path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
106198134/cell_17 | [
"text_html_output_1.png"
] | clean_names(dailyActivity) | code |
106198134/cell_5 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | library(tidyverse)
library(readr)
library(here)
library(skimr)
library(dplyr)
library(janitor) | code |
50242358/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv'
df = pd.read_csv(TRAIN_PATH)
print(f"Dataset has {len(df['city'].dropna().unique())} unique cities.")
print("_"*20)
print(f"Unique cities count:\n{df['city'].value_counts()}")
print("*"*50, end="\n\n")
fig, ax = plt.subplots(figsize=(12, 6))
sns.barplot(ax=ax, x=df['city'].value_counts().index[:10], y=df['city'].value_counts().values[:10], capsize=.2,palette="Blues_d")
def plotPerColumnDistribution(df, nGraphShown, nGraphPerRow):
nunique = df.nunique()
df = df[[col for col in df if nunique[col] > 1 and nunique[col] < 50]]
nRow, nCol = df.shape
columnNames = list(df)
nGraphRow = (nCol + nGraphPerRow - 1) / nGraphPerRow
for i in range(min(nCol, nGraphShown)):
columnDf = df.iloc[:, i]
if not np.issubdtype(type(columnDf.iloc[0]), np.number):
valueCounts = columnDf.value_counts()
plt.xticks(rotation=90)
plt.tight_layout(pad=1.0, w_pad=1.0, h_pad=1.0)
corr = df.corr()
plt.figure(num=None, figsize=(6, 6), dpi=80, facecolor='w', edgecolor='k')
corrMat = plt.matshow(corr, fignum=1)
plt.xticks(range(len(corr.columns)), corr.columns, rotation=90)
plt.yticks(range(len(corr.columns)), corr.columns)
plt.gca().xaxis.tick_bottom()
plt.colorbar(corrMat)
plt.title(f'Correlation Matrix for all Transactions', fontsize=15)
plt.savefig('./correlation.png')
plt.show() | code |
50242358/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv'
df = pd.read_csv(TRAIN_PATH)
print(f"Dataset has {len(df['education_level'].dropna().unique())} unique education level data.")
print('_' * 20)
print(f"Unique education level:\n{df['education_level'].value_counts()}")
print('*' * 50, end='\n\n') | code |
50242358/cell_9 | [
"image_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv'
df = pd.read_csv(TRAIN_PATH)
print(f"Dataset has {len(df['city_development_index'].dropna().unique())} unique city development indices.")
print('_' * 20)
print(f"Unique City Development Indices:\n{df['city_development_index'].value_counts()}")
print('*' * 50, end='\n\n') | code |
50242358/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv'
df = pd.read_csv(TRAIN_PATH)
df.head() | code |
50242358/cell_2 | [
"text_plain_output_1.png"
] | import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
50242358/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv'
df = pd.read_csv(TRAIN_PATH)
print(f"Dataset has {len(df['relevent_experience'].dropna().unique())} unique relevant experience data.")
print('_' * 20)
print(f"Unique Experiences:\n{df['relevent_experience'].value_counts()}")
print('*' * 50, end='\n\n') | code |
50242358/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv'
df = pd.read_csv(TRAIN_PATH)
print(f"Dataset has {len(df['city'].dropna().unique())} unique cities.")
print("_"*20)
print(f"Unique cities count:\n{df['city'].value_counts()}")
print("*"*50, end="\n\n")
fig, ax = plt.subplots(figsize=(12, 6))
sns.barplot(ax=ax, x=df['city'].value_counts().index[:10], y=df['city'].value_counts().values[:10], capsize=.2,palette="Blues_d")
def plotPerColumnDistribution(df, nGraphShown, nGraphPerRow):
nunique = df.nunique()
df = df[[col for col in df if nunique[col] > 1 and nunique[col] < 50]]
nRow, nCol = df.shape
columnNames = list(df)
nGraphRow = (nCol + nGraphPerRow - 1) / nGraphPerRow
plt.figure(num=None, figsize=(6 * nGraphPerRow, 8 * nGraphRow), dpi=80, facecolor='w', edgecolor='k')
for i in range(min(nCol, nGraphShown)):
plt.subplot(nGraphRow, nGraphPerRow, i + 1)
columnDf = df.iloc[:, i]
if not np.issubdtype(type(columnDf.iloc[0]), np.number):
valueCounts = columnDf.value_counts()
valueCounts.plot.bar()
else:
columnDf.hist()
plt.ylabel('counts')
plt.xticks(rotation=90)
plt.title(f'{columnNames[i]} (column {i})')
plt.tight_layout(pad=1.0, w_pad=1.0, h_pad=1.0)
plt.show()
plotPerColumnDistribution(df, 10, 5) | code |
50242358/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv'
df = pd.read_csv(TRAIN_PATH)
print(f'Dataset has {df.shape[0]} rows and {df.shape[1]} columns.')
print('*' * 50, end='\n\n')
print(f"Dataset has {len(df['enrollee_id'].dropna().unique())} unique user's data.")
print('*' * 50, end='\n\n') | code |
50242358/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv'
df = pd.read_csv(TRAIN_PATH)
print(f"Dataset has {len(df['city'].dropna().unique())} unique cities.")
print('_' * 20)
print(f"Unique cities count:\n{df['city'].value_counts()}")
print('*' * 50, end='\n\n')
fig, ax = plt.subplots(figsize=(12, 6))
sns.barplot(ax=ax, x=df['city'].value_counts().index[:10], y=df['city'].value_counts().values[:10], capsize=0.2, palette='Blues_d') | code |
50242358/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv'
df = pd.read_csv(TRAIN_PATH)
print(f"Dataset has {len(df['experience'].dropna().unique())} unique experience data.")
print('_' * 20)
print(f"Unique experiences:\n{df['experience'].value_counts()}")
print('*' * 50, end='\n\n') | code |
50242358/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv'
df = pd.read_csv(TRAIN_PATH)
print(f"Dataset has {len(df['company_size'].dropna().unique())} unique company sizes.")
print('_' * 20)
print(f"Unique company sizes:\n{df['company_size'].value_counts()}")
print('*' * 50, end='\n\n') | code |
50242358/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv'
df = pd.read_csv(TRAIN_PATH)
print(f"Dataset has {len(df['company_type'].dropna().unique())} unique company types.")
print('_' * 20)
print(f"Unique company types:\n{df['company_type'].value_counts()}")
print('*' * 50, end='\n\n') | code |
50242358/cell_14 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv'
df = pd.read_csv(TRAIN_PATH)
print(f"Dataset has {len(df['major_discipline'].dropna().unique())} unique major discipline data.")
print('_' * 20)
print(f"Unique major discipline:\n{df['major_discipline'].value_counts()}")
print('*' * 50, end='\n\n') | code |
50242358/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv'
df = pd.read_csv(TRAIN_PATH)
print(f"Dataset has {len(df['gender'].dropna().unique())} unique gender's data.")
print('_' * 20)
print(f"Unique Gender counts:\n{df['gender'].value_counts()}")
print('*' * 50, end='\n\n') | code |
50242358/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv'
df = pd.read_csv(TRAIN_PATH)
print(f"Dataset has {len(df['enrolled_university'].dropna().unique())} unique enrolled university data.")
print('_' * 20)
print(f"Unique enrolled university:\n{df['enrolled_university'].value_counts()}")
print('*' * 50, end='\n\n') | code |
50242358/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv'
df = pd.read_csv(TRAIN_PATH)
df['relevent_experience'].dropna().unique() | code |
2029228/cell_4 | [
"image_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import CountVectorizer
def draw_plot(data, start_range, end_range, total_data, bar_color, chart_title):
plt.rcdefaults()
fig, ax = plt.subplots()
data_filtered = data['title'][(data['score'] > start_range) & (data['score'] < end_range)].drop_duplicates()
cv = CountVectorizer(stop_words='english')
cv_fit = cv.fit_transform(data_filtered)
data_frame = {'Name': cv.get_feature_names(), 'Freq': cv_fit.toarray().sum(axis=0)}
data_graph = pd.DataFrame(data_frame).sort_values(by=['Freq'], ascending=False)[0:total_data]
objects = data_graph['Name'].values.tolist()
y_pos = np.arange(len(data_graph['Name']))
frequency = data_graph['Freq'].values.tolist()
ax.barh(y_pos, frequency, align='center',
color=bar_color, ecolor='black', alpha=0.5)
ax.set_yticks(y_pos)
ax.set_yticklabels(objects)
ax.invert_yaxis()
ax.set_xlabel('Frequency')
ax.set_title(chart_title)
plt.show()
file = pd.read_csv('../input/ign.csv')
draw_plot(file, 0, 10.1, 20, 'black', 'What is the popular words?') | code |
2029228/cell_6 | [
"image_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import CountVectorizer
def draw_plot(data, start_range, end_range, total_data, bar_color, chart_title):
plt.rcdefaults()
fig, ax = plt.subplots()
data_filtered = data['title'][(data['score'] > start_range) & (data['score'] < end_range)].drop_duplicates()
cv = CountVectorizer(stop_words='english')
cv_fit = cv.fit_transform(data_filtered)
data_frame = {'Name': cv.get_feature_names(), 'Freq': cv_fit.toarray().sum(axis=0)}
data_graph = pd.DataFrame(data_frame).sort_values(by=['Freq'], ascending=False)[0:total_data]
objects = data_graph['Name'].values.tolist()
y_pos = np.arange(len(data_graph['Name']))
frequency = data_graph['Freq'].values.tolist()
ax.barh(y_pos, frequency, align='center',
color=bar_color, ecolor='black', alpha=0.5)
ax.set_yticks(y_pos)
ax.set_yticklabels(objects)
ax.invert_yaxis()
ax.set_xlabel('Frequency')
ax.set_title(chart_title)
plt.show()
file = pd.read_csv('../input/ign.csv')
draw_plot(file, 9.4, 10.1, 20, 'blue', 'What made the masterpieces?') | code |
2029228/cell_8 | [
"image_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import CountVectorizer
def draw_plot(data, start_range, end_range, total_data, bar_color, chart_title):
plt.rcdefaults()
fig, ax = plt.subplots()
data_filtered = data['title'][(data['score'] > start_range) & (data['score'] < end_range)].drop_duplicates()
cv = CountVectorizer(stop_words='english')
cv_fit = cv.fit_transform(data_filtered)
data_frame = {'Name': cv.get_feature_names(), 'Freq': cv_fit.toarray().sum(axis=0)}
data_graph = pd.DataFrame(data_frame).sort_values(by=['Freq'], ascending=False)[0:total_data]
objects = data_graph['Name'].values.tolist()
y_pos = np.arange(len(data_graph['Name']))
frequency = data_graph['Freq'].values.tolist()
ax.barh(y_pos, frequency, align='center',
color=bar_color, ecolor='black', alpha=0.5)
ax.set_yticks(y_pos)
ax.set_yticklabels(objects)
ax.invert_yaxis()
ax.set_xlabel('Frequency')
ax.set_title(chart_title)
plt.show()
file = pd.read_csv('../input/ign.csv')
draw_plot(file, 5.9, 9.5, 20, 'green', 'What is the Okay and above?') | code |
2029228/cell_3 | [
"image_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import CountVectorizer
def draw_plot(data, start_range, end_range, total_data, bar_color, chart_title):
plt.rcdefaults()
fig, ax = plt.subplots()
data_filtered = data['title'][(data['score'] > start_range) & (data['score'] < end_range)].drop_duplicates()
cv = CountVectorizer(stop_words='english')
cv_fit = cv.fit_transform(data_filtered)
data_frame = {'Name': cv.get_feature_names(), 'Freq': cv_fit.toarray().sum(axis=0)}
data_graph = pd.DataFrame(data_frame).sort_values(by=['Freq'], ascending=False)[0:total_data]
objects = data_graph['Name'].values.tolist()
y_pos = np.arange(len(data_graph['Name']))
frequency = data_graph['Freq'].values.tolist()
ax.barh(y_pos, frequency, align='center',
color=bar_color, ecolor='black', alpha=0.5)
ax.set_yticks(y_pos)
ax.set_yticklabels(objects)
ax.invert_yaxis()
ax.set_xlabel('Frequency')
ax.set_title(chart_title)
plt.show()
file = pd.read_csv('../input/ign.csv')
file.head() | code |
2029228/cell_10 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import CountVectorizer
def draw_plot(data, start_range, end_range, total_data, bar_color, chart_title):
plt.rcdefaults()
fig, ax = plt.subplots()
data_filtered = data['title'][(data['score'] > start_range) & (data['score'] < end_range)].drop_duplicates()
cv = CountVectorizer(stop_words='english')
cv_fit = cv.fit_transform(data_filtered)
data_frame = {'Name': cv.get_feature_names(), 'Freq': cv_fit.toarray().sum(axis=0)}
data_graph = pd.DataFrame(data_frame).sort_values(by=['Freq'], ascending=False)[0:total_data]
objects = data_graph['Name'].values.tolist()
y_pos = np.arange(len(data_graph['Name']))
frequency = data_graph['Freq'].values.tolist()
ax.barh(y_pos, frequency, align='center',
color=bar_color, ecolor='black', alpha=0.5)
ax.set_yticks(y_pos)
ax.set_yticklabels(objects)
ax.invert_yaxis()
ax.set_xlabel('Frequency')
ax.set_title(chart_title)
plt.show()
file = pd.read_csv('../input/ign.csv')
draw_plot(file, 0, 6.0, 20, 'red', 'The Worst') | code |
129037105/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.drop('ID', axis=1, inplace=True)
df.nunique()
df.isnull().sum() | code |
129037105/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.drop('ID', axis=1, inplace=True)
df.nunique() | code |
129037105/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.drop('ID', axis=1, inplace=True)
df.nunique()
df.isnull().sum()
df.isnull().sum()
df.duplicated().sum() | code |
129037105/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.describe() | code |
129037105/cell_55 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # it's is core library for numeric and scientific computing
import numpy as np # linear algebra
import pandas as pd # it's is core library for data manipulation and data analysis
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/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.drop('ID', axis=1, inplace=True)
df.nunique()
df.isnull().sum()
df.isnull().sum()
df.duplicated().sum()
df.drop_duplicates(inplace=True)
df.duplicated().sum()
col = ['Age', 'Vintage', 'Avg_Account_Balance']
col_to_transform = ['Age', 'Vintage', 'Avg_Account_Balance']
df[col_to_transform] = df[col_to_transform].apply(lambda x: np.log(x))
# visualize the transformed data using histograms
fig,axes = plt.subplots(nrows=1 ,ncols=len(col_to_transform),figsize=(15,5))
for i,col in enumerate(col_to_transform):
axes[i].hist(df[col])
axes[i].set_xlabel(f'log({col})')
axes[i].set_ylabel('Frequency')
plt.show()
plt.figure(figsize=(10, 8))
sns.countplot(x='Gender', hue='Credit_Product', data=df) | code |
129037105/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.drop('ID', axis=1, inplace=True)
df.nunique()
df.isnull().sum()
df.isnull().sum() | code |
129037105/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # it's is core library for data manipulation and data analysis
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/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.drop('ID', axis=1, inplace=True)
df.nunique()
df.isnull().sum()
df.isnull().sum()
df.duplicated().sum()
df.drop_duplicates(inplace=True)
df.duplicated().sum()
plt.figure(figsize=(30, 10))
sns.boxplot(df['Avg_Account_Balance']) | code |
129037105/cell_52 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # it's is core library for numeric and scientific computing
import numpy as np # linear algebra
import pandas as pd # it's is core library for data manipulation and data analysis
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/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.drop('ID', axis=1, inplace=True)
df.nunique()
df.isnull().sum()
df.isnull().sum()
df.duplicated().sum()
df.drop_duplicates(inplace=True)
df.duplicated().sum()
col = ['Age', 'Vintage', 'Avg_Account_Balance']
col_to_transform = ['Age', 'Vintage', 'Avg_Account_Balance']
df[col_to_transform] = df[col_to_transform].apply(lambda x: np.log(x))
# visualize the transformed data using histograms
fig,axes = plt.subplots(nrows=1 ,ncols=len(col_to_transform),figsize=(15,5))
for i,col in enumerate(col_to_transform):
axes[i].hist(df[col])
axes[i].set_xlabel(f'log({col})')
axes[i].set_ylabel('Frequency')
plt.show()
plt.figure(figsize=(10, 8))
sns.countplot(x='Gender', hue='Is_Active', data=df) | code |
129037105/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 |
129037105/cell_45 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # it's is core library for data manipulation and data analysis
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/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.drop('ID', axis=1, inplace=True)
df.nunique()
df.isnull().sum()
df.isnull().sum()
df.duplicated().sum()
df.drop_duplicates(inplace=True)
df.duplicated().sum()
col = ['Age', 'Vintage', 'Avg_Account_Balance']
df[col].hist(bins=50, figsize=(20, 15))
plt.show() | code |
129037105/cell_49 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # it's is core library for numeric and scientific computing
import numpy as np # linear algebra
import pandas as pd # it's is core library for data manipulation and data analysis
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/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.drop('ID', axis=1, inplace=True)
df.nunique()
df.isnull().sum()
df.isnull().sum()
df.duplicated().sum()
df.drop_duplicates(inplace=True)
df.duplicated().sum()
col = ['Age', 'Vintage', 'Avg_Account_Balance']
col_to_transform = ['Age', 'Vintage', 'Avg_Account_Balance']
df[col_to_transform] = df[col_to_transform].apply(lambda x: np.log(x))
fig, axes = plt.subplots(nrows=1, ncols=len(col_to_transform), figsize=(15, 5))
for i, col in enumerate(col_to_transform):
axes[i].hist(df[col])
axes[i].set_xlabel(f'log({col})')
axes[i].set_ylabel('Frequency')
plt.show() | code |
129037105/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.info() | code |
129037105/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.drop('ID', axis=1, inplace=True)
df.nunique()
df.isnull().sum()
df.isnull().sum()
df.duplicated().sum()
df.drop_duplicates(inplace=True)
df.duplicated().sum() | code |
129037105/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/credit-card-buyers/train data credit card.csv')
df.size
df.shape | code |
129037105/cell_38 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # it's is core library for data manipulation and data analysis
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/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.drop('ID', axis=1, inplace=True)
df.nunique()
df.isnull().sum()
df.isnull().sum()
df.duplicated().sum()
df.drop_duplicates(inplace=True)
df.duplicated().sum()
sns.pairplot(df) | code |
129037105/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.drop('ID', axis=1, inplace=True)
df.nunique()
df.isnull().sum()
df.isnull().sum()
df.duplicated().sum()
df.drop_duplicates(inplace=True)
df.duplicated().sum()
df['Is_Lead'].value_counts() | code |
129037105/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/credit-card-buyers/train data credit card.csv')
df.size | code |
129037105/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/credit-card-buyers/train data credit card.csv')
df.head() | code |
129037105/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.drop('ID', axis=1, inplace=True)
df.nunique()
df.isnull().sum()
df.isnull().sum()
df.duplicated().sum()
df.drop_duplicates(inplace=True)
df.duplicated().sum()
plt.figure(figsize=(5, 5))
df['Is_Lead'].value_counts().plot(kind='pie', autopct='%1.1f%%')
plt.title('Is_Lead', size=20) | code |
33107227/cell_21 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df_1 = pd.get_dummies(df, columns=['phone_value', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus'])
df_1.dtypes.value_counts()
df_2 = df_1.dropna()
df_2.shape
X = df_1.drop('target', axis=1)
y = df_1['target']
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.25, random_state=1)
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_valid)
kf = KFold(n_splits=5, shuffle=True, random_state=22)
model_knn = KNeighborsClassifier(n_neighbors=5)
model_knn.fit(X_train, y_train)
y_pred = model_knn.predict(X_valid)
accuracy_score(y_pred, y_valid)
kf = KFold(n_splits=5, shuffle=True, random_state=22)
model_knn = KNeighborsClassifier(n_neighbors=5)
scores = cross_val_score(model_knn, X, y, cv=kf, scoring='accuracy')
print(scores)
mean_score = scores.mean()
print(mean_score) | code |
33107227/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df_1 = pd.get_dummies(df, columns=['phone_value', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus'])
df_1.dtypes.value_counts()
df_2 = df_1.dropna()
df_2.shape | code |
33107227/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df['software_os_version'].value_counts() | code |
33107227/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df_1 = pd.get_dummies(df, columns=['phone_value', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus'])
df_1.dtypes.value_counts()
df_2 = df_1.dropna()
df_2.shape
X = df_1.drop('target', axis=1)
y = df_1['target']
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.25, random_state=1)
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_valid)
kf = KFold(n_splits=5, shuffle=True, random_state=22)
model_knn = KNeighborsClassifier(n_neighbors=5)
model_knn.fit(X_train, y_train)
y_pred = model_knn.predict(X_valid)
accuracy_score(y_pred, y_valid)
kf = KFold(n_splits=5, shuffle=True, random_state=22)
model_knn = KNeighborsClassifier(n_neighbors=5)
scores = cross_val_score(model_knn, X, y, cv=kf, scoring='accuracy')
mean_score = scores.mean()
knn_params = {'n_neighbors': np.arange(1, 51)}
knn_grid = GridSearchCV(model_knn, knn_params, scoring='accuracy', cv=kf)
knn_grid.fit(X_train, y_train)
knn_grid.best_estimator_
knn_grid.best_score_ | code |
33107227/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df.head() | code |
33107227/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df_1 = pd.get_dummies(df, columns=['phone_value', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus'])
df_1.dtypes.value_counts()
df_2 = df_1.dropna()
df_2.shape
X = df_1.drop('target', axis=1)
y = df_1['target']
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.25, random_state=1)
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_valid)
kf = KFold(n_splits=5, shuffle=True, random_state=22)
model_knn = KNeighborsClassifier(n_neighbors=5)
model_knn.fit(X_train, y_train)
y_pred = model_knn.predict(X_valid)
accuracy_score(y_pred, y_valid)
kf = KFold(n_splits=5, shuffle=True, random_state=22)
model_knn = KNeighborsClassifier(n_neighbors=5)
scores = cross_val_score(model_knn, X, y, cv=kf, scoring='accuracy')
mean_score = scores.mean()
knn_params = {'n_neighbors': np.arange(1, 51)}
knn_grid = GridSearchCV(model_knn, knn_params, scoring='accuracy', cv=kf)
knn_grid.fit(X_train, y_train)
knn_grid.best_estimator_
knn_grid.best_score_
knn_grid.best_params_
knn_params = {'n_neighbors': np.arange(1, 51)}
knn_grid = GridSearchCV(knn, knn_params, scoring='accuracy', cv=5)
knn_grid.fit(X_train, y_train)
knn_grid.best_estimator_
knn_grid.best_score_ | code |
33107227/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df['device_brand'].value_counts() | code |
33107227/cell_29 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df_1 = pd.get_dummies(df, columns=['phone_value', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus'])
df_1.dtypes.value_counts()
df_2 = df_1.dropna()
df_2.shape
X = df_1.drop('target', axis=1)
y = df_1['target']
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.25, random_state=1)
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_valid)
kf = KFold(n_splits=5, shuffle=True, random_state=22)
model_knn = KNeighborsClassifier(n_neighbors=5)
model_knn.fit(X_train, y_train)
y_pred = model_knn.predict(X_valid)
accuracy_score(y_pred, y_valid)
kf = KFold(n_splits=5, shuffle=True, random_state=22)
model_knn = KNeighborsClassifier(n_neighbors=5)
scores = cross_val_score(model_knn, X, y, cv=kf, scoring='accuracy')
mean_score = scores.mean()
knn_params = {'n_neighbors': np.arange(1, 51)}
knn_grid = GridSearchCV(model_knn, knn_params, scoring='accuracy', cv=kf)
knn_grid.fit(X_train, y_train)
knn_grid.best_estimator_
knn_grid.best_score_
knn_grid.best_params_
knn_params = {'n_neighbors': np.arange(1, 51)}
knn_grid = GridSearchCV(knn, knn_params, scoring='accuracy', cv=5)
knn_grid.fit(X_train, y_train)
knn_grid.best_estimator_ | code |
33107227/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df_1 = pd.get_dummies(df, columns=['phone_value', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus'])
df_1.dtypes.value_counts()
df_2 = df_1.dropna()
df_2.shape
X = df_1.drop('target', axis=1)
y = df_1['target']
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.25, random_state=1)
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_valid)
kf = KFold(n_splits=5, shuffle=True, random_state=22)
model_knn = KNeighborsClassifier(n_neighbors=5)
model_knn.fit(X_train, y_train)
y_pred = model_knn.predict(X_valid)
accuracy_score(y_pred, y_valid)
kf = KFold(n_splits=5, shuffle=True, random_state=22)
model_knn = KNeighborsClassifier(n_neighbors=5)
scores = cross_val_score(model_knn, X, y, cv=kf, scoring='accuracy')
mean_score = scores.mean()
knn_params = {'n_neighbors': np.arange(1, 51)}
knn_grid = GridSearchCV(model_knn, knn_params, scoring='accuracy', cv=kf)
knn_grid.fit(X_train, y_train)
knn_grid.best_estimator_
knn_grid.best_score_
knn_grid.best_params_ | code |
33107227/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df_1 = pd.get_dummies(df, columns=['phone_value', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus'])
df_1.head() | code |
33107227/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df_1 = pd.get_dummies(df, columns=['phone_value', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus'])
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_valid)
pd.DataFrame(confusion_matrix(y_valid, y_pred), index=['True_' + str(i + 1) for i in range(6)], columns=['Pred' + str(i + 1) for i in range(6)]) | code |
33107227/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 |
33107227/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df['software_os_vendor'].value_counts() | code |
33107227/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_valid)
confusion_matrix(y_valid, y_pred) | code |
33107227/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df['software_os_name'].value_counts() | code |
33107227/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train) | code |
33107227/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
vodafone_subset_6.head(10) | code |
33107227/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_valid)
accuracy_score(y_pred, y_valid) | code |
33107227/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df_1 = pd.get_dummies(df, columns=['phone_value', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus'])
df_1.dtypes.value_counts()
df_2 = df_1.dropna()
df_2.shape
X = df_1.drop('target', axis=1)
y = df_1['target']
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.25, random_state=1)
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_valid)
kf = KFold(n_splits=5, shuffle=True, random_state=22)
model_knn = KNeighborsClassifier(n_neighbors=5)
model_knn.fit(X_train, y_train)
y_pred = model_knn.predict(X_valid)
accuracy_score(y_pred, y_valid)
kf = KFold(n_splits=5, shuffle=True, random_state=22)
model_knn = KNeighborsClassifier(n_neighbors=5)
scores = cross_val_score(model_knn, X, y, cv=kf, scoring='accuracy')
mean_score = scores.mean()
knn_params = {'n_neighbors': np.arange(1, 51)}
knn_grid = GridSearchCV(model_knn, knn_params, scoring='accuracy', cv=kf)
knn_grid.fit(X_train, y_train)
knn_grid.best_estimator_ | code |
33107227/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df_1 = pd.get_dummies(df, columns=['phone_value', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus'])
df_1.dtypes.value_counts()
df_2 = df_1.dropna()
df_2.shape
X = df_1.drop('target', axis=1)
y = df_1['target']
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.25, random_state=1)
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_valid)
kf = KFold(n_splits=5, shuffle=True, random_state=22)
model_knn = KNeighborsClassifier(n_neighbors=5)
model_knn.fit(X_train, y_train)
y_pred = model_knn.predict(X_valid)
accuracy_score(y_pred, y_valid)
kf = KFold(n_splits=5, shuffle=True, random_state=22)
model_knn = KNeighborsClassifier(n_neighbors=5)
scores = cross_val_score(model_knn, X, y, cv=kf, scoring='accuracy')
mean_score = scores.mean()
knn_params = {'n_neighbors': np.arange(1, 51)}
knn_grid = GridSearchCV(model_knn, knn_params, scoring='accuracy', cv=kf)
knn_grid.fit(X_train, y_train) | code |
33107227/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df['device_type_rus'].value_counts() | code |
33107227/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df_1 = pd.get_dummies(df, columns=['phone_value', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus'])
df_1.dtypes.value_counts()
df_2 = df_1.dropna()
df_2.shape
X = df_1.drop('target', axis=1)
y = df_1['target']
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.25, random_state=1)
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_valid)
kf = KFold(n_splits=5, shuffle=True, random_state=22)
model_knn = KNeighborsClassifier(n_neighbors=5)
model_knn.fit(X_train, y_train)
y_pred = model_knn.predict(X_valid)
accuracy_score(y_pred, y_valid)
kf = KFold(n_splits=5, shuffle=True, random_state=22)
model_knn = KNeighborsClassifier(n_neighbors=5)
scores = cross_val_score(model_knn, X, y, cv=kf, scoring='accuracy')
mean_score = scores.mean()
knn_params = {'n_neighbors': np.arange(1, 51)}
knn_grid = GridSearchCV(model_knn, knn_params, scoring='accuracy', cv=kf)
knn_grid.fit(X_train, y_train)
knn_grid.best_estimator_
knn_grid.best_score_
knn_grid.best_params_
knn_params = {'n_neighbors': np.arange(1, 51)}
knn_grid = GridSearchCV(knn, knn_params, scoring='accuracy', cv=5)
knn_grid.fit(X_train, y_train) | code |
33107227/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df_1 = pd.get_dummies(df, columns=['phone_value', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus'])
df_1.dtypes.value_counts() | code |
33107227/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'software_os_version', 'device_type_rus', 'AVG_ARPU', 'lifetime', 'how_long_same_model', 'ecommerce_score', 'banks_sms_count', 'instagram_volume', 'viber_volume', 'linkedin_volume', 'tinder_volume', 'telegram_volume', 'google_volume', 'whatsapp_volume', 'youtube_volume']]
df.info() | code |
128015258/cell_9 | [
"text_plain_output_1.png"
] | prompt2 = "Task Description:\nIn this task, your goal is to convert a given sentence grounded in the input table schema into a question whose answer can be the given sentence. You should use the table schema provided to generate the question.\n\nNow complete the following:\nTable Schema: 'title', 'Birth Name', 'Born', 'Residence', 'Occupation', 'Years active', 'Spouse(s)', 'Partner(s)', 'Children'\nInput: Chris Pratt has been in his current profession since the turn of the century.\nOutput:\n"
len(prompt2) | code |
128015258/cell_2 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, OPTForCausalLM
from transformers import AutoTokenizer, OPTForCausalLM
model = OPTForCausalLM.from_pretrained('facebook/opt-350m')
tokenizer = AutoTokenizer.from_pretrained('facebook/opt-350m')
prompt = 'Hey, are you consciours? Can you talk to me?' | code |
128015258/cell_11 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | prompt2 = "Task Description:\nIn this task, your goal is to convert a given sentence grounded in the input table schema into a question whose answer can be the given sentence. You should use the table schema provided to generate the question.\n\nNow complete the following:\nTable Schema: 'title', 'Birth Name', 'Born', 'Residence', 'Occupation', 'Years active', 'Spouse(s)', 'Partner(s)', 'Children'\nInput: Chris Pratt has been in his current profession since the turn of the century.\nOutput:\n"
print(generate(prompt2)) | code |
128015258/cell_1 | [
"text_plain_output_1.png"
] | pip install transformers | code |
128015258/cell_7 | [
"text_plain_output_1.png"
] | prompt1 = "Task Description:\nIn this task, your goal is to convert a given sentence grounded in the input table schema into a question whose answer can be the given sentence. You should use the table schema provided to generate the question.\n\nExamples:\n\nTable Schema: 'title', 'Birth Name', 'Born', 'Residence', 'Occupation', 'Years active', 'Spouse(s)', 'Partner(s)', 'Children'\nInput: Chris Pratt was born in Los Angeles where he currently resides.\nOutput: Where was Chris Pratt born and where does he currently reside?\n\nTable Schema: 'title', 'Capital', 'Common languages', 'Government', 'Khagan, Khan', 'Established', 'Disestablished'\nInput: Kara-Khanid Khanate had many people that were Arabic.\nOutput: Did Kara-Khanid Khanate have many people who were Arabic?\n\nTable Schema: 'Author', 'Publication date', 'Genre', 'Publisher', 'Pages', 'ISBN'\nInput: J.K. Rowling's Harry Potter and the Philosopher's Stone was published by Bloomsbury in 1997, and it has 223 pages.\nOutput: What is the author, publication date, publisher, and number of pages of Harry Potter and the Philosopher's Stone?\n\nNow complete the following:\nTable Schema: 'title', 'Birth Name', 'Born', 'Residence', 'Occupation', 'Years active', 'Spouse(s)', 'Partner(s)', 'Children'\nInput: Chris Pratt has been in his current profession since the turn of the century.\nOutput: \n"
len(prompt1) | code |
128015258/cell_10 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | prompt1 = "Task Description:\nIn this task, your goal is to convert a given sentence grounded in the input table schema into a question whose answer can be the given sentence. You should use the table schema provided to generate the question.\n\nExamples:\n\nTable Schema: 'title', 'Birth Name', 'Born', 'Residence', 'Occupation', 'Years active', 'Spouse(s)', 'Partner(s)', 'Children'\nInput: Chris Pratt was born in Los Angeles where he currently resides.\nOutput: Where was Chris Pratt born and where does he currently reside?\n\nTable Schema: 'title', 'Capital', 'Common languages', 'Government', 'Khagan, Khan', 'Established', 'Disestablished'\nInput: Kara-Khanid Khanate had many people that were Arabic.\nOutput: Did Kara-Khanid Khanate have many people who were Arabic?\n\nTable Schema: 'Author', 'Publication date', 'Genre', 'Publisher', 'Pages', 'ISBN'\nInput: J.K. Rowling's Harry Potter and the Philosopher's Stone was published by Bloomsbury in 1997, and it has 223 pages.\nOutput: What is the author, publication date, publisher, and number of pages of Harry Potter and the Philosopher's Stone?\n\nNow complete the following:\nTable Schema: 'title', 'Birth Name', 'Born', 'Residence', 'Occupation', 'Years active', 'Spouse(s)', 'Partner(s)', 'Children'\nInput: Chris Pratt has been in his current profession since the turn of the century.\nOutput: \n"
print(generate(prompt1)) | code |
128015258/cell_5 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, OPTForCausalLM
from transformers import AutoTokenizer, OPTForCausalLM
model = OPTForCausalLM.from_pretrained('facebook/opt-350m')
tokenizer = AutoTokenizer.from_pretrained('facebook/opt-350m')
prompt = 'Hey, are you consciours? Can you talk to me?'
generate(prompt) | code |
74058313/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
listings = pd.read_csv('/kaggle/input/airbnb-buenosaires/listings.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-buenosaires/calendar.csv')
listings = listings.loc[:, ['id', 'host_is_superhost', 'host_identity_verified', 'neighbourhood_cleansed', 'room_type', 'price', 'minimum_nights', 'availability_365', 'number_of_reviews', 'instant_bookable', 'accommodates', 'bathrooms_text', 'beds', 'calculated_host_listings_count', 'reviews_per_month', 'price', 'minimum_nights', 'maximum_nights']]
airbnb = listings.merge(calendar, how='inner', left_on='id', right_on='listing_id').sample(frac=0.1)
airbnb.drop(columns=['price_x', 'minimum_nights_x', 'maximum_nights_x', 'listing_id', 'adjusted_price'], inplace=True)
airbnb.rename(columns={'price_y': 'price', 'minimum_nights_y': 'minimum_nights', 'maximum_nights_y': 'maximum_nights'}, inplace=True)
airbnb.fillna({'reviews_per_month': 0}, inplace=True)
airbnb['price'] = airbnb['price'].replace('[$,]', '', regex=True).astype(float)
airbnb = airbnb.loc[airbnb['price'] > 0]
airbnb.host_is_superhost.fillna('f', inplace=True)
airbnb['host_is_superhost'] = airbnb['host_is_superhost'].replace({'f': 0})
airbnb['host_is_superhost'] = airbnb['host_is_superhost'].replace({'t': 1})
airbnb.host_identity_verified.fillna('f', inplace=True)
airbnb['host_identity_verified'] = airbnb['host_identity_verified'].replace({'f': 0})
airbnb['host_identity_verified'] = airbnb['host_identity_verified'].replace({'t': 1})
airbnb['available'] = airbnb['available'].replace({'f': 0})
airbnb['available'] = airbnb['available'].replace({'t': 1})
airbnb['instant_bookable'] = airbnb['instant_bookable'].replace({'f': 0})
airbnb['instant_bookable'] = airbnb['instant_bookable'].replace({'t': 1})
airbnb = airbnb[(airbnb['price'] > 900) & (airbnb['price'] <= 30000)]
p = airbnb.price.value_counts().sum()
print('total de registros ate 30000')
print(p)
print('-----------------------------')
l1 = airbnb[(airbnb['price'] > 900) & (airbnb['price'] <= 5000)]
print(l1['price'].value_counts().sum() / p * 100)
l2 = airbnb[(airbnb['price'] > 5000) & (airbnb['price'] <= 15000)]
print(l2['price'].value_counts().sum() / p * 100)
l3 = airbnb[airbnb['price'] > 15000]
print(l3['price'].value_counts().sum() / p * 100)
min_price = airbnb['price'].min()
max_price = airbnb['price'].max()
mean_price = airbnb['price'].mean()
print('Minimum price per listing is %d$.' % min_price)
print('Maximum price per listing is %d$' % max_price)
print('Average price per listing is %d$.' % mean_price)
airbnb = airbnb[(airbnb['price'] > 900) & (airbnb['price'] <= 15000)]
print('total de registros ate 15000')
p = airbnb.price.value_counts().sum()
print(p)
print('-----------------------------')
min_price = airbnb['price'].min()
max_price = airbnb['price'].max()
mean_price = airbnb['price'].mean()
print('Minimum price per listing is %d$.' % min_price)
print('Maximum price per listing is %d$' % max_price)
print('Average price per listing is %d$.' % mean_price)
l1 = airbnb[(airbnb['price'] > 900) & (airbnb['price'] <= 5000)]
l2 = airbnb[(airbnb['price'] > 5000) & (airbnb['price'] <= 10000)]
l3 = airbnb[airbnb['price'] > 10000] | code |
74058313/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
listings = pd.read_csv('/kaggle/input/airbnb-buenosaires/listings.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-buenosaires/calendar.csv')
listings = listings.loc[:, ['id', 'host_is_superhost', 'host_identity_verified', 'neighbourhood_cleansed', 'room_type', 'price', 'minimum_nights', 'availability_365', 'number_of_reviews', 'instant_bookable', 'accommodates', 'bathrooms_text', 'beds', 'calculated_host_listings_count', 'reviews_per_month', 'price', 'minimum_nights', 'maximum_nights']]
airbnb = listings.merge(calendar, how='inner', left_on='id', right_on='listing_id').sample(frac=0.1)
airbnb.drop(columns=['price_x', 'minimum_nights_x', 'maximum_nights_x', 'listing_id', 'adjusted_price'], inplace=True)
airbnb.rename(columns={'price_y': 'price', 'minimum_nights_y': 'minimum_nights', 'maximum_nights_y': 'maximum_nights'}, inplace=True)
airbnb.info()
print(airbnb.isna().sum()) | code |
74058313/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
listings = pd.read_csv('/kaggle/input/airbnb-buenosaires/listings.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-buenosaires/calendar.csv')
listings = listings.loc[:, ['id', 'host_is_superhost', 'host_identity_verified', 'neighbourhood_cleansed', 'room_type', 'price', 'minimum_nights', 'availability_365', 'number_of_reviews', 'instant_bookable', 'accommodates', 'bathrooms_text', 'beds', 'calculated_host_listings_count', 'reviews_per_month', 'price', 'minimum_nights', 'maximum_nights']]
airbnb = listings.merge(calendar, how='inner', left_on='id', right_on='listing_id').sample(frac=0.1)
airbnb.drop(columns=['price_x', 'minimum_nights_x', 'maximum_nights_x', 'listing_id', 'adjusted_price'], inplace=True)
airbnb.rename(columns={'price_y': 'price', 'minimum_nights_y': 'minimum_nights', 'maximum_nights_y': 'maximum_nights'}, inplace=True)
airbnb.fillna({'reviews_per_month': 0}, inplace=True)
airbnb['price'] = airbnb['price'].replace('[$,]', '', regex=True).astype(float)
airbnb = airbnb.loc[airbnb['price'] > 0]
airbnb.host_is_superhost.fillna('f', inplace=True)
airbnb['host_is_superhost'] = airbnb['host_is_superhost'].replace({'f': 0})
airbnb['host_is_superhost'] = airbnb['host_is_superhost'].replace({'t': 1})
airbnb.host_identity_verified.fillna('f', inplace=True)
airbnb['host_identity_verified'] = airbnb['host_identity_verified'].replace({'f': 0})
airbnb['host_identity_verified'] = airbnb['host_identity_verified'].replace({'t': 1})
airbnb['available'] = airbnb['available'].replace({'f': 0})
airbnb['available'] = airbnb['available'].replace({'t': 1})
airbnb['instant_bookable'] = airbnb['instant_bookable'].replace({'f': 0})
airbnb['instant_bookable'] = airbnb['instant_bookable'].replace({'t': 1})
airbnb = airbnb[(airbnb['price'] > 900) & (airbnb['price'] <= 30000)]
p = airbnb.price.value_counts().sum()
l1 = airbnb[(airbnb['price'] > 900) & (airbnb['price'] <= 5000)]
l2 = airbnb[(airbnb['price'] > 5000) & (airbnb['price'] <= 15000)]
l3 = airbnb[airbnb['price'] > 15000]
min_price = airbnb['price'].min()
max_price = airbnb['price'].max()
mean_price = airbnb['price'].mean()
airbnb = airbnb[(airbnb['price'] > 900) & (airbnb['price'] <= 15000)]
p = airbnb.price.value_counts().sum()
min_price = airbnb['price'].min()
max_price = airbnb['price'].max()
mean_price = airbnb['price'].mean()
l1 = airbnb[(airbnb['price'] > 900) & (airbnb['price'] <= 5000)]
l2 = airbnb[(airbnb['price'] > 5000) & (airbnb['price'] <= 10000)]
l3 = airbnb[airbnb['price'] > 10000]
mean_price_for_listing = airbnb.groupby('id').mean()['price']
plt.xticks(np.arange(800, 15000, step=1000))
f, ax = plt.subplots(figsize=(8, 3))
subplot(2, 3, 1)
sns.boxplot(y=l1['price'])
subplot(2, 3, 2)
sns.boxplot(y=l2['price'])
subplot(2, 3, 3)
sns.boxplot(y=l3['price'])
plt.tight_layout()
plt.draw()
plt.show() | code |
74058313/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import matplotlib.pyplot as plt
import seaborn as sns
from pylab import *
warnings.filterwarnings('ignore')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
74058313/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
listings = pd.read_csv('/kaggle/input/airbnb-buenosaires/listings.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-buenosaires/calendar.csv')
listings = listings.loc[:, ['id', 'host_is_superhost', 'host_identity_verified', 'neighbourhood_cleansed', 'room_type', 'price', 'minimum_nights', 'availability_365', 'number_of_reviews', 'instant_bookable', 'accommodates', 'bathrooms_text', 'beds', 'calculated_host_listings_count', 'reviews_per_month', 'price', 'minimum_nights', 'maximum_nights']]
airbnb = listings.merge(calendar, how='inner', left_on='id', right_on='listing_id').sample(frac=0.1)
airbnb.drop(columns=['price_x', 'minimum_nights_x', 'maximum_nights_x', 'listing_id', 'adjusted_price'], inplace=True)
airbnb.rename(columns={'price_y': 'price', 'minimum_nights_y': 'minimum_nights', 'maximum_nights_y': 'maximum_nights'}, inplace=True)
airbnb.fillna({'reviews_per_month': 0}, inplace=True)
airbnb['price'] = airbnb['price'].replace('[$,]', '', regex=True).astype(float)
airbnb = airbnb.loc[airbnb['price'] > 0]
airbnb.host_is_superhost.fillna('f', inplace=True)
airbnb['host_is_superhost'] = airbnb['host_is_superhost'].replace({'f': 0})
airbnb['host_is_superhost'] = airbnb['host_is_superhost'].replace({'t': 1})
airbnb.host_identity_verified.fillna('f', inplace=True)
airbnb['host_identity_verified'] = airbnb['host_identity_verified'].replace({'f': 0})
airbnb['host_identity_verified'] = airbnb['host_identity_verified'].replace({'t': 1})
airbnb['available'] = airbnb['available'].replace({'f': 0})
airbnb['available'] = airbnb['available'].replace({'t': 1})
airbnb['instant_bookable'] = airbnb['instant_bookable'].replace({'f': 0})
airbnb['instant_bookable'] = airbnb['instant_bookable'].replace({'t': 1})
print(airbnb.isna().sum()) | code |
74058313/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
listings = pd.read_csv('/kaggle/input/airbnb-buenosaires/listings.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-buenosaires/calendar.csv')
listings = listings.loc[:, ['id', 'host_is_superhost', 'host_identity_verified', 'neighbourhood_cleansed', 'room_type', 'price', 'minimum_nights', 'availability_365', 'number_of_reviews', 'instant_bookable', 'accommodates', 'bathrooms_text', 'beds', 'calculated_host_listings_count', 'reviews_per_month', 'price', 'minimum_nights', 'maximum_nights']]
airbnb = listings.merge(calendar, how='inner', left_on='id', right_on='listing_id').sample(frac=0.1)
airbnb.drop(columns=['price_x', 'minimum_nights_x', 'maximum_nights_x', 'listing_id', 'adjusted_price'], inplace=True)
airbnb.rename(columns={'price_y': 'price', 'minimum_nights_y': 'minimum_nights', 'maximum_nights_y': 'maximum_nights'}, inplace=True)
airbnb.fillna({'reviews_per_month': 0}, inplace=True)
airbnb['price'] = airbnb['price'].replace('[$,]', '', regex=True).astype(float)
airbnb = airbnb.loc[airbnb['price'] > 0]
airbnb.host_is_superhost.fillna('f', inplace=True)
airbnb['host_is_superhost'] = airbnb['host_is_superhost'].replace({'f': 0})
airbnb['host_is_superhost'] = airbnb['host_is_superhost'].replace({'t': 1})
airbnb.host_identity_verified.fillna('f', inplace=True)
airbnb['host_identity_verified'] = airbnb['host_identity_verified'].replace({'f': 0})
airbnb['host_identity_verified'] = airbnb['host_identity_verified'].replace({'t': 1})
airbnb['available'] = airbnb['available'].replace({'f': 0})
airbnb['available'] = airbnb['available'].replace({'t': 1})
airbnb['instant_bookable'] = airbnb['instant_bookable'].replace({'f': 0})
airbnb['instant_bookable'] = airbnb['instant_bookable'].replace({'t': 1})
airbnb = airbnb[(airbnb['price'] > 900) & (airbnb['price'] <= 30000)]
p = airbnb.price.value_counts().sum()
l1 = airbnb[(airbnb['price'] > 900) & (airbnb['price'] <= 5000)]
l2 = airbnb[(airbnb['price'] > 5000) & (airbnb['price'] <= 15000)]
l3 = airbnb[airbnb['price'] > 15000]
min_price = airbnb['price'].min()
max_price = airbnb['price'].max()
mean_price = airbnb['price'].mean()
airbnb = airbnb[(airbnb['price'] > 900) & (airbnb['price'] <= 15000)]
p = airbnb.price.value_counts().sum()
min_price = airbnb['price'].min()
max_price = airbnb['price'].max()
mean_price = airbnb['price'].mean()
l1 = airbnb[(airbnb['price'] > 900) & (airbnb['price'] <= 5000)]
l2 = airbnb[(airbnb['price'] > 5000) & (airbnb['price'] <= 10000)]
l3 = airbnb[airbnb['price'] > 10000]
mean_price_for_listing = airbnb.groupby('id').mean()['price']
plt.figure(figsize=(20, 5))
plt.hist(mean_price_for_listing, bins=20)
plt.xticks(np.arange(800, 15000, step=1000))
plt.ylabel('Number of listings')
plt.xlabel('Price, $')
plt.title('Number of listings depending on price')
plt.savefig('Price distrubution.png')
plt.show() | code |
74058313/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
listings = pd.read_csv('/kaggle/input/airbnb-buenosaires/listings.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-buenosaires/calendar.csv')
listings = listings.loc[:, ['id', 'host_is_superhost', 'host_identity_verified', 'neighbourhood_cleansed', 'room_type', 'price', 'minimum_nights', 'availability_365', 'number_of_reviews', 'instant_bookable', 'accommodates', 'bathrooms_text', 'beds', 'calculated_host_listings_count', 'reviews_per_month', 'price', 'minimum_nights', 'maximum_nights']]
airbnb = listings.merge(calendar, how='inner', left_on='id', right_on='listing_id').sample(frac=0.1)
airbnb.drop(columns=['price_x', 'minimum_nights_x', 'maximum_nights_x', 'listing_id', 'adjusted_price'], inplace=True)
airbnb.rename(columns={'price_y': 'price', 'minimum_nights_y': 'minimum_nights', 'maximum_nights_y': 'maximum_nights'}, inplace=True)
airbnb.fillna({'reviews_per_month': 0}, inplace=True)
airbnb['price'] = airbnb['price'].replace('[$,]', '', regex=True).astype(float)
airbnb = airbnb.loc[airbnb['price'] > 0]
airbnb.host_is_superhost.fillna('f', inplace=True)
airbnb['host_is_superhost'] = airbnb['host_is_superhost'].replace({'f': 0})
airbnb['host_is_superhost'] = airbnb['host_is_superhost'].replace({'t': 1})
airbnb.host_identity_verified.fillna('f', inplace=True)
airbnb['host_identity_verified'] = airbnb['host_identity_verified'].replace({'f': 0})
airbnb['host_identity_verified'] = airbnb['host_identity_verified'].replace({'t': 1})
airbnb['available'] = airbnb['available'].replace({'f': 0})
airbnb['available'] = airbnb['available'].replace({'t': 1})
airbnb['instant_bookable'] = airbnb['instant_bookable'].replace({'f': 0})
airbnb['instant_bookable'] = airbnb['instant_bookable'].replace({'t': 1})
airbnb = airbnb[(airbnb['price'] > 900) & (airbnb['price'] <= 30000)]
p = airbnb.price.value_counts().sum()
l1 = airbnb[(airbnb['price'] > 900) & (airbnb['price'] <= 5000)]
l2 = airbnb[(airbnb['price'] > 5000) & (airbnb['price'] <= 15000)]
l3 = airbnb[airbnb['price'] > 15000]
min_price = airbnb['price'].min()
max_price = airbnb['price'].max()
mean_price = airbnb['price'].mean()
airbnb = airbnb[(airbnb['price'] > 900) & (airbnb['price'] <= 15000)]
p = airbnb.price.value_counts().sum()
min_price = airbnb['price'].min()
max_price = airbnb['price'].max()
mean_price = airbnb['price'].mean()
l1 = airbnb[(airbnb['price'] > 900) & (airbnb['price'] <= 5000)]
l2 = airbnb[(airbnb['price'] > 5000) & (airbnb['price'] <= 10000)]
l3 = airbnb[airbnb['price'] > 10000]
mean_price_for_listing = airbnb.groupby('id').mean()['price']
plt.xticks(np.arange(800, 15000, step=1000))
print(airbnb.bathrooms_text.unique())
airbnb['shared_bathrooms'] = airbnb['bathrooms_text'].where(airbnb['bathrooms_text'].str.contains('shared') == True)
airbnb.shared_bathrooms.fillna(0, inplace=True)
airbnb['shared_bathrooms'] = airbnb['shared_bathrooms'].replace('[ shared bath| shared baths]', '', regex=True).astype(float)
airbnb['private_bathrooms'] = airbnb['bathrooms_text'].where(airbnb['bathrooms_text'].str.contains('shared') == False)
airbnb.private_bathrooms.fillna(0, inplace=True)
airbnb['private_bathrooms'] = airbnb['private_bathrooms'].replace('[ private bath| bath| baths,]', '', regex=True)
airbnb['private_bathrooms'] = airbnb['private_bathrooms'].replace('[Plf-|Hlf-|Sdlf-]', '', regex=True)
airbnb['private_bathrooms'] = airbnb['private_bathrooms'].replace('', 0, regex=True).astype(float)
print(airbnb.private_bathrooms.unique())
airbnb.drop(columns=['bathrooms_text'], inplace=True)
print(airbnb.isna().sum()) | code |
106214297/cell_20 | [
"text_html_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV,train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler,OneHotEncoder
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
ss = StandardScaler()
OneHot = OneHotEncoder()
Imputer = SimpleImputer()
train_data = pd.read_csv('../input/titanic/train.csv')
train_data.keys()
drop_train_data = train_data.drop(['Name', 'PassengerId', 'Cabin', 'Ticket'], axis=1).dropna(axis=0)
train_data['Family'] = train_data['SibSp'] + train_data['Parch']
train_data.corr()
y = drop_train_data.pop('Survived')
OneHot.fit_transform(drop_train_data['Embarked'])
num_pipeline = Pipeline([('imputer', Imputer), ('scaler', ss)])
num_attrib = ['Fare', 'Age']
txt_attrib = ['Sex', 'Embarked']
full_pipeline = ColumnTransformer([('num', num_pipeline, num_attrib), ('txt_trans', OneHot, txt_attrib)])
data_pred = full_pipeline.fit_transform(drop_train_data)
data = pd.DataFrame(data_pred)
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.ensemble import RandomForestClassifier
sgd = SGDClassifier()
RandForest = RandomForestClassifier()
param_sgd = {'max_iter': [400, 600, 1000, 1500, 2000], 'loss': ['hinge', 'modified_huber'], 'penalty': ['l2', 'l1', 'elasticnet']}
grid_search_sgd = GridSearchCV(sgd, param_sgd)
grid_search_sgd.fit(x_train, y_train)
final_sgd = grid_search_sgd.best_estimator_
final_sgd.fit(x_train, y_train)
y_pred_sgd = final_sgd.predict(x_test)
accuracy_score(y_test, y_pred_sgd)
test_data = pd.read_csv('../input/titanic/test.csv')
drop_test_data = test_data.drop(['Name', 'PassengerId', 'Cabin', 'Ticket'], axis=1).dropna(axis=0)
drop_test_data['Family'] = drop_test_data['SibSp'] + drop_test_data['Parch']
drop_test_data
num_attrib = ['Fare', 'Age']
txt_attrib = ['Sex', 'Embarked']
full_pipeline = ColumnTransformer([('num', num_pipeline, num_attrib), ('txt_trans', OneHot, txt_attrib)])
data_pred_test = full_pipeline.fit_transform(drop_test_data)
data_pred_test.shape
final_sgd.predict(data_pred_test) | code |
106214297/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
train_data.keys() | code |
106214297/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler,OneHotEncoder
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
ss = StandardScaler()
OneHot = OneHotEncoder()
Imputer = SimpleImputer()
train_data = pd.read_csv('../input/titanic/train.csv')
train_data.keys()
drop_train_data = train_data.drop(['Name', 'PassengerId', 'Cabin', 'Ticket'], axis=1).dropna(axis=0)
train_data['Family'] = train_data['SibSp'] + train_data['Parch']
train_data.corr()
y = drop_train_data.pop('Survived')
OneHot.fit_transform(drop_train_data['Embarked'])
num_pipeline = Pipeline([('imputer', Imputer), ('scaler', ss)])
num_attrib = ['Fare', 'Age']
txt_attrib = ['Sex', 'Embarked']
full_pipeline = ColumnTransformer([('num', num_pipeline, num_attrib), ('txt_trans', OneHot, txt_attrib)])
data_pred = full_pipeline.fit_transform(drop_train_data)
data = pd.DataFrame(data_pred)
test_data = pd.read_csv('../input/titanic/test.csv')
drop_test_data = test_data.drop(['Name', 'PassengerId', 'Cabin', 'Ticket'], axis=1).dropna(axis=0)
drop_test_data['Family'] = drop_test_data['SibSp'] + drop_test_data['Parch']
drop_test_data
num_attrib = ['Fare', 'Age']
txt_attrib = ['Sex', 'Embarked']
full_pipeline = ColumnTransformer([('num', num_pipeline, num_attrib), ('txt_trans', OneHot, txt_attrib)])
data_pred_test = full_pipeline.fit_transform(drop_test_data)
data_pred_test.shape | code |
106214297/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler,OneHotEncoder
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
ss = StandardScaler()
OneHot = OneHotEncoder()
Imputer = SimpleImputer()
train_data = pd.read_csv('../input/titanic/train.csv')
train_data.keys()
drop_train_data = train_data.drop(['Name', 'PassengerId', 'Cabin', 'Ticket'], axis=1).dropna(axis=0)
train_data['Family'] = train_data['SibSp'] + train_data['Parch']
train_data.corr()
y = drop_train_data.pop('Survived')
OneHot.fit_transform(drop_train_data['Embarked'])
num_pipeline = Pipeline([('imputer', Imputer), ('scaler', ss)])
num_attrib = ['Fare', 'Age']
txt_attrib = ['Sex', 'Embarked']
full_pipeline = ColumnTransformer([('num', num_pipeline, num_attrib), ('txt_trans', OneHot, txt_attrib)])
data_pred = full_pipeline.fit_transform(drop_train_data)
data = pd.DataFrame(data_pred)
test_data = pd.read_csv('../input/titanic/test.csv')
drop_test_data = test_data.drop(['Name', 'PassengerId', 'Cabin', 'Ticket'], axis=1).dropna(axis=0)
drop_test_data['Family'] = drop_test_data['SibSp'] + drop_test_data['Parch']
drop_test_data | code |
106214297/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import accuracy_score
from sklearn.ensemble import VotingClassifier
voting = VotingClassifier(estimators=[('sgd', SGDClassifier()), ('randForest', RandomForestClassifier())], voting='hard')
voting.fit(x_train, y_train)
y_pred_voting = voting.predict(x_test)
accuracy_score(y_test, y_pred_voting) | code |
106214297/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
train_data.keys()
drop_train_data = train_data.drop(['Name', 'PassengerId', 'Cabin', 'Ticket'], axis=1).dropna(axis=0)
train_data['Family'] = train_data['SibSp'] + train_data['Parch']
train_data.corr() | code |
106214297/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV,train_test_split
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.ensemble import RandomForestClassifier
sgd = SGDClassifier()
RandForest = RandomForestClassifier()
param_sgd = {'max_iter': [400, 600, 1000, 1500, 2000], 'loss': ['hinge', 'modified_huber'], 'penalty': ['l2', 'l1', 'elasticnet']}
grid_search_sgd = GridSearchCV(sgd, param_sgd)
grid_search_sgd.fit(x_train, y_train)
final_sgd = grid_search_sgd.best_estimator_
final_sgd.fit(x_train, y_train)
y_pred_sgd = final_sgd.predict(x_test)
accuracy_score(y_test, y_pred_sgd) | code |
106214297/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import GridSearchCV,train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler,OneHotEncoder
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
ss = StandardScaler()
OneHot = OneHotEncoder()
Imputer = SimpleImputer()
train_data = pd.read_csv('../input/titanic/train.csv')
train_data.keys()
drop_train_data = train_data.drop(['Name', 'PassengerId', 'Cabin', 'Ticket'], axis=1).dropna(axis=0)
train_data['Family'] = train_data['SibSp'] + train_data['Parch']
train_data.corr()
y = drop_train_data.pop('Survived')
OneHot.fit_transform(drop_train_data['Embarked'])
num_pipeline = Pipeline([('imputer', Imputer), ('scaler', ss)])
num_attrib = ['Fare', 'Age']
txt_attrib = ['Sex', 'Embarked']
full_pipeline = ColumnTransformer([('num', num_pipeline, num_attrib), ('txt_trans', OneHot, txt_attrib)])
data_pred = full_pipeline.fit_transform(drop_train_data)
data = pd.DataFrame(data_pred)
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.ensemble import RandomForestClassifier
sgd = SGDClassifier()
RandForest = RandomForestClassifier()
x_train, x_test, y_train, y_test = train_test_split(data_pred, y, test_size=0.2)
param_rand = {'n_estimators': [5, 10, 20], 'max_depth': [1, 5, 8, 10]}
grid_search_rand = GridSearchCV(RandForest, param_rand, n_jobs=-1)
grid_search_rand.fit(x_train, y_train)
best_param_tree = grid_search_rand.best_estimator_
best_param_tree | code |
106214297/cell_12 | [
"text_html_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV,train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler,OneHotEncoder
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
ss = StandardScaler()
OneHot = OneHotEncoder()
Imputer = SimpleImputer()
train_data = pd.read_csv('../input/titanic/train.csv')
train_data.keys()
drop_train_data = train_data.drop(['Name', 'PassengerId', 'Cabin', 'Ticket'], axis=1).dropna(axis=0)
train_data['Family'] = train_data['SibSp'] + train_data['Parch']
train_data.corr()
y = drop_train_data.pop('Survived')
OneHot.fit_transform(drop_train_data['Embarked'])
num_pipeline = Pipeline([('imputer', Imputer), ('scaler', ss)])
num_attrib = ['Fare', 'Age']
txt_attrib = ['Sex', 'Embarked']
full_pipeline = ColumnTransformer([('num', num_pipeline, num_attrib), ('txt_trans', OneHot, txt_attrib)])
data_pred = full_pipeline.fit_transform(drop_train_data)
data = pd.DataFrame(data_pred)
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.ensemble import RandomForestClassifier
sgd = SGDClassifier()
RandForest = RandomForestClassifier()
x_train, x_test, y_train, y_test = train_test_split(data_pred, y, test_size=0.2)
param_rand = {'n_estimators': [5, 10, 20], 'max_depth': [1, 5, 8, 10]}
grid_search_rand = GridSearchCV(RandForest, param_rand, n_jobs=-1)
grid_search_rand.fit(x_train, y_train)
best_param_tree = grid_search_rand.best_estimator_
best_param_tree
final_rand = grid_search_rand.best_estimator_
final_rand.fit(x_train, y_train)
y_pred_rand = final_rand.predict(x_test)
accuracy_score(y_test, y_pred_rand) | code |
106214297/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler,OneHotEncoder
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
ss = StandardScaler()
OneHot = OneHotEncoder()
Imputer = SimpleImputer()
train_data = pd.read_csv('../input/titanic/train.csv')
train_data.keys()
drop_train_data = train_data.drop(['Name', 'PassengerId', 'Cabin', 'Ticket'], axis=1).dropna(axis=0)
train_data['Family'] = train_data['SibSp'] + train_data['Parch']
train_data.corr()
y = drop_train_data.pop('Survived')
OneHot.fit_transform(drop_train_data['Embarked']) | code |
2007984/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
x_train = train[:, 1:].values.astype('float32')
y_train = train[:, 0].values.astype('int32')
x_test = test.values.astype('float32')
x_train.shape
x_train = x_train.reshape(x_train.shape[0], 28, 28)
x_train.shape
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
(x_train.shape, x_test.shape) | code |
2007984/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
x_train = train[:, 1:].values.astype('float32')
y_train = train[:, 0].values.astype('int32')
x_test = test.values.astype('float32')
x_train.shape
x_train = x_train.reshape(x_train.shape[0], 28, 28)
x_train.shape | code |
2007984/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense,Dropout, Activation,Lambda,Flatten
from keras.models import Sequential
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
x_train = train[:, 1:].values.astype('float32')
y_train = train[:, 0].values.astype('int32')
x_test = test.values.astype('float32')
x_train.shape
x_train = x_train.reshape(x_train.shape[0], 28, 28)
x_train.shape
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
(x_train.shape, x_test.shape)
mean_px = x_train.mean().astype(np.float32)
std_px = x_train.std().astype(np.float32)
def standardize(x):
return (x - mean_px) / std_px
model = Sequential()
model.add(Lambda(standardize, input_shape=(28, 28, 1)))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))
model.summary() | code |
2007984/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
x_train = train[:, 1:].values.astype('float32')
y_train = train[:, 0].values.astype('int32')
x_test = test.values.astype('float32') | code |
2007984/cell_34 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense,Dropout, Activation,Lambda,Flatten
from keras.models import Sequential
from keras.optimizers import Adam , RMSprop
from keras.preprocessing import image
from keras.utils.np_utils import to_categorical
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
x_train = train[:, 1:].values.astype('float32')
y_train = train[:, 0].values.astype('int32')
x_test = test.values.astype('float32')
x_train.shape
y_train.shape
x_train = x_train.reshape(x_train.shape[0], 28, 28)
x_train.shape
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
(x_train.shape, x_test.shape)
from keras.utils.np_utils import to_categorical
y_train = to_categorical(y_train, num_classes=10)
y_train.shape
mean_px = x_train.mean().astype(np.float32)
std_px = x_train.std().astype(np.float32)
def standardize(x):
return (x - mean_px) / std_px
x_train.reshape
model = Sequential()
model.add(Lambda(standardize, input_shape=(28, 28, 1)))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))
model.summary()
model.compile(optimizer=RMSprop(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])
from keras.preprocessing import image
gen = image.ImageDataGenerator()
X_train, X_val, Y_train, Y_val = train_test_split(x_train, y_train, test_size=0.1, random_state=34)
batches = gen.flow(X_train, Y_train, batch_size=64)
val_batches = gen.flow(X_val, Y_val, batch_size=64)
cache = model.fit_generator(batches, batches.n, nb_epoch=1, validation_data=val_batches, nb_val_samples=val_batches.n)
model.optimizer.lr = 0.01
gen = image.ImageDataGenerator()
batches = gen.flow(X_train, Y_train, batch_size=64)
history = model.fit_generator(batches, batches.n, nb_epoch=1)
preds = model.predict_classes(x_test, verbose=0)
preds[0:5] | code |
2007984/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense,Dropout, Activation,Lambda,Flatten
from keras.models import Sequential
from keras.optimizers import Adam , RMSprop
from keras.preprocessing import image
from keras.utils.np_utils import to_categorical
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
x_train = train[:, 1:].values.astype('float32')
y_train = train[:, 0].values.astype('int32')
x_test = test.values.astype('float32')
x_train.shape
y_train.shape
x_train = x_train.reshape(x_train.shape[0], 28, 28)
x_train.shape
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
(x_train.shape, x_test.shape)
from keras.utils.np_utils import to_categorical
y_train = to_categorical(y_train, num_classes=10)
y_train.shape
mean_px = x_train.mean().astype(np.float32)
std_px = x_train.std().astype(np.float32)
def standardize(x):
return (x - mean_px) / std_px
x_train.reshape
model = Sequential()
model.add(Lambda(standardize, input_shape=(28, 28, 1)))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))
model.summary()
model.compile(optimizer=RMSprop(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])
from keras.preprocessing import image
gen = image.ImageDataGenerator()
X_train, X_val, Y_train, Y_val = train_test_split(x_train, y_train, test_size=0.1, random_state=34)
batches = gen.flow(X_train, Y_train, batch_size=64)
val_batches = gen.flow(X_val, Y_val, batch_size=64)
cache = model.fit_generator(batches, batches.n, nb_epoch=1, validation_data=val_batches, nb_val_samples=val_batches.n)
cache.history | code |
2007984/cell_33 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense,Dropout, Activation,Lambda,Flatten
from keras.models import Sequential
from keras.optimizers import Adam , RMSprop
from keras.preprocessing import image
from keras.utils.np_utils import to_categorical
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
x_train = train[:, 1:].values.astype('float32')
y_train = train[:, 0].values.astype('int32')
x_test = test.values.astype('float32')
x_train.shape
y_train.shape
x_train = x_train.reshape(x_train.shape[0], 28, 28)
x_train.shape
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
(x_train.shape, x_test.shape)
from keras.utils.np_utils import to_categorical
y_train = to_categorical(y_train, num_classes=10)
y_train.shape
mean_px = x_train.mean().astype(np.float32)
std_px = x_train.std().astype(np.float32)
def standardize(x):
return (x - mean_px) / std_px
x_train.reshape
model = Sequential()
model.add(Lambda(standardize, input_shape=(28, 28, 1)))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))
model.summary()
model.compile(optimizer=RMSprop(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])
from keras.preprocessing import image
gen = image.ImageDataGenerator()
X_train, X_val, Y_train, Y_val = train_test_split(x_train, y_train, test_size=0.1, random_state=34)
batches = gen.flow(X_train, Y_train, batch_size=64)
val_batches = gen.flow(X_val, Y_val, batch_size=64)
cache = model.fit_generator(batches, batches.n, nb_epoch=1, validation_data=val_batches, nb_val_samples=val_batches.n)
model.optimizer.lr = 0.01
gen = image.ImageDataGenerator()
batches = gen.flow(X_train, Y_train, batch_size=64)
history = model.fit_generator(batches, batches.n, nb_epoch=1)
preds = model.predict_classes(x_test, verbose=0) | code |
2007984/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
x_train = train[:, 1:].values.astype('float32')
y_train = train[:, 0].values.astype('int32')
x_test = test.values.astype('float32')
y_train.shape | code |
2007984/cell_29 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense,Dropout, Activation,Lambda,Flatten
from keras.models import Sequential
from keras.optimizers import Adam , RMSprop
from keras.preprocessing import image
from keras.utils.np_utils import to_categorical
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
x_train = train[:, 1:].values.astype('float32')
y_train = train[:, 0].values.astype('int32')
x_test = test.values.astype('float32')
x_train.shape
y_train.shape
x_train = x_train.reshape(x_train.shape[0], 28, 28)
x_train.shape
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
(x_train.shape, x_test.shape)
from keras.utils.np_utils import to_categorical
y_train = to_categorical(y_train, num_classes=10)
y_train.shape
mean_px = x_train.mean().astype(np.float32)
std_px = x_train.std().astype(np.float32)
def standardize(x):
return (x - mean_px) / std_px
x_train.reshape
model = Sequential()
model.add(Lambda(standardize, input_shape=(28, 28, 1)))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))
model.summary()
model.compile(optimizer=RMSprop(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])
from keras.preprocessing import image
gen = image.ImageDataGenerator()
X_train, X_val, Y_train, Y_val = train_test_split(x_train, y_train, test_size=0.1, random_state=34)
batches = gen.flow(X_train, Y_train, batch_size=64)
val_batches = gen.flow(X_val, Y_val, batch_size=64)
cache = model.fit_generator(batches, batches.n, nb_epoch=1, validation_data=val_batches, nb_val_samples=val_batches.n) | code |
2007984/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense,Dropout, Activation,Lambda,Flatten
from keras.models import Sequential
from keras.optimizers import Adam , RMSprop
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
x_train = train[:, 1:].values.astype('float32')
y_train = train[:, 0].values.astype('int32')
x_test = test.values.astype('float32')
x_train.shape
x_train = x_train.reshape(x_train.shape[0], 28, 28)
x_train.shape
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
(x_train.shape, x_test.shape)
mean_px = x_train.mean().astype(np.float32)
std_px = x_train.std().astype(np.float32)
def standardize(x):
return (x - mean_px) / std_px
model = Sequential()
model.add(Lambda(standardize, input_shape=(28, 28, 1)))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))
model.summary()
model.compile(optimizer=RMSprop(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy']) | code |
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