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stringlengths 13
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34120249/cell_4 | [
"image_output_1.png"
] | import pandas as pd
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses.head(3) | code |
34120249/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_courses['price'] = udemy_courses['price'].astype('float')
udemy_courses['number_of_contents'] = udemy_courses['content_duration'].str.extract('([\\d\\.]+)\\s[\\w]+').astype('float')
udemy_courses['content_duration_type'] = udemy_courses['content_duration'].str.extract('[\\d\\.]+\\s([\\w]+)')
udemy_courses.drop('content_duration', axis=1, inplace=True)
udemy_courses.dropna(inplace=True)
udemy_courses[udemy_courses['is_paid'] == 'True'].groupby('level')['price'].mean().sort_values(ascending=False) | code |
34120249/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_courses['price'] = udemy_courses['price'].astype('float')
udemy_courses['number_of_contents'] = udemy_courses['content_duration'].str.extract('([\\d\\.]+)\\s[\\w]+').astype('float')
udemy_courses['content_duration_type'] = udemy_courses['content_duration'].str.extract('[\\d\\.]+\\s([\\w]+)')
udemy_courses.drop('content_duration', axis=1, inplace=True)
udemy_courses.dropna(inplace=True)
udemy_courses.groupby('price_category')['num_lectures'].mean().sort_values(ascending=False)
udemy_courses[udemy_courses['content_duration_type'] == 'questions'] | code |
34120249/cell_33 | [
"image_output_1.png"
] | import pandas as pd
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_courses['price'] = udemy_courses['price'].astype('float')
udemy_courses['number_of_contents'] = udemy_courses['content_duration'].str.extract('([\\d\\.]+)\\s[\\w]+').astype('float')
udemy_courses['content_duration_type'] = udemy_courses['content_duration'].str.extract('[\\d\\.]+\\s([\\w]+)')
udemy_courses.drop('content_duration', axis=1, inplace=True)
udemy_courses.dropna(inplace=True)
pd.pivot_table(index='is_paid', values='num_subscribers', data=udemy_courses, aggfunc='mean')
udemy_courses.groupby('price_category')['num_lectures'].mean().sort_values(ascending=False)
udemy_courses['published_year'] = udemy_courses['published_timestamp'].dt.year
pd.pivot_table(index='published_year', columns='subject', values='course_id', data=udemy_courses, aggfunc='count', fill_value=0) | code |
34120249/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_courses['price'] = udemy_courses['price'].astype('float')
udemy_courses['number_of_contents'] = udemy_courses['content_duration'].str.extract('([\\d\\.]+)\\s[\\w]+').astype('float')
udemy_courses['content_duration_type'] = udemy_courses['content_duration'].str.extract('[\\d\\.]+\\s([\\w]+)')
udemy_courses.drop('content_duration', axis=1, inplace=True)
udemy_courses.dropna(inplace=True)
g=sns.catplot(x='subject',
data=udemy_courses,
kind='count',
hue='is_paid')
g.fig.suptitle('free/paid categories comparison', y=1.03)
plt.xticks(rotation=90)
plt.show()
g = sns.catplot(x='level', data=udemy_courses, kind='count', hue='is_paid')
g.fig.suptitle('free/paid lavel comparison', y=1.03)
plt.xticks(rotation=90)
plt.show() | code |
34120249/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_courses['price'] = udemy_courses['price'].astype('float')
udemy_courses['number_of_contents'] = udemy_courses['content_duration'].str.extract('([\\d\\.]+)\\s[\\w]+').astype('float')
udemy_courses['content_duration_type'] = udemy_courses['content_duration'].str.extract('[\\d\\.]+\\s([\\w]+)')
udemy_courses.drop('content_duration', axis=1, inplace=True)
udemy_courses.dropna(inplace=True)
udemy_courses.groupby('price_category')['num_lectures'].mean().sort_values(ascending=False) | code |
34120249/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_courses['price'] = udemy_courses['price'].astype('float')
udemy_courses['number_of_contents'] = udemy_courses['content_duration'].str.extract('([\\d\\.]+)\\s[\\w]+').astype('float')
udemy_courses['content_duration_type'] = udemy_courses['content_duration'].str.extract('[\\d\\.]+\\s([\\w]+)')
udemy_courses.drop('content_duration', axis=1, inplace=True)
udemy_courses.dropna(inplace=True)
udemy_courses['is_paid'].value_counts() | code |
34120249/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_courses['price'] = udemy_courses['price'].astype('float')
udemy_courses['number_of_contents'] = udemy_courses['content_duration'].str.extract('([\\d\\.]+)\\s[\\w]+').astype('float')
udemy_courses['content_duration_type'] = udemy_courses['content_duration'].str.extract('[\\d\\.]+\\s([\\w]+)')
udemy_courses.drop('content_duration', axis=1, inplace=True)
udemy_courses.dropna(inplace=True)
pd.pivot_table(index='is_paid', values='num_subscribers', data=udemy_courses, aggfunc='mean') | code |
34120249/cell_32 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_courses['price'] = udemy_courses['price'].astype('float')
udemy_courses['number_of_contents'] = udemy_courses['content_duration'].str.extract('([\\d\\.]+)\\s[\\w]+').astype('float')
udemy_courses['content_duration_type'] = udemy_courses['content_duration'].str.extract('[\\d\\.]+\\s([\\w]+)')
udemy_courses.drop('content_duration', axis=1, inplace=True)
udemy_courses.dropna(inplace=True)
g=sns.catplot(x='subject',
data=udemy_courses,
kind='count',
hue='is_paid')
g.fig.suptitle('free/paid categories comparison', y=1.03)
plt.xticks(rotation=90)
plt.show()
g=sns.catplot(x='level',
data=udemy_courses,
kind='count',
hue='is_paid')
g.fig.suptitle('free/paid lavel comparison', y=1.03)
plt.xticks(rotation=90)
plt.show()
# Box-plot, CDF, histogram
def cdf(lst):
x=np.sort(lst)
y=np.arange(1, len(x)+1)/len(x)
return x, y
fig, ax=plt.subplots(1,3, figsize=(15,5))
x_price, y_price=cdf(udemy_courses['price'])
ax[0].plot(x_price, y_price)
ax[0].set_title('CDF of prices')
ax[1].hist(udemy_courses['price'])
ax[1].set_title('histogram distribution of prices')
ax[2].boxplot(udemy_courses['price'])
ax[2].set_title('boxplot of prices')
plt.show()
print('median: ',udemy_courses['price'].median())
print('mean: ',udemy_courses['price'].mean())
udemy_courses.groupby('price_category')['num_lectures'].mean().sort_values(ascending=False)
mins_courses=udemy_courses[udemy_courses['content_duration_type']=='mins']
g=sns.catplot(x='price_category',
data=mins_courses,
kind='count',
hue='level')
g.fig.suptitle("price category (minutes courses) in each level.", y=1.03, x=0.4)
plt.show()
udemy_courses['published_timestamp'].dt.year.value_counts().plot.bar()
plt.xlabel('published year')
plt.ylabel('frequency')
plt.title('number of courses published each year(2011-2017)')
plt.show() | code |
34120249/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_courses['price'] = udemy_courses['price'].astype('float')
udemy_courses['number_of_contents'] = udemy_courses['content_duration'].str.extract('([\\d\\.]+)\\s[\\w]+').astype('float')
udemy_courses['content_duration_type'] = udemy_courses['content_duration'].str.extract('[\\d\\.]+\\s([\\w]+)')
udemy_courses.drop('content_duration', axis=1, inplace=True)
udemy_courses.dropna(inplace=True)
udemy_courses.groupby('price_category')['num_lectures'].mean().sort_values(ascending=False)
udemy_courses['content_duration_type'].value_counts() | code |
34120249/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_courses['price'] = udemy_courses['price'].astype('float')
udemy_courses['number_of_contents'] = udemy_courses['content_duration'].str.extract('([\\d\\.]+)\\s[\\w]+').astype('float')
udemy_courses['content_duration_type'] = udemy_courses['content_duration'].str.extract('[\\d\\.]+\\s([\\w]+)')
udemy_courses.drop('content_duration', axis=1, inplace=True)
udemy_courses.dropna(inplace=True)
udemy_courses['is_paid'].value_counts() | code |
34120249/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_courses['price'] = udemy_courses['price'].astype('float')
udemy_courses['number_of_contents'] = udemy_courses['content_duration'].str.extract('([\\d\\.]+)\\s[\\w]+').astype('float')
udemy_courses['content_duration_type'] = udemy_courses['content_duration'].str.extract('[\\d\\.]+\\s([\\w]+)')
udemy_courses.drop('content_duration', axis=1, inplace=True)
udemy_courses.dropna(inplace=True)
g = sns.catplot(x='subject', data=udemy_courses, kind='count', hue='is_paid')
g.fig.suptitle('free/paid categories comparison', y=1.03)
plt.xticks(rotation=90)
plt.show() | code |
34120249/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import datetime as dt
import os
import re
plt.style.use('ggplot')
sns.set(style='darkgrid', context='notebook')
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
34120249/cell_31 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses['price'] = udemy_courses['price'].str.replace('Free', '0').str.replace('TRUE', '0')
udemy_courses['price'] = udemy_courses['price'].astype('float')
udemy_courses['number_of_contents'] = udemy_courses['content_duration'].str.extract('([\\d\\.]+)\\s[\\w]+').astype('float')
udemy_courses['content_duration_type'] = udemy_courses['content_duration'].str.extract('[\\d\\.]+\\s([\\w]+)')
udemy_courses.drop('content_duration', axis=1, inplace=True)
udemy_courses.dropna(inplace=True)
g=sns.catplot(x='subject',
data=udemy_courses,
kind='count',
hue='is_paid')
g.fig.suptitle('free/paid categories comparison', y=1.03)
plt.xticks(rotation=90)
plt.show()
g=sns.catplot(x='level',
data=udemy_courses,
kind='count',
hue='is_paid')
g.fig.suptitle('free/paid lavel comparison', y=1.03)
plt.xticks(rotation=90)
plt.show()
# Box-plot, CDF, histogram
def cdf(lst):
x=np.sort(lst)
y=np.arange(1, len(x)+1)/len(x)
return x, y
fig, ax=plt.subplots(1,3, figsize=(15,5))
x_price, y_price=cdf(udemy_courses['price'])
ax[0].plot(x_price, y_price)
ax[0].set_title('CDF of prices')
ax[1].hist(udemy_courses['price'])
ax[1].set_title('histogram distribution of prices')
ax[2].boxplot(udemy_courses['price'])
ax[2].set_title('boxplot of prices')
plt.show()
print('median: ',udemy_courses['price'].median())
print('mean: ',udemy_courses['price'].mean())
udemy_courses.groupby('price_category')['num_lectures'].mean().sort_values(ascending=False)
mins_courses = udemy_courses[udemy_courses['content_duration_type'] == 'mins']
g = sns.catplot(x='price_category', data=mins_courses, kind='count', hue='level')
g.fig.suptitle('price category (minutes courses) in each level.', y=1.03, x=0.4)
plt.show() | code |
34120249/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
udemy_courses = pd.read_csv('/kaggle/input/udemy-courses/udemy_courses.csv', parse_dates=['published_timestamp'])
udemy_courses.info() | code |
329956/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import trueskill as ts
import pandas as pd
import numpy as np
import trueskill as ts
pd.set_option('display.max_rows', len(dfRatings))
def cleanResults(raceColumns,dfResultsTemp,appendScore):
for raceCol in raceColumns:
dfResultsTemp.index = dfResultsTemp.index.str.replace(r"(\w)([A-Z])", r"\1 \2")
dfResultsTemp.index = dfResultsTemp.index.str.title()
dfResultsTemp.index = dfResultsTemp.index.str.replace('\([A-Z\ 0-9]*\)','')
dfResultsTemp.index = dfResultsTemp.index.str.strip()
dfResultsTemp.index = dfResultsTemp.index.str.replace('Riccardo Andrea Leccese','Rikki Leccese')
dfResultsTemp.index = dfResultsTemp.index.str.replace('Nicolas Parlier','Nico Parlier')
dfResultsTemp.index = dfResultsTemp.index.str.replace('Alejandro Climent Hernã¥_ Ndez', 'Alejandro Climent Hernandez')
dfResultsTemp.index = dfResultsTemp.index.str.replace('Alexandre Caizergues','Alex Caizergues')
dfResultsTemp.index = dfResultsTemp.index.str.replace('Florian Trittel Paul','Florian Trittel')
dfResultsTemp.index = dfResultsTemp.index.str.replace('Jean Guillaume Rivaud','Jean-Guillaume Rivaud')
dfResultsTemp.index = dfResultsTemp.index.str.replace('^Kieran Le$','Kieran Le Borgne')
dfResultsTemp.index = dfResultsTemp.index.str.replace('Marvin Baumeisterschoenian','Marvin Baumeister Schoenian')
dfResultsTemp.index = dfResultsTemp.index.str.replace('Theo De Ramecourt','Theo De-Ramecourt')
dfResultsTemp.index = dfResultsTemp.index.str.replace('James Johnson','James Johnsen')
dfResultsTemp[raceCol] = dfResultsTemp[raceCol].astype(str)
dfResultsTemp[raceCol] = dfResultsTemp[raceCol].str.replace('D\+D|DSQ|D\+0|^-[A-Z0-9]*$|\([A-Z0-9\.-]*\)|UFD|SCP|RDG|RCT|DCT|DNS-[0-9]*|DNC-[0-9]*|OCS-[0-9]*|[0-9\.]*DNC|\/','')
dfResultsTemp[raceCol] = dfResultsTemp[raceCol].str.replace('DNS','')
#Count DNF or Retired as last place
dfResultsTemp[raceCol] = dfResultsTemp[raceCol].str.replace('RET[0-9]*|DNF-[0-9]*|^DNF$|[0-9\.]*DNF',str(len(dfResultsTemp)+1))
dfResultsTemp[raceCol] = pd.to_numeric(dfResultsTemp[raceCol])
dfResultsTemp[raceCol] = dfResultsTemp[raceCol] + appendScore
return dfResultsTemp
def mergeResults(raceColumns, raceName, dfResultsTemp, dfResults):
for raceCol in raceColumns:
raceIndex = raceName + '-' + raceCol
dfResultsTemp[raceIndex] = dfResultsTemp[raceCol]
del dfResultsTemp[raceCol]
dfResults = pd.merge(dfResults, dfResultsTemp[[raceIndex]], left_index=True, right_index=True, how='outer')
return dfResults
def doRating(dfResults, dfRatings):
dfRatings = pd.merge(dfRatings, dfResults, left_on=['Name'], right_index=True, how='outer')
dfRatings['Name'] = dfRatings.index
ratingsColumns = ['Name', 'mu_minus_3sigma', 'NumRaces', 'Rating']
dfRatings = dfRatings[ratingsColumns]
dfRatings['Rating'][dfRatings['Rating'].isnull()] = pd.Series(np.repeat(ts.Rating(), len(dfRatings['Rating'].isnull()))).T.values.tolist()
for raceCol in dfResults:
competed = dfRatings['Name'].isin(dfResults.index[dfResults[raceCol].notnull()])
rating_group = list(zip(dfRatings['Rating'][competed].T.values.tolist()))
dfRatings['Rating'][competed] = ts.rate(rating_group, ranks=dfResults[raceCol][competed].T.values.tolist())
dfRatings = pd.DataFrame(dfRatings)
dfRatings['mu_minus_3sigma'] = pd.Series(np.repeat(0.0, len(dfRatings)))
for i in range(0, len(dfRatings['Rating'])):
dfRatings['mu_minus_3sigma'][i] = float(dfRatings['Rating'][i].mu) - 3 * float(dfRatings['Rating'][i].sigma)
dfRatings['Name'] = dfRatings.index
dfRatings.index = dfRatings['mu_minus_3sigma'].rank(ascending=False).astype(int)
dfRatings.index.names = ['Rank']
return dfRatings.sort('mu_minus_3sigma', ascending=False)
ratingsColumns = ['Name', 'mu_minus_3sigma', 'NumRaces', 'Rating']
dfRatings = pd.DataFrame(columns=ratingsColumns)
dfResults = pd.DataFrame()
raceName = '20160323-LaVentana-HydrofoilProTour'
raceColumns = ['Q1', 'Q2', 'R1', 'R2', 'R3', 'R4', 'R5', 'R6']
dfResultsTempGold = pd.read_csv('../input/' + raceName + '-Gold.csv')
dfResultsTempGold = dfResultsTempGold.set_index(dfResultsTempGold['Name'] + ' ' + dfResultsTempGold['LastName'])
dfResultsTempGold = cleanResults(raceColumns, dfResultsTempGold, 0)
dfResultsTempSilver = pd.read_csv('../input/' + raceName + '-Silver.csv')
dfResultsTempSilver = dfResultsTempSilver.set_index(dfResultsTempSilver['Name'] + ' ' + dfResultsTempSilver['LastName'])
dfResultsTempSilver = cleanResults(raceColumns, dfResultsTempSilver, len(dfResultsTempGold))
dfResultsTemp = dfResultsTempGold.append(dfResultsTempSilver)
dfResults = mergeResults(raceColumns, raceName, dfResultsTemp, dfResults)
dfResults = pd.DataFrame()
raceName = '20160516-MontPellier-IFKOSilverCup'
raceColumns = ['CO 1', 'CO 2', 'CO 3', 'CO 4', 'CO 5', 'CO 6', 'CO 7', 'CO 8', 'CO 9', 'CO 10', 'CO 11', 'CO 12']
dfResultsTemp = pd.read_csv('../input/' + raceName + '.csv')
for i in range(0, len(dfResultsTemp)):
numNames = len(dfResultsTemp['Name'].str.split(' ')[i])
dfResultsTemp['Name'][i] = dfResultsTemp['Name'].str.split(' ')[i][numNames - 1] + ' ' + dfResultsTemp['Name'].str.split(' ')[i][0]
dfResultsTemp = dfResultsTemp.set_index(dfResultsTemp['Name'].str.lower())
dfResultsTemp = cleanResults(raceColumns, dfResultsTemp, 0)
for i in (dfResultsTemp[raceColumns].isnull().sum(axis=1) < 3).index:
toDelete = 3 - dfResultsTemp[raceColumns][dfResultsTemp.index == i].isnull().sum(axis=1).values[0]
if toDelete > 0:
for j in range(1, toDelete + 1):
maxToDelete = dfResultsTemp[raceColumns][dfResultsTemp.index == i].idxmax(axis=1).values[0]
dfResultsTemp[maxToDelete][dfResultsTemp.index == i] = np.nan
dfResults = mergeResults(raceColumns, raceName, dfResultsTemp, dfResults) | code |
329956/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import trueskill as ts
pd.set_option('display.max_rows', len(dfRatings)) | code |
329956/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import trueskill as ts
import pandas as pd
import numpy as np
import trueskill as ts
pd.set_option('display.max_rows', len(dfRatings))
def cleanResults(raceColumns,dfResultsTemp,appendScore):
for raceCol in raceColumns:
dfResultsTemp.index = dfResultsTemp.index.str.replace(r"(\w)([A-Z])", r"\1 \2")
dfResultsTemp.index = dfResultsTemp.index.str.title()
dfResultsTemp.index = dfResultsTemp.index.str.replace('\([A-Z\ 0-9]*\)','')
dfResultsTemp.index = dfResultsTemp.index.str.strip()
dfResultsTemp.index = dfResultsTemp.index.str.replace('Riccardo Andrea Leccese','Rikki Leccese')
dfResultsTemp.index = dfResultsTemp.index.str.replace('Nicolas Parlier','Nico Parlier')
dfResultsTemp.index = dfResultsTemp.index.str.replace('Alejandro Climent Hernã¥_ Ndez', 'Alejandro Climent Hernandez')
dfResultsTemp.index = dfResultsTemp.index.str.replace('Alexandre Caizergues','Alex Caizergues')
dfResultsTemp.index = dfResultsTemp.index.str.replace('Florian Trittel Paul','Florian Trittel')
dfResultsTemp.index = dfResultsTemp.index.str.replace('Jean Guillaume Rivaud','Jean-Guillaume Rivaud')
dfResultsTemp.index = dfResultsTemp.index.str.replace('^Kieran Le$','Kieran Le Borgne')
dfResultsTemp.index = dfResultsTemp.index.str.replace('Marvin Baumeisterschoenian','Marvin Baumeister Schoenian')
dfResultsTemp.index = dfResultsTemp.index.str.replace('Theo De Ramecourt','Theo De-Ramecourt')
dfResultsTemp.index = dfResultsTemp.index.str.replace('James Johnson','James Johnsen')
dfResultsTemp[raceCol] = dfResultsTemp[raceCol].astype(str)
dfResultsTemp[raceCol] = dfResultsTemp[raceCol].str.replace('D\+D|DSQ|D\+0|^-[A-Z0-9]*$|\([A-Z0-9\.-]*\)|UFD|SCP|RDG|RCT|DCT|DNS-[0-9]*|DNC-[0-9]*|OCS-[0-9]*|[0-9\.]*DNC|\/','')
dfResultsTemp[raceCol] = dfResultsTemp[raceCol].str.replace('DNS','')
#Count DNF or Retired as last place
dfResultsTemp[raceCol] = dfResultsTemp[raceCol].str.replace('RET[0-9]*|DNF-[0-9]*|^DNF$|[0-9\.]*DNF',str(len(dfResultsTemp)+1))
dfResultsTemp[raceCol] = pd.to_numeric(dfResultsTemp[raceCol])
dfResultsTemp[raceCol] = dfResultsTemp[raceCol] + appendScore
return dfResultsTemp
def mergeResults(raceColumns, raceName, dfResultsTemp, dfResults):
for raceCol in raceColumns:
raceIndex = raceName + '-' + raceCol
dfResultsTemp[raceIndex] = dfResultsTemp[raceCol]
del dfResultsTemp[raceCol]
dfResults = pd.merge(dfResults, dfResultsTemp[[raceIndex]], left_index=True, right_index=True, how='outer')
return dfResults
def doRating(dfResults, dfRatings):
dfRatings = pd.merge(dfRatings, dfResults, left_on=['Name'], right_index=True, how='outer')
dfRatings['Name'] = dfRatings.index
ratingsColumns = ['Name', 'mu_minus_3sigma', 'NumRaces', 'Rating']
dfRatings = dfRatings[ratingsColumns]
dfRatings['Rating'][dfRatings['Rating'].isnull()] = pd.Series(np.repeat(ts.Rating(), len(dfRatings['Rating'].isnull()))).T.values.tolist()
for raceCol in dfResults:
competed = dfRatings['Name'].isin(dfResults.index[dfResults[raceCol].notnull()])
rating_group = list(zip(dfRatings['Rating'][competed].T.values.tolist()))
dfRatings['Rating'][competed] = ts.rate(rating_group, ranks=dfResults[raceCol][competed].T.values.tolist())
dfRatings = pd.DataFrame(dfRatings)
dfRatings['mu_minus_3sigma'] = pd.Series(np.repeat(0.0, len(dfRatings)))
for i in range(0, len(dfRatings['Rating'])):
dfRatings['mu_minus_3sigma'][i] = float(dfRatings['Rating'][i].mu) - 3 * float(dfRatings['Rating'][i].sigma)
dfRatings['Name'] = dfRatings.index
dfRatings.index = dfRatings['mu_minus_3sigma'].rank(ascending=False).astype(int)
dfRatings.index.names = ['Rank']
return dfRatings.sort('mu_minus_3sigma', ascending=False)
ratingsColumns = ['Name', 'mu_minus_3sigma', 'NumRaces', 'Rating']
dfRatings = pd.DataFrame(columns=ratingsColumns)
dfResults = pd.DataFrame()
raceName = '20160323-LaVentana-HydrofoilProTour'
raceColumns = ['Q1', 'Q2', 'R1', 'R2', 'R3', 'R4', 'R5', 'R6']
dfResultsTempGold = pd.read_csv('../input/' + raceName + '-Gold.csv')
dfResultsTempGold = dfResultsTempGold.set_index(dfResultsTempGold['Name'] + ' ' + dfResultsTempGold['LastName'])
dfResultsTempGold = cleanResults(raceColumns, dfResultsTempGold, 0)
dfResultsTempSilver = pd.read_csv('../input/' + raceName + '-Silver.csv')
dfResultsTempSilver = dfResultsTempSilver.set_index(dfResultsTempSilver['Name'] + ' ' + dfResultsTempSilver['LastName'])
dfResultsTempSilver = cleanResults(raceColumns, dfResultsTempSilver, len(dfResultsTempGold))
dfResultsTemp = dfResultsTempGold.append(dfResultsTempSilver)
dfResults = mergeResults(raceColumns, raceName, dfResultsTemp, dfResults)
dfRatings = doRating(dfResults, dfRatings)
dfRatings | code |
329956/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import trueskill as ts
import pandas as pd
import numpy as np
import trueskill as ts
pd.set_option('display.max_rows', len(dfRatings))
def cleanResults(raceColumns,dfResultsTemp,appendScore):
for raceCol in raceColumns:
dfResultsTemp.index = dfResultsTemp.index.str.replace(r"(\w)([A-Z])", r"\1 \2")
dfResultsTemp.index = dfResultsTemp.index.str.title()
dfResultsTemp.index = dfResultsTemp.index.str.replace('\([A-Z\ 0-9]*\)','')
dfResultsTemp.index = dfResultsTemp.index.str.strip()
dfResultsTemp.index = dfResultsTemp.index.str.replace('Riccardo Andrea Leccese','Rikki Leccese')
dfResultsTemp.index = dfResultsTemp.index.str.replace('Nicolas Parlier','Nico Parlier')
dfResultsTemp.index = dfResultsTemp.index.str.replace('Alejandro Climent Hernã¥_ Ndez', 'Alejandro Climent Hernandez')
dfResultsTemp.index = dfResultsTemp.index.str.replace('Alexandre Caizergues','Alex Caizergues')
dfResultsTemp.index = dfResultsTemp.index.str.replace('Florian Trittel Paul','Florian Trittel')
dfResultsTemp.index = dfResultsTemp.index.str.replace('Jean Guillaume Rivaud','Jean-Guillaume Rivaud')
dfResultsTemp.index = dfResultsTemp.index.str.replace('^Kieran Le$','Kieran Le Borgne')
dfResultsTemp.index = dfResultsTemp.index.str.replace('Marvin Baumeisterschoenian','Marvin Baumeister Schoenian')
dfResultsTemp.index = dfResultsTemp.index.str.replace('Theo De Ramecourt','Theo De-Ramecourt')
dfResultsTemp.index = dfResultsTemp.index.str.replace('James Johnson','James Johnsen')
dfResultsTemp[raceCol] = dfResultsTemp[raceCol].astype(str)
dfResultsTemp[raceCol] = dfResultsTemp[raceCol].str.replace('D\+D|DSQ|D\+0|^-[A-Z0-9]*$|\([A-Z0-9\.-]*\)|UFD|SCP|RDG|RCT|DCT|DNS-[0-9]*|DNC-[0-9]*|OCS-[0-9]*|[0-9\.]*DNC|\/','')
dfResultsTemp[raceCol] = dfResultsTemp[raceCol].str.replace('DNS','')
#Count DNF or Retired as last place
dfResultsTemp[raceCol] = dfResultsTemp[raceCol].str.replace('RET[0-9]*|DNF-[0-9]*|^DNF$|[0-9\.]*DNF',str(len(dfResultsTemp)+1))
dfResultsTemp[raceCol] = pd.to_numeric(dfResultsTemp[raceCol])
dfResultsTemp[raceCol] = dfResultsTemp[raceCol] + appendScore
return dfResultsTemp
def mergeResults(raceColumns, raceName, dfResultsTemp, dfResults):
for raceCol in raceColumns:
raceIndex = raceName + '-' + raceCol
dfResultsTemp[raceIndex] = dfResultsTemp[raceCol]
del dfResultsTemp[raceCol]
dfResults = pd.merge(dfResults, dfResultsTemp[[raceIndex]], left_index=True, right_index=True, how='outer')
return dfResults
def doRating(dfResults, dfRatings):
dfRatings = pd.merge(dfRatings, dfResults, left_on=['Name'], right_index=True, how='outer')
dfRatings['Name'] = dfRatings.index
ratingsColumns = ['Name', 'mu_minus_3sigma', 'NumRaces', 'Rating']
dfRatings = dfRatings[ratingsColumns]
dfRatings['Rating'][dfRatings['Rating'].isnull()] = pd.Series(np.repeat(ts.Rating(), len(dfRatings['Rating'].isnull()))).T.values.tolist()
for raceCol in dfResults:
competed = dfRatings['Name'].isin(dfResults.index[dfResults[raceCol].notnull()])
rating_group = list(zip(dfRatings['Rating'][competed].T.values.tolist()))
dfRatings['Rating'][competed] = ts.rate(rating_group, ranks=dfResults[raceCol][competed].T.values.tolist())
dfRatings = pd.DataFrame(dfRatings)
dfRatings['mu_minus_3sigma'] = pd.Series(np.repeat(0.0, len(dfRatings)))
for i in range(0, len(dfRatings['Rating'])):
dfRatings['mu_minus_3sigma'][i] = float(dfRatings['Rating'][i].mu) - 3 * float(dfRatings['Rating'][i].sigma)
dfRatings['Name'] = dfRatings.index
dfRatings.index = dfRatings['mu_minus_3sigma'].rank(ascending=False).astype(int)
dfRatings.index.names = ['Rank']
return dfRatings.sort('mu_minus_3sigma', ascending=False)
ratingsColumns = ['Name', 'mu_minus_3sigma', 'NumRaces', 'Rating']
dfRatings = pd.DataFrame(columns=ratingsColumns)
dfResults = pd.DataFrame()
raceName = '20160323-LaVentana-HydrofoilProTour'
raceColumns = ['Q1', 'Q2', 'R1', 'R2', 'R3', 'R4', 'R5', 'R6']
dfResultsTempGold = pd.read_csv('../input/' + raceName + '-Gold.csv')
dfResultsTempGold = dfResultsTempGold.set_index(dfResultsTempGold['Name'] + ' ' + dfResultsTempGold['LastName'])
dfResultsTempGold = cleanResults(raceColumns, dfResultsTempGold, 0)
dfResultsTempSilver = pd.read_csv('../input/' + raceName + '-Silver.csv')
dfResultsTempSilver = dfResultsTempSilver.set_index(dfResultsTempSilver['Name'] + ' ' + dfResultsTempSilver['LastName'])
dfResultsTempSilver = cleanResults(raceColumns, dfResultsTempSilver, len(dfResultsTempGold))
dfResultsTemp = dfResultsTempGold.append(dfResultsTempSilver)
dfResults = mergeResults(raceColumns, raceName, dfResultsTemp, dfResults)
dfRatings = doRating(dfResults, dfRatings)
dfRatings
dfResults = pd.DataFrame()
raceName = '20160516-MontPellier-IFKOSilverCup'
raceColumns = ['CO 1', 'CO 2', 'CO 3', 'CO 4', 'CO 5', 'CO 6', 'CO 7', 'CO 8', 'CO 9', 'CO 10', 'CO 11', 'CO 12']
dfResultsTemp = pd.read_csv('../input/' + raceName + '.csv')
for i in range(0, len(dfResultsTemp)):
numNames = len(dfResultsTemp['Name'].str.split(' ')[i])
dfResultsTemp['Name'][i] = dfResultsTemp['Name'].str.split(' ')[i][numNames - 1] + ' ' + dfResultsTemp['Name'].str.split(' ')[i][0]
dfResultsTemp = dfResultsTemp.set_index(dfResultsTemp['Name'].str.lower())
dfResultsTemp = cleanResults(raceColumns, dfResultsTemp, 0)
for i in (dfResultsTemp[raceColumns].isnull().sum(axis=1) < 3).index:
toDelete = 3 - dfResultsTemp[raceColumns][dfResultsTemp.index == i].isnull().sum(axis=1).values[0]
if toDelete > 0:
for j in range(1, toDelete + 1):
maxToDelete = dfResultsTemp[raceColumns][dfResultsTemp.index == i].idxmax(axis=1).values[0]
dfResultsTemp[maxToDelete][dfResultsTemp.index == i] = np.nan
dfResults = mergeResults(raceColumns, raceName, dfResultsTemp, dfResults)
dfRatings = doRating(dfResults, dfRatings) | code |
32063168/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
clf.fit(X_train, y_train)
pred = clf.predict(X_test)
clf.feature_importances_ | code |
32063168/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/did-it-rain-in-seattle-19482017/seattleWeather_1948-2017.csv')
df.shape
df.info() | code |
32063168/cell_20 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
clf.fit(X_train, y_train)
pred = clf.predict(X_test)
from sklearn import metrics
print(metrics.classification_report(y_test, pred)) | code |
32063168/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/did-it-rain-in-seattle-19482017/seattleWeather_1948-2017.csv')
df.shape
df[df['RAIN'].isnull()] | code |
32063168/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/did-it-rain-in-seattle-19482017/seattleWeather_1948-2017.csv')
df.head() | code |
32063168/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
clf.fit(X_train, y_train)
pred = clf.predict(X_test)
from sklearn.metrics import confusion_matrix
print(confusion_matrix(y_test, pred)) | code |
32063168/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
32063168/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/did-it-rain-in-seattle-19482017/seattleWeather_1948-2017.csv')
df.shape
df = df.dropna()
df.shape | code |
32063168/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/did-it-rain-in-seattle-19482017/seattleWeather_1948-2017.csv')
df.shape | code |
32063168/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
clf.fit(X_train, y_train) | code |
32063168/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/did-it-rain-in-seattle-19482017/seattleWeather_1948-2017.csv')
df.shape
df = df.dropna()
df.shape
df['RAIN'].value_counts() | code |
32063168/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/did-it-rain-in-seattle-19482017/seattleWeather_1948-2017.csv')
df.shape
df = df.dropna()
df.shape
df = df.drop('DATE', axis=1)
import seaborn as sns
plt.figure(figsize=(8, 8))
sns.heatmap(df.corr(), annot=True) | code |
32063168/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/did-it-rain-in-seattle-19482017/seattleWeather_1948-2017.csv')
df.shape
df['RAIN'].unique() | code |
33118937/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.style as style
import numpy as np
import pandas as pd
import seaborn as sns
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape
Tgt_Col = 'SalePrice'
Num_Col = Sample.select_dtypes(exclude='object').drop(Tgt_Col, axis=1).columns
Cat_Col = Sample.select_dtypes(include='object').columns
plt.ticklabel_format(style='plain', axis='y')
plt.ticklabel_format(style='plain', axis='y')
fig = plt.figure(figsize=(12,18))
for idx,col in enumerate(Num_Col):
fig.add_subplot(9,4,idx+1)
sns.distplot(Sample[col].dropna(), kde_kws={'bw':0.1})
plt.xlabel(col)
plt.tight_layout()
plt.show()
cor = Sample.corr()
import matplotlib.style as style
style.use('ggplot')
sns.set_style('whitegrid')
mask = np.zeros_like(cor, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
Sample[Num_Col].isna().sum().sort_values(ascending=False).head() | code |
33118937/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape
Tgt_Col = 'SalePrice'
Num_Col = Sample.select_dtypes(exclude='object').drop(Tgt_Col, axis=1).columns
Cat_Col = Sample.select_dtypes(include='object').columns
sns.distplot(Sample[Tgt_Col])
plt.ticklabel_format(style='plain', axis='y')
plt.title("SalePrice's Distribution")
plt.show()
print('Skewness : ', str(Sample[Tgt_Col].skew())) | code |
33118937/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape
Sample.head() | code |
33118937/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.style as style
import numpy as np
import pandas as pd
import seaborn as sns
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape
Tgt_Col = 'SalePrice'
Num_Col = Sample.select_dtypes(exclude='object').drop(Tgt_Col, axis=1).columns
Cat_Col = Sample.select_dtypes(include='object').columns
plt.ticklabel_format(style='plain', axis='y')
plt.ticklabel_format(style='plain', axis='y')
fig = plt.figure(figsize=(12,18))
for idx,col in enumerate(Num_Col):
fig.add_subplot(9,4,idx+1)
sns.distplot(Sample[col].dropna(), kde_kws={'bw':0.1})
plt.xlabel(col)
plt.tight_layout()
plt.show()
cor = Sample.corr()
import matplotlib.style as style
style.use('ggplot')
sns.set_style('whitegrid')
mask = np.zeros_like(cor, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
for col in Sample[Cat_Col]:
if Sample[col].isnull().sum() > 0:
print(col, ' : ', Sample[col].isnull().sum(), Sample[col].unique()) | code |
33118937/cell_30 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.style as style
import numpy as np
import pandas as pd
import seaborn as sns
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape
Tgt_Col = 'SalePrice'
Num_Col = Sample.select_dtypes(exclude='object').drop(Tgt_Col, axis=1).columns
Cat_Col = Sample.select_dtypes(include='object').columns
plt.ticklabel_format(style='plain', axis='y')
plt.ticklabel_format(style='plain', axis='y')
fig = plt.figure(figsize=(12,18))
for idx,col in enumerate(Num_Col):
fig.add_subplot(9,4,idx+1)
sns.distplot(Sample[col].dropna(), kde_kws={'bw':0.1})
plt.xlabel(col)
plt.tight_layout()
plt.show()
cor = Sample.corr()
import matplotlib.style as style
style.use('ggplot')
sns.set_style('whitegrid')
mask = np.zeros_like(cor, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
Sample_copy = Sample.copy()
Sample_copy['MasVnrArea'] = Sample['MasVnrArea'].fillna(0)
Cat_Cols_Fill_NA = ['PoolQC', 'MiscFeature', 'Alley', 'Fence', 'FireplaceQu', 'MasVnrType', 'BsmtFinType2', 'BsmtExposure', 'BsmtFinType1', 'BsmtQual', 'BsmtCond', 'GarageQual', 'GarageFinish', 'GarageType', 'GarageCond']
for cat in Cat_Cols_Fill_NA:
Sample_copy[cat] = Sample_copy[cat].fillna('NA')
Sample_copy.isna().sum().sort_values(ascending=False).head() | code |
33118937/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.style as style
import numpy as np
import pandas as pd
import seaborn as sns
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape
Tgt_Col = 'SalePrice'
Num_Col = Sample.select_dtypes(exclude='object').drop(Tgt_Col, axis=1).columns
Cat_Col = Sample.select_dtypes(include='object').columns
plt.ticklabel_format(style='plain', axis='y')
plt.ticklabel_format(style='plain', axis='y')
fig = plt.figure(figsize=(12,18))
for idx,col in enumerate(Num_Col):
fig.add_subplot(9,4,idx+1)
sns.distplot(Sample[col].dropna(), kde_kws={'bw':0.1})
plt.xlabel(col)
plt.tight_layout()
plt.show()
cor = Sample.corr()
import matplotlib.style as style
style.use('ggplot')
sns.set_style('whitegrid')
mask = np.zeros_like(cor, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
fig = plt.figure(figsize=(12, 18))
for idx, col in enumerate(Num_Col):
fig.add_subplot(9, 4, idx + 1)
if abs(cor.iloc[-1, idx]) < 0.1:
sns.scatterplot(Sample[col], Sample[Tgt_Col], color='red')
elif abs(cor.iloc[-1, idx]) >= 0.5:
sns.scatterplot(Sample[col], Sample[Tgt_Col], color='green')
else:
sns.scatterplot(Sample[col], Sample[Tgt_Col], color='blue')
plt.title('Corr to SalePrice : ' + np.round(cor.iloc[-1, idx], decimals=2).astype(str))
plt.tight_layout()
plt.show() | code |
33118937/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.style as style
import numpy as np
import pandas as pd
import seaborn as sns
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape
Tgt_Col = 'SalePrice'
Num_Col = Sample.select_dtypes(exclude='object').drop(Tgt_Col, axis=1).columns
Cat_Col = Sample.select_dtypes(include='object').columns
plt.ticklabel_format(style='plain', axis='y')
plt.ticklabel_format(style='plain', axis='y')
fig = plt.figure(figsize=(12,18))
for idx,col in enumerate(Num_Col):
fig.add_subplot(9,4,idx+1)
sns.distplot(Sample[col].dropna(), kde_kws={'bw':0.1})
plt.xlabel(col)
plt.tight_layout()
plt.show()
cor = Sample.corr()
import matplotlib.style as style
style.use('ggplot')
sns.set_style('whitegrid')
plt.subplots(figsize=(30, 20))
mask = np.zeros_like(cor, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
sns.heatmap(cor, cmap=sns.diverging_palette(8, 150, n=10), mask=mask, annot=True, vmin=-1, vmax=1)
plt.title('Heatmap of all the Features', fontsize=30) | code |
33118937/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape
Tgt_Col = 'SalePrice'
Num_Col = Sample.select_dtypes(exclude='object').drop(Tgt_Col, axis=1).columns
Cat_Col = Sample.select_dtypes(include='object').columns
plt.ticklabel_format(style='plain', axis='y')
plt.ticklabel_format(style='plain', axis='y')
fig = plt.figure(figsize=(12, 18))
for idx, col in enumerate(Num_Col):
fig.add_subplot(9, 4, idx + 1)
sns.distplot(Sample[col].dropna(), kde_kws={'bw': 0.1})
plt.xlabel(col)
plt.tight_layout()
plt.show() | code |
33118937/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape | code |
33118937/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape
Tgt_Col = 'SalePrice'
Num_Col = Sample.select_dtypes(exclude='object').drop(Tgt_Col, axis=1).columns
Cat_Col = Sample.select_dtypes(include='object').columns
Sample[Num_Col].describe().round(decimals=2) | code |
33118937/cell_24 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.style as style
import numpy as np
import pandas as pd
import seaborn as sns
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape
Tgt_Col = 'SalePrice'
Num_Col = Sample.select_dtypes(exclude='object').drop(Tgt_Col, axis=1).columns
Cat_Col = Sample.select_dtypes(include='object').columns
plt.ticklabel_format(style='plain', axis='y')
plt.ticklabel_format(style='plain', axis='y')
fig = plt.figure(figsize=(12,18))
for idx,col in enumerate(Num_Col):
fig.add_subplot(9,4,idx+1)
sns.distplot(Sample[col].dropna(), kde_kws={'bw':0.1})
plt.xlabel(col)
plt.tight_layout()
plt.show()
cor = Sample.corr()
import matplotlib.style as style
style.use('ggplot')
sns.set_style('whitegrid')
mask = np.zeros_like(cor, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
Sample[Cat_Col].describe() | code |
33118937/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape
Tgt_Col = 'SalePrice'
Num_Col = Sample.select_dtypes(exclude='object').drop(Tgt_Col, axis=1).columns
Cat_Col = Sample.select_dtypes(include='object').columns
plt.ticklabel_format(style='plain', axis='y')
sns.distplot(np.log(Sample[Tgt_Col] + 1))
plt.ticklabel_format(style='plain', axis='y')
plt.title("SalePrice's Distribution")
plt.show()
print('Skewness : ', str(np.log(Sample[Tgt_Col] + 1).skew())) | code |
33118937/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
Sample = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv', index_col='Id')
Test = pd.read_csv('/kaggle/input/home-data-for-ml-course/test.csv', index_col='Id')
Sample.shape
Tgt_Col = 'SalePrice'
Num_Col = Sample.select_dtypes(exclude='object').drop(Tgt_Col, axis=1).columns
Cat_Col = Sample.select_dtypes(include='object').columns
print('Numerical Columns : ', len(Num_Col))
print('Categorical Columns : ', len(Cat_Col)) | code |
16111026/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16111026/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
file = '../input/COTAHIST_A2009_to_A2018P.csv'
df = pd.read_csv(file)
df.head(2) | code |
73077140/cell_4 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.DataFrame(np.array([['Species 1', np.nan, np.nan, np.nan], ['Species 1', 'Ocotber 2011', np.nan, np.nan], ['Species 1', np.nan, np.nan, 'Decmber 2011'], ['Species 2', 'Ocotber 2011', np.nan, np.nan], ['Species 2', np.nan, 'November 2011', np.nan], ['Species 2', np.nan, np.nan, 'Decmber 2011'], ['Species 3', 'Ocotber 2011', np.nan, np.nan], ['Species 3', np.nan, 'Novermber 2011', np.nan]]), columns=['Species', 'Oct-11', 'Nov-11', 'Dec-11'])
df
df2 = pd.DataFrame(np.array([['Gene 1', '100001ABC', 0], ['Gene 2', '100001ABC', 0], ['Gene 2', '100001ABC', 1], ['Gene 3', '100001ABC', 0], ['Gene 3', '100001ABC', 0], ['Gene 3', '100001ABC', -1], ['Gene 4', '100001ABC', -1], ['Gene 4', '100001ABC', 1], ['Gene 5', '999999XYZ', 0], ['Gene 6', '999999XYZ', -1], ['Gene 7', '999999XYZ', 1]]), columns=['Gene', 'nip', 'PathInf'])
df2 | code |
73077140/cell_2 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.DataFrame(np.array([['Species 1', np.nan, np.nan, np.nan], ['Species 1', 'Ocotber 2011', np.nan, np.nan], ['Species 1', np.nan, np.nan, 'Decmber 2011'], ['Species 2', 'Ocotber 2011', np.nan, np.nan], ['Species 2', np.nan, 'November 2011', np.nan], ['Species 2', np.nan, np.nan, 'Decmber 2011'], ['Species 3', 'Ocotber 2011', np.nan, np.nan], ['Species 3', np.nan, 'Novermber 2011', np.nan]]), columns=['Species', 'Oct-11', 'Nov-11', 'Dec-11'])
df | code |
73077140/cell_3 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.DataFrame(np.array([['Species 1', np.nan, np.nan, np.nan], ['Species 1', 'Ocotber 2011', np.nan, np.nan], ['Species 1', np.nan, np.nan, 'Decmber 2011'], ['Species 2', 'Ocotber 2011', np.nan, np.nan], ['Species 2', np.nan, 'November 2011', np.nan], ['Species 2', np.nan, np.nan, 'Decmber 2011'], ['Species 3', 'Ocotber 2011', np.nan, np.nan], ['Species 3', np.nan, 'Novermber 2011', np.nan]]), columns=['Species', 'Oct-11', 'Nov-11', 'Dec-11'])
df
agg_funcs = {}
for col in df.columns:
agg_funcs[col] = 'min'
agg_funcs
df.groupby(['Species'], as_index=False).agg(agg_funcs) | code |
73077140/cell_5 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.DataFrame(np.array([['Species 1', np.nan, np.nan, np.nan], ['Species 1', 'Ocotber 2011', np.nan, np.nan], ['Species 1', np.nan, np.nan, 'Decmber 2011'], ['Species 2', 'Ocotber 2011', np.nan, np.nan], ['Species 2', np.nan, 'November 2011', np.nan], ['Species 2', np.nan, np.nan, 'Decmber 2011'], ['Species 3', 'Ocotber 2011', np.nan, np.nan], ['Species 3', np.nan, 'Novermber 2011', np.nan]]), columns=['Species', 'Oct-11', 'Nov-11', 'Dec-11'])
df
df2 = pd.DataFrame(np.array([['Gene 1', '100001ABC', 0], ['Gene 2', '100001ABC', 0], ['Gene 2', '100001ABC', 1], ['Gene 3', '100001ABC', 0], ['Gene 3', '100001ABC', 0], ['Gene 3', '100001ABC', -1], ['Gene 4', '100001ABC', -1], ['Gene 4', '100001ABC', 1], ['Gene 5', '999999XYZ', 0], ['Gene 6', '999999XYZ', -1], ['Gene 7', '999999XYZ', 1]]), columns=['Gene', 'nip', 'PathInf'])
df2
df2.groupby(['Gene', 'nip'], as_index=False).agg(lambda x: x[x != '0'].max()).fillna(0) | code |
50238455/cell_6 | [
"text_plain_output_1.png"
] | from sklearn import tree
import pandas as pd
DATA_DIR = '/kaggle/input/mds-misis-test/'
train = pd.read_csv(DATA_DIR + 'train.csv')
X = train.drop(['Outcome'], axis=1)
y = train.Outcome
clf = tree.DecisionTreeClassifier(criterion='entropy', max_depth=6)
clf.fit(X, y) | code |
50238455/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
DATA_DIR = '/kaggle/input/mds-misis-test/'
train = pd.read_csv(DATA_DIR + 'train.csv')
test = pd.read_csv(DATA_DIR + 'test.csv')
sample_submission = pd.read_csv(DATA_DIR + 'sample_submission.csv')
sample_submission.head() | code |
50238455/cell_1 | [
"text_plain_output_1.png"
] | import os
from sklearn import tree
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 |
50238455/cell_7 | [
"text_html_output_1.png"
] | from IPython.display import SVG
from IPython.display import display
from graphviz import Source
from sklearn import tree
import pandas as pd
DATA_DIR = '/kaggle/input/mds-misis-test/'
train = pd.read_csv(DATA_DIR + 'train.csv')
X = train.drop(['Outcome'], axis=1)
y = train.Outcome
clf = tree.DecisionTreeClassifier(criterion='entropy', max_depth=6)
clf.fit(X, y)
graph = Source(tree.export_graphviz(clf, out_file=None, feature_names=list(X), class_names=['1', '0'], filled=True))
display(SVG(graph.pipe(format='svg'))) | code |
50238455/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
DATA_DIR = '/kaggle/input/mds-misis-test/'
train = pd.read_csv(DATA_DIR + 'train.csv')
test = pd.read_csv(DATA_DIR + 'test.csv')
test.head() | code |
50238455/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
DATA_DIR = '/kaggle/input/mds-misis-test/'
train = pd.read_csv(DATA_DIR + 'train.csv')
train.head() | code |
50238455/cell_10 | [
"text_html_output_1.png"
] | from sklearn import tree
import pandas as pd
DATA_DIR = '/kaggle/input/mds-misis-test/'
train = pd.read_csv(DATA_DIR + 'train.csv')
X = train.drop(['Outcome'], axis=1)
y = train.Outcome
clf = tree.DecisionTreeClassifier(criterion='entropy', max_depth=6)
clf.fit(X, y)
test = pd.read_csv(DATA_DIR + 'test.csv')
submission = clf.predict(test)
submission | code |
50238455/cell_12 | [
"text_plain_output_1.png"
] | from sklearn import tree
import pandas as pd
DATA_DIR = '/kaggle/input/mds-misis-test/'
train = pd.read_csv(DATA_DIR + 'train.csv')
X = train.drop(['Outcome'], axis=1)
y = train.Outcome
clf = tree.DecisionTreeClassifier(criterion='entropy', max_depth=6)
clf.fit(X, y)
test = pd.read_csv(DATA_DIR + 'test.csv')
submission = clf.predict(test)
sample_submission = pd.read_csv(DATA_DIR + 'sample_submission.csv')
sample_submission['Outcome'] = submission
sample_submission.to_csv('submission.csv', index=False)
sample_submission.head(10) | code |
1008790/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import xgboost
from sklearn import cross_validation
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression | code |
1008790/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
import xgboost
clf = xgboost.XGBClassifier()
clf.fit(xtrain, ytrain)
pred = clf.predict(xtest)
acc = accuracy_score(ytest, pred)
print('%0.2f%%' % (acc * 100.0)) | code |
1008790/cell_5 | [
"text_plain_output_1.png"
] | import xgboost
clf = xgboost.XGBClassifier()
clf.fit(xtrain, ytrain) | code |
34126020/cell_13 | [
"text_plain_output_1.png"
] | stack = [3, 4, 5]
stack.append(6)
stack.append(7)
stack | code |
34126020/cell_9 | [
"text_plain_output_1.png"
] | fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
fruits.count('apple')
fruits.count('tangerine')
fruits.index('banana')
fruits.index('banana', 4)
fruits.reverse()
fruits
fruits.append('grape')
fruits | code |
34126020/cell_25 | [
"text_plain_output_1.png"
] | t = (12345, 54321, 'hello!')
t[0] | code |
34126020/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
fruits.count('apple') | code |
34126020/cell_6 | [
"text_plain_output_1.png"
] | fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
fruits.count('apple')
fruits.count('tangerine')
fruits.index('banana') | code |
34126020/cell_40 | [
"text_plain_output_1.png"
] | a = set('abracadabra')
b = set('alacazam')
a | code |
34126020/cell_29 | [
"text_plain_output_1.png"
] | v = ([1, 2, 3], [3, 2, 1])
v | code |
34126020/cell_39 | [
"text_plain_output_1.png"
] | basket = {'apple', 'orange', 'apple', 'pear', 'orange', 'banana'}
'crabgrass' in basket | code |
34126020/cell_26 | [
"text_plain_output_1.png"
] | t = (12345, 54321, 'hello!')
t[0]
t | code |
34126020/cell_48 | [
"text_plain_output_1.png"
] | tel = {'jack': 4098, 'sape': 4139}
tel['guido'] = 4127
tel
del tel['sape']
tel | code |
34126020/cell_11 | [
"text_plain_output_1.png"
] | fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
fruits.count('apple')
fruits.count('tangerine')
fruits.index('banana')
fruits.index('banana', 4)
fruits.reverse()
fruits
fruits.append('grape')
fruits
fruits.sort()
fruits
fruits.pop() | code |
34126020/cell_7 | [
"text_plain_output_1.png"
] | fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
fruits.count('apple')
fruits.count('tangerine')
fruits.index('banana')
fruits.index('banana', 4) | code |
34126020/cell_28 | [
"text_plain_output_1.png"
] | t = (12345, 54321, 'hello!')
t[0]
t[0] = 88888 | code |
34126020/cell_8 | [
"text_plain_output_1.png"
] | fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
fruits.count('apple')
fruits.count('tangerine')
fruits.index('banana')
fruits.index('banana', 4)
fruits.reverse()
fruits | code |
34126020/cell_15 | [
"text_plain_output_1.png"
] | stack = [3, 4, 5]
stack.append(6)
stack.append(7)
stack
stack.pop()
stack | code |
34126020/cell_16 | [
"text_plain_output_1.png"
] | stack = [3, 4, 5]
stack.append(6)
stack.append(7)
stack
stack.pop()
stack.pop()
stack.pop()
stack | code |
34126020/cell_38 | [
"text_plain_output_1.png"
] | basket = {'apple', 'orange', 'apple', 'pear', 'orange', 'banana'}
'orange' in basket | code |
34126020/cell_47 | [
"text_plain_output_1.png"
] | tel = {'jack': 4098, 'sape': 4139}
tel['guido'] = 4127
tel
tel['jack'] | code |
34126020/cell_35 | [
"text_plain_output_1.png"
] | t = (12345, 54321, 'hello!')
t[0]
x, y, z = t
print(x, y, z) | code |
34126020/cell_46 | [
"text_plain_output_1.png"
] | tel = {'jack': 4098, 'sape': 4139}
tel['guido'] = 4127
tel | code |
34126020/cell_14 | [
"text_plain_output_1.png"
] | stack = [3, 4, 5]
stack.append(6)
stack.append(7)
stack
stack.pop() | code |
34126020/cell_10 | [
"text_plain_output_1.png"
] | fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
fruits.count('apple')
fruits.count('tangerine')
fruits.index('banana')
fruits.index('banana', 4)
fruits.reverse()
fruits
fruits.append('grape')
fruits
fruits.sort()
fruits | code |
34126020/cell_27 | [
"text_plain_output_1.png"
] | t = (12345, 54321, 'hello!')
t[0]
u = (t, (1, 2, 3, 4, 5))
u | code |
34126020/cell_37 | [
"text_plain_output_1.png"
] | basket = {'apple', 'orange', 'apple', 'pear', 'orange', 'banana'}
print(basket) | code |
34126020/cell_5 | [
"text_plain_output_1.png"
] | fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
fruits.count('apple')
fruits.count('tangerine') | code |
90149707/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
df = df.drop(columns=['parking', 'bedrooms', 'bathrooms'])
X = df[['area', 'stories']]
y = df['price']
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_
model.coef_
model.predict([[6000, 3]])
model.predict([[6000, 4]])
model.predict([[10000, 3]]) | code |
90149707/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
df = df.drop(columns=['parking', 'bedrooms', 'bathrooms'])
X = df[['area', 'stories']]
y = df['price']
X.head() | code |
90149707/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
df = df.drop(columns=['parking', 'bedrooms', 'bathrooms'])
df.head() | code |
90149707/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
sns.lmplot(x='area', y='price', data=df, ci=None) | code |
90149707/cell_20 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
df = df.drop(columns=['parking', 'bedrooms', 'bathrooms'])
X = df[['area', 'stories']]
y = df['price']
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_
model.coef_
model.predict([[6000, 3]])
model.predict([[6000, 4]]) | code |
90149707/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
sns.kdeplot(x='area', data=df) | code |
90149707/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
df = df.drop(columns=['parking', 'bedrooms', 'bathrooms'])
len(df) | code |
90149707/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
df = df.drop(columns=['parking', 'bedrooms', 'bathrooms'])
X = df[['area', 'stories']]
y = df['price']
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_
model.coef_
model.predict([[6000, 3]]) | code |
90149707/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
sns.kdeplot(x='stories', data=df) | code |
90149707/cell_18 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
df = df.drop(columns=['parking', 'bedrooms', 'bathrooms'])
X = df[['area', 'stories']]
y = df['price']
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_
model.coef_ | code |
90149707/cell_8 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
df.info() | code |
90149707/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
df = df.drop(columns=['parking', 'bedrooms', 'bathrooms'])
X = df[['area', 'stories']]
y = df['price']
model = LinearRegression()
model.fit(X, y) | code |
90149707/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/housing-prices-dataset/Housing.csv')
df = df.drop(columns=['parking', 'bedrooms', 'bathrooms'])
X = df[['area', 'stories']]
y = df['price']
model = LinearRegression()
model.fit(X, y)
model.score(X, y) | code |
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