplot(1, 3, count + 1)
plt.title(f'{value} distribution')
plot_count(df_train[value]) | 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'].mean()
data['Gender'].value_counts(normalize=True) * 100 | 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.Gender.value_counts()
data.groupby('Gender')['Purchase'].mean()
data.groupby('Marital_Status')['Purchase'].mean()
plt.figure(figsize=(12, 8))
sns.countplot(data['Occupation'])
plt.show() | 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.Gender.value_counts()
sns.countplot(data['Gender'])
plt.show() | 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.Gender.value_counts()
plt.figure(figsize=(12, 8))
sns.boxplot(x='Age', y='Purchase', data=data, hue='Gender', palette='Set3')
plt.show() | code |
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