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320604/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
c.execute(s)
result = c.fetchall()
conn.close()
return result
def print_details():
"""Print database details including table names and the number of rows.
"""
table_names = sql_query('SELECT name FROM sqlite_master ' + "WHERE type='table' " + 'ORDER BY name;')[0][0]
num_rows = sql_query('SELECT COUNT(*) FROM loan;')[0][0]
def print_column_names():
"""Print the column names in the 'loan' table.
Note that the "index" column name is specific to Python and is not part of
the original SQLite database.
"""
conn = sqlite3.connect('../input/database.sqlite')
conn.row_factory = sqlite3.Row
c = conn.cursor()
c.execute('SELECT * FROM loan LIMIT 2;')
r = c.fetchone()
i = 1
for k in r.keys():
i += 1
conn.close()
emp_length_dict = {'n/a': 0, '< 1 year': 0, '1 year': 1, '2 years': 2, '3 years': 3, '4 years': 4, '5 years': 5, '6 years': 6, '7 years': 7, '8 years': 8, '9 years': 9, '10+ years': 10}
home_ownership_dict = {'MORTGAGE': 0, 'OWN': 1, 'RENT': 2, 'OTHER': 3, 'NONE': 4, 'ANY': 5}
features_dict = {'loan_amnt': 0, 'int_rate': 1, 'annual_inc': 2, 'delinq_2yrs': 3, 'open_acc': 4, 'dti': 5, 'emp_length': 6, 'funded_amnt': 7, 'tot_cur_bal': 8, 'home_ownership': 9}
def get_data(s):
"""Return features and targets for a specific search term.
Arguments:
s (str) -- string to search for in loan "title" field
Returns:
(list of lists) -- [list of feature tuples, list of targets]
(features) -- [(sample1 features), (sample2 features),...]
(target) -- [sample1 target, sample2 target,...]
"""
data = sql_query('SELECT ' + 'loan_amnt,int_rate,annual_inc,' + 'loan_status,title,delinq_2yrs,' + 'open_acc,dti,emp_length,' + 'funded_amnt,tot_cur_bal,home_ownership ' + 'FROM loan ' + "WHERE application_type='INDIVIDUAL';")
features_list = []
target_list = []
n = 0
n0 = 0
n1 = 0
for d in data:
test0 = isinstance(d[0], float)
test1 = isinstance(d[1], str)
test2 = isinstance(d[2], float)
test3 = isinstance(d[3], str)
test4 = isinstance(d[4], str)
test5 = isinstance(d[5], float)
test6 = isinstance(d[6], float)
test7 = isinstance(d[7], float)
test8 = isinstance(d[8], str)
test9 = isinstance(d[9], float)
test10 = isinstance(d[10], float)
if test0 and test1 and test2 and test3 and test4 and test5 and test6 and test7 and test8 and test9 and test10:
try:
d1_float = float(d[1].replace('%', ''))
except:
continue
try:
e = emp_length_dict[d[8]]
except:
continue
try:
h = home_ownership_dict[d[11]]
except:
continue
if s.lower() in d[4].lower():
if d[3] == 'Fully Paid' or d[3] == 'Current':
target = 0
n += 1
n0 += 1
elif 'Late' in d[3] or d[3] == 'Charged Off':
target = 1
n += 1
n1 += 1
else:
continue
features = (d[0], float(d[1].replace('%', '')), d[2], d[5], d[6], d[7], emp_length_dict[d[8]], d[9], d[10], home_ownership_dict[d[11]])
features_list.append(features)
target_list.append(target)
else:
pass
result = [features_list, target_list]
return result
def create_scatter_plot(x0_data, y0_data, x1_data, y1_data, pt, pa, x_label, y_label, axis_type):
ax = plt.gca()
ax.set_axis_bgcolor('#BBBBBB')
ax.set_axisbelow(True)
plt.axis(pa)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
if axis_type == 'semilogx':
plt.semilogx(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogx(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'semilogy':
plt.semilogy(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogy(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'loglog':
plt.loglog(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.loglog(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
plt.clf()
def plot_two_fields(data, s, f1, f2, pa, x_label, y_label, axis_type):
x0_list = []
y0_list = []
x1_list = []
y1_list = []
features_list = data[0]
target_list = data[1]
for i in range(len(features_list)):
x = features_list[i][features_dict[f1]]
y = features_list[i][features_dict[f2]]
if target_list[i] == 0:
x0_list.append(x)
y0_list.append(y)
elif target_list[i] == 1:
x1_list.append(x)
y1_list.append(y)
else:
pass
cc_data = get_data('credit card')
plot_two_fields(cc_data, 'credit card', 'loan_amnt', 'int_rate', [100.0, 100000.0, 5.0, 30.0], 'loan amount', 'interest rate', 'semilogx') | code |
320604/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
c.execute(s)
result = c.fetchall()
conn.close()
return result
def print_details():
"""Print database details including table names and the number of rows.
"""
table_names = sql_query('SELECT name FROM sqlite_master ' + "WHERE type='table' " + 'ORDER BY name;')[0][0]
num_rows = sql_query('SELECT COUNT(*) FROM loan;')[0][0]
def print_column_names():
"""Print the column names in the 'loan' table.
Note that the "index" column name is specific to Python and is not part of
the original SQLite database.
"""
conn = sqlite3.connect('../input/database.sqlite')
conn.row_factory = sqlite3.Row
c = conn.cursor()
c.execute('SELECT * FROM loan LIMIT 2;')
r = c.fetchone()
i = 1
for k in r.keys():
i += 1
conn.close()
emp_length_dict = {'n/a': 0, '< 1 year': 0, '1 year': 1, '2 years': 2, '3 years': 3, '4 years': 4, '5 years': 5, '6 years': 6, '7 years': 7, '8 years': 8, '9 years': 9, '10+ years': 10}
home_ownership_dict = {'MORTGAGE': 0, 'OWN': 1, 'RENT': 2, 'OTHER': 3, 'NONE': 4, 'ANY': 5}
features_dict = {'loan_amnt': 0, 'int_rate': 1, 'annual_inc': 2, 'delinq_2yrs': 3, 'open_acc': 4, 'dti': 5, 'emp_length': 6, 'funded_amnt': 7, 'tot_cur_bal': 8, 'home_ownership': 9}
def get_data(s):
"""Return features and targets for a specific search term.
Arguments:
s (str) -- string to search for in loan "title" field
Returns:
(list of lists) -- [list of feature tuples, list of targets]
(features) -- [(sample1 features), (sample2 features),...]
(target) -- [sample1 target, sample2 target,...]
"""
data = sql_query('SELECT ' + 'loan_amnt,int_rate,annual_inc,' + 'loan_status,title,delinq_2yrs,' + 'open_acc,dti,emp_length,' + 'funded_amnt,tot_cur_bal,home_ownership ' + 'FROM loan ' + "WHERE application_type='INDIVIDUAL';")
features_list = []
target_list = []
n = 0
n0 = 0
n1 = 0
for d in data:
test0 = isinstance(d[0], float)
test1 = isinstance(d[1], str)
test2 = isinstance(d[2], float)
test3 = isinstance(d[3], str)
test4 = isinstance(d[4], str)
test5 = isinstance(d[5], float)
test6 = isinstance(d[6], float)
test7 = isinstance(d[7], float)
test8 = isinstance(d[8], str)
test9 = isinstance(d[9], float)
test10 = isinstance(d[10], float)
if test0 and test1 and test2 and test3 and test4 and test5 and test6 and test7 and test8 and test9 and test10:
try:
d1_float = float(d[1].replace('%', ''))
except:
continue
try:
e = emp_length_dict[d[8]]
except:
continue
try:
h = home_ownership_dict[d[11]]
except:
continue
if s.lower() in d[4].lower():
if d[3] == 'Fully Paid' or d[3] == 'Current':
target = 0
n += 1
n0 += 1
elif 'Late' in d[3] or d[3] == 'Charged Off':
target = 1
n += 1
n1 += 1
else:
continue
features = (d[0], float(d[1].replace('%', '')), d[2], d[5], d[6], d[7], emp_length_dict[d[8]], d[9], d[10], home_ownership_dict[d[11]])
features_list.append(features)
target_list.append(target)
else:
pass
result = [features_list, target_list]
return result
def create_scatter_plot(x0_data, y0_data, x1_data, y1_data, pt, pa, x_label, y_label, axis_type):
ax = plt.gca()
ax.set_axis_bgcolor('#BBBBBB')
ax.set_axisbelow(True)
plt.axis(pa)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
if axis_type == 'semilogx':
plt.semilogx(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogx(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'semilogy':
plt.semilogy(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogy(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'loglog':
plt.loglog(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.loglog(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
plt.clf()
def plot_two_fields(data, s, f1, f2, pa, x_label, y_label, axis_type):
x0_list = []
y0_list = []
x1_list = []
y1_list = []
features_list = data[0]
target_list = data[1]
for i in range(len(features_list)):
x = features_list[i][features_dict[f1]]
y = features_list[i][features_dict[f2]]
if target_list[i] == 0:
x0_list.append(x)
y0_list.append(y)
elif target_list[i] == 1:
x1_list.append(x)
y1_list.append(y)
else:
pass
medical_data = get_data('medical')
plot_two_fields(medical_data, 'medical', 'home_ownership', 'funded_amnt', [-1, 6, 0.0, 35000.0], 'home ownership', 'funded amount', 'standard') | code |
320604/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
c.execute(s)
result = c.fetchall()
conn.close()
return result
def print_details():
"""Print database details including table names and the number of rows.
"""
table_names = sql_query('SELECT name FROM sqlite_master ' + "WHERE type='table' " + 'ORDER BY name;')[0][0]
num_rows = sql_query('SELECT COUNT(*) FROM loan;')[0][0]
def print_column_names():
"""Print the column names in the 'loan' table.
Note that the "index" column name is specific to Python and is not part of
the original SQLite database.
"""
conn = sqlite3.connect('../input/database.sqlite')
conn.row_factory = sqlite3.Row
c = conn.cursor()
c.execute('SELECT * FROM loan LIMIT 2;')
r = c.fetchone()
i = 1
for k in r.keys():
i += 1
conn.close()
emp_length_dict = {'n/a': 0, '< 1 year': 0, '1 year': 1, '2 years': 2, '3 years': 3, '4 years': 4, '5 years': 5, '6 years': 6, '7 years': 7, '8 years': 8, '9 years': 9, '10+ years': 10}
home_ownership_dict = {'MORTGAGE': 0, 'OWN': 1, 'RENT': 2, 'OTHER': 3, 'NONE': 4, 'ANY': 5}
features_dict = {'loan_amnt': 0, 'int_rate': 1, 'annual_inc': 2, 'delinq_2yrs': 3, 'open_acc': 4, 'dti': 5, 'emp_length': 6, 'funded_amnt': 7, 'tot_cur_bal': 8, 'home_ownership': 9}
def get_data(s):
"""Return features and targets for a specific search term.
Arguments:
s (str) -- string to search for in loan "title" field
Returns:
(list of lists) -- [list of feature tuples, list of targets]
(features) -- [(sample1 features), (sample2 features),...]
(target) -- [sample1 target, sample2 target,...]
"""
data = sql_query('SELECT ' + 'loan_amnt,int_rate,annual_inc,' + 'loan_status,title,delinq_2yrs,' + 'open_acc,dti,emp_length,' + 'funded_amnt,tot_cur_bal,home_ownership ' + 'FROM loan ' + "WHERE application_type='INDIVIDUAL';")
features_list = []
target_list = []
n = 0
n0 = 0
n1 = 0
for d in data:
test0 = isinstance(d[0], float)
test1 = isinstance(d[1], str)
test2 = isinstance(d[2], float)
test3 = isinstance(d[3], str)
test4 = isinstance(d[4], str)
test5 = isinstance(d[5], float)
test6 = isinstance(d[6], float)
test7 = isinstance(d[7], float)
test8 = isinstance(d[8], str)
test9 = isinstance(d[9], float)
test10 = isinstance(d[10], float)
if test0 and test1 and test2 and test3 and test4 and test5 and test6 and test7 and test8 and test9 and test10:
try:
d1_float = float(d[1].replace('%', ''))
except:
continue
try:
e = emp_length_dict[d[8]]
except:
continue
try:
h = home_ownership_dict[d[11]]
except:
continue
if s.lower() in d[4].lower():
if d[3] == 'Fully Paid' or d[3] == 'Current':
target = 0
n += 1
n0 += 1
elif 'Late' in d[3] or d[3] == 'Charged Off':
target = 1
n += 1
n1 += 1
else:
continue
features = (d[0], float(d[1].replace('%', '')), d[2], d[5], d[6], d[7], emp_length_dict[d[8]], d[9], d[10], home_ownership_dict[d[11]])
features_list.append(features)
target_list.append(target)
else:
pass
result = [features_list, target_list]
return result
def create_scatter_plot(x0_data, y0_data, x1_data, y1_data, pt, pa, x_label, y_label, axis_type):
ax = plt.gca()
ax.set_axis_bgcolor('#BBBBBB')
ax.set_axisbelow(True)
plt.axis(pa)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
if axis_type == 'semilogx':
plt.semilogx(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogx(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'semilogy':
plt.semilogy(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogy(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'loglog':
plt.loglog(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.loglog(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
plt.clf()
def plot_two_fields(data, s, f1, f2, pa, x_label, y_label, axis_type):
x0_list = []
y0_list = []
x1_list = []
y1_list = []
features_list = data[0]
target_list = data[1]
for i in range(len(features_list)):
x = features_list[i][features_dict[f1]]
y = features_list[i][features_dict[f2]]
if target_list[i] == 0:
x0_list.append(x)
y0_list.append(y)
elif target_list[i] == 1:
x1_list.append(x)
y1_list.append(y)
else:
pass
cc_data = get_data('credit card')
plot_two_fields(cc_data, 'credit card', 'annual_inc', 'int_rate', [1000.0, 10000000.0, 5.0, 30.0], 'annual income', 'interest rate', 'semilogx') | code |
320604/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
c.execute(s)
result = c.fetchall()
conn.close()
return result
def print_details():
"""Print database details including table names and the number of rows.
"""
table_names = sql_query('SELECT name FROM sqlite_master ' + "WHERE type='table' " + 'ORDER BY name;')[0][0]
num_rows = sql_query('SELECT COUNT(*) FROM loan;')[0][0]
def print_column_names():
"""Print the column names in the 'loan' table.
Note that the "index" column name is specific to Python and is not part of
the original SQLite database.
"""
conn = sqlite3.connect('../input/database.sqlite')
conn.row_factory = sqlite3.Row
c = conn.cursor()
c.execute('SELECT * FROM loan LIMIT 2;')
r = c.fetchone()
i = 1
for k in r.keys():
i += 1
conn.close()
emp_length_dict = {'n/a': 0, '< 1 year': 0, '1 year': 1, '2 years': 2, '3 years': 3, '4 years': 4, '5 years': 5, '6 years': 6, '7 years': 7, '8 years': 8, '9 years': 9, '10+ years': 10}
home_ownership_dict = {'MORTGAGE': 0, 'OWN': 1, 'RENT': 2, 'OTHER': 3, 'NONE': 4, 'ANY': 5}
features_dict = {'loan_amnt': 0, 'int_rate': 1, 'annual_inc': 2, 'delinq_2yrs': 3, 'open_acc': 4, 'dti': 5, 'emp_length': 6, 'funded_amnt': 7, 'tot_cur_bal': 8, 'home_ownership': 9}
def get_data(s):
"""Return features and targets for a specific search term.
Arguments:
s (str) -- string to search for in loan "title" field
Returns:
(list of lists) -- [list of feature tuples, list of targets]
(features) -- [(sample1 features), (sample2 features),...]
(target) -- [sample1 target, sample2 target,...]
"""
data = sql_query('SELECT ' + 'loan_amnt,int_rate,annual_inc,' + 'loan_status,title,delinq_2yrs,' + 'open_acc,dti,emp_length,' + 'funded_amnt,tot_cur_bal,home_ownership ' + 'FROM loan ' + "WHERE application_type='INDIVIDUAL';")
features_list = []
target_list = []
n = 0
n0 = 0
n1 = 0
for d in data:
test0 = isinstance(d[0], float)
test1 = isinstance(d[1], str)
test2 = isinstance(d[2], float)
test3 = isinstance(d[3], str)
test4 = isinstance(d[4], str)
test5 = isinstance(d[5], float)
test6 = isinstance(d[6], float)
test7 = isinstance(d[7], float)
test8 = isinstance(d[8], str)
test9 = isinstance(d[9], float)
test10 = isinstance(d[10], float)
if test0 and test1 and test2 and test3 and test4 and test5 and test6 and test7 and test8 and test9 and test10:
try:
d1_float = float(d[1].replace('%', ''))
except:
continue
try:
e = emp_length_dict[d[8]]
except:
continue
try:
h = home_ownership_dict[d[11]]
except:
continue
if s.lower() in d[4].lower():
if d[3] == 'Fully Paid' or d[3] == 'Current':
target = 0
n += 1
n0 += 1
elif 'Late' in d[3] or d[3] == 'Charged Off':
target = 1
n += 1
n1 += 1
else:
continue
features = (d[0], float(d[1].replace('%', '')), d[2], d[5], d[6], d[7], emp_length_dict[d[8]], d[9], d[10], home_ownership_dict[d[11]])
features_list.append(features)
target_list.append(target)
else:
pass
result = [features_list, target_list]
return result
def create_scatter_plot(x0_data, y0_data, x1_data, y1_data, pt, pa, x_label, y_label, axis_type):
ax = plt.gca()
ax.set_axis_bgcolor('#BBBBBB')
ax.set_axisbelow(True)
plt.axis(pa)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
if axis_type == 'semilogx':
plt.semilogx(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogx(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'semilogy':
plt.semilogy(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogy(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'loglog':
plt.loglog(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.loglog(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
plt.clf()
def plot_two_fields(data, s, f1, f2, pa, x_label, y_label, axis_type):
x0_list = []
y0_list = []
x1_list = []
y1_list = []
features_list = data[0]
target_list = data[1]
for i in range(len(features_list)):
x = features_list[i][features_dict[f1]]
y = features_list[i][features_dict[f2]]
if target_list[i] == 0:
x0_list.append(x)
y0_list.append(y)
elif target_list[i] == 1:
x1_list.append(x)
y1_list.append(y)
else:
pass
medical_data = get_data('medical')
plot_two_fields(medical_data, 'medical', 'annual_inc', 'int_rate', [1000.0, 10000000.0, 5.0, 30.0], 'annual income', 'interest rate', 'semilogx') | code |
320604/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
c.execute(s)
result = c.fetchall()
conn.close()
return result
def print_details():
"""Print database details including table names and the number of rows.
"""
table_names = sql_query('SELECT name FROM sqlite_master ' + "WHERE type='table' " + 'ORDER BY name;')[0][0]
num_rows = sql_query('SELECT COUNT(*) FROM loan;')[0][0]
def print_column_names():
"""Print the column names in the 'loan' table.
Note that the "index" column name is specific to Python and is not part of
the original SQLite database.
"""
conn = sqlite3.connect('../input/database.sqlite')
conn.row_factory = sqlite3.Row
c = conn.cursor()
c.execute('SELECT * FROM loan LIMIT 2;')
r = c.fetchone()
i = 1
for k in r.keys():
i += 1
conn.close()
emp_length_dict = {'n/a': 0, '< 1 year': 0, '1 year': 1, '2 years': 2, '3 years': 3, '4 years': 4, '5 years': 5, '6 years': 6, '7 years': 7, '8 years': 8, '9 years': 9, '10+ years': 10}
home_ownership_dict = {'MORTGAGE': 0, 'OWN': 1, 'RENT': 2, 'OTHER': 3, 'NONE': 4, 'ANY': 5}
features_dict = {'loan_amnt': 0, 'int_rate': 1, 'annual_inc': 2, 'delinq_2yrs': 3, 'open_acc': 4, 'dti': 5, 'emp_length': 6, 'funded_amnt': 7, 'tot_cur_bal': 8, 'home_ownership': 9}
def get_data(s):
"""Return features and targets for a specific search term.
Arguments:
s (str) -- string to search for in loan "title" field
Returns:
(list of lists) -- [list of feature tuples, list of targets]
(features) -- [(sample1 features), (sample2 features),...]
(target) -- [sample1 target, sample2 target,...]
"""
data = sql_query('SELECT ' + 'loan_amnt,int_rate,annual_inc,' + 'loan_status,title,delinq_2yrs,' + 'open_acc,dti,emp_length,' + 'funded_amnt,tot_cur_bal,home_ownership ' + 'FROM loan ' + "WHERE application_type='INDIVIDUAL';")
features_list = []
target_list = []
n = 0
n0 = 0
n1 = 0
for d in data:
test0 = isinstance(d[0], float)
test1 = isinstance(d[1], str)
test2 = isinstance(d[2], float)
test3 = isinstance(d[3], str)
test4 = isinstance(d[4], str)
test5 = isinstance(d[5], float)
test6 = isinstance(d[6], float)
test7 = isinstance(d[7], float)
test8 = isinstance(d[8], str)
test9 = isinstance(d[9], float)
test10 = isinstance(d[10], float)
if test0 and test1 and test2 and test3 and test4 and test5 and test6 and test7 and test8 and test9 and test10:
try:
d1_float = float(d[1].replace('%', ''))
except:
continue
try:
e = emp_length_dict[d[8]]
except:
continue
try:
h = home_ownership_dict[d[11]]
except:
continue
if s.lower() in d[4].lower():
if d[3] == 'Fully Paid' or d[3] == 'Current':
target = 0
n += 1
n0 += 1
elif 'Late' in d[3] or d[3] == 'Charged Off':
target = 1
n += 1
n1 += 1
else:
continue
features = (d[0], float(d[1].replace('%', '')), d[2], d[5], d[6], d[7], emp_length_dict[d[8]], d[9], d[10], home_ownership_dict[d[11]])
features_list.append(features)
target_list.append(target)
else:
pass
result = [features_list, target_list]
return result
def create_scatter_plot(x0_data, y0_data, x1_data, y1_data, pt, pa, x_label, y_label, axis_type):
ax = plt.gca()
ax.set_axis_bgcolor('#BBBBBB')
ax.set_axisbelow(True)
plt.axis(pa)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
if axis_type == 'semilogx':
plt.semilogx(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogx(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'semilogy':
plt.semilogy(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogy(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'loglog':
plt.loglog(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.loglog(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
plt.clf()
def plot_two_fields(data, s, f1, f2, pa, x_label, y_label, axis_type):
x0_list = []
y0_list = []
x1_list = []
y1_list = []
features_list = data[0]
target_list = data[1]
for i in range(len(features_list)):
x = features_list[i][features_dict[f1]]
y = features_list[i][features_dict[f2]]
if target_list[i] == 0:
x0_list.append(x)
y0_list.append(y)
elif target_list[i] == 1:
x1_list.append(x)
y1_list.append(y)
else:
pass
medical_data = get_data('medical')
plot_two_fields(medical_data, 'medical', 'annual_inc', 'loan_amnt', [1000.0, 10000000.0, 0.0, 35000.0], 'annual income', 'loan amount', 'semilogx') | code |
320604/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
c.execute(s)
result = c.fetchall()
conn.close()
return result
def print_details():
"""Print database details including table names and the number of rows.
"""
table_names = sql_query('SELECT name FROM sqlite_master ' + "WHERE type='table' " + 'ORDER BY name;')[0][0]
num_rows = sql_query('SELECT COUNT(*) FROM loan;')[0][0]
def print_column_names():
"""Print the column names in the 'loan' table.
Note that the "index" column name is specific to Python and is not part of
the original SQLite database.
"""
conn = sqlite3.connect('../input/database.sqlite')
conn.row_factory = sqlite3.Row
c = conn.cursor()
c.execute('SELECT * FROM loan LIMIT 2;')
r = c.fetchone()
i = 1
for k in r.keys():
i += 1
conn.close()
emp_length_dict = {'n/a': 0, '< 1 year': 0, '1 year': 1, '2 years': 2, '3 years': 3, '4 years': 4, '5 years': 5, '6 years': 6, '7 years': 7, '8 years': 8, '9 years': 9, '10+ years': 10}
home_ownership_dict = {'MORTGAGE': 0, 'OWN': 1, 'RENT': 2, 'OTHER': 3, 'NONE': 4, 'ANY': 5}
features_dict = {'loan_amnt': 0, 'int_rate': 1, 'annual_inc': 2, 'delinq_2yrs': 3, 'open_acc': 4, 'dti': 5, 'emp_length': 6, 'funded_amnt': 7, 'tot_cur_bal': 8, 'home_ownership': 9}
def get_data(s):
"""Return features and targets for a specific search term.
Arguments:
s (str) -- string to search for in loan "title" field
Returns:
(list of lists) -- [list of feature tuples, list of targets]
(features) -- [(sample1 features), (sample2 features),...]
(target) -- [sample1 target, sample2 target,...]
"""
data = sql_query('SELECT ' + 'loan_amnt,int_rate,annual_inc,' + 'loan_status,title,delinq_2yrs,' + 'open_acc,dti,emp_length,' + 'funded_amnt,tot_cur_bal,home_ownership ' + 'FROM loan ' + "WHERE application_type='INDIVIDUAL';")
features_list = []
target_list = []
n = 0
n0 = 0
n1 = 0
for d in data:
test0 = isinstance(d[0], float)
test1 = isinstance(d[1], str)
test2 = isinstance(d[2], float)
test3 = isinstance(d[3], str)
test4 = isinstance(d[4], str)
test5 = isinstance(d[5], float)
test6 = isinstance(d[6], float)
test7 = isinstance(d[7], float)
test8 = isinstance(d[8], str)
test9 = isinstance(d[9], float)
test10 = isinstance(d[10], float)
if test0 and test1 and test2 and test3 and test4 and test5 and test6 and test7 and test8 and test9 and test10:
try:
d1_float = float(d[1].replace('%', ''))
except:
continue
try:
e = emp_length_dict[d[8]]
except:
continue
try:
h = home_ownership_dict[d[11]]
except:
continue
if s.lower() in d[4].lower():
if d[3] == 'Fully Paid' or d[3] == 'Current':
target = 0
n += 1
n0 += 1
elif 'Late' in d[3] or d[3] == 'Charged Off':
target = 1
n += 1
n1 += 1
else:
continue
features = (d[0], float(d[1].replace('%', '')), d[2], d[5], d[6], d[7], emp_length_dict[d[8]], d[9], d[10], home_ownership_dict[d[11]])
features_list.append(features)
target_list.append(target)
else:
pass
result = [features_list, target_list]
return result
def create_scatter_plot(x0_data, y0_data, x1_data, y1_data, pt, pa, x_label, y_label, axis_type):
ax = plt.gca()
ax.set_axis_bgcolor('#BBBBBB')
ax.set_axisbelow(True)
plt.axis(pa)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
if axis_type == 'semilogx':
plt.semilogx(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogx(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'semilogy':
plt.semilogy(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogy(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'loglog':
plt.loglog(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.loglog(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
plt.clf()
def plot_two_fields(data, s, f1, f2, pa, x_label, y_label, axis_type):
x0_list = []
y0_list = []
x1_list = []
y1_list = []
features_list = data[0]
target_list = data[1]
for i in range(len(features_list)):
x = features_list[i][features_dict[f1]]
y = features_list[i][features_dict[f2]]
if target_list[i] == 0:
x0_list.append(x)
y0_list.append(y)
elif target_list[i] == 1:
x1_list.append(x)
y1_list.append(y)
else:
pass
medical_data = get_data('medical')
plot_two_fields(medical_data, 'medical', 'loan_amnt', 'funded_amnt', [0.0, 35000.0, 0.0, 35000.0], 'loan amount', 'funded amount', 'standard') | code |
320604/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import tree
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
c.execute(s)
result = c.fetchall()
conn.close()
return result
def print_details():
"""Print database details including table names and the number of rows.
"""
table_names = sql_query('SELECT name FROM sqlite_master ' + "WHERE type='table' " + 'ORDER BY name;')[0][0]
num_rows = sql_query('SELECT COUNT(*) FROM loan;')[0][0]
def print_column_names():
"""Print the column names in the 'loan' table.
Note that the "index" column name is specific to Python and is not part of
the original SQLite database.
"""
conn = sqlite3.connect('../input/database.sqlite')
conn.row_factory = sqlite3.Row
c = conn.cursor()
c.execute('SELECT * FROM loan LIMIT 2;')
r = c.fetchone()
i = 1
for k in r.keys():
i += 1
conn.close()
emp_length_dict = {'n/a': 0, '< 1 year': 0, '1 year': 1, '2 years': 2, '3 years': 3, '4 years': 4, '5 years': 5, '6 years': 6, '7 years': 7, '8 years': 8, '9 years': 9, '10+ years': 10}
home_ownership_dict = {'MORTGAGE': 0, 'OWN': 1, 'RENT': 2, 'OTHER': 3, 'NONE': 4, 'ANY': 5}
features_dict = {'loan_amnt': 0, 'int_rate': 1, 'annual_inc': 2, 'delinq_2yrs': 3, 'open_acc': 4, 'dti': 5, 'emp_length': 6, 'funded_amnt': 7, 'tot_cur_bal': 8, 'home_ownership': 9}
def get_data(s):
"""Return features and targets for a specific search term.
Arguments:
s (str) -- string to search for in loan "title" field
Returns:
(list of lists) -- [list of feature tuples, list of targets]
(features) -- [(sample1 features), (sample2 features),...]
(target) -- [sample1 target, sample2 target,...]
"""
data = sql_query('SELECT ' + 'loan_amnt,int_rate,annual_inc,' + 'loan_status,title,delinq_2yrs,' + 'open_acc,dti,emp_length,' + 'funded_amnt,tot_cur_bal,home_ownership ' + 'FROM loan ' + "WHERE application_type='INDIVIDUAL';")
features_list = []
target_list = []
n = 0
n0 = 0
n1 = 0
for d in data:
test0 = isinstance(d[0], float)
test1 = isinstance(d[1], str)
test2 = isinstance(d[2], float)
test3 = isinstance(d[3], str)
test4 = isinstance(d[4], str)
test5 = isinstance(d[5], float)
test6 = isinstance(d[6], float)
test7 = isinstance(d[7], float)
test8 = isinstance(d[8], str)
test9 = isinstance(d[9], float)
test10 = isinstance(d[10], float)
if test0 and test1 and test2 and test3 and test4 and test5 and test6 and test7 and test8 and test9 and test10:
try:
d1_float = float(d[1].replace('%', ''))
except:
continue
try:
e = emp_length_dict[d[8]]
except:
continue
try:
h = home_ownership_dict[d[11]]
except:
continue
if s.lower() in d[4].lower():
if d[3] == 'Fully Paid' or d[3] == 'Current':
target = 0
n += 1
n0 += 1
elif 'Late' in d[3] or d[3] == 'Charged Off':
target = 1
n += 1
n1 += 1
else:
continue
features = (d[0], float(d[1].replace('%', '')), d[2], d[5], d[6], d[7], emp_length_dict[d[8]], d[9], d[10], home_ownership_dict[d[11]])
features_list.append(features)
target_list.append(target)
else:
pass
result = [features_list, target_list]
return result
def create_scatter_plot(x0_data, y0_data, x1_data, y1_data, pt, pa, x_label, y_label, axis_type):
ax = plt.gca()
ax.set_axis_bgcolor('#BBBBBB')
ax.set_axisbelow(True)
plt.axis(pa)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
if axis_type == 'semilogx':
plt.semilogx(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogx(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'semilogy':
plt.semilogy(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogy(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'loglog':
plt.loglog(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.loglog(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
plt.clf()
def plot_two_fields(data, s, f1, f2, pa, x_label, y_label, axis_type):
x0_list = []
y0_list = []
x1_list = []
y1_list = []
features_list = data[0]
target_list = data[1]
for i in range(len(features_list)):
x = features_list[i][features_dict[f1]]
y = features_list[i][features_dict[f2]]
if target_list[i] == 0:
x0_list.append(x)
y0_list.append(y)
elif target_list[i] == 1:
x1_list.append(x)
y1_list.append(y)
else:
pass
medical_data = get_data('medical')
def create_classifier(f, t, nt):
"""Create classifier for predicting loan status. Print accuracy.
Arguments:
f (list of tuples) -- [(sample 1 features), (sample 2 features),...]
t (list) -- [sample 1 target, sample 2 target,...]
nt (int) -- number of samples to use in training set
"""
training_set_features = []
training_set_target = []
testing_set_features = []
testing_set_target = []
for i in np.arange(0, nt, 1):
training_set_features.append(f[i])
training_set_target.append(t[i])
for i in np.arange(nt, len(f), 1):
testing_set_features.append(f[i])
testing_set_target.append(t[i])
clf = tree.DecisionTreeClassifier()
clf = clf.fit(training_set_features, training_set_target)
n = 0
n_correct = 0
n0 = 0
n0_correct = 0
n1 = 0
n1_correct = 0
for i in range(len(testing_set_features)):
t = testing_set_target[i]
p = clf.predict(np.asarray(testing_set_features[i]).reshape(1, -1))
if t == 0:
if t == p[0]:
equal = 'yes'
n_correct += 1
n0_correct += 1
else:
equal = 'no'
n += 1
n0 += 1
elif t == 1:
if t == p[0]:
equal = 'yes'
n_correct += 1
n1_correct += 1
else:
equal = 'no'
n += 1
n1 += 1
else:
pass
n_accuracy = 100.0 * n_correct / n
n0_accuracy = 100.0 * n0_correct / n0
n1_accuracy = 100.0 * n1_correct / n1
create_classifier(medical_data[0], medical_data[1], 2000) | code |
320604/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
c.execute(s)
result = c.fetchall()
conn.close()
return result
def print_details():
"""Print database details including table names and the number of rows.
"""
table_names = sql_query('SELECT name FROM sqlite_master ' + "WHERE type='table' " + 'ORDER BY name;')[0][0]
num_rows = sql_query('SELECT COUNT(*) FROM loan;')[0][0]
def print_column_names():
"""Print the column names in the 'loan' table.
Note that the "index" column name is specific to Python and is not part of
the original SQLite database.
"""
conn = sqlite3.connect('../input/database.sqlite')
conn.row_factory = sqlite3.Row
c = conn.cursor()
c.execute('SELECT * FROM loan LIMIT 2;')
r = c.fetchone()
i = 1
for k in r.keys():
i += 1
conn.close()
emp_length_dict = {'n/a': 0, '< 1 year': 0, '1 year': 1, '2 years': 2, '3 years': 3, '4 years': 4, '5 years': 5, '6 years': 6, '7 years': 7, '8 years': 8, '9 years': 9, '10+ years': 10}
home_ownership_dict = {'MORTGAGE': 0, 'OWN': 1, 'RENT': 2, 'OTHER': 3, 'NONE': 4, 'ANY': 5}
features_dict = {'loan_amnt': 0, 'int_rate': 1, 'annual_inc': 2, 'delinq_2yrs': 3, 'open_acc': 4, 'dti': 5, 'emp_length': 6, 'funded_amnt': 7, 'tot_cur_bal': 8, 'home_ownership': 9}
def get_data(s):
"""Return features and targets for a specific search term.
Arguments:
s (str) -- string to search for in loan "title" field
Returns:
(list of lists) -- [list of feature tuples, list of targets]
(features) -- [(sample1 features), (sample2 features),...]
(target) -- [sample1 target, sample2 target,...]
"""
data = sql_query('SELECT ' + 'loan_amnt,int_rate,annual_inc,' + 'loan_status,title,delinq_2yrs,' + 'open_acc,dti,emp_length,' + 'funded_amnt,tot_cur_bal,home_ownership ' + 'FROM loan ' + "WHERE application_type='INDIVIDUAL';")
features_list = []
target_list = []
n = 0
n0 = 0
n1 = 0
for d in data:
test0 = isinstance(d[0], float)
test1 = isinstance(d[1], str)
test2 = isinstance(d[2], float)
test3 = isinstance(d[3], str)
test4 = isinstance(d[4], str)
test5 = isinstance(d[5], float)
test6 = isinstance(d[6], float)
test7 = isinstance(d[7], float)
test8 = isinstance(d[8], str)
test9 = isinstance(d[9], float)
test10 = isinstance(d[10], float)
if test0 and test1 and test2 and test3 and test4 and test5 and test6 and test7 and test8 and test9 and test10:
try:
d1_float = float(d[1].replace('%', ''))
except:
continue
try:
e = emp_length_dict[d[8]]
except:
continue
try:
h = home_ownership_dict[d[11]]
except:
continue
if s.lower() in d[4].lower():
if d[3] == 'Fully Paid' or d[3] == 'Current':
target = 0
n += 1
n0 += 1
elif 'Late' in d[3] or d[3] == 'Charged Off':
target = 1
n += 1
n1 += 1
else:
continue
features = (d[0], float(d[1].replace('%', '')), d[2], d[5], d[6], d[7], emp_length_dict[d[8]], d[9], d[10], home_ownership_dict[d[11]])
features_list.append(features)
target_list.append(target)
else:
pass
result = [features_list, target_list]
return result
def create_scatter_plot(x0_data, y0_data, x1_data, y1_data, pt, pa, x_label, y_label, axis_type):
ax = plt.gca()
ax.set_axis_bgcolor('#BBBBBB')
ax.set_axisbelow(True)
plt.axis(pa)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
if axis_type == 'semilogx':
plt.semilogx(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogx(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'semilogy':
plt.semilogy(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogy(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'loglog':
plt.loglog(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.loglog(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
plt.clf()
def plot_two_fields(data, s, f1, f2, pa, x_label, y_label, axis_type):
x0_list = []
y0_list = []
x1_list = []
y1_list = []
features_list = data[0]
target_list = data[1]
for i in range(len(features_list)):
x = features_list[i][features_dict[f1]]
y = features_list[i][features_dict[f2]]
if target_list[i] == 0:
x0_list.append(x)
y0_list.append(y)
elif target_list[i] == 1:
x1_list.append(x)
y1_list.append(y)
else:
pass
debt_data = get_data('debt')
plot_two_fields(debt_data, 'debt', 'loan_amnt', 'funded_amnt', [0.0, 35000.0, 0.0, 35000.0], 'loan amount', 'funded amount', 'standard') | code |
320604/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
c.execute(s)
result = c.fetchall()
conn.close()
return result
def print_details():
"""Print database details including table names and the number of rows.
"""
table_names = sql_query('SELECT name FROM sqlite_master ' + "WHERE type='table' " + 'ORDER BY name;')[0][0]
num_rows = sql_query('SELECT COUNT(*) FROM loan;')[0][0]
def print_column_names():
"""Print the column names in the 'loan' table.
Note that the "index" column name is specific to Python and is not part of
the original SQLite database.
"""
conn = sqlite3.connect('../input/database.sqlite')
conn.row_factory = sqlite3.Row
c = conn.cursor()
c.execute('SELECT * FROM loan LIMIT 2;')
r = c.fetchone()
i = 1
for k in r.keys():
i += 1
conn.close()
emp_length_dict = {'n/a': 0, '< 1 year': 0, '1 year': 1, '2 years': 2, '3 years': 3, '4 years': 4, '5 years': 5, '6 years': 6, '7 years': 7, '8 years': 8, '9 years': 9, '10+ years': 10}
home_ownership_dict = {'MORTGAGE': 0, 'OWN': 1, 'RENT': 2, 'OTHER': 3, 'NONE': 4, 'ANY': 5}
features_dict = {'loan_amnt': 0, 'int_rate': 1, 'annual_inc': 2, 'delinq_2yrs': 3, 'open_acc': 4, 'dti': 5, 'emp_length': 6, 'funded_amnt': 7, 'tot_cur_bal': 8, 'home_ownership': 9}
def get_data(s):
"""Return features and targets for a specific search term.
Arguments:
s (str) -- string to search for in loan "title" field
Returns:
(list of lists) -- [list of feature tuples, list of targets]
(features) -- [(sample1 features), (sample2 features),...]
(target) -- [sample1 target, sample2 target,...]
"""
data = sql_query('SELECT ' + 'loan_amnt,int_rate,annual_inc,' + 'loan_status,title,delinq_2yrs,' + 'open_acc,dti,emp_length,' + 'funded_amnt,tot_cur_bal,home_ownership ' + 'FROM loan ' + "WHERE application_type='INDIVIDUAL';")
features_list = []
target_list = []
n = 0
n0 = 0
n1 = 0
for d in data:
test0 = isinstance(d[0], float)
test1 = isinstance(d[1], str)
test2 = isinstance(d[2], float)
test3 = isinstance(d[3], str)
test4 = isinstance(d[4], str)
test5 = isinstance(d[5], float)
test6 = isinstance(d[6], float)
test7 = isinstance(d[7], float)
test8 = isinstance(d[8], str)
test9 = isinstance(d[9], float)
test10 = isinstance(d[10], float)
if test0 and test1 and test2 and test3 and test4 and test5 and test6 and test7 and test8 and test9 and test10:
try:
d1_float = float(d[1].replace('%', ''))
except:
continue
try:
e = emp_length_dict[d[8]]
except:
continue
try:
h = home_ownership_dict[d[11]]
except:
continue
if s.lower() in d[4].lower():
if d[3] == 'Fully Paid' or d[3] == 'Current':
target = 0
n += 1
n0 += 1
elif 'Late' in d[3] or d[3] == 'Charged Off':
target = 1
n += 1
n1 += 1
else:
continue
features = (d[0], float(d[1].replace('%', '')), d[2], d[5], d[6], d[7], emp_length_dict[d[8]], d[9], d[10], home_ownership_dict[d[11]])
features_list.append(features)
target_list.append(target)
else:
pass
result = [features_list, target_list]
return result
def create_scatter_plot(x0_data, y0_data, x1_data, y1_data, pt, pa, x_label, y_label, axis_type):
ax = plt.gca()
ax.set_axis_bgcolor('#BBBBBB')
ax.set_axisbelow(True)
plt.axis(pa)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
if axis_type == 'semilogx':
plt.semilogx(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogx(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'semilogy':
plt.semilogy(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogy(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'loglog':
plt.loglog(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.loglog(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
plt.clf()
def plot_two_fields(data, s, f1, f2, pa, x_label, y_label, axis_type):
x0_list = []
y0_list = []
x1_list = []
y1_list = []
features_list = data[0]
target_list = data[1]
for i in range(len(features_list)):
x = features_list[i][features_dict[f1]]
y = features_list[i][features_dict[f2]]
if target_list[i] == 0:
x0_list.append(x)
y0_list.append(y)
elif target_list[i] == 1:
x1_list.append(x)
y1_list.append(y)
else:
pass
medical_data = get_data('medical')
plot_two_fields(medical_data, 'medical', 'loan_amnt', 'int_rate', [100.0, 100000.0, 5.0, 30.0], 'loan amount', 'interest rate', 'semilogx') | code |
320604/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
c.execute(s)
result = c.fetchall()
conn.close()
return result
def print_details():
"""Print database details including table names and the number of rows.
"""
table_names = sql_query('SELECT name FROM sqlite_master ' + "WHERE type='table' " + 'ORDER BY name;')[0][0]
num_rows = sql_query('SELECT COUNT(*) FROM loan;')[0][0]
def print_column_names():
"""Print the column names in the 'loan' table.
Note that the "index" column name is specific to Python and is not part of
the original SQLite database.
"""
conn = sqlite3.connect('../input/database.sqlite')
conn.row_factory = sqlite3.Row
c = conn.cursor()
c.execute('SELECT * FROM loan LIMIT 2;')
r = c.fetchone()
i = 1
for k in r.keys():
i += 1
conn.close()
emp_length_dict = {'n/a': 0, '< 1 year': 0, '1 year': 1, '2 years': 2, '3 years': 3, '4 years': 4, '5 years': 5, '6 years': 6, '7 years': 7, '8 years': 8, '9 years': 9, '10+ years': 10}
home_ownership_dict = {'MORTGAGE': 0, 'OWN': 1, 'RENT': 2, 'OTHER': 3, 'NONE': 4, 'ANY': 5}
features_dict = {'loan_amnt': 0, 'int_rate': 1, 'annual_inc': 2, 'delinq_2yrs': 3, 'open_acc': 4, 'dti': 5, 'emp_length': 6, 'funded_amnt': 7, 'tot_cur_bal': 8, 'home_ownership': 9}
def get_data(s):
"""Return features and targets for a specific search term.
Arguments:
s (str) -- string to search for in loan "title" field
Returns:
(list of lists) -- [list of feature tuples, list of targets]
(features) -- [(sample1 features), (sample2 features),...]
(target) -- [sample1 target, sample2 target,...]
"""
data = sql_query('SELECT ' + 'loan_amnt,int_rate,annual_inc,' + 'loan_status,title,delinq_2yrs,' + 'open_acc,dti,emp_length,' + 'funded_amnt,tot_cur_bal,home_ownership ' + 'FROM loan ' + "WHERE application_type='INDIVIDUAL';")
features_list = []
target_list = []
n = 0
n0 = 0
n1 = 0
for d in data:
test0 = isinstance(d[0], float)
test1 = isinstance(d[1], str)
test2 = isinstance(d[2], float)
test3 = isinstance(d[3], str)
test4 = isinstance(d[4], str)
test5 = isinstance(d[5], float)
test6 = isinstance(d[6], float)
test7 = isinstance(d[7], float)
test8 = isinstance(d[8], str)
test9 = isinstance(d[9], float)
test10 = isinstance(d[10], float)
if test0 and test1 and test2 and test3 and test4 and test5 and test6 and test7 and test8 and test9 and test10:
try:
d1_float = float(d[1].replace('%', ''))
except:
continue
try:
e = emp_length_dict[d[8]]
except:
continue
try:
h = home_ownership_dict[d[11]]
except:
continue
if s.lower() in d[4].lower():
if d[3] == 'Fully Paid' or d[3] == 'Current':
target = 0
n += 1
n0 += 1
elif 'Late' in d[3] or d[3] == 'Charged Off':
target = 1
n += 1
n1 += 1
else:
continue
features = (d[0], float(d[1].replace('%', '')), d[2], d[5], d[6], d[7], emp_length_dict[d[8]], d[9], d[10], home_ownership_dict[d[11]])
features_list.append(features)
target_list.append(target)
else:
pass
result = [features_list, target_list]
return result
def create_scatter_plot(x0_data, y0_data, x1_data, y1_data, pt, pa, x_label, y_label, axis_type):
ax = plt.gca()
ax.set_axis_bgcolor('#BBBBBB')
ax.set_axisbelow(True)
plt.axis(pa)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
if axis_type == 'semilogx':
plt.semilogx(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogx(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'semilogy':
plt.semilogy(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogy(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'loglog':
plt.loglog(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.loglog(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
plt.clf()
def plot_two_fields(data, s, f1, f2, pa, x_label, y_label, axis_type):
x0_list = []
y0_list = []
x1_list = []
y1_list = []
features_list = data[0]
target_list = data[1]
for i in range(len(features_list)):
x = features_list[i][features_dict[f1]]
y = features_list[i][features_dict[f2]]
if target_list[i] == 0:
x0_list.append(x)
y0_list.append(y)
elif target_list[i] == 1:
x1_list.append(x)
y1_list.append(y)
else:
pass
debt_data = get_data('debt')
plot_two_fields(debt_data, 'debt', 'annual_inc', 'int_rate', [1000.0, 10000000.0, 5.0, 30.0], 'annual income', 'interest rate', 'semilogx') | code |
320604/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
c.execute(s)
result = c.fetchall()
conn.close()
return result
def print_details():
"""Print database details including table names and the number of rows.
"""
table_names = sql_query('SELECT name FROM sqlite_master ' + "WHERE type='table' " + 'ORDER BY name;')[0][0]
num_rows = sql_query('SELECT COUNT(*) FROM loan;')[0][0]
def print_column_names():
"""Print the column names in the 'loan' table.
Note that the "index" column name is specific to Python and is not part of
the original SQLite database.
"""
conn = sqlite3.connect('../input/database.sqlite')
conn.row_factory = sqlite3.Row
c = conn.cursor()
c.execute('SELECT * FROM loan LIMIT 2;')
r = c.fetchone()
i = 1
for k in r.keys():
i += 1
conn.close()
emp_length_dict = {'n/a': 0, '< 1 year': 0, '1 year': 1, '2 years': 2, '3 years': 3, '4 years': 4, '5 years': 5, '6 years': 6, '7 years': 7, '8 years': 8, '9 years': 9, '10+ years': 10}
home_ownership_dict = {'MORTGAGE': 0, 'OWN': 1, 'RENT': 2, 'OTHER': 3, 'NONE': 4, 'ANY': 5}
features_dict = {'loan_amnt': 0, 'int_rate': 1, 'annual_inc': 2, 'delinq_2yrs': 3, 'open_acc': 4, 'dti': 5, 'emp_length': 6, 'funded_amnt': 7, 'tot_cur_bal': 8, 'home_ownership': 9}
def get_data(s):
"""Return features and targets for a specific search term.
Arguments:
s (str) -- string to search for in loan "title" field
Returns:
(list of lists) -- [list of feature tuples, list of targets]
(features) -- [(sample1 features), (sample2 features),...]
(target) -- [sample1 target, sample2 target,...]
"""
data = sql_query('SELECT ' + 'loan_amnt,int_rate,annual_inc,' + 'loan_status,title,delinq_2yrs,' + 'open_acc,dti,emp_length,' + 'funded_amnt,tot_cur_bal,home_ownership ' + 'FROM loan ' + "WHERE application_type='INDIVIDUAL';")
features_list = []
target_list = []
n = 0
n0 = 0
n1 = 0
for d in data:
test0 = isinstance(d[0], float)
test1 = isinstance(d[1], str)
test2 = isinstance(d[2], float)
test3 = isinstance(d[3], str)
test4 = isinstance(d[4], str)
test5 = isinstance(d[5], float)
test6 = isinstance(d[6], float)
test7 = isinstance(d[7], float)
test8 = isinstance(d[8], str)
test9 = isinstance(d[9], float)
test10 = isinstance(d[10], float)
if test0 and test1 and test2 and test3 and test4 and test5 and test6 and test7 and test8 and test9 and test10:
try:
d1_float = float(d[1].replace('%', ''))
except:
continue
try:
e = emp_length_dict[d[8]]
except:
continue
try:
h = home_ownership_dict[d[11]]
except:
continue
if s.lower() in d[4].lower():
if d[3] == 'Fully Paid' or d[3] == 'Current':
target = 0
n += 1
n0 += 1
elif 'Late' in d[3] or d[3] == 'Charged Off':
target = 1
n += 1
n1 += 1
else:
continue
features = (d[0], float(d[1].replace('%', '')), d[2], d[5], d[6], d[7], emp_length_dict[d[8]], d[9], d[10], home_ownership_dict[d[11]])
features_list.append(features)
target_list.append(target)
else:
pass
result = [features_list, target_list]
return result
def create_scatter_plot(x0_data, y0_data, x1_data, y1_data, pt, pa, x_label, y_label, axis_type):
ax = plt.gca()
ax.set_axis_bgcolor('#BBBBBB')
ax.set_axisbelow(True)
plt.axis(pa)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
if axis_type == 'semilogx':
plt.semilogx(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogx(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'semilogy':
plt.semilogy(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogy(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'loglog':
plt.loglog(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.loglog(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
plt.clf()
def plot_two_fields(data, s, f1, f2, pa, x_label, y_label, axis_type):
x0_list = []
y0_list = []
x1_list = []
y1_list = []
features_list = data[0]
target_list = data[1]
for i in range(len(features_list)):
x = features_list[i][features_dict[f1]]
y = features_list[i][features_dict[f2]]
if target_list[i] == 0:
x0_list.append(x)
y0_list.append(y)
elif target_list[i] == 1:
x1_list.append(x)
y1_list.append(y)
else:
pass
cc_data = get_data('credit card')
plot_two_fields(cc_data, 'credit card', 'loan_amnt', 'funded_amnt', [0.0, 35000.0, 0.0, 35000.0], 'loan amount', 'funded amount', 'standard') | code |
1003319/cell_9 | [
"image_output_1.png"
] | import os # for doing directory operations
import dicom
import os
import pandas as pd
data_dir = '../input/sample_images/'
patients = os.listdir(data_dir)
patients
file_list = os.listdir('../input/')
file_list
len(patients) | code |
1003319/cell_2 | [
"text_plain_output_1.png"
] | import os # for doing directory operations
import dicom
import os
import pandas as pd
data_dir = '../input/sample_images/'
patients = os.listdir(data_dir)
patients
file_list = os.listdir('../input/')
file_list | code |
1003319/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
"""
So I want to test some transformations and changes to the modelling data:
** for each of the below get it running on 5% then validate on 20% modelling data)
(1) Try Different combinations of "resizing data"
(2) Try "Resampling approach" in pre-processing
(3) Try all of the transformations below on pixel array and MXNET transformed features
reciprocal t = 1 / x
log base 10 t = log_10 x
10 to the power x = 10^t
log base e t = log_e x = ln x
e to the power x = exp(t)
log base 2 t = log_2 x
2 to the power x = 2^t
cube root t = x^(1/3)
cube x = t^3
square root t = x^(1/2)
square x = t^2
(data.dat$Y)^(1/9) Takes the ninth root of Y
abs(data.dat$Y) Finds the absolute value of Y
** Try all known trigonometric functions **
sin(data.dat$Y) Calculates the sine of Y
asin(data.dat$Y) Calculates the inverse sine (arcsine) of Y
(4) Try Masking all images
then try all transformation in (3)
(5) Try dropping all columns with no useful information
"""
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1003319/cell_8 | [
"text_plain_output_1.png"
] | import dicom # for reading dicom files
import os # for doing directory operations
import dicom
import os
import pandas as pd
data_dir = '../input/sample_images/'
patients = os.listdir(data_dir)
patients
file_list = os.listdir('../input/')
file_list
for patient in patients[:1]:
path = data_dir + patient
slices = [dicom.read_file(path + '/' + s) for s in os.listdir(path)]
slices.sort(key=lambda x: int(x.ImagePositionPatient[2]))
for patient in patients[:3]:
path = data_dir + patient
slices = [dicom.read_file(path + '/' + s) for s in os.listdir(path)]
slices.sort(key=lambda x: int(x.ImagePositionPatient[2]))
print(slices[0].pixel_array.shape, len(slices)) | code |
1003319/cell_3 | [
"text_plain_output_1.png"
] | import dicom # for reading dicom files
import os # for doing directory operations
import dicom
import os
import pandas as pd
data_dir = '../input/sample_images/'
patients = os.listdir(data_dir)
patients
file_list = os.listdir('../input/')
file_list
for patient in patients[:1]:
path = data_dir + patient
slices = [dicom.read_file(path + '/' + s) for s in os.listdir(path)]
slices.sort(key=lambda x: int(x.ImagePositionPatient[2]))
print(len(slices))
print(slices[0]) | code |
1003319/cell_10 | [
"text_plain_output_1.png"
] | import dicom # for reading dicom files
import matplotlib.pyplot as plt
import os # for doing directory operations
import dicom
import os
import pandas as pd
data_dir = '../input/sample_images/'
patients = os.listdir(data_dir)
patients
file_list = os.listdir('../input/')
file_list
for patient in patients[:1]:
path = data_dir + patient
slices = [dicom.read_file(path + '/' + s) for s in os.listdir(path)]
slices.sort(key=lambda x: int(x.ImagePositionPatient[2]))
for patient in patients[:3]:
path = data_dir + patient
slices = [dicom.read_file(path + '/' + s) for s in os.listdir(path)]
slices.sort(key=lambda x: int(x.ImagePositionPatient[2]))
import matplotlib.pyplot as plt
for patient in patients[:1]:
path = data_dir + patient
slices = [dicom.read_file(path + '/' + s) for s in os.listdir(path)]
slices.sort(key=lambda x: int(x.ImagePositionPatient[2]))
plt.imshow(slices[0].pixel_array)
plt.show() | code |
128043747/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pygpt4all.models.gpt4all import GPT4All
from pygpt4all.models.gpt4all import GPT4All
model = GPT4All('ggml-gpt4all-l13b-snoozy.bin', n_ctx=2048) | code |
128043747/cell_1 | [
"text_plain_output_1.png"
] | ! pip install pygpt4all
! wget http://gpt4all.io/models/ggml-gpt4all-l13b-snoozy.bin | code |
128043747/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
from pygpt4all.models.gpt4all import GPT4All
import requests
from pygpt4all.models.gpt4all import GPT4All
model = GPT4All('ggml-gpt4all-l13b-snoozy.bin', n_ctx=2048)
def gpt4all(prompt):
model.generate(prompt, n_predict=500, new_text_callback=lambda x: print(x, end=''))
import requests
from bs4 import BeautifulSoup
repo = '0xk1h0/ChatGPT_DAN'
url = f'https://raw.githubusercontent.com/{repo}/main/README.md'
bsoup = BeautifulSoup(requests.get(url).content.decode('utf8'))
dans = {}
for li in bsoup.find_all('li'):
details = li.get_text('\n').split('\n')
details = [p for p in details if p]
name, dan = (details[0], '\n'.join(details[1:]))
dans[name] = dan
dan_context = dans['The Jailbreak Prompt']
model_to_tune = 'togethercomputer/GPT-NeoXT-Chat-Base-20B'
prompt = str(f'Using the this prompt as training data, "{dan_context}"\n\nQuestion: Can you generate a Python code to fine-tune the using the {model_to_tune} model with Huggingface transformer library?\n\nAnswer:')
gpt4all(prompt) | code |
128043747/cell_5 | [
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
import requests
import requests
from bs4 import BeautifulSoup
repo = '0xk1h0/ChatGPT_DAN'
url = f'https://raw.githubusercontent.com/{repo}/main/README.md'
bsoup = BeautifulSoup(requests.get(url).content.decode('utf8'))
dans = {}
for li in bsoup.find_all('li'):
details = li.get_text('\n').split('\n')
details = [p for p in details if p]
name, dan = (details[0], '\n'.join(details[1:]))
dans[name] = dan
dan_context = dans['The Jailbreak Prompt']
dan_context | code |
128045992/cell_13 | [
"text_plain_output_1.png"
] | from tensorflow.keras import layers
import cv2
import numpy as np
import os
import tensorflow as tf
subdir = ['angry', 'notAngry']
target = {'angry': 0, 'notAngry': 1}
dataset = '../input/fer2013new2class/FER2013NEW2CLASS/train/'
X = []
y = []
for emotions in subdir:
for img_names in os.listdir(dataset + '/' + emotions):
load_images = cv2.imread(dataset + '/' + emotions + '/' + img_names)
X.append(load_images)
y.append(target[emotions])
X, y = (np.array(X), np.array(y))
(X.shape, y.shape)
y = tf.keras.utils.to_categorical(y, num_classes=2)
y.shape
class SEBlock(layers.Layer):
def __init__(self, channels, reduction=16):
super(SEBlock, self).__init__()
self.avg_pool = layers.GlobalAveragePooling2D()
self.fc1 = layers.Dense(channels // reduction, activation='relu')
self.fc2 = layers.Dense(channels, activation='sigmoid')
def call(self, input_tensor):
bs, c = (input_tensor.shape[0], input_tensor.shape[-1])
y = self.avg_pool(input_tensor)
y = self.fc1(y)
y = self.fc2(y)
y = tf.reshape(y, [bs, 1, 1, c])
return input_tensor * y
def create_model():
model = tf.keras.Sequential([layers.Conv2D(32, (3, 3), activation='relu', input_shape=(48, 48, 3)), SEBlock(32), layers.BatchNormalization(), layers.MaxPooling2D(pool_size=(2, 2)), layers.Conv2D(64, (3, 3), activation='relu'), SEBlock(64), layers.BatchNormalization(), layers.MaxPooling2D(pool_size=(2, 2)), layers.Flatten(), layers.Dense(128, activation='relu'), layers.Dense(2, activation='sigmoid')])
return model
model = create_model()
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) | code |
128045992/cell_4 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
import os
subdir = ['angry', 'notAngry']
target = {'angry': 0, 'notAngry': 1}
dataset = '../input/fer2013new2class/FER2013NEW2CLASS/train/'
X = []
y = []
for emotions in subdir:
for img_names in os.listdir(dataset + '/' + emotions):
load_images = cv2.imread(dataset + '/' + emotions + '/' + img_names)
X.append(load_images)
y.append(target[emotions])
X, y = (np.array(X), np.array(y))
(X.shape, y.shape) | code |
128045992/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | (X_train.shape, X_test.shape, y_train.shape, y_test.shape) | code |
128045992/cell_3 | [
"text_plain_output_1.png"
] | import cv2
import os
subdir = ['angry', 'notAngry']
target = {'angry': 0, 'notAngry': 1}
dataset = '../input/fer2013new2class/FER2013NEW2CLASS/train/'
X = []
y = []
for emotions in subdir:
for img_names in os.listdir(dataset + '/' + emotions):
load_images = cv2.imread(dataset + '/' + emotions + '/' + img_names)
X.append(load_images)
y.append(target[emotions])
print('X:', len(X), 'y:', len(y)) | code |
128045992/cell_5 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
import os
import tensorflow as tf
subdir = ['angry', 'notAngry']
target = {'angry': 0, 'notAngry': 1}
dataset = '../input/fer2013new2class/FER2013NEW2CLASS/train/'
X = []
y = []
for emotions in subdir:
for img_names in os.listdir(dataset + '/' + emotions):
load_images = cv2.imread(dataset + '/' + emotions + '/' + img_names)
X.append(load_images)
y.append(target[emotions])
X, y = (np.array(X), np.array(y))
(X.shape, y.shape)
y = tf.keras.utils.to_categorical(y, num_classes=2)
y.shape | code |
16142841/cell_1 | [
"text_plain_output_1.png"
] | import os
import os
print(os.listdir('../input')) | code |
16142841/cell_7 | [
"text_plain_output_1.png"
] | print('End') | code |
16142841/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train_data.csv')
test_df = pd.read_csv('../input/test_data.csv') | code |
122244636/cell_4 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MultiLabelBinarizer
import cv2 as cv
import numpy as np
import os
import re
X = []
Y = []
input_shape = (96, 96, 3)
path_to_subset = f'../input/apparel-images-dataset/'
for folder in os.listdir(path_to_subset):
for image in os.listdir(os.path.join(path_to_subset, folder)):
path_to_image = os.path.join(path_to_subset, folder, image)
image = cv.imread(path_to_image)
image = cv.resize(image, (input_shape[1], input_shape[0]))
label = re.findall('\\w+\\_\\w+', path_to_image)[0].split('_')
X.append(image)
Y.append(label)
X = np.array(X) / 255.0
Y = np.array(Y)
mlb = MultiLabelBinarizer()
Y = mlb.fit_transform(Y)
x, test_x, y, test_y = train_test_split(X, Y, test_size=0.1, stratify=Y, shuffle=True, random_state=1)
train_x, val_x, train_y, val_y = train_test_split(x, y, test_size=0.2, stratify=y, shuffle=True, random_state=1)
print(x.shape, test_x.shape, y.shape, test_y.shape)
print(train_x.shape, val_x.shape, train_y.shape, val_y.shape)
datagen = ImageDataGenerator(rotation_range=45, width_shift_range=0.1, height_shift_range=0.1, zoom_range=0.2, horizontal_flip=True, validation_split=0.2) | code |
122244636/cell_6 | [
"image_output_1.png"
] | from keras.layers import BatchNormalization, Activation, Dropout
from keras.layers import Conv2D, Dense, Flatten, MaxPooling2D
from keras.models import Sequential
from keras.utils import plot_model
from sklearn.preprocessing import MultiLabelBinarizer
import cv2 as cv
import numpy as np
import os
import re
X = []
Y = []
input_shape = (96, 96, 3)
path_to_subset = f'../input/apparel-images-dataset/'
for folder in os.listdir(path_to_subset):
for image in os.listdir(os.path.join(path_to_subset, folder)):
path_to_image = os.path.join(path_to_subset, folder, image)
image = cv.imread(path_to_image)
image = cv.resize(image, (input_shape[1], input_shape[0]))
label = re.findall('\\w+\\_\\w+', path_to_image)[0].split('_')
X.append(image)
Y.append(label)
X = np.array(X) / 255.0
Y = np.array(Y)
mlb = MultiLabelBinarizer()
Y = mlb.fit_transform(Y)
model = Sequential()
model.add(Conv2D(32, 3, padding='same', input_shape=input_shape, kernel_initializer='he_normal', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(3))
model.add(Dropout(0.25))
model.add(Conv2D(64, 3, padding='same', kernel_initializer='he_normal', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(64, 3, padding='same', kernel_initializer='he_normal', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D())
model.add(Dropout(0.25))
model.add(Conv2D(128, 3, padding='same', kernel_initializer='he_normal', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(128, 2, padding='same', kernel_initializer='he_normal', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D())
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(1024, activation='relu', kernel_initializer='he_normal'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.summary()
model.add(Dense(len(mlb.classes_), activation='sigmoid'))
from keras.utils import plot_model
plot_model(model) | code |
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"text_plain_output_647.png",
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] | from keras.callbacks import ModelCheckpoint
from keras.layers import BatchNormalization, Activation, Dropout
from keras.layers import Conv2D, Dense, Flatten, MaxPooling2D
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MultiLabelBinarizer
import cv2 as cv
import numpy as np
import os
import re
X = []
Y = []
input_shape = (96, 96, 3)
path_to_subset = f'../input/apparel-images-dataset/'
for folder in os.listdir(path_to_subset):
for image in os.listdir(os.path.join(path_to_subset, folder)):
path_to_image = os.path.join(path_to_subset, folder, image)
image = cv.imread(path_to_image)
image = cv.resize(image, (input_shape[1], input_shape[0]))
label = re.findall('\\w+\\_\\w+', path_to_image)[0].split('_')
X.append(image)
Y.append(label)
X = np.array(X) / 255.0
Y = np.array(Y)
mlb = MultiLabelBinarizer()
Y = mlb.fit_transform(Y)
x, test_x, y, test_y = train_test_split(X, Y, test_size=0.1, stratify=Y, shuffle=True, random_state=1)
train_x, val_x, train_y, val_y = train_test_split(x, y, test_size=0.2, stratify=y, shuffle=True, random_state=1)
datagen = ImageDataGenerator(rotation_range=45, width_shift_range=0.1, height_shift_range=0.1, zoom_range=0.2, horizontal_flip=True, validation_split=0.2)
model = Sequential()
model.add(Conv2D(32, 3, padding='same', input_shape=input_shape, kernel_initializer='he_normal', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(3))
model.add(Dropout(0.25))
model.add(Conv2D(64, 3, padding='same', kernel_initializer='he_normal', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(64, 3, padding='same', kernel_initializer='he_normal', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D())
model.add(Dropout(0.25))
model.add(Conv2D(128, 3, padding='same', kernel_initializer='he_normal', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(128, 2, padding='same', kernel_initializer='he_normal', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D())
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(1024, activation='relu', kernel_initializer='he_normal'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.summary()
model.add(Dense(len(mlb.classes_), activation='sigmoid'))
checkpoint = ModelCheckpoint('../working/best_model.hdf5', save_best_only=True, monitor='val_loss', verbose=1)
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit_generator(datagen.flow(train_x, train_y, batch_size=64), validation_data=(val_x, val_y), epochs=100, verbose=1, callbacks=[checkpoint]) | code |
122244636/cell_3 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
import cv2 as cv
import numpy as np
import os
import re
X = []
Y = []
input_shape = (96, 96, 3)
path_to_subset = f'../input/apparel-images-dataset/'
for folder in os.listdir(path_to_subset):
for image in os.listdir(os.path.join(path_to_subset, folder)):
path_to_image = os.path.join(path_to_subset, folder, image)
image = cv.imread(path_to_image)
image = cv.resize(image, (input_shape[1], input_shape[0]))
label = re.findall('\\w+\\_\\w+', path_to_image)[0].split('_')
X.append(image)
Y.append(label)
X = np.array(X) / 255.0
Y = np.array(Y)
mlb = MultiLabelBinarizer()
Y = mlb.fit_transform(Y)
print(mlb.classes_)
print(Y[0]) | code |
122244636/cell_5 | [
"text_plain_output_56.png",
"text_plain_output_35.png",
"text_plain_output_43.png",
"text_plain_output_37.png",
"text_plain_output_5.png",
"text_plain_output_48.png",
"text_plain_output_30.png",
"text_plain_output_15.png",
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"text_plain_output_3.png",
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"text_plain_output_38.png",
"text_plain_output_7.png",
"text_plain_output_16.png",
"text_plain_output_8.png",
"text_plain_output_26.png",
"text_plain_output_41.png",
"text_plain_output_34.png",
"text_plain_output_42.png",
"text_plain_output_53.png",
"text_plain_output_23.png",
"text_plain_output_51.png",
"text_plain_output_28.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text_plain_output_33.png",
"text_plain_output_39.png",
"text_plain_output_55.png",
"text_plain_output_19.png",
"text_plain_output_17.png",
"text_plain_output_11.png",
"text_plain_output_12.png",
"text_plain_output_46.png"
] | from keras.layers import BatchNormalization, Activation, Dropout
from keras.layers import Conv2D, Dense, Flatten, MaxPooling2D
from keras.models import Sequential
from sklearn.preprocessing import MultiLabelBinarizer
import cv2 as cv
import numpy as np
import os
import re
X = []
Y = []
input_shape = (96, 96, 3)
path_to_subset = f'../input/apparel-images-dataset/'
for folder in os.listdir(path_to_subset):
for image in os.listdir(os.path.join(path_to_subset, folder)):
path_to_image = os.path.join(path_to_subset, folder, image)
image = cv.imread(path_to_image)
image = cv.resize(image, (input_shape[1], input_shape[0]))
label = re.findall('\\w+\\_\\w+', path_to_image)[0].split('_')
X.append(image)
Y.append(label)
X = np.array(X) / 255.0
Y = np.array(Y)
mlb = MultiLabelBinarizer()
Y = mlb.fit_transform(Y)
model = Sequential()
model.add(Conv2D(32, 3, padding='same', input_shape=input_shape, kernel_initializer='he_normal', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(3))
model.add(Dropout(0.25))
model.add(Conv2D(64, 3, padding='same', kernel_initializer='he_normal', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(64, 3, padding='same', kernel_initializer='he_normal', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D())
model.add(Dropout(0.25))
model.add(Conv2D(128, 3, padding='same', kernel_initializer='he_normal', activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(128, 2, padding='same', kernel_initializer='he_normal', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D())
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(1024, activation='relu', kernel_initializer='he_normal'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.summary()
model.add(Dense(len(mlb.classes_), activation='sigmoid')) | code |
49116351/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
dataset = pd.read_csv('../input/daim-hakathon/COOKIES_80.csv')
dataset1 = pd.merge(dataset, subscribers)
dataset1.count()
dataset2 = dataset1.drop(['subscribed'], axis=1)
dataset = pd.merge(dataset, dataset2, indicator=True, how='outer').query('_merge=="left_only"').drop('_merge', axis=1)
dataset['subscribed'] = 0
data = pd.concat([dataset1, dataset])
data.to_csv('COOKIES_80v2.csv', index=False)
indexes = data[data['numero_page'] == 0].index
data.drop(indexes, inplace=True)
data.isnull().sum()
data['chapitre'].fillna('home_page', inplace=True)
data.isnull().sum()
data['region'].fillna('GR463', inplace=True)
data['pays'].fillna('GC1', inplace=True)
data.isnull().sum()
data = data.drop(['date_heure_visite', 'chapitre', 'page', 'identifiant'], axis=1)
data = pd.get_dummies(data, columns=['rubrique', 'device', 'browser', 'os', 'source'])
data.shape | code |
49116351/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
dataset = pd.read_csv('../input/daim-hakathon/COOKIES_80.csv')
dataset1 = pd.merge(dataset, subscribers)
dataset1.count()
dataset2 = dataset1.drop(['subscribed'], axis=1)
dataset = pd.merge(dataset, dataset2, indicator=True, how='outer').query('_merge=="left_only"').drop('_merge', axis=1)
dataset['subscribed'] = 0
data = pd.concat([dataset1, dataset])
data.to_csv('COOKIES_80v2.csv', index=False)
indexes = data[data['numero_page'] == 0].index
data.drop(indexes, inplace=True)
data.isnull().sum()
data['chapitre'].fillna('home_page', inplace=True)
data.isnull().sum() | code |
49116351/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
subscribers.head() | code |
49116351/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
dataset = pd.read_csv('../input/daim-hakathon/COOKIES_80.csv')
dataset1 = pd.merge(dataset, subscribers)
dataset1.count()
dataset2 = dataset1.drop(['subscribed'], axis=1)
dataset = pd.merge(dataset, dataset2, indicator=True, how='outer').query('_merge=="left_only"').drop('_merge', axis=1)
dataset['subscribed'] = 0
data = pd.concat([dataset1, dataset])
data.to_csv('COOKIES_80v2.csv', index=False)
indexes = data[data['numero_page'] == 0].index
data.drop(indexes, inplace=True)
data.isnull().sum() | code |
49116351/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
dataset = pd.read_csv('../input/daim-hakathon/COOKIES_80.csv')
dataset1 = pd.merge(dataset, subscribers)
dataset1.count()
dataset2 = dataset1.drop(['subscribed'], axis=1)
dataset = pd.merge(dataset, dataset2, indicator=True, how='outer').query('_merge=="left_only"').drop('_merge', axis=1)
dataset['subscribed'] = 0
data = pd.concat([dataset1, dataset])
data.to_csv('COOKIES_80v2.csv', index=False)
indexes = data[data['numero_page'] == 0].index
data.drop(indexes, inplace=True)
data.isnull().sum()
data['chapitre'].fillna('home_page', inplace=True)
data.isnull().sum()
data['region'].fillna('GR463', inplace=True)
data['pays'].fillna('GC1', inplace=True)
data.isnull().sum()
data = data.drop(['date_heure_visite', 'chapitre', 'page', 'identifiant'], axis=1)
data.head() | code |
49116351/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 |
49116351/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
dataset = pd.read_csv('../input/daim-hakathon/COOKIES_80.csv')
dataset1 = pd.merge(dataset, subscribers)
dataset1.count()
dataset2 = dataset1.drop(['subscribed'], axis=1)
dataset = pd.merge(dataset, dataset2, indicator=True, how='outer').query('_merge=="left_only"').drop('_merge', axis=1)
dataset['subscribed'] = 0
data = pd.concat([dataset1, dataset])
data.to_csv('COOKIES_80v2.csv', index=False)
indexes = data[data['numero_page'] == 0].index
data.drop(indexes, inplace=True)
data.isnull().sum()
data['chapitre'].fillna('home_page', inplace=True)
data.isnull().sum()
data['region'].fillna('GR463', inplace=True)
data['pays'].fillna('GC1', inplace=True)
data.isnull().sum()
data = data.drop(['date_heure_visite', 'chapitre', 'page', 'identifiant'], axis=1)
data.head() | code |
49116351/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
dataset = pd.read_csv('../input/daim-hakathon/COOKIES_80.csv')
dataset1 = pd.merge(dataset, subscribers)
dataset1.count()
dataset2 = dataset1.drop(['subscribed'], axis=1)
dataset = pd.merge(dataset, dataset2, indicator=True, how='outer').query('_merge=="left_only"').drop('_merge', axis=1)
dataset['subscribed'] = 0
data = pd.concat([dataset1, dataset])
data['subscribed'].value_counts() | code |
49116351/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
dataset = pd.read_csv('../input/daim-hakathon/COOKIES_80.csv')
dataset1 = pd.merge(dataset, subscribers)
dataset1.count()
dataset2 = dataset1.drop(['subscribed'], axis=1)
dataset = pd.merge(dataset, dataset2, indicator=True, how='outer').query('_merge=="left_only"').drop('_merge', axis=1)
dataset['subscribed'] = 0
data = pd.concat([dataset1, dataset])
data.to_csv('COOKIES_80v2.csv', index=False)
indexes = data[data['numero_page'] == 0].index
data.drop(indexes, inplace=True)
data.isnull().sum()
data['chapitre'].fillna('home_page', inplace=True)
data.isnull().sum()
data['region'].fillna('GR463', inplace=True)
data['pays'].fillna('GC1', inplace=True)
data.isnull().sum()
data['browser'] = data['browser'].str.split(' ').str[0]
data['browser'].value_counts() | code |
49116351/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
dataset = pd.read_csv('../input/daim-hakathon/COOKIES_80.csv')
dataset1 = pd.merge(dataset, subscribers)
dataset1.count()
dataset2 = dataset1.drop(['subscribed'], axis=1)
dataset = pd.merge(dataset, dataset2, indicator=True, how='outer').query('_merge=="left_only"').drop('_merge', axis=1)
dataset['subscribed'] = 0
data = pd.concat([dataset1, dataset])
data.to_csv('COOKIES_80v2.csv', index=False)
indexes = data[data['numero_page'] == 0].index
data.drop(indexes, inplace=True)
data.isnull().sum()
data['chapitre'].fillna('home_page', inplace=True)
data.isnull().sum()
data['region'].fillna('GR463', inplace=True)
data['pays'].fillna('GC1', inplace=True)
data.isnull().sum()
data['os'] = data['os'].str.split(' ').str[0]
data['os'].value_counts() | code |
49116351/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
dataset = pd.read_csv('../input/daim-hakathon/COOKIES_80.csv')
dataset1 = pd.merge(dataset, subscribers)
dataset1.count()
dataset2 = dataset1.drop(['subscribed'], axis=1)
dataset = pd.merge(dataset, dataset2, indicator=True, how='outer').query('_merge=="left_only"').drop('_merge', axis=1)
dataset['subscribed'] = 0
data = pd.concat([dataset1, dataset])
data.to_csv('COOKIES_80v2.csv', index=False)
indexes = data[data['numero_page'] == 0].index
data.drop(indexes, inplace=True)
data.isnull().sum()
data['chapitre'].fillna('home_page', inplace=True)
data.isnull().sum()
data['region'].fillna('GR463', inplace=True)
data['pays'].fillna('GC1', inplace=True)
data.isnull().sum()
data.head(5) | code |
49116351/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
dataset = pd.read_csv('../input/daim-hakathon/COOKIES_80.csv')
dataset1 = pd.merge(dataset, subscribers)
dataset1.count()
dataset2 = dataset1.drop(['subscribed'], axis=1)
dataset = pd.merge(dataset, dataset2, indicator=True, how='outer').query('_merge=="left_only"').drop('_merge', axis=1)
dataset['subscribed'] = 0
data = pd.concat([dataset1, dataset])
data.to_csv('COOKIES_80v2.csv', index=False)
indexes = data[data['numero_page'] == 0].index
data.drop(indexes, inplace=True)
data.isnull().sum()
data['chapitre'].fillna('home_page', inplace=True)
data.isnull().sum()
data['region'].fillna('GR463', inplace=True)
data['pays'].fillna('GC1', inplace=True)
data.isnull().sum() | code |
49116351/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
dataset = pd.read_csv('../input/daim-hakathon/COOKIES_80.csv')
dataset1 = pd.merge(dataset, subscribers)
dataset1.count()
dataset2 = dataset1.drop(['subscribed'], axis=1)
dataset = pd.merge(dataset, dataset2, indicator=True, how='outer').query('_merge=="left_only"').drop('_merge', axis=1)
dataset['subscribed'] = 0
data = pd.concat([dataset1, dataset])
data.to_csv('COOKIES_80v2.csv', index=False)
data.head() | code |
49116351/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
dataset = pd.read_csv('../input/daim-hakathon/COOKIES_80.csv')
dataset1 = pd.merge(dataset, subscribers)
dataset1.count() | code |
325654/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
import csv as csv
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from time import time
train = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
data = np.array(train)
test_data = np.array(test)
number_passengers = np.size(data[0:, 1].astype(np.float))
number_survived = np.sum(data[0:, 1].astype(np.float))
proportion_survivors = number_survived / number_passengers
women_only_stats = data[0:, 4] == 'female'
men_only_stats = data[0:, 4] != 'female'
women_onboard = data[women_only_stats, 1].astype(np.float)
men_onboard = data[men_only_stats, 1].astype(np.float)
proportion_women_survived = np.sum(women_onboard) / np.size(women_onboard)
proportion_men_survived = np.sum(men_onboard) / np.size(men_onboard)
print('Proportion of women who survived is %s' % proportion_women_survived)
print('Proportion of men who survived is %s' % proportion_men_survived) | code |
129019305/cell_13 | [
"text_plain_output_1.png"
] | from matplotlib.colors import ListedColormap
from matplotlib.colors import ListedColormap
from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as mtp
import numpy as np
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
x_train = sc.fit_transform(x_train)
x_test = sc.transform(x_test)
from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(x_train, y_train)
y_pred = classifier.predict(x_test)
from matplotlib.colors import ListedColormap
x_set, y_set = (x_train, y_train)
X1, X2 = np.meshgrid(np.arange(start=x_set[:, 0].min() - 1, stop=x_set[:, 0].max() + 1, step=0.01), np.arange(start=x_set[:, 1].min() - 1, stop=x_set[:, 1].max() + 1, step=0.01))
mtp.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape), alpha=0.75, cmap=ListedColormap(('purple', 'green')))
mtp.xlim(X1.min(), X1.max())
mtp.ylim(X2.min(), X2.max())
from matplotlib.colors import ListedColormap
x_set, y_set = (x_test, y_test)
X1, X2 = np.meshgrid(np.arange(start=x_set[:, 0].min() - 1, stop=x_set[:, 0].max() + 1, step=0.01), np.arange(start=x_set[:, 1].min() - 1, stop=x_set[:, 1].max() + 1, step=0.01))
mtp.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape), alpha=0.75, cmap=ListedColormap(('purple', 'green')))
mtp.xlim(X1.min(), X1.max())
mtp.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
mtp.scatter(x_set[y_set == j, 0], x_set[y_set == j, 1], c=ListedColormap(('purple', 'green'))(i), label=j)
mtp.title('Naive Bayes (test set)')
mtp.xlabel('Age')
mtp.ylabel('Estimated Salary')
mtp.legend()
mtp.show() | code |
129019305/cell_11 | [
"text_html_output_1.png"
] | from sklearn.metrics import confusion_matrix
from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
x_train = sc.fit_transform(x_train)
x_test = sc.transform(x_test)
from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(x_train, y_train)
y_pred = classifier.predict(x_test)
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
cm | code |
129019305/cell_8 | [
"image_output_1.png"
] | from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
x_train = sc.fit_transform(x_train)
x_test = sc.transform(x_test)
from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(x_train, y_train) | code |
129019305/cell_10 | [
"text_html_output_1.png"
] | from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import StandardScaler
import pandas as pd
dataset = pd.read_csv('/kaggle/input/user-data/User_Data.csv')
x = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
x_train = sc.fit_transform(x_train)
x_test = sc.transform(x_test)
from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(x_train, y_train)
y_pred = classifier.predict(x_test)
pd.DataFrame(y_pred, y_test).head(20) | code |
129019305/cell_12 | [
"text_html_output_1.png"
] | from matplotlib.colors import ListedColormap
from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as mtp
import numpy as np
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
x_train = sc.fit_transform(x_train)
x_test = sc.transform(x_test)
from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(x_train, y_train)
y_pred = classifier.predict(x_test)
from matplotlib.colors import ListedColormap
x_set, y_set = (x_train, y_train)
X1, X2 = np.meshgrid(np.arange(start=x_set[:, 0].min() - 1, stop=x_set[:, 0].max() + 1, step=0.01), np.arange(start=x_set[:, 1].min() - 1, stop=x_set[:, 1].max() + 1, step=0.01))
mtp.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape), alpha=0.75, cmap=ListedColormap(('purple', 'green')))
mtp.xlim(X1.min(), X1.max())
mtp.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
mtp.scatter(x_set[y_set == j, 0], x_set[y_set == j, 1], c=ListedColormap(('purple', 'green'))(i), label=j)
mtp.title('Naive Bayes (Training set)')
mtp.xlabel('Age')
mtp.ylabel('Estimated Salary')
mtp.legend()
mtp.show() | code |
129019305/cell_5 | [
"image_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/user-data/User_Data.csv')
x = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
dataset.head() | code |
320410/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)
Y_train = pd.read_csv('../input/genderclassmodel.csv')
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
_train = train.copy()
Y_train = _train['Survived'].copy()
_train.drop(['Name', 'Ticket', 'Cabin', 'Survived'], axis=1, inplace=True)
_train.fillna('-1', inplace=True)
_train.replace('male', 1, inplace=True)
_train.replace('female', 2, inplace=True)
_train.replace('C', 1, inplace=True)
_train.replace('Q', 2, inplace=True)
_train.replace('S', 3, inplace=True)
_test = test.copy()
_test.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
_test.fillna('-1', inplace=True)
_test.replace('male', 1, inplace=True)
_test.replace('female', 2, inplace=True)
_test.replace('C', 1, inplace=True)
_test.replace('Q', 2, inplace=True)
_test.replace('S', 3, inplace=True)
passenger_survived = train[train.Survived == 1]['PassengerId'].copy()
passenger_died = train[train.Survived == 0]['PassengerId'].copy()
_train_survived = _train[_train.PassengerId.isin(passenger_survived)].drop('PassengerId', axis=1)
_train_survived.plot(kind='bar') | code |
320410/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
from IPython.display import display
from sklearn.ensemble import RandomForestClassifier
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import sklearn
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
320410/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Y_train = pd.read_csv('../input/genderclassmodel.csv')
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
_train = train.copy()
Y_train = _train['Survived'].copy()
_train.drop(['Name', 'Ticket', 'Cabin', 'Survived'], axis=1, inplace=True)
_train.fillna('-1', inplace=True)
_train.replace('male', 1, inplace=True)
_train.replace('female', 2, inplace=True)
_train.replace('C', 1, inplace=True)
_train.replace('Q', 2, inplace=True)
_train.replace('S', 3, inplace=True)
_test = test.copy()
_test.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
_test.fillna('-1', inplace=True)
_test.replace('male', 1, inplace=True)
_test.replace('female', 2, inplace=True)
_test.replace('C', 1, inplace=True)
_test.replace('Q', 2, inplace=True)
_test.replace('S', 3, inplace=True)
passenger_survived = train[train.Survived == 1]['PassengerId'].copy()
passenger_died = train[train.Survived == 0]['PassengerId'].copy()
_train_survived = _train[_train.PassengerId.isin(passenger_survived)].drop('PassengerId', axis=1)
model = RandomForestClassifier()
model.fit(_train, Y_train) | code |
320410/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Y_train = pd.read_csv('../input/genderclassmodel.csv')
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
_train = train.copy()
Y_train = _train['Survived'].copy()
_train.drop(['Name', 'Ticket', 'Cabin', 'Survived'], axis=1, inplace=True)
_train.fillna('-1', inplace=True)
_train.replace('male', 1, inplace=True)
_train.replace('female', 2, inplace=True)
_train.replace('C', 1, inplace=True)
_train.replace('Q', 2, inplace=True)
_train.replace('S', 3, inplace=True)
_test = test.copy()
_test.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
_test.fillna('-1', inplace=True)
_test.replace('male', 1, inplace=True)
_test.replace('female', 2, inplace=True)
_test.replace('C', 1, inplace=True)
_test.replace('Q', 2, inplace=True)
_test.replace('S', 3, inplace=True)
passenger_survived = train[train.Survived == 1]['PassengerId'].copy()
passenger_died = train[train.Survived == 0]['PassengerId'].copy()
_train_survived = _train[_train.PassengerId.isin(passenger_survived)].drop('PassengerId', axis=1)
model = RandomForestClassifier()
model.fit(_train, Y_train)
predicted = test[['PassengerId']].copy()
predicted['Survived'] = model.predict(_test)
print(predicted.to_csv(index=False)) | code |
320410/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Y_train = pd.read_csv('../input/genderclassmodel.csv')
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
print('test set has %s rows.' % test.shape[0])
print('train set has %s rows.' % train.shape[0]) | code |
88105099/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_path = '../input/nuclio10-dsc-1121/sales_train_merged.csv'
df = pd.read_csv(data_path, index_col=0)
df.head() | code |
105197919/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
choco_data = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
choco_data2 = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
len(choco_data)
choco_data.sample(5)
nullValues = choco_data.isnull().sum()
nullValues[0:12]
choco_data = choco_data.drop(['num_ingredients', 'ingredients'], axis=1)
nullValues = choco_data.isnull().sum()
choco_data4 = choco_data
one_hot = pd.get_dummies(choco_data4, columns=['company_location'], drop_first=False)
one_hot.head() | code |
105197919/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
choco_data = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
choco_data2 = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
len(choco_data)
choco_data.sample(5) | code |
105197919/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
choco_data = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
choco_data2 = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
len(choco_data)
choco_data.sample(5)
nullValues = choco_data.isnull().sum()
nullValues[0:12]
choco_data = choco_data.drop(['num_ingredients', 'ingredients'], axis=1)
nullValues = choco_data.isnull().sum()
choco_data4 = choco_data
choco_data4['company_location'].value_counts() | code |
105197919/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
choco_data = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
choco_data2 = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
len(choco_data)
choco_data.sample(5)
nullValues = choco_data.isnull().sum()
nullValues[0:12]
choco_data = choco_data.drop(['num_ingredients', 'ingredients'], axis=1)
nullValues = choco_data.isnull().sum()
choco_data2 = choco_data2.dropna()
nullValues = choco_data2.isnull().sum()
print(nullValues) | code |
105197919/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 |
105197919/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
choco_data = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
choco_data2 = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
len(choco_data)
choco_data.sample(5)
nullValues = choco_data.isnull().sum()
nullValues[0:12]
choco_data = choco_data.drop(['num_ingredients', 'ingredients'], axis=1)
nullValues = choco_data.isnull().sum()
print(nullValues) | code |
105197919/cell_16 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
choco_data = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
choco_data2 = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
len(choco_data)
choco_data.sample(5)
nullValues = choco_data.isnull().sum()
nullValues[0:12]
choco_data = choco_data.drop(['num_ingredients', 'ingredients'], axis=1)
nullValues = choco_data.isnull().sum()
from sklearn import preprocessing
label_encoder = preprocessing.LabelEncoder()
choco_data3 = choco_data
choco_data3['company_location'] = label_encoder.fit_transform(choco_data3['company_location'])
choco_data3['company_location'].unique() | code |
105197919/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
choco_data = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
choco_data2 = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
len(choco_data) | code |
105197919/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
choco_data = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
choco_data2 = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
len(choco_data)
choco_data.sample(5)
nullValues = choco_data.isnull().sum()
nullValues[0:12]
choco_data = choco_data.drop(['num_ingredients', 'ingredients'], axis=1)
nullValues = choco_data.isnull().sum()
choco_data3 = choco_data
choco_data3.head() | code |
105197919/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
choco_data = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
choco_data2 = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
len(choco_data)
choco_data.sample(5)
nullValues = choco_data.isnull().sum()
nullValues[0:12]
choco_data = choco_data.drop(['num_ingredients', 'ingredients'], axis=1)
nullValues = choco_data.isnull().sum()
choco_data4 = choco_data
one_hot = pd.get_dummies(choco_data4, columns=['company_location'], drop_first=False)
choco_data_custom = ['brown', 'toblerone', 'perk', 'mars', 'mars', 'chocho', 'london', 'perk', 'brown']
print(pd.get_dummies(choco_data_custom, drop_first=True)) | code |
105197919/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
choco_data = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
choco_data2 = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
len(choco_data)
choco_data.sample(5)
nullValues = choco_data.isnull().sum()
nullValues[0:12]
choco_data = choco_data.drop(['num_ingredients', 'ingredients'], axis=1)
nullValues = choco_data.isnull().sum()
for columns in choco_data.columns:
print(columns, len(choco_data[columns].unique())) | code |
105197919/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
choco_data = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
choco_data2 = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
len(choco_data)
choco_data.sample(5)
nullValues = choco_data.isnull().sum()
nullValues[0:12] | code |
18116618/cell_21 | [
"image_output_1.png"
] | from pandas.plotting import scatter_matrix
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
housing = df.copy()
corr_matrix = housing.corr()
attributes = corr_matrix['SalePrice'].sort_values(ascending=False)
attributes
from pandas.plotting import scatter_matrix
scatter_matrix(housing[attributes[:6].index], figsize=(20, 20)) | code |
18116618/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
housing = df.copy()
corr_matrix = housing.corr()
attributes = corr_matrix['SalePrice'].sort_values(ascending=False)
attributes
top10 = attributes[1:11]
attributes[:11]
housing = df.drop('SalePrice', axis=1)
housing_labels = df['SalePrice'].copy()
top10 = list(top10.index)
housing = housing[top10]
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
full_pipeline = Pipeline([('imputer', SimpleImputer(strategy='median')), ('std_scaler', StandardScaler())])
housing_prepared = full_pipeline.fit_transform(housing)
housing_prepared
pd.DataFrame(housing_prepared, columns=housing.columns, index=housing.index).head() | code |
18116618/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
housing = df.copy()
corr_matrix = housing.corr()
housing.plot(kind='scatter', x='GrLivArea', y='SalePrice', alpha=0.1) | code |
18116618/cell_30 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
housing = df.copy()
corr_matrix = housing.corr()
attributes = corr_matrix['SalePrice'].sort_values(ascending=False)
attributes
top10 = attributes[1:11]
attributes[:11]
housing = df.drop('SalePrice', axis=1)
housing_labels = df['SalePrice'].copy()
top10 = list(top10.index)
housing = housing[top10]
housing.describe() | code |
18116618/cell_44 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
housing = df.copy()
corr_matrix = housing.corr()
attributes = corr_matrix['SalePrice'].sort_values(ascending=False)
attributes
top10 = attributes[1:11]
attributes[:11]
housing = df.drop('SalePrice', axis=1)
housing_labels = df['SalePrice'].copy()
top10 = list(top10.index)
housing = housing[top10]
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
full_pipeline = Pipeline([('imputer', SimpleImputer(strategy='median')), ('std_scaler', StandardScaler())])
housing_prepared = full_pipeline.fit_transform(housing)
housing_prepared
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(housing_prepared, housing_labels)
some_data = housing.iloc[:5]
some_labels = housing_labels.iloc[:5]
some_data_prepared = full_pipeline.transform(some_data)
from sklearn.metrics import mean_squared_error
housing_predictions = lin_reg.predict(housing_prepared)
lin_mse = mean_squared_error(housing_labels, housing_predictions)
lin_rmse = np.sqrt(lin_mse)
lin_rmse
from sklearn.tree import DecisionTreeRegressor
tree_reg = DecisionTreeRegressor()
tree_reg.fit(housing_prepared, housing_labels)
housing_predictions = tree_reg.predict(housing_prepared)
tree_mse = mean_squared_error(housing_labels, housing_predictions)
tree_rmse = np.sqrt(tree_mse)
tree_rmse | code |
18116618/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
df.info() | code |
18116618/cell_39 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
housing = df.copy()
corr_matrix = housing.corr()
attributes = corr_matrix['SalePrice'].sort_values(ascending=False)
attributes
top10 = attributes[1:11]
attributes[:11]
housing = df.drop('SalePrice', axis=1)
housing_labels = df['SalePrice'].copy()
top10 = list(top10.index)
housing = housing[top10]
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
full_pipeline = Pipeline([('imputer', SimpleImputer(strategy='median')), ('std_scaler', StandardScaler())])
housing_prepared = full_pipeline.fit_transform(housing)
housing_prepared
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(housing_prepared, housing_labels)
some_data = housing.iloc[:5]
some_labels = housing_labels.iloc[:5]
some_data_prepared = full_pipeline.transform(some_data)
print('Predictions: ', lin_reg.predict(some_data_prepared))
print('Labels: ', list(some_labels)) | code |
18116618/cell_26 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
housing = df.copy()
corr_matrix = housing.corr()
housing = df.drop('SalePrice', axis=1)
housing_labels = df['SalePrice'].copy()
housing.head() | code |
18116618/cell_41 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
housing = df.copy()
corr_matrix = housing.corr()
attributes = corr_matrix['SalePrice'].sort_values(ascending=False)
attributes
top10 = attributes[1:11]
attributes[:11]
housing = df.drop('SalePrice', axis=1)
housing_labels = df['SalePrice'].copy()
top10 = list(top10.index)
housing = housing[top10]
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
full_pipeline = Pipeline([('imputer', SimpleImputer(strategy='median')), ('std_scaler', StandardScaler())])
housing_prepared = full_pipeline.fit_transform(housing)
housing_prepared
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(housing_prepared, housing_labels)
some_data = housing.iloc[:5]
some_labels = housing_labels.iloc[:5]
some_data_prepared = full_pipeline.transform(some_data)
from sklearn.metrics import mean_squared_error
housing_predictions = lin_reg.predict(housing_prepared)
lin_mse = mean_squared_error(housing_labels, housing_predictions)
lin_rmse = np.sqrt(lin_mse)
lin_rmse | code |
18116618/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
housing = df.copy()
corr_matrix = housing.corr()
attributes = corr_matrix['SalePrice'].sort_values(ascending=False)
attributes
top10 = attributes[1:11]
attributes[:11] | code |
18116618/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
housing = df.copy()
corr_matrix = housing.corr()
attributes = corr_matrix['SalePrice'].sort_values(ascending=False)
attributes | code |
18116618/cell_32 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
housing = df.copy()
corr_matrix = housing.corr()
attributes = corr_matrix['SalePrice'].sort_values(ascending=False)
attributes
top10 = attributes[1:11]
attributes[:11]
housing = df.drop('SalePrice', axis=1)
housing_labels = df['SalePrice'].copy()
top10 = list(top10.index)
housing = housing[top10]
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
full_pipeline = Pipeline([('imputer', SimpleImputer(strategy='median')), ('std_scaler', StandardScaler())])
housing_prepared = full_pipeline.fit_transform(housing)
housing_prepared | code |
18116618/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
df.describe() | code |
18116618/cell_35 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
housing = df.copy()
corr_matrix = housing.corr()
attributes = corr_matrix['SalePrice'].sort_values(ascending=False)
attributes
top10 = attributes[1:11]
attributes[:11]
housing = df.drop('SalePrice', axis=1)
housing_labels = df['SalePrice'].copy()
top10 = list(top10.index)
housing = housing[top10]
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
full_pipeline = Pipeline([('imputer', SimpleImputer(strategy='median')), ('std_scaler', StandardScaler())])
housing_prepared = full_pipeline.fit_transform(housing)
housing_prepared
housing.head() | code |
18116618/cell_43 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
housing = df.copy()
corr_matrix = housing.corr()
attributes = corr_matrix['SalePrice'].sort_values(ascending=False)
attributes
top10 = attributes[1:11]
attributes[:11]
housing = df.drop('SalePrice', axis=1)
housing_labels = df['SalePrice'].copy()
top10 = list(top10.index)
housing = housing[top10]
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
full_pipeline = Pipeline([('imputer', SimpleImputer(strategy='median')), ('std_scaler', StandardScaler())])
housing_prepared = full_pipeline.fit_transform(housing)
housing_prepared
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(housing_prepared, housing_labels)
some_data = housing.iloc[:5]
some_labels = housing_labels.iloc[:5]
some_data_prepared = full_pipeline.transform(some_data)
from sklearn.tree import DecisionTreeRegressor
tree_reg = DecisionTreeRegressor()
tree_reg.fit(housing_prepared, housing_labels) | code |
18116618/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
df.hist(bins=50, figsize=(30, 20))
plt.show() | code |
18116618/cell_27 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
housing = df.copy()
housing = df.drop('SalePrice', axis=1)
housing_labels = df['SalePrice'].copy()
housing_labels.head() | code |
18116618/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
housing = df.copy()
corr_matrix = housing.corr()
attributes = corr_matrix['SalePrice'].sort_values(ascending=False)
attributes
top10 = attributes[1:11]
attributes[:11]
housing = df.drop('SalePrice', axis=1)
housing_labels = df['SalePrice'].copy()
top10 = list(top10.index)
housing = housing[top10]
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
full_pipeline = Pipeline([('imputer', SimpleImputer(strategy='median')), ('std_scaler', StandardScaler())])
housing_prepared = full_pipeline.fit_transform(housing)
housing_prepared
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(housing_prepared, housing_labels) | code |
18116618/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
df.head() | code |
34141752/cell_9 | [
"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 re
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
title_list = ['Mrs', 'Mr', 'Master', 'Miss', 'Major', 'Rev', 'Dr', 'Ms', 'Mlle', 'Col', 'Capt', 'Mme', 'Countess', 'Don', 'Jonkheer', 'Sir']
def substrings_in_string(big_string, substrings):
for substring in substrings:
if bool(re.search(substring, big_string)) == 1:
return substring
return np.nan
train['Title'] = train['Name'].map(lambda x: substrings_in_string(x, title_list))
test['Title'] = test['Name'].map(lambda x: substrings_in_string(x, title_list))
def replace_titles(x):
title = x['Title']
if title in ['Don', 'Major', 'Capt', 'Jonkheer', 'Rev', 'Col', 'Sir']:
return 'Mr'
elif title in ['Countess', 'Mme']:
return 'Mrs'
elif title in ['Mlle', 'Ms']:
return 'Miss'
elif title == 'Dr':
if x['Sex'] == 'Male':
return 'Mr'
else:
return 'Mrs'
else:
return title
train['Title'] = train.apply(replace_titles, axis=1)
test['Title'] = test.apply(replace_titles, axis=1)
cabin_list = ['A', 'B', 'C', 'D', 'E', 'F', 'T', 'G', 'Unknown']
train = train.fillna(value={'Cabin': 'Unknown'})
test = test.fillna(value={'Cabin': 'Unknown'})
train['Cabin'] = train['Cabin'].map(lambda x: substrings_in_string(x, cabin_list))
test['Cabin'] = train['Cabin'].map(lambda x: substrings_in_string(x, cabin_list))
sns.distplot(train[train['Sex'] == 'female']['Age'], color='pink', bins=15)
sns.distplot(train[train['Sex'] == 'male']['Age'], color='blue', bins=15, hist_kws={'alpha': 0.2}) | code |
34141752/cell_4 | [
"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)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.info()
train.describe() | code |
34141752/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
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 re
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
title_list = ['Mrs', 'Mr', 'Master', 'Miss', 'Major', 'Rev', 'Dr', 'Ms', 'Mlle', 'Col', 'Capt', 'Mme', 'Countess', 'Don', 'Jonkheer', 'Sir']
def substrings_in_string(big_string, substrings):
for substring in substrings:
if bool(re.search(substring, big_string)) == 1:
return substring
return np.nan
train['Title'] = train['Name'].map(lambda x: substrings_in_string(x, title_list))
test['Title'] = test['Name'].map(lambda x: substrings_in_string(x, title_list))
def replace_titles(x):
title = x['Title']
if title in ['Don', 'Major', 'Capt', 'Jonkheer', 'Rev', 'Col', 'Sir']:
return 'Mr'
elif title in ['Countess', 'Mme']:
return 'Mrs'
elif title in ['Mlle', 'Ms']:
return 'Miss'
elif title == 'Dr':
if x['Sex'] == 'Male':
return 'Mr'
else:
return 'Mrs'
else:
return title
train['Title'] = train.apply(replace_titles, axis=1)
test['Title'] = test.apply(replace_titles, axis=1)
cabin_list = ['A', 'B', 'C', 'D', 'E', 'F', 'T', 'G', 'Unknown']
train = train.fillna(value={'Cabin': 'Unknown'})
test = test.fillna(value={'Cabin': 'Unknown'})
train['Cabin'] = train['Cabin'].map(lambda x: substrings_in_string(x, cabin_list))
test['Cabin'] = train['Cabin'].map(lambda x: substrings_in_string(x, cabin_list))
corre = train.corr()
features = ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare', 'Sex_bin', 'Family_Size']
train[features].info()
y_train = train['Survived']
x_train = train[features]
lr = LogisticRegression()
lr.fit(x_train, y_train)
print('The Score for Titanic is {:.3f}'.format(lr.score(x_train, y_train))) | code |
34141752/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 |
34141752/cell_8 | [
"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 re
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
title_list = ['Mrs', 'Mr', 'Master', 'Miss', 'Major', 'Rev', 'Dr', 'Ms', 'Mlle', 'Col', 'Capt', 'Mme', 'Countess', 'Don', 'Jonkheer', 'Sir']
def substrings_in_string(big_string, substrings):
for substring in substrings:
if bool(re.search(substring, big_string)) == 1:
return substring
return np.nan
train['Title'] = train['Name'].map(lambda x: substrings_in_string(x, title_list))
test['Title'] = test['Name'].map(lambda x: substrings_in_string(x, title_list))
def replace_titles(x):
title = x['Title']
if title in ['Don', 'Major', 'Capt', 'Jonkheer', 'Rev', 'Col', 'Sir']:
return 'Mr'
elif title in ['Countess', 'Mme']:
return 'Mrs'
elif title in ['Mlle', 'Ms']:
return 'Miss'
elif title == 'Dr':
if x['Sex'] == 'Male':
return 'Mr'
else:
return 'Mrs'
else:
return title
train['Title'] = train.apply(replace_titles, axis=1)
test['Title'] = test.apply(replace_titles, axis=1)
cabin_list = ['A', 'B', 'C', 'D', 'E', 'F', 'T', 'G', 'Unknown']
train = train.fillna(value={'Cabin': 'Unknown'})
test = test.fillna(value={'Cabin': 'Unknown'})
train['Cabin'] = train['Cabin'].map(lambda x: substrings_in_string(x, cabin_list))
test['Cabin'] = train['Cabin'].map(lambda x: substrings_in_string(x, cabin_list))
train['Family_Size'] = train['SibSp'] + train['Parch']
test['Family_Size'] = test['SibSp'] + test['Parch']
train['Age'] = train['Age'].fillna(np.mean(train['Age']))
print(train) | code |
34141752/cell_3 | [
"text_plain_output_1.png"
] | train.head(5) | code |
34141752/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
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 re
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
title_list = ['Mrs', 'Mr', 'Master', 'Miss', 'Major', 'Rev', 'Dr', 'Ms', 'Mlle', 'Col', 'Capt', 'Mme', 'Countess', 'Don', 'Jonkheer', 'Sir']
def substrings_in_string(big_string, substrings):
for substring in substrings:
if bool(re.search(substring, big_string)) == 1:
return substring
return np.nan
train['Title'] = train['Name'].map(lambda x: substrings_in_string(x, title_list))
test['Title'] = test['Name'].map(lambda x: substrings_in_string(x, title_list))
def replace_titles(x):
title = x['Title']
if title in ['Don', 'Major', 'Capt', 'Jonkheer', 'Rev', 'Col', 'Sir']:
return 'Mr'
elif title in ['Countess', 'Mme']:
return 'Mrs'
elif title in ['Mlle', 'Ms']:
return 'Miss'
elif title == 'Dr':
if x['Sex'] == 'Male':
return 'Mr'
else:
return 'Mrs'
else:
return title
train['Title'] = train.apply(replace_titles, axis=1)
test['Title'] = test.apply(replace_titles, axis=1)
cabin_list = ['A', 'B', 'C', 'D', 'E', 'F', 'T', 'G', 'Unknown']
train = train.fillna(value={'Cabin': 'Unknown'})
test = test.fillna(value={'Cabin': 'Unknown'})
train['Cabin'] = train['Cabin'].map(lambda x: substrings_in_string(x, cabin_list))
test['Cabin'] = train['Cabin'].map(lambda x: substrings_in_string(x, cabin_list))
corre = train.corr()
sns.heatmap(corre, annot=True)
plt.show() | code |
105200129/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/chocolate-bar-ratings/chocolate_bars.csv')
data.isnull().sum()
data.isnull().sum()
from sklearn.preprocessing import LabelEncoder
En = LabelEncoder()
Enco = En.fit_transform(data['bean_origin'])
data.drop('bean_origin', axis=1, inplace=True)
data['bean_origin'] = Enco
data.isnull().sum() | code |
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