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stringlengths 13
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|---|---|---|---|
73082264/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/broadband-customers-base-churn-analysis/bbs_cust_base_scfy_20200210.csv')
df = data.copy()
df
df.drop('Unnamed: 19', axis=1, inplace=True)
df.churn.replace('N', '0', inplace=True)
df.churn.replace('Y', '1', inplace=True)
df.current_mth_churn.replace('N', '0', inplace=True)
df.current_mth_churn.replace('Y', '1', inplace=True)
df.head() | code |
73082264/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/broadband-customers-base-churn-analysis/bbs_cust_base_scfy_20200210.csv')
df = data.copy()
df
df.drop('Unnamed: 19', axis=1, inplace=True)
df.churn.replace('N', '0', inplace=True)
df.churn.replace('Y', '1', inplace=True)
df.current_mth_churn.replace('N', '0', inplace=True)
df.current_mth_churn.replace('Y', '1', inplace=True)
df.churn = df.churn.astype(int)
df.current_mth_churn = df.churn.astype(int)
df.describe().T
def summary(df):
Types = df.dtypes
Counts = df.apply(lambda x: x.count())
Uniques = df.apply(lambda x: x.unique().shape[0])
Nulls = df.apply(lambda x: x.isnull().sum())
cols = ['Types', 'Counts', 'Uniques', 'Nulls']
str = pd.concat([Types, Counts, Uniques, Nulls], axis=1, sort=True)
str.columns = cols
summary(df)
col = df.columns.to_list()
catcol = [_ for _ in col if df[_].nunique() < 30]
termination_reasion_code = df.term_reas_code.unique()
termination_reasion_code_description = df.term_reas_desc.unique()
termination_reasion = dict(zip(termination_reasion_code, termination_reasion_code_description))
termination_reasion | code |
73082264/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/broadband-customers-base-churn-analysis/bbs_cust_base_scfy_20200210.csv')
df = data.copy()
df | code |
73082264/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/broadband-customers-base-churn-analysis/bbs_cust_base_scfy_20200210.csv')
df = data.copy()
df
df.drop('Unnamed: 19', axis=1, inplace=True)
df.churn.replace('N', '0', inplace=True)
df.churn.replace('Y', '1', inplace=True)
df.current_mth_churn.replace('N', '0', inplace=True)
df.current_mth_churn.replace('Y', '1', inplace=True)
df.churn = df.churn.astype(int)
df.current_mth_churn = df.churn.astype(int)
df.describe().T
def summary(df):
Types = df.dtypes
Counts = df.apply(lambda x: x.count())
Uniques = df.apply(lambda x: x.unique().shape[0])
Nulls = df.apply(lambda x: x.isnull().sum())
cols = ['Types', 'Counts', 'Uniques', 'Nulls']
str = pd.concat([Types, Counts, Uniques, Nulls], axis=1, sort=True)
str.columns = cols
summary(df)
col = df.columns.to_list()
catcol = [_ for _ in col if df[_].nunique() < 30]
termination_reasion_code = df.term_reas_code.unique()
termination_reasion_code_description = df.term_reas_desc.unique()
termination_reasion = dict(zip(termination_reasion_code, termination_reasion_code_description))
termination_reasion
df.drop(columns=['bill_cycl', 'serv_type', 'serv_code', 'term_reas_desc'], inplace=True)
df.head() | code |
73082264/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/broadband-customers-base-churn-analysis/bbs_cust_base_scfy_20200210.csv')
df = data.copy()
df
df.drop('Unnamed: 19', axis=1, inplace=True)
df.churn.replace('N', '0', inplace=True)
df.churn.replace('Y', '1', inplace=True)
df.current_mth_churn.replace('N', '0', inplace=True)
df.current_mth_churn.replace('Y', '1', inplace=True)
df.churn = df.churn.astype(int)
df.current_mth_churn = df.churn.astype(int)
df.describe().T
def summary(df):
Types = df.dtypes
Counts = df.apply(lambda x: x.count())
Uniques = df.apply(lambda x: x.unique().shape[0])
Nulls = df.apply(lambda x: x.isnull().sum())
cols = ['Types', 'Counts', 'Uniques', 'Nulls']
str = pd.concat([Types, Counts, Uniques, Nulls], axis=1, sort=True)
str.columns = cols
summary(df)
col = df.columns.to_list()
catcol = [_ for _ in col if df[_].nunique() < 30]
for _ in catcol:
print('{} has {} unique value/s - {}\n'.format(_, df[_].nunique(), df[_].unique())) | code |
73082264/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/broadband-customers-base-churn-analysis/bbs_cust_base_scfy_20200210.csv')
df = data.copy()
df
df.drop('Unnamed: 19', axis=1, inplace=True)
df.churn.replace('N', '0', inplace=True)
df.churn.replace('Y', '1', inplace=True)
df.current_mth_churn.replace('N', '0', inplace=True)
df.current_mth_churn.replace('Y', '1', inplace=True)
df.churn = df.churn.astype(int)
df.current_mth_churn = df.churn.astype(int)
df.describe().T
def summary(df):
Types = df.dtypes
Counts = df.apply(lambda x: x.count())
Uniques = df.apply(lambda x: x.unique().shape[0])
Nulls = df.apply(lambda x: x.isnull().sum())
cols = ['Types', 'Counts', 'Uniques', 'Nulls']
str = pd.concat([Types, Counts, Uniques, Nulls], axis=1, sort=True)
str.columns = cols
display(str.sort_values(by='Nulls', ascending=False))
print('__________Data Types__________\n')
print(str.Types.value_counts())
summary(df) | code |
73082264/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/broadband-customers-base-churn-analysis/bbs_cust_base_scfy_20200210.csv')
df = data.copy()
df
df.drop('Unnamed: 19', axis=1, inplace=True)
print(df.shape)
print(df.ndim)
print(df.size) | code |
122260629/cell_9 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_percentage_error
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/no-ground/fake2no_train.csv')
x = np.array(data[['theta', 'phi', 'power_fakeDB']]).reshape(len(data), 3)
y = np.array(data[['theta', 'phi', 'power_noDB']]).reshape(len(data), 3)
poly_reg = Pipeline([('poly', PolynomialFeatures(degree=9)), ('std_scale', StandardScaler()), ('lin_reg', LinearRegression())])
poly_reg.fit(x, y)
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_percentage_error
data_1 = pd.read_csv('/kaggle/input/no-ground/fake2no_test.csv')
x_1 = np.array(data_1[['theta', 'phi', 'power_fakeDB']]).reshape(len(data_1), 3)
y_1 = np.array(data_1[['theta', 'phi', 'power_noDB']]).reshape(len(data_1), 3)
y_predict = poly_reg.predict(x_1)
MAE = mean_absolute_error(y_1[:, 2], y_predict[:, 2])
MAPE = mean_absolute_percentage_error(y_1[:, 2], y_predict[:, 2])
y_predict_csv = pd.DataFrame(y_predict)
y_predict_csv.to_csv('y_predict.csv')
y_predict_csv.loc[122:152]
y_predict_csv.loc[1692:1722] | code |
122260629/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 |
122260629/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_percentage_error
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/no-ground/fake2no_train.csv')
x = np.array(data[['theta', 'phi', 'power_fakeDB']]).reshape(len(data), 3)
y = np.array(data[['theta', 'phi', 'power_noDB']]).reshape(len(data), 3)
poly_reg = Pipeline([('poly', PolynomialFeatures(degree=9)), ('std_scale', StandardScaler()), ('lin_reg', LinearRegression())])
poly_reg.fit(x, y)
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_percentage_error
data_1 = pd.read_csv('/kaggle/input/no-ground/fake2no_test.csv')
x_1 = np.array(data_1[['theta', 'phi', 'power_fakeDB']]).reshape(len(data_1), 3)
y_1 = np.array(data_1[['theta', 'phi', 'power_noDB']]).reshape(len(data_1), 3)
y_predict = poly_reg.predict(x_1)
MAE = mean_absolute_error(y_1[:, 2], y_predict[:, 2])
MAPE = mean_absolute_percentage_error(y_1[:, 2], y_predict[:, 2])
print(MAE)
print(MAPE) | code |
122260629/cell_8 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_percentage_error
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/no-ground/fake2no_train.csv')
x = np.array(data[['theta', 'phi', 'power_fakeDB']]).reshape(len(data), 3)
y = np.array(data[['theta', 'phi', 'power_noDB']]).reshape(len(data), 3)
poly_reg = Pipeline([('poly', PolynomialFeatures(degree=9)), ('std_scale', StandardScaler()), ('lin_reg', LinearRegression())])
poly_reg.fit(x, y)
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_percentage_error
data_1 = pd.read_csv('/kaggle/input/no-ground/fake2no_test.csv')
x_1 = np.array(data_1[['theta', 'phi', 'power_fakeDB']]).reshape(len(data_1), 3)
y_1 = np.array(data_1[['theta', 'phi', 'power_noDB']]).reshape(len(data_1), 3)
y_predict = poly_reg.predict(x_1)
MAE = mean_absolute_error(y_1[:, 2], y_predict[:, 2])
MAPE = mean_absolute_percentage_error(y_1[:, 2], y_predict[:, 2])
y_predict_csv = pd.DataFrame(y_predict)
y_predict_csv.to_csv('y_predict.csv')
y_predict_csv.loc[122:152] | code |
122260629/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/no-ground/fake2no_train.csv')
x = np.array(data[['theta', 'phi', 'power_fakeDB']]).reshape(len(data), 3)
y = np.array(data[['theta', 'phi', 'power_noDB']]).reshape(len(data), 3)
poly_reg = Pipeline([('poly', PolynomialFeatures(degree=9)), ('std_scale', StandardScaler()), ('lin_reg', LinearRegression())])
poly_reg.fit(x, y) | code |
1003966/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
## Rate of crimes solved
solved = pd.DataFrame(data, columns = ['Crime Solved'])
resolution = solved.stack().value_counts()
ax = resolution.plot(kind = 'pie',
title = 'Crimes solved between 1980 & 2014 (in %)',
startangle = 10,
autopct='%.2f')
ax.set_ylabel('')
#Gender of victims
sex = pd.DataFrame(data, columns = ['Victim Sex'])
count_sex = sex.stack().value_counts()
ax = count_sex.plot(kind = 'pie',
title = 'Sex of the victims',
startangle = 10,
autopct='%.2f')
ax.set_ylabel('')
#Race of Victims
race = pd.DataFrame(data, columns = ['Victim Race'])
count_race = race.stack().value_counts()
ax = count_race.plot(kind = 'pie',
title = 'Race of the victims',
startangle = 10,
autopct='%.2f',
explode=(0, 0, 0.7, 1, 1.3))
ax.set_ylabel('')
data['Victim Age'] = data['Victim Age'].astype('int')
mask = data['Victim Age'] < 21
young_victims = pd.DataFrame(data.loc[mask], columns=['Year'])
count_years = young_victims.stack().value_counts()
homicides_young = count_years.sort_index(axis=0, ascending=False)
mask2 = data['Victim Age'] > 21
adult_victims = pd.DataFrame(data.loc[mask2], columns=['Year'])
count_years = adult_victims.stack().value_counts()
homicides_adult = count_years.sort_index(axis=0, ascending=False)
## Comparation between victims by age // ToDo adjust plot
homicides_adult.to_frame()
homicides_young.to_frame()
homicides = pd.DataFrame({'Adult': homicides_adult,'Young':homicides_young})
homicides.sort_index(inplace=True)
pos = list(range(len(homicides['Adult'])))
width = 0.25
# Plotting the bars
fig, ax = plt.subplots(figsize=(25,15))
# in position pos,
plt.bar(pos,
#using homicides['Adult'] data,
homicides['Adult'],
# of width
width,
# with alpha 0.5
alpha=0.5,
# with color
color='#EE3224',
# with label the first value in year
label=homicides.index[0])
# Create a bar with young data,
# in position pos + some width buffer,
plt.bar([p + width for p in pos],
#using homicides['Young'] data,
homicides['Young'],
# of width
width,
# with alpha 0.5
alpha=0.5,
# with color
color='#F78F1E',
# with label the second value in year
label=homicides.index[1])
# Set the y axis label
ax.set_ylabel('Adult / Young')
# Set the chart's title
ax.set_title('Comparation between victims by age')
# Set the position of the x ticks
ax.set_xticks([p + 1.5 * width for p in pos])
# Set the labels for the x ticks
ax.set_xticklabels(homicides.index)
# Setting the x-axis and y-axis limits
plt.xlim(min(pos)-width, max(pos)+width*4)
plt.ylim([0, max(homicides['Adult'] + homicides['Young'])] )
# Adding the legend and showing the plot
plt.legend(['Adult', 'Young'], loc='upper left')
plt.grid()
plt.show()
# Sex of the perpetrators
perpetrator_sex = pd.DataFrame(data, columns = ['Perpetrator Sex'])
count_perpetrator_sex = perpetrator_sex.stack().value_counts()
ax = count_perpetrator_sex.plot(kind = 'pie',
title = 'Sex of the perpetrators',
startangle = 10,
autopct='%.2f')
ax.set_ylabel('')
crime_types = pd.DataFrame(data, columns=['Crime Type'])
count_types = crime_types.stack().value_counts()
count_crime_types = count_types.sort_index(axis=0, ascending=False)
ax = count_crime_types.plot(kind='pie', title='Crime Types', startangle=25, autopct='%.2f')
ax.set_ylabel('') | code |
1003966/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
## Rate of crimes solved
solved = pd.DataFrame(data, columns = ['Crime Solved'])
resolution = solved.stack().value_counts()
ax = resolution.plot(kind = 'pie',
title = 'Crimes solved between 1980 & 2014 (in %)',
startangle = 10,
autopct='%.2f')
ax.set_ylabel('')
sex = pd.DataFrame(data, columns=['Victim Sex'])
count_sex = sex.stack().value_counts()
ax = count_sex.plot(kind='pie', title='Sex of the victims', startangle=10, autopct='%.2f')
ax.set_ylabel('') | code |
1003966/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
## Rate of crimes solved
solved = pd.DataFrame(data, columns = ['Crime Solved'])
resolution = solved.stack().value_counts()
ax = resolution.plot(kind = 'pie',
title = 'Crimes solved between 1980 & 2014 (in %)',
startangle = 10,
autopct='%.2f')
ax.set_ylabel('')
#Gender of victims
sex = pd.DataFrame(data, columns = ['Victim Sex'])
count_sex = sex.stack().value_counts()
ax = count_sex.plot(kind = 'pie',
title = 'Sex of the victims',
startangle = 10,
autopct='%.2f')
ax.set_ylabel('')
#Race of Victims
race = pd.DataFrame(data, columns = ['Victim Race'])
count_race = race.stack().value_counts()
ax = count_race.plot(kind = 'pie',
title = 'Race of the victims',
startangle = 10,
autopct='%.2f',
explode=(0, 0, 0.7, 1, 1.3))
ax.set_ylabel('')
data['Victim Age'] = data['Victim Age'].astype('int')
mask = data['Victim Age'] < 21
young_victims = pd.DataFrame(data.loc[mask], columns=['Year'])
count_years = young_victims.stack().value_counts()
homicides_young = count_years.sort_index(axis=0, ascending=False)
mask2 = data['Victim Age'] > 21
adult_victims = pd.DataFrame(data.loc[mask2], columns=['Year'])
count_years = adult_victims.stack().value_counts()
homicides_adult = count_years.sort_index(axis=0, ascending=False)
print(homicides_young.plot(kind='barh', fontsize=10, width=0.5, figsize=(12, 10), title='Victims under 21 years old')) | code |
1003966/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
data = pd.read_csv('../input/database.csv', na_values=['NA'], dtype='unicode')
years = pd.DataFrame(data, columns=['Year'])
count_years = years.stack().value_counts()
homicides = count_years.sort_index(axis=0, ascending=False)
homicides.plot(kind='barh', fontsize=10, width=0.5, figsize=(12, 10), title='Homicides in EEUU between 1980 and 2014') | code |
1003966/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
## Rate of crimes solved
solved = pd.DataFrame(data, columns = ['Crime Solved'])
resolution = solved.stack().value_counts()
ax = resolution.plot(kind = 'pie',
title = 'Crimes solved between 1980 & 2014 (in %)',
startangle = 10,
autopct='%.2f')
ax.set_ylabel('')
#Gender of victims
sex = pd.DataFrame(data, columns = ['Victim Sex'])
count_sex = sex.stack().value_counts()
ax = count_sex.plot(kind = 'pie',
title = 'Sex of the victims',
startangle = 10,
autopct='%.2f')
ax.set_ylabel('')
#Race of Victims
race = pd.DataFrame(data, columns = ['Victim Race'])
count_race = race.stack().value_counts()
ax = count_race.plot(kind = 'pie',
title = 'Race of the victims',
startangle = 10,
autopct='%.2f',
explode=(0, 0, 0.7, 1, 1.3))
ax.set_ylabel('')
data['Victim Age'] = data['Victim Age'].astype('int')
mask = data['Victim Age'] < 21
young_victims = pd.DataFrame(data.loc[mask], columns=['Year'])
count_years = young_victims.stack().value_counts()
homicides_young = count_years.sort_index(axis=0, ascending=False)
mask2 = data['Victim Age'] > 21
adult_victims = pd.DataFrame(data.loc[mask2], columns=['Year'])
count_years = adult_victims.stack().value_counts()
homicides_adult = count_years.sort_index(axis=0, ascending=False)
homicides_adult.to_frame()
homicides_young.to_frame()
homicides = pd.DataFrame({'Adult': homicides_adult, 'Young': homicides_young})
homicides.sort_index(inplace=True)
pos = list(range(len(homicides['Adult'])))
width = 0.25
fig, ax = plt.subplots(figsize=(25, 15))
plt.bar(pos, homicides['Adult'], width, alpha=0.5, color='#EE3224', label=homicides.index[0])
plt.bar([p + width for p in pos], homicides['Young'], width, alpha=0.5, color='#F78F1E', label=homicides.index[1])
ax.set_ylabel('Adult / Young')
ax.set_title('Comparation between victims by age')
ax.set_xticks([p + 1.5 * width for p in pos])
ax.set_xticklabels(homicides.index)
plt.xlim(min(pos) - width, max(pos) + width * 4)
plt.ylim([0, max(homicides['Adult'] + homicides['Young'])])
plt.legend(['Adult', 'Young'], loc='upper left')
plt.grid()
plt.show() | code |
1003966/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
## Rate of crimes solved
solved = pd.DataFrame(data, columns = ['Crime Solved'])
resolution = solved.stack().value_counts()
ax = resolution.plot(kind = 'pie',
title = 'Crimes solved between 1980 & 2014 (in %)',
startangle = 10,
autopct='%.2f')
ax.set_ylabel('')
#Gender of victims
sex = pd.DataFrame(data, columns = ['Victim Sex'])
count_sex = sex.stack().value_counts()
ax = count_sex.plot(kind = 'pie',
title = 'Sex of the victims',
startangle = 10,
autopct='%.2f')
ax.set_ylabel('')
#Race of Victims
race = pd.DataFrame(data, columns = ['Victim Race'])
count_race = race.stack().value_counts()
ax = count_race.plot(kind = 'pie',
title = 'Race of the victims',
startangle = 10,
autopct='%.2f',
explode=(0, 0, 0.7, 1, 1.3))
ax.set_ylabel('')
data['Victim Age'] = data['Victim Age'].astype('int')
mask = data['Victim Age'] < 21
young_victims = pd.DataFrame(data.loc[mask], columns=['Year'])
count_years = young_victims.stack().value_counts()
homicides_young = count_years.sort_index(axis=0, ascending=False)
mask2 = data['Victim Age'] > 21
adult_victims = pd.DataFrame(data.loc[mask2], columns=['Year'])
count_years = adult_victims.stack().value_counts()
homicides_adult = count_years.sort_index(axis=0, ascending=False)
## Comparation between victims by age // ToDo adjust plot
homicides_adult.to_frame()
homicides_young.to_frame()
homicides = pd.DataFrame({'Adult': homicides_adult,'Young':homicides_young})
homicides.sort_index(inplace=True)
pos = list(range(len(homicides['Adult'])))
width = 0.25
# Plotting the bars
fig, ax = plt.subplots(figsize=(25,15))
# in position pos,
plt.bar(pos,
#using homicides['Adult'] data,
homicides['Adult'],
# of width
width,
# with alpha 0.5
alpha=0.5,
# with color
color='#EE3224',
# with label the first value in year
label=homicides.index[0])
# Create a bar with young data,
# in position pos + some width buffer,
plt.bar([p + width for p in pos],
#using homicides['Young'] data,
homicides['Young'],
# of width
width,
# with alpha 0.5
alpha=0.5,
# with color
color='#F78F1E',
# with label the second value in year
label=homicides.index[1])
# Set the y axis label
ax.set_ylabel('Adult / Young')
# Set the chart's title
ax.set_title('Comparation between victims by age')
# Set the position of the x ticks
ax.set_xticks([p + 1.5 * width for p in pos])
# Set the labels for the x ticks
ax.set_xticklabels(homicides.index)
# Setting the x-axis and y-axis limits
plt.xlim(min(pos)-width, max(pos)+width*4)
plt.ylim([0, max(homicides['Adult'] + homicides['Young'])] )
# Adding the legend and showing the plot
plt.legend(['Adult', 'Young'], loc='upper left')
plt.grid()
plt.show()
perpetrator_sex = pd.DataFrame(data, columns=['Perpetrator Sex'])
count_perpetrator_sex = perpetrator_sex.stack().value_counts()
ax = count_perpetrator_sex.plot(kind='pie', title='Sex of the perpetrators', startangle=10, autopct='%.2f')
ax.set_ylabel('') | code |
1003966/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
solved = pd.DataFrame(data, columns=['Crime Solved'])
resolution = solved.stack().value_counts()
ax = resolution.plot(kind='pie', title='Crimes solved between 1980 & 2014 (in %)', startangle=10, autopct='%.2f')
ax.set_ylabel('') | code |
1003966/cell_10 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
## Rate of crimes solved
solved = pd.DataFrame(data, columns = ['Crime Solved'])
resolution = solved.stack().value_counts()
ax = resolution.plot(kind = 'pie',
title = 'Crimes solved between 1980 & 2014 (in %)',
startangle = 10,
autopct='%.2f')
ax.set_ylabel('')
#Gender of victims
sex = pd.DataFrame(data, columns = ['Victim Sex'])
count_sex = sex.stack().value_counts()
ax = count_sex.plot(kind = 'pie',
title = 'Sex of the victims',
startangle = 10,
autopct='%.2f')
ax.set_ylabel('')
#Race of Victims
race = pd.DataFrame(data, columns = ['Victim Race'])
count_race = race.stack().value_counts()
ax = count_race.plot(kind = 'pie',
title = 'Race of the victims',
startangle = 10,
autopct='%.2f',
explode=(0, 0, 0.7, 1, 1.3))
ax.set_ylabel('')
data['Victim Age'] = data['Victim Age'].astype('int')
mask = data['Victim Age'] < 21
young_victims = pd.DataFrame(data.loc[mask], columns=['Year'])
count_years = young_victims.stack().value_counts()
homicides_young = count_years.sort_index(axis=0, ascending=False)
mask2 = data['Victim Age'] > 21
adult_victims = pd.DataFrame(data.loc[mask2], columns=['Year'])
count_years = adult_victims.stack().value_counts()
homicides_adult = count_years.sort_index(axis=0, ascending=False)
## Comparation between victims by age // ToDo adjust plot
homicides_adult.to_frame()
homicides_young.to_frame()
homicides = pd.DataFrame({'Adult': homicides_adult,'Young':homicides_young})
homicides.sort_index(inplace=True)
pos = list(range(len(homicides['Adult'])))
width = 0.25
# Plotting the bars
fig, ax = plt.subplots(figsize=(25,15))
# in position pos,
plt.bar(pos,
#using homicides['Adult'] data,
homicides['Adult'],
# of width
width,
# with alpha 0.5
alpha=0.5,
# with color
color='#EE3224',
# with label the first value in year
label=homicides.index[0])
# Create a bar with young data,
# in position pos + some width buffer,
plt.bar([p + width for p in pos],
#using homicides['Young'] data,
homicides['Young'],
# of width
width,
# with alpha 0.5
alpha=0.5,
# with color
color='#F78F1E',
# with label the second value in year
label=homicides.index[1])
# Set the y axis label
ax.set_ylabel('Adult / Young')
# Set the chart's title
ax.set_title('Comparation between victims by age')
# Set the position of the x ticks
ax.set_xticks([p + 1.5 * width for p in pos])
# Set the labels for the x ticks
ax.set_xticklabels(homicides.index)
# Setting the x-axis and y-axis limits
plt.xlim(min(pos)-width, max(pos)+width*4)
plt.ylim([0, max(homicides['Adult'] + homicides['Young'])] )
# Adding the legend and showing the plot
plt.legend(['Adult', 'Young'], loc='upper left')
plt.grid()
plt.show()
# Sex of the perpetrators
perpetrator_sex = pd.DataFrame(data, columns = ['Perpetrator Sex'])
count_perpetrator_sex = perpetrator_sex.stack().value_counts()
ax = count_perpetrator_sex.plot(kind = 'pie',
title = 'Sex of the perpetrators',
startangle = 10,
autopct='%.2f')
ax.set_ylabel('')
#Crime types
crime_types = pd.DataFrame(data, columns = ['Crime Type'])
count_types = crime_types.stack().value_counts()
count_crime_types = count_types.sort_index(axis=0, ascending=False)
#plot the total of homicides
ax = count_crime_types.plot(kind = 'pie',
title = 'Crime Types',
startangle = 25,
autopct='%.2f')
ax.set_ylabel('')
state = pd.DataFrame(data, columns=['State'])
count_states = state.stack().value_counts()
states = count_states.sort_index(axis=0, ascending=False)
print(states.plot(kind='barh', fontsize=10, width=0.5, figsize=(12, 10), title='Homicides in EEUU by State between 1980 and 2014')) | code |
1003966/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib.collections import PatchCollection
from matplotlib.colors import Normalize
from matplotlib.patches import Polygon
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
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)
## Rate of crimes solved
solved = pd.DataFrame(data, columns = ['Crime Solved'])
resolution = solved.stack().value_counts()
ax = resolution.plot(kind = 'pie',
title = 'Crimes solved between 1980 & 2014 (in %)',
startangle = 10,
autopct='%.2f')
ax.set_ylabel('')
#Gender of victims
sex = pd.DataFrame(data, columns = ['Victim Sex'])
count_sex = sex.stack().value_counts()
ax = count_sex.plot(kind = 'pie',
title = 'Sex of the victims',
startangle = 10,
autopct='%.2f')
ax.set_ylabel('')
#Race of Victims
race = pd.DataFrame(data, columns = ['Victim Race'])
count_race = race.stack().value_counts()
ax = count_race.plot(kind = 'pie',
title = 'Race of the victims',
startangle = 10,
autopct='%.2f',
explode=(0, 0, 0.7, 1, 1.3))
ax.set_ylabel('')
data['Victim Age'] = data['Victim Age'].astype('int')
mask = data['Victim Age'] < 21
young_victims = pd.DataFrame(data.loc[mask], columns=['Year'])
count_years = young_victims.stack().value_counts()
homicides_young = count_years.sort_index(axis=0, ascending=False)
mask2 = data['Victim Age'] > 21
adult_victims = pd.DataFrame(data.loc[mask2], columns=['Year'])
count_years = adult_victims.stack().value_counts()
homicides_adult = count_years.sort_index(axis=0, ascending=False)
## Comparation between victims by age // ToDo adjust plot
homicides_adult.to_frame()
homicides_young.to_frame()
homicides = pd.DataFrame({'Adult': homicides_adult,'Young':homicides_young})
homicides.sort_index(inplace=True)
pos = list(range(len(homicides['Adult'])))
width = 0.25
# Plotting the bars
fig, ax = plt.subplots(figsize=(25,15))
# in position pos,
plt.bar(pos,
#using homicides['Adult'] data,
homicides['Adult'],
# of width
width,
# with alpha 0.5
alpha=0.5,
# with color
color='#EE3224',
# with label the first value in year
label=homicides.index[0])
# Create a bar with young data,
# in position pos + some width buffer,
plt.bar([p + width for p in pos],
#using homicides['Young'] data,
homicides['Young'],
# of width
width,
# with alpha 0.5
alpha=0.5,
# with color
color='#F78F1E',
# with label the second value in year
label=homicides.index[1])
# Set the y axis label
ax.set_ylabel('Adult / Young')
# Set the chart's title
ax.set_title('Comparation between victims by age')
# Set the position of the x ticks
ax.set_xticks([p + 1.5 * width for p in pos])
# Set the labels for the x ticks
ax.set_xticklabels(homicides.index)
# Setting the x-axis and y-axis limits
plt.xlim(min(pos)-width, max(pos)+width*4)
plt.ylim([0, max(homicides['Adult'] + homicides['Young'])] )
# Adding the legend and showing the plot
plt.legend(['Adult', 'Young'], loc='upper left')
plt.grid()
plt.show()
# Sex of the perpetrators
perpetrator_sex = pd.DataFrame(data, columns = ['Perpetrator Sex'])
count_perpetrator_sex = perpetrator_sex.stack().value_counts()
ax = count_perpetrator_sex.plot(kind = 'pie',
title = 'Sex of the perpetrators',
startangle = 10,
autopct='%.2f')
ax.set_ylabel('')
#Crime types
crime_types = pd.DataFrame(data, columns = ['Crime Type'])
count_types = crime_types.stack().value_counts()
count_crime_types = count_types.sort_index(axis=0, ascending=False)
#plot the total of homicides
ax = count_crime_types.plot(kind = 'pie',
title = 'Crime Types',
startangle = 25,
autopct='%.2f')
ax.set_ylabel('')
state = pd.DataFrame(data, columns=['State'])
count_states = state.stack().value_counts()
states = count_states.sort_index(axis=0, ascending=False)
import matplotlib.pyplot as plt
import matplotlib.cm
from mpl_toolkits.basemap import Basemap
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
from matplotlib.colors import Normalize
states_eeuu = pd.DataFrame({'homicides': states, 'state': states.index})
states_name = states_eeuu.index
fig, ax = plt.subplots(figsize=(20, 10))
m = Basemap(resolution='h', projection='lcc', lat_1=33, lat_2=45, lon_0=-95, llcrnrlon=-119, llcrnrlat=22, urcrnrlon=-64, urcrnrlat=49)
m.readshapefile('../input/st99_d00', 'states')
m.drawmapboundary(fill_color='#46bcec')
m.fillcontinents(color='#f2f2f2', lake_color='#46bcec')
m.drawcoastlines()
geo = pd.DataFrame({'shapes': [Polygon(np.array(shape), True) for shape in m.states], 'state': [state['NAME'] for state in m.states_info]})
geo = geo.merge(states_eeuu, on='state', how='left')
cmap = plt.get_cmap('Oranges')
pc = PatchCollection(geo.shapes, zorder=2)
norm = Normalize()
pc.set_facecolor(cmap(norm(geo['homicides'].fillna(0).values)))
ax.add_collection(pc)
mapper = matplotlib.cm.ScalarMappable(norm=norm, cmap=cmap)
mapper.set_array(geo['homicides'])
plt.colorbar(mapper, shrink=0.4)
plt.title('Geographic homicide distribution') | code |
1003966/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
## Rate of crimes solved
solved = pd.DataFrame(data, columns = ['Crime Solved'])
resolution = solved.stack().value_counts()
ax = resolution.plot(kind = 'pie',
title = 'Crimes solved between 1980 & 2014 (in %)',
startangle = 10,
autopct='%.2f')
ax.set_ylabel('')
#Gender of victims
sex = pd.DataFrame(data, columns = ['Victim Sex'])
count_sex = sex.stack().value_counts()
ax = count_sex.plot(kind = 'pie',
title = 'Sex of the victims',
startangle = 10,
autopct='%.2f')
ax.set_ylabel('')
race = pd.DataFrame(data, columns=['Victim Race'])
count_race = race.stack().value_counts()
ax = count_race.plot(kind='pie', title='Race of the victims', startangle=10, autopct='%.2f', explode=(0, 0, 0.7, 1, 1.3))
ax.set_ylabel('') | code |
90106983/cell_42 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.duplicated()
data = data.drop_duplicates()
DuplicatedDataSum = data.duplicated().sum()
corr = data.corr()
corr
## Draw the RelationShip between Data
# Generate a mask for the upper triangle
mask = np.triu(np.ones_like(corr, dtype=bool))
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(11, 9))
# Generate a custom diverging colormap
cmap = sns.diverging_palette(230, 20, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,
square=True, linewidths=.5, cbar_kws={"shrink": .5})
model = np.corrcoef(data['model'], data['price'])
model
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5), sharex=True)
sns.kdeplot(data=model, ax=ax1)
sns.heatmap(model,annot=True,ax= ax2)
year = np.corrcoef(data['year'], data['price'])
year
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5), sharex=True)
sns.kdeplot(data=year, ax=ax1)
sns.heatmap(year,annot=True,ax= ax2)
transmission = np.corrcoef(data['transmission'], data['price'])
transmission
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5), sharex=True)
sns.kdeplot(data=transmission, ax=ax1)
sns.heatmap(transmission, annot=True, ax=ax2) | code |
90106983/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.duplicated()
print(DuplicatedData) | code |
90106983/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
data.info() | code |
90106983/cell_34 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.duplicated()
data = data.drop_duplicates()
DuplicatedDataSum = data.duplicated().sum()
corr = data.corr()
corr
## Draw the RelationShip between Data
# Generate a mask for the upper triangle
mask = np.triu(np.ones_like(corr, dtype=bool))
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(11, 9))
# Generate a custom diverging colormap
cmap = sns.diverging_palette(230, 20, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,
square=True, linewidths=.5, cbar_kws={"shrink": .5})
model = np.corrcoef(data['model'], data['price'])
model
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5), sharex=True)
sns.kdeplot(data=model, ax=ax1)
sns.heatmap(model, annot=True, ax=ax2) | code |
90106983/cell_44 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.duplicated()
data = data.drop_duplicates()
DuplicatedDataSum = data.duplicated().sum()
corr = data.corr()
corr
## Draw the RelationShip between Data
# Generate a mask for the upper triangle
mask = np.triu(np.ones_like(corr, dtype=bool))
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(11, 9))
# Generate a custom diverging colormap
cmap = sns.diverging_palette(230, 20, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,
square=True, linewidths=.5, cbar_kws={"shrink": .5})
model = np.corrcoef(data['model'], data['price'])
model
year = np.corrcoef(data['year'], data['price'])
year
transmission = np.corrcoef(data['transmission'], data['price'])
transmission
mileage = np.corrcoef(data['mileage'], data['price'])
mileage | code |
90106983/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.duplicated()
data = data.drop_duplicates()
DuplicatedDataSum = data.duplicated().sum()
corr = data.corr()
corr
## Draw the RelationShip between Data
# Generate a mask for the upper triangle
mask = np.triu(np.ones_like(corr, dtype=bool))
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(11, 9))
# Generate a custom diverging colormap
cmap = sns.diverging_palette(230, 20, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,
square=True, linewidths=.5, cbar_kws={"shrink": .5})
plt.figure(figsize=(15, 10))
sns.heatmap(corr, annot=True) | code |
90106983/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
print(data.shape)
data.head() | code |
90106983/cell_40 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.duplicated()
data = data.drop_duplicates()
DuplicatedDataSum = data.duplicated().sum()
corr = data.corr()
corr
## Draw the RelationShip between Data
# Generate a mask for the upper triangle
mask = np.triu(np.ones_like(corr, dtype=bool))
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(11, 9))
# Generate a custom diverging colormap
cmap = sns.diverging_palette(230, 20, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,
square=True, linewidths=.5, cbar_kws={"shrink": .5})
model = np.corrcoef(data['model'], data['price'])
model
year = np.corrcoef(data['year'], data['price'])
year
transmission = np.corrcoef(data['transmission'], data['price'])
transmission | code |
90106983/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.duplicated()
data = data.drop_duplicates()
DuplicatedDataSum = data.duplicated().sum()
corr = data.corr()
corr
data.head() | code |
90106983/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
print(data.isnull().sum()) | code |
90106983/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.duplicated()
data = data.drop_duplicates()
DuplicatedDataSum = data.duplicated().sum()
corr = data.corr()
corr
mask = np.triu(np.ones_like(corr, dtype=bool))
f, ax = plt.subplots(figsize=(11, 9))
cmap = sns.diverging_palette(230, 20, as_cmap=True)
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=0.3, center=0, square=True, linewidths=0.5, cbar_kws={'shrink': 0.5}) | code |
90106983/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 |
90106983/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.duplicated()
data = data.drop_duplicates()
DuplicatedDataSum = data.duplicated().sum()
corr = data.corr()
corr
## Draw the RelationShip between Data
# Generate a mask for the upper triangle
mask = np.triu(np.ones_like(corr, dtype=bool))
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(11, 9))
# Generate a custom diverging colormap
cmap = sns.diverging_palette(230, 20, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,
square=True, linewidths=.5, cbar_kws={"shrink": .5})
model = np.corrcoef(data['model'], data['price'])
model | code |
90106983/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
print(data.columns.values) | code |
90106983/cell_38 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.duplicated()
data = data.drop_duplicates()
DuplicatedDataSum = data.duplicated().sum()
corr = data.corr()
corr
## Draw the RelationShip between Data
# Generate a mask for the upper triangle
mask = np.triu(np.ones_like(corr, dtype=bool))
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(11, 9))
# Generate a custom diverging colormap
cmap = sns.diverging_palette(230, 20, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,
square=True, linewidths=.5, cbar_kws={"shrink": .5})
model = np.corrcoef(data['model'], data['price'])
model
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5), sharex=True)
sns.kdeplot(data=model, ax=ax1)
sns.heatmap(model,annot=True,ax= ax2)
year = np.corrcoef(data['year'], data['price'])
year
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5), sharex=True)
sns.kdeplot(data=year, ax=ax1)
sns.heatmap(year, annot=True, ax=ax2) | code |
90106983/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.duplicated()
data = data.drop_duplicates()
DuplicatedDataSum = data.duplicated().sum()
corr = data.corr()
corr | code |
90106983/cell_24 | [
"text_html_output_1.png"
] | from pandas_profiling import ProfileReport
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.duplicated()
data = data.drop_duplicates()
DuplicatedDataSum = data.duplicated().sum()
corr = data.corr()
corr
from pandas_profiling import ProfileReport
profile = ProfileReport(data, title='Pandas profiling report ', html={'style': {'full_width': True}})
profile.to_notebook_iframe() | code |
90106983/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.duplicated()
data = data.drop_duplicates()
DuplicatedDataSum = data.duplicated().sum()
print('Sum of the Dublicate in Data', DuplicatedDataSum) | code |
90106983/cell_10 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
data.describe() | code |
90106983/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
print('Sum of the Dublicate in Data', DuplicatedDataSum) | code |
90106983/cell_36 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.duplicated()
data = data.drop_duplicates()
DuplicatedDataSum = data.duplicated().sum()
corr = data.corr()
corr
## Draw the RelationShip between Data
# Generate a mask for the upper triangle
mask = np.triu(np.ones_like(corr, dtype=bool))
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(11, 9))
# Generate a custom diverging colormap
cmap = sns.diverging_palette(230, 20, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,
square=True, linewidths=.5, cbar_kws={"shrink": .5})
model = np.corrcoef(data['model'], data['price'])
model
year = np.corrcoef(data['year'], data['price'])
year | code |
32067324/cell_2 | [
"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 |
72085259/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
submission_df = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train.head() | code |
72085259/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
submission_df = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
data = pd.concat([train.assign(ind='train'), test.assign(ind='test')], ignore_index=True)
data['Age'] = data.groupby(['Sex', 'Pclass'])['Age'].apply(lambda x: x.fillna(x.median()))
data['Age'] = data['Age'].astype(int)
data.loc[data['Age'] <= 15, 'Age'] = 0
data.loc[(data['Age'] > 15) & (data['Age'] <= 30), 'Age'] = 1
data.loc[(data['Age'] > 30) & (data['Age'] <= 45), 'Age'] = 2
data.loc[(data['Age'] > 45) & (data['Age'] <= 60), 'Age'] = 3
data.loc[data['Age'] > 60, 'Age'] = 4
data.describe(include='all') | code |
72085259/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
submission_df = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
print('train:\n', train.isnull().sum())
print()
print('test:\n', test.isnull().sum()) | code |
72085259/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | test_data = test_data.drop(['ind'], axis=1)
train_data = train_data.drop(['ind'], axis=1)
train_data.head() | code |
72085259/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
submission_df = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
data = pd.concat([train.assign(ind='train'), test.assign(ind='test')], ignore_index=True)
sns.barplot(x=data['Deck'], y=data['Survived']) | code |
72085259/cell_38 | [
"text_html_output_1.png"
] | from lightgbm import LGBMClassifier
from sklearn import metrics
from sklearn import svm
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
submission_df = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
data = pd.concat([train.assign(ind='train'), test.assign(ind='test')], ignore_index=True)
data['Age'] = data.groupby(['Sex', 'Pclass'])['Age'].apply(lambda x: x.fillna(x.median()))
data['Age'] = data['Age'].astype(int)
data.loc[data['Age'] <= 15, 'Age'] = 0
data.loc[(data['Age'] > 15) & (data['Age'] <= 30), 'Age'] = 1
data.loc[(data['Age'] > 30) & (data['Age'] <= 45), 'Age'] = 2
data.loc[(data['Age'] > 45) & (data['Age'] <= 60), 'Age'] = 3
data.loc[data['Age'] > 60, 'Age'] = 4
data['Fare_bins'] = pd.cut(data['Fare'], bins=[0.0, 7.895, 14.45, 31.275, 512.329], labels=[0, 1, 2, 3])
data['Fare_bins'] = data['Fare_bins'].fillna(0)
data['Fare_bins'] = data['Fare_bins'].astype(int)
test_data = test_data.drop(['ind'], axis=1)
train_data = train_data.drop(['ind'], axis=1)
X = train_data.drop(['Survived'], axis=1)
y = train_data['Survived']
traindf_X = pd.get_dummies(X, columns=['Sex', 'Embarked', 'Fare_bins', 'Deck'], prefix=['Sex', 'Embarked', 'Fare_type', 'Deck'])
testdf = pd.get_dummies(test_data, columns=['Sex', 'Embarked', 'Fare_bins', 'Deck'], prefix=['Sex', 'Embarked', 'Fare_type', 'Deck']).drop(['Survived'], axis=1)
from sklearn import metrics
def get_scores(y_preds, y):
return {'Accuracy': metrics.accuracy_score(y_preds, y), 'Precision': metrics.precision_score(y_preds, y), 'Recall': metrics.recall_score(y_preds, y), 'F1': metrics.f1_score(y_preds, y), 'ROC_AUC': metrics.roc_auc_score(y_preds, y)}
def train_model(model):
model_ = model
model_.fit(X_train, y_train)
y_pred = model_.predict(X_val)
return get_scores(y_pred, y_val)
model_list = [DecisionTreeClassifier(random_state=42), RandomForestClassifier(random_state=42), XGBClassifier(random_state=42), LGBMClassifier(random_state=42, is_unbalance=True), LogisticRegression(random_state=42), svm.SVC(random_state=42), AdaBoostClassifier(random_state=42), KNeighborsClassifier(), GaussianNB()]
model_names = ['Decision Tree', 'Random Forest', 'XGB Classifier', 'LGBM Classifier', 'Logistic Regression', 'SVC', 'AdaBoost ', 'KNN', 'GaussianNB']
scores = pd.DataFrame(columns=['Name', 'Accuracy', 'Precision', 'Recall', 'F1', 'ROC_AUC'])
for i in range(len(model_list)):
score = train_model(model_list[i])
scores.loc[i] = [model_names[i]] + list(score.values())
figure, axis = plt.subplots(2, 3)
figure.set_figheight(15)
figure.set_figwidth(30)
for i in range(2):
for j in range(3):
axis[i, j].set_xlim([0.5, 0.9])
axis[0, 0].barh(scores['Name'], scores['Accuracy'], height=0.5)
axis[0, 0].set_title('Accuracy Score')
axis[0, 1].barh(scores['Name'], scores['Precision'], height=0.5)
axis[0, 1].set_title('Precision')
axis[1, 0].barh(scores['Name'], scores['Recall'], height=0.5)
axis[1, 0].set_title('Recall')
axis[1, 2].barh(scores['Name'], scores['F1'], height=0.5)
axis[1, 2].set_title('F1')
axis[0, 2].barh(scores['Name'], scores['ROC_AUC'], height=0.5)
axis[0, 2].set_title('ROC_AUC')
axis[1, 1].set_visible(False)
plt.show() | code |
72085259/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
submission_df = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
data = pd.concat([train.assign(ind='train'), test.assign(ind='test')], ignore_index=True)
data['Age'] = data.groupby(['Sex', 'Pclass'])['Age'].apply(lambda x: x.fillna(x.median()))
data['Age'] = data['Age'].astype(int)
data.loc[data['Age'] <= 15, 'Age'] = 0
data.loc[(data['Age'] > 15) & (data['Age'] <= 30), 'Age'] = 1
data.loc[(data['Age'] > 30) & (data['Age'] <= 45), 'Age'] = 2
data.loc[(data['Age'] > 45) & (data['Age'] <= 60), 'Age'] = 3
data.loc[data['Age'] > 60, 'Age'] = 4
data.head() | code |
72085259/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
submission_df = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
data = pd.concat([train.assign(ind='train'), test.assign(ind='test')], ignore_index=True)
sns.barplot(x=data['Deck'], y=data['Survived']) | code |
72085259/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
submission_df = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
data = pd.concat([train.assign(ind='train'), test.assign(ind='test')], ignore_index=True)
print(train.Cabin.unique()) | code |
72085259/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
submission_df = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
test.head() | code |
128002897/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from numba import njit, prange
from tqdm.notebook import tnrange
import numpy as np
import numpy as np
import numpy as np
tracedata = np.load('/kaggle/input/sca-simple-xor-cipher-dataset/2023.04.08-14.10.20_0traces.npy')
textindata = np.load('/kaggle/input/sca-simple-xor-cipher-dataset/2023.04.08-14.10.20_0textin.npy')
textoutdata = np.load('/kaggle/input/sca-simple-xor-cipher-dataset/2023.04.08-14.10.20_0textin.npy')
key = np.load('/kaggle/input/sca-simple-xor-cipher-dataset/2023.04.08-14.10.20_0knownkey.npy', allow_pickle=True)
import numpy as np
from tqdm.notebook import tnrange
from numba import njit, prange
def mean(X):
return np.sum(X, axis=0) / len(X)
def std_dev(X, X_bar):
return np.sqrt(np.sum((X - X_bar) ** 2, axis=0))
def cov(X, X_bar, Y, Y_bar):
return np.sum((X - X_bar) * (Y - Y_bar), axis=0)
def xor_internal(inputdata, key):
return inputdata ^ key
HW = [bin(n).count('1') for n in range(0, 256)]
t_bar = np.sum(tracedata[:1000], axis=0) / len(tracedata[:1000])
o_t = np.sqrt(np.sum((tracedata[:1000] - t_bar) ** 2, axis=0))
cparefs = [0] * 16
bestguess = [0] * 16
for bnum in tnrange(0, 16):
maxcpa = [0] * 256
for kguess in prange(0, 256):
hws = np.array([[HW[xor_internal(textin[bnum], kguess)] for textin in textindata]]).transpose()
hws_bar = mean(hws)
o_hws = std_dev(hws, hws_bar)
correlation = cov(tracedata[:1000], t_bar, hws, hws_bar)
cpaoutput = correlation / (o_t * o_hws)
maxcpa[kguess] = max(abs(cpaoutput))
bestguess[bnum] = np.argmax(maxcpa)
cparefs[bnum] = max(maxcpa)
print('Best Key Guess: ', end='')
for b in bestguess:
print('%02x ' % b, end='')
print('\n', cparefs) | code |
128002897/cell_1 | [
"text_plain_output_1.png"
] | !pip install numpy
!pip install numba
!pip install matplotlib | code |
128002897/cell_3 | [
"image_output_1.png"
] | import matplotlib.pylab as plt
import binascii
plt.plot(np.mean(tracedata, axis=0), 'r')
plt.legend()
plt.show() | code |
2032344/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def computeCost(x, y, theta):
h = x.dot(theta)
cost = sum(pow(h - y, 2)) / (2 * m)
return cost
def gradientDescent(x, y, theta, alpha, iterations):
computed_theta = theta
for i in range(0, iterations):
h = x.dot(computed_theta)
computed_theta[0] = computed_theta[0] - alpha * (1 / m) * sum(h - y)
computed_theta[1] = computed_theta[1] - alpha * (1 / m) * sum((h - y) * X[:, 1])
return computed_theta
data = pd.read_csv('../input/ex1data1.txt', header=None)
X = data.iloc[:, 0].values
y = data.iloc[:, 1].values
m = y.size
plt.scatter(X, y, marker='x')
plt.xlabel('Population of City in 10,000s')
plt.ylabel('Profit in $10,000s')
plt.show()
X = np.concatenate((np.ones((m, 1), dtype=np.int), X.reshape(m, 1)), axis=1)
print(X.size)
print('Testing the cost function with theta = [0 ; 0]')
J = computeCost(X, y, np.array([0, 0]))
print('Expected cost value (approx): 32.07')
print('Actual cost value: {}\n'.format(J))
print('Testing the cost function with theta = [-1 ; 2]')
J = computeCost(X, y, np.array([-1, 2]))
print('Expected cost value (approx): 54.24')
print('Actual cost value: {}\n'.format(J))
theta = np.zeros(2)
alpha = 0.01
iterations = 1500
print('Running Gradient Descent')
theta = gradientDescent(X, y, theta, alpha, iterations)
print('Expected theta value (approx): [-3.6303, 1.1664]')
print('Actual theta value: {}\n'.format(theta))
plt.scatter(X[:, 1], y, marker='x', label='Training data')
plt.plot(X[:, 1], X.dot(theta), color='r', label='Linear regression')
plt.xlabel('Population of City in 10,000s')
plt.ylabel('Profit in $10,000s')
plt.legend()
plt.show()
predict1 = np.array([1, 3.5]).dot(theta)
print('For population of 35,000 we predict a profit of {}'.format(predict1 * 10000))
predict2 = np.array([1, 7]).dot(theta)
print('For population of 70,000 we predict a profit of {}'.format(predict2 * 10000)) | code |
106195648/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
from sklearn.preprocessing import StandardScaler
df = pd.read_csv('../input/tabular-playground-series-jul-2022/data.csv')
df
from sklearn.cluster import KMeans
km = KMeans(init='random', random_state=0)
y_km = km.fit_predict(df)
km = KMeans(max_iter=300, n_init=10)
elbow_k = kelbow_visualizer(km, df, k=(2, 15)) | code |
106195648/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
from sklearn.preprocessing import StandardScaler
df = pd.read_csv('../input/tabular-playground-series-jul-2022/data.csv')
df | code |
106195648/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 |
106195648/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
from sklearn.preprocessing import StandardScaler
df = pd.read_csv('../input/tabular-playground-series-jul-2022/data.csv')
df
from sklearn.cluster import KMeans
km = KMeans(init='random', random_state=0)
y_km = km.fit_predict(df)
km = KMeans(max_iter=300, n_init=10)
elbow_k = kelbow_visualizer(km, df, k=(2, 15))
kmeans = KMeans(n_clusters=5, n_init=10, max_iter=300)
label_pred = kmeans.fit_predict(df)
print('The best number of cluster is: ', elbow_k.elbow_value_) | code |
106195648/cell_8 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
from sklearn.preprocessing import StandardScaler
df = pd.read_csv('../input/tabular-playground-series-jul-2022/data.csv')
df
from sklearn.cluster import KMeans
km = KMeans(init='random', random_state=0)
y_km = km.fit_predict(df)
km = KMeans(max_iter=300, n_init=10)
elbow_k = kelbow_visualizer(km, df, k=(2, 15))
kmeans = KMeans(n_clusters=5, n_init=10, max_iter=300)
label_pred = kmeans.fit_predict(df)
silhouette = silhouette_visualizer(kmeans, df)
print('The Average Silhouette score is: ', silhouette.silhouette_score_) | code |
106195648/cell_3 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
from sklearn.preprocessing import StandardScaler
df = pd.read_csv('../input/tabular-playground-series-jul-2022/data.csv')
df
df_st = StandardScaler()
df_st.fit(df)
df_ = df_st.transform(df)
df_ | code |
106195648/cell_10 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
from sklearn.preprocessing import StandardScaler
df = pd.read_csv('../input/tabular-playground-series-jul-2022/data.csv')
df
from sklearn.cluster import KMeans
km = KMeans(init='random', random_state=0)
y_km = km.fit_predict(df)
km = KMeans(max_iter=300, n_init=10)
elbow_k = kelbow_visualizer(km, df, k=(2, 15))
kmeans = KMeans(n_clusters=5, n_init=10, max_iter=300)
label_pred = kmeans.fit_predict(df)
df_submit = pd.read_csv('../input/tabular-playground-series-jul-2022/sample_submission.csv')
df_submit['Predicted'] = label_pred
df_submit | code |
90148683/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_data = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
submission = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv')
test_data.head() | code |
90148683/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_data = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
submission = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv')
train_data = train_data.drop(['location'], axis=1)
test_data = test_data.drop(['location'], axis=1)
train_data.shape | code |
90148683/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 |
90148683/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_data = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
submission = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv')
submission.head() | code |
90148683/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_data = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
submission = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv')
print('Number of missing data for column keyword: ', train_data['keyword'].isna().sum())
print('Number of missing data for column location: ', train_data['location'].isna().sum())
print('Number of missing data for column text: ', train_data['text'].isna().sum())
print('Number of missing data for column target: ', train_data['target'].isna().sum()) | code |
90148683/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_data = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
submission = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv')
train_data = train_data.drop(['location'], axis=1)
test_data = test_data.drop(['location'], axis=1)
test_data.shape | code |
90148683/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_data = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
submission = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv')
train_data.head() | code |
122249638/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import cv2
import cv2
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import tensorflow as tf
path = '/kaggle/input/fruit-images-for-object-detection/train_zip/train'
fileslst = os.listdir(path)
imgs = []
for fle in fileslst:
if fle.endswith('.jpg'):
imgs.append(fle)
img = cv2.imread('/kaggle/input/fruit-images-for-object-detection/train_zip/train/' + imgs[1])
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
imgedg = []
imgedg0 = cv2.Canny(img[:, :, 0], 120, 200)
imgedg1 = cv2.Canny(img[:, :, 1], 120, 200)
imgedg2 = cv2.Canny(img[:, :, 2], 120, 200)
import numpy as np
import pandas as pd
import os
path = '/kaggle/input/fruit-images-for-object-detection/train_zip/train'
fileslst = os.listdir(path)
imgs = []
for fle in fileslst:
if fle.endswith('.jpg'):
imgs.append(fle)
imgar = []
y = pd.DataFrame(columns=['apl', 'ban', 'orn', 'mix'])
for i in range(len(imgs)):
img = cv2.imread('/kaggle/input/fruit-images-for-object-detection/train_zip/train/' + imgs[i])
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (28, 28))
imgar.append(np.array(img))
if imgs[i][0] == 'a':
y.loc[i, 'apl'] = 1
elif imgs[i][0] == 'b':
y.loc[i, 'ban'] = 1
elif imgs[i][0] == 'o':
y.loc[i, 'orn'] = 1
else:
y.loc[i, 'mix'] = 1
imgarr = np.array([imgar])
imgarr = np.reshape(imgarr, (240, 1, 28, 28, 3))
y = y.replace(np.nan, 0)
y = np.array([y])
y = np.reshape(y, (240, 4))
img = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
imgedg = []
imgedg0 = cv2.Canny(img[:, :, 0], 120, 200)
imgedg1 = cv2.Canny(img[:, :, 1], 120, 200)
imgedg2 = cv2.Canny(img[:, :, 2], 120, 200)
modl = tf.keras.Sequential()
modl.add(tf.keras.layers.InputLayer(input_shape=(1, 28, 28, 3)))
modl.add(tf.keras.layers.Conv2D(32, (3, 3), activation='relu'))
modl.add(tf.keras.layers.Flatten())
modl.add(tf.keras.layers.Dense(units=64, activation='relu'))
modl.add(tf.keras.layers.Dense(32, activation='relu'))
modl.add(tf.keras.layers.Dense(4, activation='softmax'))
modl.summary()
optm = tf.keras.optimizers.Adam(learning_rate=0.001)
modl.compile(loss='categorical_crossentropy', optimizer=optm, metrics='accuracy')
modl.fit(imgarr, y, epochs=50)
print(np.shape(imgarr))
yprd = modl.predict(np.array([imgarr[0]]))
print(yprd)
img = cv2.imread('/kaggle/input/fruit-images-for-object-detection/test_zip/test/' + 'banana_80.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (28, 28))
xnw = np.array([img])
yprd = modl.predict(xnw)
print(yprd) | code |
122249638/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import os
import os
path = '/kaggle/input/fruit-images-for-object-detection/train_zip/train'
fileslst = os.listdir(path)
imgs = []
for fle in fileslst:
if fle.endswith('.jpg'):
imgs.append(fle)
img = cv2.imread('/kaggle/input/fruit-images-for-object-detection/train_zip/train/' + imgs[1])
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
imgedg = []
imgedg0 = cv2.Canny(img[:, :, 0], 120, 200)
imgedg1 = cv2.Canny(img[:, :, 1], 120, 200)
imgedg2 = cv2.Canny(img[:, :, 2], 120, 200)
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 |
122249638/cell_6 | [
"text_plain_output_1.png"
] | import cv2
import cv2
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/fruit-images-for-object-detection/train_zip/train'
fileslst = os.listdir(path)
imgs = []
for fle in fileslst:
if fle.endswith('.jpg'):
imgs.append(fle)
img = cv2.imread('/kaggle/input/fruit-images-for-object-detection/train_zip/train/' + imgs[1])
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
imgedg = []
imgedg0 = cv2.Canny(img[:, :, 0], 120, 200)
imgedg1 = cv2.Canny(img[:, :, 1], 120, 200)
imgedg2 = cv2.Canny(img[:, :, 2], 120, 200)
import numpy as np
import pandas as pd
import os
path = '/kaggle/input/fruit-images-for-object-detection/train_zip/train'
fileslst = os.listdir(path)
imgs = []
for fle in fileslst:
if fle.endswith('.jpg'):
imgs.append(fle)
imgar = []
y = pd.DataFrame(columns=['apl', 'ban', 'orn', 'mix'])
print(fileslst[:20])
for i in range(len(imgs)):
img = cv2.imread('/kaggle/input/fruit-images-for-object-detection/train_zip/train/' + imgs[i])
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (28, 28))
imgar.append(np.array(img))
if imgs[i][0] == 'a':
y.loc[i, 'apl'] = 1
elif imgs[i][0] == 'b':
y.loc[i, 'ban'] = 1
elif imgs[i][0] == 'o':
y.loc[i, 'orn'] = 1
else:
y.loc[i, 'mix'] = 1
imgarr = np.array([imgar])
imgarr = np.reshape(imgarr, (240, 1, 28, 28, 3))
print(np.shape(imgarr))
y = y.replace(np.nan, 0)
y = np.array([y])
y = np.reshape(y, (240, 4))
print(y[0])
plt.imshow(img)
img = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
plt.subplot(231)
plt.imshow(img[:, :, 0])
plt.subplot(232)
plt.imshow(img[:, :, 1])
plt.subplot(233)
plt.imshow(img[:, :, 2])
imgedg = []
imgedg0 = cv2.Canny(img[:, :, 0], 120, 200)
imgedg1 = cv2.Canny(img[:, :, 1], 120, 200)
imgedg2 = cv2.Canny(img[:, :, 2], 120, 200)
plt.subplot(234)
plt.imshow(imgedg0)
plt.subplot(235)
plt.imshow(imgedg1)
plt.subplot(236)
plt.imshow(imgedg2) | code |
122249638/cell_2 | [
"text_plain_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import os
path = '/kaggle/input/fruit-images-for-object-detection/train_zip/train'
fileslst = os.listdir(path)
imgs = []
for fle in fileslst:
if fle.endswith('.jpg'):
imgs.append(fle)
img = cv2.imread('/kaggle/input/fruit-images-for-object-detection/train_zip/train/' + imgs[1])
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.imshow(img)
img = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
plt.subplot(231)
plt.imshow(img[:, :, 0])
plt.subplot(232)
plt.imshow(img[:, :, 1])
plt.subplot(233)
plt.imshow(img[:, :, 2])
imgedg = []
imgedg0 = cv2.Canny(img[:, :, 0], 120, 200)
imgedg1 = cv2.Canny(img[:, :, 1], 120, 200)
imgedg2 = cv2.Canny(img[:, :, 2], 120, 200)
plt.subplot(234)
plt.imshow(imgedg0)
plt.subplot(235)
plt.imshow(imgedg1)
plt.subplot(236)
plt.imshow(imgedg2) | code |
122249638/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import tensorflow as tf
import tensorflow as tf
modl = tf.keras.Sequential()
modl.add(tf.keras.layers.InputLayer(input_shape=(1, 28, 28, 3)))
modl.add(tf.keras.layers.Conv2D(32, (3, 3), activation='relu'))
modl.add(tf.keras.layers.Flatten())
modl.add(tf.keras.layers.Dense(units=64, activation='relu'))
modl.add(tf.keras.layers.Dense(32, activation='relu'))
modl.add(tf.keras.layers.Dense(4, activation='softmax'))
modl.summary()
optm = tf.keras.optimizers.Adam(learning_rate=0.001)
modl.compile(loss='categorical_crossentropy', optimizer=optm, metrics='accuracy') | code |
122249638/cell_8 | [
"text_plain_output_1.png"
] | import cv2
import cv2
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import tensorflow as tf
path = '/kaggle/input/fruit-images-for-object-detection/train_zip/train'
fileslst = os.listdir(path)
imgs = []
for fle in fileslst:
if fle.endswith('.jpg'):
imgs.append(fle)
img = cv2.imread('/kaggle/input/fruit-images-for-object-detection/train_zip/train/' + imgs[1])
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
imgedg = []
imgedg0 = cv2.Canny(img[:, :, 0], 120, 200)
imgedg1 = cv2.Canny(img[:, :, 1], 120, 200)
imgedg2 = cv2.Canny(img[:, :, 2], 120, 200)
import numpy as np
import pandas as pd
import os
path = '/kaggle/input/fruit-images-for-object-detection/train_zip/train'
fileslst = os.listdir(path)
imgs = []
for fle in fileslst:
if fle.endswith('.jpg'):
imgs.append(fle)
imgar = []
y = pd.DataFrame(columns=['apl', 'ban', 'orn', 'mix'])
for i in range(len(imgs)):
img = cv2.imread('/kaggle/input/fruit-images-for-object-detection/train_zip/train/' + imgs[i])
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (28, 28))
imgar.append(np.array(img))
if imgs[i][0] == 'a':
y.loc[i, 'apl'] = 1
elif imgs[i][0] == 'b':
y.loc[i, 'ban'] = 1
elif imgs[i][0] == 'o':
y.loc[i, 'orn'] = 1
else:
y.loc[i, 'mix'] = 1
imgarr = np.array([imgar])
imgarr = np.reshape(imgarr, (240, 1, 28, 28, 3))
y = y.replace(np.nan, 0)
y = np.array([y])
y = np.reshape(y, (240, 4))
img = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
imgedg = []
imgedg0 = cv2.Canny(img[:, :, 0], 120, 200)
imgedg1 = cv2.Canny(img[:, :, 1], 120, 200)
imgedg2 = cv2.Canny(img[:, :, 2], 120, 200)
modl = tf.keras.Sequential()
modl.add(tf.keras.layers.InputLayer(input_shape=(1, 28, 28, 3)))
modl.add(tf.keras.layers.Conv2D(32, (3, 3), activation='relu'))
modl.add(tf.keras.layers.Flatten())
modl.add(tf.keras.layers.Dense(units=64, activation='relu'))
modl.add(tf.keras.layers.Dense(32, activation='relu'))
modl.add(tf.keras.layers.Dense(4, activation='softmax'))
modl.summary()
optm = tf.keras.optimizers.Adam(learning_rate=0.001)
modl.compile(loss='categorical_crossentropy', optimizer=optm, metrics='accuracy')
modl.fit(imgarr, y, epochs=50) | code |
122249638/cell_10 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
import cv2
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import tensorflow as tf
path = '/kaggle/input/fruit-images-for-object-detection/train_zip/train'
fileslst = os.listdir(path)
imgs = []
for fle in fileslst:
if fle.endswith('.jpg'):
imgs.append(fle)
img = cv2.imread('/kaggle/input/fruit-images-for-object-detection/train_zip/train/' + imgs[1])
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
imgedg = []
imgedg0 = cv2.Canny(img[:, :, 0], 120, 200)
imgedg1 = cv2.Canny(img[:, :, 1], 120, 200)
imgedg2 = cv2.Canny(img[:, :, 2], 120, 200)
import numpy as np
import pandas as pd
import os
path = '/kaggle/input/fruit-images-for-object-detection/train_zip/train'
fileslst = os.listdir(path)
imgs = []
for fle in fileslst:
if fle.endswith('.jpg'):
imgs.append(fle)
imgar = []
y = pd.DataFrame(columns=['apl', 'ban', 'orn', 'mix'])
for i in range(len(imgs)):
img = cv2.imread('/kaggle/input/fruit-images-for-object-detection/train_zip/train/' + imgs[i])
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (28, 28))
imgar.append(np.array(img))
if imgs[i][0] == 'a':
y.loc[i, 'apl'] = 1
elif imgs[i][0] == 'b':
y.loc[i, 'ban'] = 1
elif imgs[i][0] == 'o':
y.loc[i, 'orn'] = 1
else:
y.loc[i, 'mix'] = 1
imgarr = np.array([imgar])
imgarr = np.reshape(imgarr, (240, 1, 28, 28, 3))
y = y.replace(np.nan, 0)
y = np.array([y])
y = np.reshape(y, (240, 4))
img = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
imgedg = []
imgedg0 = cv2.Canny(img[:, :, 0], 120, 200)
imgedg1 = cv2.Canny(img[:, :, 1], 120, 200)
imgedg2 = cv2.Canny(img[:, :, 2], 120, 200)
modl = tf.keras.Sequential()
modl.add(tf.keras.layers.InputLayer(input_shape=(1, 28, 28, 3)))
modl.add(tf.keras.layers.Conv2D(32, (3, 3), activation='relu'))
modl.add(tf.keras.layers.Flatten())
modl.add(tf.keras.layers.Dense(units=64, activation='relu'))
modl.add(tf.keras.layers.Dense(32, activation='relu'))
modl.add(tf.keras.layers.Dense(4, activation='softmax'))
modl.summary()
optm = tf.keras.optimizers.Adam(learning_rate=0.001)
modl.compile(loss='categorical_crossentropy', optimizer=optm, metrics='accuracy')
modl.fit(imgarr, y, epochs=50)
yprd = modl.predict(np.array([imgarr[0]]))
img = cv2.imread('/kaggle/input/fruit-images-for-object-detection/test_zip/test/' + 'banana_80.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (28, 28))
xnw = np.array([img])
yprd = modl.predict(xnw)
modl.predict() | code |
105184129/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df1 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df1.isna().sum()
ax=sns.barplot(x=list(df1['track_id'].unique()),y=list(df1['track_id'].value_counts()))
for i in ax.containers:
ax.bar_label(i,)
ax=sns.barplot(x=list(df1['track_id'].unique()),y=list(df1.groupby(['track_id'])['program_number'].nunique()))
for i in ax.containers:
ax.bar_label(i,)
ax = sns.barplot(x=list(df1['track_id'].unique()), y=list(df1.groupby(['track_id'])['race_number'].nunique()))
for i in ax.containers:
ax.bar_label(i) | code |
105184129/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df1 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df1.isna().sum()
ax = sns.barplot(x=list(df1['track_id'].unique()), y=list(df1['track_id'].value_counts()))
for i in ax.containers:
ax.bar_label(i) | code |
105184129/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df1.isna().sum() | code |
105184129/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df1 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df1.isna().sum()
ax=sns.barplot(x=list(df1['track_id'].unique()),y=list(df1['track_id'].value_counts()))
for i in ax.containers:
ax.bar_label(i,)
ax = sns.barplot(x=list(df1['track_id'].unique()), y=list(df1.groupby(['track_id'])['program_number'].nunique()))
for i in ax.containers:
ax.bar_label(i) | code |
105184129/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))
import seaborn as sns | code |
105184129/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df1.isna().sum()
df1['track_id'].unique() | code |
105184129/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df1 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df1.isna().sum()
ax=sns.barplot(x=list(df1['track_id'].unique()),y=list(df1['track_id'].value_counts()))
for i in ax.containers:
ax.bar_label(i,)
ax=sns.barplot(x=list(df1['track_id'].unique()),y=list(df1.groupby(['track_id'])['program_number'].nunique()))
for i in ax.containers:
ax.bar_label(i,)
ax=sns.barplot(x=list(df1['track_id'].unique()),y=list(df1.groupby(['track_id'])['race_number'].nunique()))
for i in ax.containers:
ax.bar_label(i,)
ax = sns.barplot(x=list(df1['track_id'].unique()), y=list(df1.groupby(['track_id'])['race_date'].nunique()))
for i in ax.containers:
ax.bar_label(i) | code |
105184129/cell_17 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df1 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df1.isna().sum()
ax=sns.barplot(x=list(df1['track_id'].unique()),y=list(df1['track_id'].value_counts()))
for i in ax.containers:
ax.bar_label(i,)
ax=sns.barplot(x=list(df1['track_id'].unique()),y=list(df1.groupby(['track_id'])['program_number'].nunique()))
for i in ax.containers:
ax.bar_label(i,)
ax=sns.barplot(x=list(df1['track_id'].unique()),y=list(df1.groupby(['track_id'])['race_number'].nunique()))
for i in ax.containers:
ax.bar_label(i,)
ax=sns.barplot(x=list(df1['track_id'].unique()),y=list(df1.groupby(['track_id'])['race_date'].nunique()))
for i in ax.containers:
ax.bar_label(i,)
ax = sns.barplot(x=list(df1['track_id'].unique()), y=list(df1.groupby(['track_id'])['jockey'].nunique()))
for i in ax.containers:
ax.bar_label(i) | code |
105184129/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df1.isna().sum()
df1.head() | code |
1007792/cell_4 | [
"image_output_1.png"
] | from sklearn import cluster
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import time
import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn import cluster
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import StandardScaler
from pylab import rcParams
np.random.seed(0)
n_samples = 500
colors = np.array([x for x in 'bgrcmykbgrcmykbgrcmykbgrcmyk'])
colors = np.hstack([colors] * 20)
datasets = [[[0, 0]], [[0, 0]], [[0, 0]], [[0, 0]]]
mu = 0.3
centers = [[(0, 0.45), (0.9, 0.5), (0.45, 0.9), (0.45, 0)], [(0, 0.2), (0.8, 0), (0.2, 1), (1, 0.8)], [(0, 0), (0.9, 0.9), (0, 0.9), (0.9, 0)], [(0, 0), (0.9, 0), (0.45, 0.779), (0.45, 0.259)]]
for i, c in enumerate(centers):
for x, y in c:
num1 = np.random.normal(x, mu, n_samples)
num2 = np.random.normal(y, mu, n_samples)
nums = np.vstack((num1, num2)).T
datasets[i] = np.vstack((datasets[i], nums))
datasets = list(zip(datasets, ['a', 'b', 'c', 'd']))
plot_num = 1
for (X, lbl), c in zip(datasets, centers):
center_colors = colors[:len(centers)]
plt.xlim(-1, 2)
plt.ylim(-1, 2)
plt.xticks(())
plt.yticks(())
plt.text(0.01, 0.01, lbl, transform=plt.gca().transAxes, size=15, horizontalalignment='left')
plot_num += 1
plot_num = 1
for i_dataset, dataset in enumerate(datasets):
X, lbl = dataset
X = StandardScaler().fit_transform(X)
two_means = cluster.MiniBatchKMeans(n_clusters=4)
algorithm = two_means
name = 'MiniBatchKMeans'
t0 = time.time()
algorithm.fit(X)
t1 = time.time()
if hasattr(algorithm, 'labels_'):
y_pred = algorithm.labels_.astype(np.int)
else:
y_pred = algorithm.predict(X)
if hasattr(algorithm, 'cluster_centers_'):
centers = algorithm.cluster_centers_
center_colors = colors[:len(centers)]
plt.xlim(-2, 2)
plt.ylim(-2, 2)
plt.xticks(())
plt.yticks(())
plt.text(0.99, 0.01, ('%.2fs' % (t1 - t0)).lstrip('0'), transform=plt.gca().transAxes, size=15, horizontalalignment='right')
plt.text(0.01, 0.01, lbl, transform=plt.gca().transAxes, size=15, horizontalalignment='left')
plot_num += 1
plot_num = 1
for i_dataset, dataset in enumerate(datasets):
X, y = dataset
X = StandardScaler().fit_transform(X)
bandwidth = cluster.estimate_bandwidth(X, quantile=0.3)
ms = cluster.MeanShift(bandwidth=bandwidth, bin_seeding=True)
algorithm = ms
name = 'MeanShift'
t0 = time.time()
algorithm.fit(X)
t1 = time.time()
if hasattr(algorithm, 'labels_'):
y_pred = algorithm.labels_.astype(np.int)
else:
y_pred = algorithm.predict(X)
plt.subplot(2, 2, plot_num)
if i_dataset == 0:
plt.title(name, size=18)
plt.scatter(X[:, 0], X[:, 1], color=colors[y_pred].tolist(), s=10)
if hasattr(algorithm, 'cluster_centers_'):
centers = algorithm.cluster_centers_
center_colors = colors[:len(centers)]
plt.scatter(centers[:, 0], centers[:, 1], s=100, c=center_colors)
plt.xlim(-2, 2)
plt.ylim(-2, 2)
plt.xticks(())
plt.yticks(())
plt.text(0.99, 0.01, ('%.2fs' % (t1 - t0)).lstrip('0'), transform=plt.gca().transAxes, size=15, horizontalalignment='right')
plt.text(0.01, 0.01, lbl, transform=plt.gca().transAxes, size=15, horizontalalignment='left')
plot_num += 1
plt.show() | code |
1007792/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn import cluster
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import StandardScaler
from pylab import rcParams
np.random.seed(0)
n_samples = 500
colors = np.array([x for x in 'bgrcmykbgrcmykbgrcmykbgrcmyk'])
colors = np.hstack([colors] * 20)
datasets = [[[0, 0]], [[0, 0]], [[0, 0]], [[0, 0]]]
mu = 0.3
centers = [[(0, 0.45), (0.9, 0.5), (0.45, 0.9), (0.45, 0)], [(0, 0.2), (0.8, 0), (0.2, 1), (1, 0.8)], [(0, 0), (0.9, 0.9), (0, 0.9), (0.9, 0)], [(0, 0), (0.9, 0), (0.45, 0.779), (0.45, 0.259)]]
for i, c in enumerate(centers):
for x, y in c:
num1 = np.random.normal(x, mu, n_samples)
num2 = np.random.normal(y, mu, n_samples)
nums = np.vstack((num1, num2)).T
datasets[i] = np.vstack((datasets[i], nums))
datasets = list(zip(datasets, ['a', 'b', 'c', 'd']))
plot_num = 1
for (X, lbl), c in zip(datasets, centers):
plt.subplot(2, 2, plot_num)
if i_dataset == 0:
plt.title(name, size=18)
plt.scatter(X[:, 0], X[:, 1], s=10)
center_colors = colors[:len(centers)]
plt.scatter(list(zip(*c))[0], list(zip(*c))[1], s=100, c=center_colors)
plt.xlim(-1, 2)
plt.ylim(-1, 2)
plt.xticks(())
plt.yticks(())
plt.text(0.01, 0.01, lbl, transform=plt.gca().transAxes, size=15, horizontalalignment='left')
plot_num += 1
plt.show() | code |
1007792/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn import cluster
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import StandardScaler
from pylab import rcParams
np.random.seed(0)
n_samples = 500
colors = np.array([x for x in 'bgrcmykbgrcmykbgrcmykbgrcmyk'])
colors = np.hstack([colors] * 20)
plt.figure(figsize=((len(clustering_names) * 2 + 3) * 2, 9.5 * 2))
plt.subplots_adjust(left=0.02, right=0.98, bottom=0.001, top=0.96, wspace=0.05, hspace=0.01) | code |
1007792/cell_3 | [
"image_output_1.png"
] | from sklearn import cluster
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import time
import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn import cluster
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import StandardScaler
from pylab import rcParams
np.random.seed(0)
n_samples = 500
colors = np.array([x for x in 'bgrcmykbgrcmykbgrcmykbgrcmyk'])
colors = np.hstack([colors] * 20)
datasets = [[[0, 0]], [[0, 0]], [[0, 0]], [[0, 0]]]
mu = 0.3
centers = [[(0, 0.45), (0.9, 0.5), (0.45, 0.9), (0.45, 0)], [(0, 0.2), (0.8, 0), (0.2, 1), (1, 0.8)], [(0, 0), (0.9, 0.9), (0, 0.9), (0.9, 0)], [(0, 0), (0.9, 0), (0.45, 0.779), (0.45, 0.259)]]
for i, c in enumerate(centers):
for x, y in c:
num1 = np.random.normal(x, mu, n_samples)
num2 = np.random.normal(y, mu, n_samples)
nums = np.vstack((num1, num2)).T
datasets[i] = np.vstack((datasets[i], nums))
datasets = list(zip(datasets, ['a', 'b', 'c', 'd']))
plot_num = 1
for (X, lbl), c in zip(datasets, centers):
center_colors = colors[:len(centers)]
plt.xlim(-1, 2)
plt.ylim(-1, 2)
plt.xticks(())
plt.yticks(())
plt.text(0.01, 0.01, lbl, transform=plt.gca().transAxes, size=15, horizontalalignment='left')
plot_num += 1
plot_num = 1
for i_dataset, dataset in enumerate(datasets):
X, lbl = dataset
X = StandardScaler().fit_transform(X)
two_means = cluster.MiniBatchKMeans(n_clusters=4)
algorithm = two_means
name = 'MiniBatchKMeans'
t0 = time.time()
algorithm.fit(X)
t1 = time.time()
if hasattr(algorithm, 'labels_'):
y_pred = algorithm.labels_.astype(np.int)
else:
y_pred = algorithm.predict(X)
plt.subplot(2, 2, plot_num)
if i_dataset == 0:
plt.title(name, size=18)
plt.scatter(X[:, 0], X[:, 1], color=colors[y_pred].tolist(), s=10)
if hasattr(algorithm, 'cluster_centers_'):
centers = algorithm.cluster_centers_
center_colors = colors[:len(centers)]
plt.scatter(centers[:, 0], centers[:, 1], s=100, c=center_colors)
plt.xlim(-2, 2)
plt.ylim(-2, 2)
plt.xticks(())
plt.yticks(())
plt.text(0.99, 0.01, ('%.2fs' % (t1 - t0)).lstrip('0'), transform=plt.gca().transAxes, size=15, horizontalalignment='right')
plt.text(0.01, 0.01, lbl, transform=plt.gca().transAxes, size=15, horizontalalignment='left')
plot_num += 1
plt.show() | code |
129018650/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from IPython.display import clear_output
from tensorflow.keras import layers, models, optimizers, losses
import chess
import numpy as np
import chess
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers, models, optimizers, losses
from tensorflow.keras.layers import LeakyReLU
import concurrent.futures
from IPython.display import clear_output
import time
def create_chess_model():
inputs = layers.Input(shape=(12, 8, 8))
x = layers.Conv2D(32, (3, 3), padding='same', activation='relu')(inputs)
x = layers.Conv2D(64, (3, 3), padding='same', activation='relu')(x)
x = layers.Conv2D(8, (3, 3), padding='same', activation='relu')(x)
x = layers.Attention(use_scale=True)([x, inputs])
x = layers.Flatten()(x)
x = layers.Dense(256, activation='relu')(x)
x = layers.Dense(256, activation='relu')(x)
outputs = layers.Dense(1)(x)
model = models.Model(inputs=inputs, outputs=outputs)
model.compile(optimizer=optimizers.Adam(learning_rate=0.001), loss=losses.MeanSquaredError())
return model
def board_to_tensor(board):
piece_map = board.piece_map()
tensor = np.zeros((12, 8, 8), dtype=np.float32)
for pos, piece in piece_map.items():
x, y = (pos % 8, pos // 8)
piece_index = piece.piece_type - 1 + (6 if piece.color == chess.BLACK else 0)
tensor[piece_index, x, y] = 1
return tensor[np.newaxis, :, :, :]
def evaluate(board, model):
tensor = board_to_tensor(board)
if model == chess_model_black:
evaluations = -model(tensor).numpy().item()
if model == chess_model_white:
evaluations = model(tensor).numpy().item()
return evaluations
def select_move(board, model, temperature=0.2):
legal_moves = list(board.legal_moves)
scores = []
for move in legal_moves:
board.push(move)
scores.append(evaluate(board, model))
board.pop()
probs = np.exp(np.array(scores) / temperature)
probs /= probs.sum()
move_index = np.random.choice(len(legal_moves), p=probs)
return legal_moves[move_index]
def play_single_game(model_white, model_black, game_id):
board = chess.Board()
game_moves = []
game_values = []
while not board.is_game_over():
model = model_white if board.turn == chess.WHITE else model_black
move = select_move(board, model)
value = evaluate(board, model)
game_moves.append(move)
game_values.append(value)
board.push(move)
yield (game_id, board)
result = board.result()
target_values = np.zeros(len(game_values))
if result == '1-0':
target_values[-1] = 100
elif result == '0-1':
target_values[-1] = -100
elif result == '1/2-1/2':
target_values[-1] = 0
for i in range(len(game_values) - 2, -1, -2):
target_values[i] = -target_values[i + 1]
train_data = []
temp_board = chess.Board()
for move in game_moves:
temp_board.push(move)
train_data.append(board_to_tensor(temp_board))
train_data = np.vstack(train_data)
yield (game_id, train_data, target_values)
def train_self_play(model_white, model_black, num_games=2, num_epochs=10000, num_workers=2):
scores = {'white_wins': 0, 'black_wins': 0, 'draws': 0}
for epoch in range(num_epochs):
executor = concurrent.futures.ThreadPoolExecutor(max_workers=num_workers)
game_progress = [play_single_game(model_white, model_black, game_id) for game_id in range(num_games)]
finished_games = 0
while finished_games < num_games:
clear_output(wait=True)
total_games = scores['white_wins'] + scores['black_wins'] + scores['draws']
white_win_rate = 0
black_win_rate = 0
if scores['white_wins'] > 0:
white_win_rate = scores['white_wins'] / total_games * 100
if scores['black_wins'] > 0:
black_win_rate = scores['black_wins'] / total_games * 100
print(f'Epoch {epoch + 1}/{num_epochs}')
print(f'Finished games: {finished_games}/{num_games}')
print('Scores after epoch {}: {}'.format(epoch + 1, scores))
print(f'White win rate: {white_win_rate:.2f}%')
print(f'Black win rate: {black_win_rate:.2f}%')
print()
displayed_game = False
for game_id, game_gen in enumerate(game_progress):
if game_gen is None:
continue
try:
game_state = next(game_gen)
if len(game_state) == 3:
game_id, train_data, target_values = game_state
model_white.fit(train_data[::2], target_values[::2], batch_size=len(target_values[::2]), verbose=0)
model_black.fit(train_data[1::2], target_values[1::2], batch_size=len(target_values[1::2]), verbose=0)
last_target_value = target_values[-1]
if last_target_value == 1:
scores['white_wins'] += 1
elif last_target_value == -1:
scores['black_wins'] += 1
else:
scores['draws'] += 1
game_progress[game_id] = None
finished_games += 1
elif game_id < 3 and (not displayed_game):
game_id, board = game_state
print(f'Game {game_id}:')
print(board)
print()
displayed_game = True
except StopIteration:
game_progress[game_id] = None
executor.shutdown(wait=True)
if __name__ == '__main__':
chess_model_white = create_chess_model()
chess_model_black = create_chess_model()
train_self_play(chess_model_white, chess_model_black) | code |
129018650/cell_1 | [
"text_plain_output_1.png"
] | !pip install chess | code |
73067804/cell_6 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_9.png",
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_6.png",
"application_vnd.jupyter.stderr_output_8.png",
"application_vnd.jupyter.stderr_output_10.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_1.png",
"text_plain_output_11.png"
] | from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import OrdinalEncoder
from xgboost import XGBRegressor
import pandas as pd
train = pd.read_csv('../input/train10fold/train-folds (1).csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
submission_data = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
features = [col for col in train.columns if col not in ('id', 'target', 'kfold')]
object_cols = [col for col in features if 'cat' in col]
ordinal_encoder = OrdinalEncoder()
xtest = test[features]
xtest = xtest.copy()
xtest[object_cols] = ordinal_encoder.fit_transform(xtest[object_cols])
final_preds = []
for fold in range(5):
xtrain = train[train.kfold != fold].reset_index(drop=True)
xvalid = train[train.kfold == fold].reset_index(drop=True)
ytrain = xtrain.target
yvalid = xvalid.target
xtrain = xtrain[features]
xvalid = xvalid[features]
xtrain[object_cols] = ordinal_encoder.fit_transform(xtrain[object_cols])
xvalid[object_cols] = ordinal_encoder.fit_transform(xvalid[object_cols])
best_params = {'learning_rate': 0.07853392035787837, 'colsample_bytree': 0.170759104940733, 'max_depth': 3, 'reg_lambda': 1.7549293092194938e-05, 'reg_alpha': 14.68267919457715, 'subsample': 0.8031450486786944, 'alpha': 30}
model = XGBRegressor(objective='reg:squarederror', n_estimators=5000, random_state=0, **best_params)
model.fit(xtrain, ytrain, early_stopping_rounds=300, eval_set=[(xvalid, yvalid)], verbose=1000)
preds_valid = model.predict(xvalid)
test_preds = model.predict(xtest)
final_preds.append(test_preds)
print(fold, mean_squared_error(yvalid, preds_valid, squared=False)) | code |
73067804/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train10fold/train-folds (1).csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
submission_data = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.head() | code |
73067804/cell_5 | [
"text_plain_output_1.png"
] | """def run(trial):
#optimize in one fold
fold = 0
xtrain = train[train.kfold != fold].reset_index(drop=True)
xvalid = train[train.kfold == fold].reset_index(drop=True)
ytrain = xtrain.target
yvalid = xvalid.target
xtrain = xtrain[features]
xvalid = xvalid[features]
xtrain[object_cols]= ordinal_encoder.fit_transform(xtrain[object_cols])
xvalid[object_cols] = ordinal_encoder.fit_transform(xvalid[object_cols])
learning_rate = trial.suggest_float("learning_rate", 1e-2, 0.8, log=True)
colsample_bytree = trial.suggest_float('colsample_bytree', 0.1, 0.6)
max_depth = trial.suggest_int('max_depth', 1, 9)
subsample = trial.suggest_float('subsample', 0.1, 0.6)
reg_lambda = trial.suggest_float('reg_lambda', 1e-5, 100.0)
reg_alpha = trial.suggest_float('reg_alpha', 1e-5, 100.0)
alpha = trial.suggest_int('alpha', 0, 100)
model = XGBRegressor(random_state = 0,
alpha=alpha,
n_estimators=200,
tree_method='gpu_hist',
gpu_id=0, predictor='gpu_predictor',
learning_rate = learning_rate,
colsample_bytree = colsample_bytree,
max_depth = max_depth,
subsample = subsample,
reg_lambda = reg_lambda,
reg_alpha = reg_alpha)
model.fit(xtrain, ytrain)
preds_valid = model.predict(xvalid)
rmse = mean_squared_error(yvalid, preds_valid, squared=False)
return rmse
study = optuna.create_study(direction="minimize")
study.optimize(run, n_trials=5000)
#study.best_params
final_preds = []
for fold in range(5):
xtrain = train[train.kfold != fold].reset_index(drop=True)
xvalid = train[train.kfold == fold].reset_index(drop=True)
ytrain = xtrain.target
yvalid = xvalid.target
xtrain = xtrain[features]
xvalid = xvalid[features]
xtrain[object_cols]= ordinal_encoder.fit_transform(xtrain[object_cols])
xvalid[object_cols] = ordinal_encoder.fit_transform(xvalid[object_cols])
best_params = {'learning_rate': 0.34090767065203226,
'colsample_bytree': 0.12289350813119115,
'max_depth': 7,
'reg_lambda': 5.830490094721956,
'reg_alpha': 49.68136144185203,
'alpha': 30
}
model = XGBRegressor(objective='reg:squarederror',
n_estimators=200,
random_state=0,
**best_params
#tree_method='gpu_hist',
#gpu_id=0,
#predictor='gpu_predictor'
)
model.fit(xtrain, ytrain)
preds_valid = model.predict(xvalid)
test_preds = model.predict(xtest)
final_preds.append(test_preds)
print(fold, mean_squared_error(yvalid, preds_valid, squared=False))
""" | code |
17099787/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import Lasso
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error,mean_absolute_error
from sklearn.neighbors import KNeighborsRegressor
from xgboost import XGBRegressor
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def Model_Comparision_Train_Test(AllModels, x_train, y_train, x_test, y_test):
return_df = pd.DataFrame(columns=['Model', 'MSE', 'RMSE', 'MAE'])
for myModel in AllModels:
myModel.fit(x_train, y_train)
y_pred_train = myModel.predict(x_train)
mse_train, rmse_train, mae_train = extract_metrics_from_predicted(y_train, y_pred_train)
y_pred_test = myModel.predict(x_test)
mse_test, rmse_test, mae_test = extract_metrics_from_predicted(y_test, y_pred_test)
summary = pd.DataFrame([[type(myModel).__name__, ''.join([str(round(mse_test, 3)), '(', str(round(mse_train, 3)), ')']), ''.join([str(round(rmse_test, 3)), '(', str(round(rmse_train, 3)), ')']), ''.join([str(round(mae_test, 3)), '(', str(round(mae_test, 3)), ')'])]], columns=['Model', 'MSE', 'RMSE', 'MAE'])
return_df = pd.concat([return_df, summary], axis=0)
return_df.set_index('Model', inplace=True)
return return_df
def extract_metrics_from_predicted(y_true, y_pred):
from sklearn.metrics import mean_squared_error, mean_absolute_error
mse = mean_squared_error(y_true, y_pred)
rmse = np.sqrt(mse)
mae = mean_absolute_error(y_true, y_pred)
return (mse, rmse, mae)
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Lasso
from sklearn.linear_model import Ridge
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import AdaBoostRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.ensemble import RandomForestRegressor
from xgboost import XGBRegressor
Lasso = Lasso()
Ridge = Ridge()
KNNR = KNeighborsRegressor()
RFR = RandomForestRegressor(bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=500)
XgbR = XGBRegressor(colsample_bytree=0.9, learning_rate=0.4, n_estimators=500, reg_alpha=0.4)
skLearn_Model_Comparision_Train_Test([KNNR, RFR, XgbR, Lasso, Ridge], X_train, np.ravel(y_train), X_test, np.ravel(y_test)) | code |
17099787/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17099787/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import Lasso
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error,mean_absolute_error
from sklearn.neighbors import KNeighborsRegressor
from xgboost import XGBRegressor
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def Model_Comparision_Train_Test(AllModels, x_train, y_train, x_test, y_test):
return_df = pd.DataFrame(columns=['Model', 'MSE', 'RMSE', 'MAE'])
for myModel in AllModels:
myModel.fit(x_train, y_train)
y_pred_train = myModel.predict(x_train)
mse_train, rmse_train, mae_train = extract_metrics_from_predicted(y_train, y_pred_train)
y_pred_test = myModel.predict(x_test)
mse_test, rmse_test, mae_test = extract_metrics_from_predicted(y_test, y_pred_test)
summary = pd.DataFrame([[type(myModel).__name__, ''.join([str(round(mse_test, 3)), '(', str(round(mse_train, 3)), ')']), ''.join([str(round(rmse_test, 3)), '(', str(round(rmse_train, 3)), ')']), ''.join([str(round(mae_test, 3)), '(', str(round(mae_test, 3)), ')'])]], columns=['Model', 'MSE', 'RMSE', 'MAE'])
return_df = pd.concat([return_df, summary], axis=0)
return_df.set_index('Model', inplace=True)
return return_df
def extract_metrics_from_predicted(y_true, y_pred):
from sklearn.metrics import mean_squared_error, mean_absolute_error
mse = mean_squared_error(y_true, y_pred)
rmse = np.sqrt(mse)
mae = mean_absolute_error(y_true, y_pred)
return (mse, rmse, mae)
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Lasso
from sklearn.linear_model import Ridge
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import AdaBoostRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.ensemble import RandomForestRegressor
from xgboost import XGBRegressor
Lasso = Lasso()
Ridge = Ridge()
KNNR = KNeighborsRegressor()
RFR = RandomForestRegressor(bootstrap=True, max_depth=80, max_features=3, min_samples_leaf=3, min_samples_split=8, n_estimators=500)
XgbR = XGBRegressor(colsample_bytree=0.9, learning_rate=0.4, n_estimators=500, reg_alpha=0.4)
skLearn_Model_Comparision_Train_Test([KNNR, RFR, XgbR, Lasso, Ridge], X_train, np.ravel(y_train), X_test, np.ravel(y_test))
def mape(y_true, y_pred):
y_true, y_pred = (np.array(y_true), np.array(y_pred))
return np.mean(np.abs((y_true - y_pred) / y_true)) * 100
print('mape:', mape(y_test, preds_test)) | code |
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