path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
2032867/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
import warnings # We want to suppress warnings
warnings.filterwarnings('ignore')
HRData = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv')
HRData.isnull().any()
hrdunique = HRData.nunique()
hrdunique = hrdunique.sort_values()
hrdunique
hrd = HRData.copy()
hrd.drop('Over18', axis=1, inplace=True)
hrd.drop('StandardHours', axis=1, inplace=True)
hrd.drop('EmployeeNumber', axis=1, inplace=True)
hrd.drop('EmployeeCount', axis=1, inplace=True)
hrd.groupby('Attrition').mean()
df = pd.DataFrame(hrd, columns=['Gender', 'Department', 'Attrition'])
groupby_DeptnAttrition = df['Gender'].groupby([df['Department'], df['Attrition']])
num_bins = 30
pd.crosstab(hrd.Department, hrd.Attrition).plot(kind='bar')
plt.title('Attrition rate by Department')
plt.xlabel('Department')
plt.ylabel('Frequency of Attrition') | code |
2032867/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings # We want to suppress warnings
warnings.filterwarnings('ignore')
HRData = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv')
HRData.isnull().any()
hrdunique = HRData.nunique()
hrdunique = hrdunique.sort_values()
hrdunique
hrd = HRData.copy()
hrd.drop('Over18', axis=1, inplace=True)
hrd.drop('StandardHours', axis=1, inplace=True)
hrd.drop('EmployeeNumber', axis=1, inplace=True)
hrd.drop('EmployeeCount', axis=1, inplace=True)
hrd.groupby('Attrition').mean()
df = pd.DataFrame(hrd, columns=['Gender', 'Department', 'Attrition'])
groupby_DeptnAttrition = df['Gender'].groupby([df['Department'], df['Attrition']])
num_bins = 30
fig,ax = plt.subplots(1, 2, figsize=(18,4))
sns.barplot(x = 'Attrition', y = 'DistanceFromHome', data = hrd, ax = ax[0])
sns.barplot(x = 'Gender',y = 'JobSatisfaction', data = hrd, ax = ax[1])
fig = plt.figure(figsize=(12, 12))
sns.distplot(hrd.JobSatisfaction, hist=False, kde=True, label='Job Satisfaction', hist_kws={'histtype': 'step', 'linewidth': 3, 'alpha': 1, 'color': sns.xkcd_rgb['blue']})
sns.distplot(hrd.EnvironmentSatisfaction, hist=False, kde=True, label='Environment Satisfaction', hist_kws={'histtype': 'step', 'linewidth': 3, 'alpha': 1, 'color': sns.xkcd_rgb['red']})
sns.distplot(hrd.RelationshipSatisfaction, hist=False, kde=True, label='Relationship Satisfaction', hist_kws={'histtype': 'step', 'linewidth': 3, 'alpha': 1, 'color': sns.xkcd_rgb['green']})
plt.suptitle('Satisfaction Levels of Employees', size=22, x=0.5, y=0.94)
plt.xlabel('Satisfaction Levels', size=10)
plt.legend() | code |
2032867/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings # We want to suppress warnings
warnings.filterwarnings('ignore')
HRData = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv')
HRData.isnull().any()
hrdunique = HRData.nunique()
hrdunique = hrdunique.sort_values()
hrdunique
hrd = HRData.copy()
hrd.drop('Over18', axis=1, inplace=True)
hrd.drop('StandardHours', axis=1, inplace=True)
hrd.drop('EmployeeNumber', axis=1, inplace=True)
hrd.drop('EmployeeCount', axis=1, inplace=True)
hrd.groupby('Attrition').mean()
df = pd.DataFrame(hrd, columns=['Gender', 'Department', 'Attrition'])
groupby_DeptnAttrition = df['Gender'].groupby([df['Department'], df['Attrition']])
num_bins = 30
fig, ax = plt.subplots(1, 2, figsize=(18, 4))
sns.barplot(x='Attrition', y='DistanceFromHome', data=hrd, ax=ax[0])
sns.barplot(x='Gender', y='JobSatisfaction', data=hrd, ax=ax[1]) | code |
2032867/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import warnings # We want to suppress warnings
warnings.filterwarnings('ignore')
HRData = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv')
HRData.isnull().any()
hrdunique = HRData.nunique()
hrdunique = hrdunique.sort_values()
hrdunique | code |
2032867/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings # We want to suppress warnings
warnings.filterwarnings('ignore')
HRData = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv')
HRData.isnull().any()
hrdunique = HRData.nunique()
hrdunique = hrdunique.sort_values()
hrdunique
hrd = HRData.copy()
hrd.drop('Over18', axis=1, inplace=True)
hrd.drop('StandardHours', axis=1, inplace=True)
hrd.drop('EmployeeNumber', axis=1, inplace=True)
hrd.drop('EmployeeCount', axis=1, inplace=True)
hrd.groupby('Attrition').mean()
df = pd.DataFrame(hrd, columns=['Gender', 'Department', 'Attrition'])
groupby_DeptnAttrition = df['Gender'].groupby([df['Department'], df['Attrition']])
num_bins = 30
fig,ax = plt.subplots(1, 2, figsize=(18,4))
sns.barplot(x = 'Attrition', y = 'DistanceFromHome', data = hrd, ax = ax[0])
sns.barplot(x = 'Gender',y = 'JobSatisfaction', data = hrd, ax = ax[1])
fig = plt.figure(figsize=(12,12))
sns.distplot(hrd.JobSatisfaction, hist=False, kde=True, label='Job Satisfaction', hist_kws={"histtype": "step", "linewidth": 3, "alpha": 1, "color": sns.xkcd_rgb["blue"]})
sns.distplot(hrd.EnvironmentSatisfaction, hist=False, kde=True, label='Environment Satisfaction', hist_kws={"histtype": "step", "linewidth": 3,"alpha": 1, "color": sns.xkcd_rgb["red"]})
sns.distplot(hrd.RelationshipSatisfaction, hist=False, kde=True, label='Relationship Satisfaction', hist_kws={"histtype": "step", "linewidth": 3,"alpha": 1, "color": sns.xkcd_rgb["green"]})
plt.suptitle('Satisfaction Levels of Employees', size=22, x=0.5, y=0.94)
plt.xlabel('Satisfaction Levels', size=10)
plt.legend()
sns.jointplot(x='Age', y='MonthlyIncome', data=hrd) | code |
2032867/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import warnings # We want to suppress warnings
warnings.filterwarnings('ignore')
HRData = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv')
HRData.isnull().any()
hrdunique = HRData.nunique()
hrdunique = hrdunique.sort_values()
hrdunique
hrd = HRData.copy()
hrd.drop('Over18', axis=1, inplace=True)
hrd.drop('StandardHours', axis=1, inplace=True)
hrd.drop('EmployeeNumber', axis=1, inplace=True)
hrd.drop('EmployeeCount', axis=1, inplace=True)
hrd.groupby('Attrition').mean()
num_bins = 30
hrd.hist(bins=num_bins, figsize=(20, 15)) | code |
2032867/cell_38 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib import colors
import numpy as np
import pandas as pd
import seaborn as sns
import warnings # We want to suppress warnings
warnings.filterwarnings('ignore')
HRData = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv')
HRData.isnull().any()
hrdunique = HRData.nunique()
hrdunique = hrdunique.sort_values()
hrdunique
hrd = HRData.copy()
hrd.drop('Over18', axis=1, inplace=True)
hrd.drop('StandardHours', axis=1, inplace=True)
hrd.drop('EmployeeNumber', axis=1, inplace=True)
hrd.drop('EmployeeCount', axis=1, inplace=True)
hrd.groupby('Attrition').mean()
df = pd.DataFrame(hrd, columns=['Gender', 'Department', 'Attrition'])
groupby_DeptnAttrition = df['Gender'].groupby([df['Department'], df['Attrition']])
num_bins = 30
fig,ax = plt.subplots(1, 2, figsize=(18,4))
sns.barplot(x = 'Attrition', y = 'DistanceFromHome', data = hrd, ax = ax[0])
sns.barplot(x = 'Gender',y = 'JobSatisfaction', data = hrd, ax = ax[1])
fig = plt.figure(figsize=(12,12))
sns.distplot(hrd.JobSatisfaction, hist=False, kde=True, label='Job Satisfaction', hist_kws={"histtype": "step", "linewidth": 3, "alpha": 1, "color": sns.xkcd_rgb["blue"]})
sns.distplot(hrd.EnvironmentSatisfaction, hist=False, kde=True, label='Environment Satisfaction', hist_kws={"histtype": "step", "linewidth": 3,"alpha": 1, "color": sns.xkcd_rgb["red"]})
sns.distplot(hrd.RelationshipSatisfaction, hist=False, kde=True, label='Relationship Satisfaction', hist_kws={"histtype": "step", "linewidth": 3,"alpha": 1, "color": sns.xkcd_rgb["green"]})
plt.suptitle('Satisfaction Levels of Employees', size=22, x=0.5, y=0.94)
plt.xlabel('Satisfaction Levels', size=10)
plt.legend()
colors = 'red'
area = np.pi * 3
fig = plt.figure(figsize=(12, 12))
r = sns.heatmap(hrd.corr(), cmap='BuPu', linewidths=0.5, annot=True, fmt='.1f')
r.set_title('Heatmap of IBM HR Data') | code |
2032867/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import warnings # We want to suppress warnings
warnings.filterwarnings('ignore')
HRData = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv')
HRData.isnull().any()
hrdunique = HRData.nunique()
hrdunique = hrdunique.sort_values()
hrdunique
hrd = HRData.copy()
hrd.drop('Over18', axis=1, inplace=True)
hrd.drop('StandardHours', axis=1, inplace=True)
hrd.drop('EmployeeNumber', axis=1, inplace=True)
hrd.drop('EmployeeCount', axis=1, inplace=True)
hrd.groupby('Attrition').mean()
df = pd.DataFrame(hrd, columns=['Gender', 'Department', 'Attrition'])
groupby_DeptnAttrition = df['Gender'].groupby([df['Department'], df['Attrition']])
pd.crosstab(df.Department, df.Gender).plot(kind='bar')
plt.title('Genderwise employee distribution in all Department')
plt.xlabel('Department')
plt.ylabel('Employee') | code |
2032867/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib import colors
import numpy as np
import pandas as pd
import seaborn as sns
import warnings # We want to suppress warnings
warnings.filterwarnings('ignore')
HRData = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv')
HRData.isnull().any()
hrdunique = HRData.nunique()
hrdunique = hrdunique.sort_values()
hrdunique
hrd = HRData.copy()
hrd.drop('Over18', axis=1, inplace=True)
hrd.drop('StandardHours', axis=1, inplace=True)
hrd.drop('EmployeeNumber', axis=1, inplace=True)
hrd.drop('EmployeeCount', axis=1, inplace=True)
hrd.groupby('Attrition').mean()
df = pd.DataFrame(hrd, columns=['Gender', 'Department', 'Attrition'])
groupby_DeptnAttrition = df['Gender'].groupby([df['Department'], df['Attrition']])
num_bins = 30
fig,ax = plt.subplots(1, 2, figsize=(18,4))
sns.barplot(x = 'Attrition', y = 'DistanceFromHome', data = hrd, ax = ax[0])
sns.barplot(x = 'Gender',y = 'JobSatisfaction', data = hrd, ax = ax[1])
fig = plt.figure(figsize=(12,12))
sns.distplot(hrd.JobSatisfaction, hist=False, kde=True, label='Job Satisfaction', hist_kws={"histtype": "step", "linewidth": 3, "alpha": 1, "color": sns.xkcd_rgb["blue"]})
sns.distplot(hrd.EnvironmentSatisfaction, hist=False, kde=True, label='Environment Satisfaction', hist_kws={"histtype": "step", "linewidth": 3,"alpha": 1, "color": sns.xkcd_rgb["red"]})
sns.distplot(hrd.RelationshipSatisfaction, hist=False, kde=True, label='Relationship Satisfaction', hist_kws={"histtype": "step", "linewidth": 3,"alpha": 1, "color": sns.xkcd_rgb["green"]})
plt.suptitle('Satisfaction Levels of Employees', size=22, x=0.5, y=0.94)
plt.xlabel('Satisfaction Levels', size=10)
plt.legend()
colors = 'red'
area = np.pi * 3
plt.scatter(x='Age', y='YearsAtCompany', data=hrd, c=colors, s=area, alpha=0.5)
plt.title('Scatter plot')
plt.xlabel('Age')
plt.ylabel('YearsAtCompany')
plt.legend(loc=2) | code |
2032867/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import warnings # We want to suppress warnings
warnings.filterwarnings('ignore')
HRData = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv')
HRData.isnull().any()
hrdunique = HRData.nunique()
hrdunique = hrdunique.sort_values()
hrdunique
hrd = HRData.copy()
hrd.drop('Over18', axis=1, inplace=True)
hrd.drop('StandardHours', axis=1, inplace=True)
hrd.drop('EmployeeNumber', axis=1, inplace=True)
hrd.drop('EmployeeCount', axis=1, inplace=True)
hrd.groupby('Attrition').mean() | code |
2032867/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import warnings # We want to suppress warnings
warnings.filterwarnings('ignore')
HRData = pd.read_csv('../input/WA_Fn-UseC_-HR-Employee-Attrition.csv')
HRData.info()
HRData.isnull().any() | code |
50233984/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/credit-card-customers/BankChurners.csv')
data = data.drop(columns=['Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1', 'Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2'])
data.columns
len(data.columns) | code |
50233984/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/credit-card-customers/BankChurners.csv')
data.head(5) | code |
50233984/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/credit-card-customers/BankChurners.csv')
data = data.drop(columns=['Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1', 'Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2'])
data.columns | code |
88094945/cell_63 | [
"text_plain_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value_counts()
loan.Credit_History.value_counts()
loan.groupby('Credit_History')['Loan_Status'].value_counts()
loan.Dependents.count()
loan.Dependents.value_counts()
loan.Dependents.value_counts(normalize=True).plot.bar(title='Dependents') | code |
88094945/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
sns.barplot(x='Gender', hue='Loan_Status', y='ApplicantIncome', data=loan) | code |
88094945/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan['Gender'].value_counts() | code |
88094945/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan['Married'].count() | code |
88094945/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.info() | code |
88094945/cell_57 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value_counts()
loan.Credit_History.value_counts()
loan.groupby('Credit_History')['Loan_Status'].value_counts()
sns.barplot(x='Credit_History', hue='Loan_Status', y='ApplicantIncome', data=loan) | code |
88094945/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
sns.barplot(x='Married', hue='Loan_Status', y='LoanAmount', data=loan) | code |
88094945/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
sns.catplot(x='Gender', hue='Loan_Status', y='ApplicantIncome', data=loan, kind='bar', col='Education') | code |
88094945/cell_44 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value_counts()
sns.countplot(x='Self_Employed', hue='Loan_Status', data=loan) | code |
88094945/cell_55 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value_counts()
loan.Credit_History.value_counts()
loan.groupby('Credit_History')['Loan_Status'].value_counts()
sns.countplot(x='Credit_History', hue='Loan_Status', data=loan) | code |
88094945/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes | code |
88094945/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan['Married'].value_counts(normalize=True).plot.bar(title='Married') | code |
88094945/cell_39 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan['Self_Employed'].value_counts() | code |
88094945/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan['Married'].value_counts() | code |
88094945/cell_65 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value_counts()
loan.Credit_History.value_counts()
loan.groupby('Credit_History')['Loan_Status'].value_counts()
loan.Dependents.count()
loan.Dependents.value_counts()
loan.groupby('Dependents')['Loan_Status'].value_counts() | code |
88094945/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan['Self_Employed'].value_counts(normalize=True).plot.bar(title='Self Employed') | code |
88094945/cell_61 | [
"text_plain_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value_counts()
loan.Credit_History.value_counts()
loan.groupby('Credit_History')['Loan_Status'].value_counts()
loan.Dependents.count()
loan.Dependents.value_counts() | code |
88094945/cell_54 | [
"text_plain_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value_counts()
loan.Credit_History.value_counts()
loan.groupby('Credit_History')['Loan_Status'].value_counts() | code |
88094945/cell_60 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value_counts()
loan.Credit_History.value_counts()
loan.groupby('Credit_History')['Loan_Status'].value_counts()
loan.Dependents.count() | code |
88094945/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
sns.countplot(x='Gender', hue='Loan_Status', data=loan) | code |
88094945/cell_50 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value_counts()
loan.Credit_History.value_counts() | code |
88094945/cell_52 | [
"text_plain_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value_counts()
loan.Credit_History.value_counts()
loan.Credit_History.value_counts(normalize=True).plot.bar(title='Credit History') | code |
88094945/cell_49 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value_counts()
loan['Credit_History'].count() | code |
88094945/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts() | code |
88094945/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
sns.countplot(x='Married', hue='Loan_Status', data=loan) | code |
88094945/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
sns.countplot(data=loan, x='Married') | code |
88094945/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan['Loan_Status'].value_counts() | code |
88094945/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
sns.countplot(data=loan, x='Gender') | code |
88094945/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan['Gender'].value_counts(normalize=True).plot.bar(title='Gender') | code |
88094945/cell_38 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan['Self_Employed'].count() | code |
88094945/cell_47 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value_counts()
sns.barplot(x='Self_Employed', hue='Loan_Status', y='ApplicantIncome', data=loan) | code |
88094945/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.head() | code |
88094945/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
sns.barplot(x='Married', hue='Loan_Status', y='ApplicantIncome', data=loan) | code |
88094945/cell_43 | [
"text_plain_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value_counts() | code |
88094945/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts() | code |
88094945/cell_46 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
loan.groupby('Self_Employed')['Loan_Status'].value_counts()
sns.barplot(x='Self_Employed', hue='Loan_Status', y='LoanAmount', data=loan) | code |
88094945/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
sns.barplot(x='Gender', hue='Loan_Status', y='LoanAmount', data=loan) | code |
88094945/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
sns.countplot(data=loan, x='Loan_Status') | code |
88094945/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum() | code |
88094945/cell_36 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
loan = pd.read_csv('../input/loan-prediction-analytics-vidhya/train_ctrUa4K.csv')
loan.isnull().sum()
loan.dtypes
loan.groupby('Gender')['Loan_Status'].value_counts()
loan.groupby('Married')['Loan_Status'].value_counts()
sns.catplot(x='Married', hue='Loan_Status', y='ApplicantIncome', data=loan, kind='bar', col='Education') | code |
32062089/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import interpolate
df = pd.read_csv('../input/datasetauteur.csv')
df_maj = pd.read_csv('../input/MJ.csv', delimiter=';', encoding='utf-8')
df = df.append(df_maj, ignore_index=True, verify_integrity=True, sort=False)
df = df.drop(columns=['Source', 'Thématique', 'Questionnement', 'Problématique'])
df.loc[997, 'Nombre de titres'] = 3
nan_index = df[df['Nombre de titres'].isnull()].index
df.loc[nan_index, 'Nombre de titres'] = 1
niveaux_ordonnes = ['sixième', 'cinquième', 'quatrième', 'troisième', 'terminale L', 'classes préparatoires aux grandes écoles scientifiques', "concours A/L de l'ENS", 'agrégation externe de lettres modernes']
niveaux_ordonnes_court = ['6e', '5e', '4e', '3e', 'Tle', 'Prépa Sc', 'Prépa A/L', 'Agreg']
df = df[df['Genre'] != '-']
df_niveau = df.groupby(['Programme'])['Genre'].value_counts(normalize=True) * 100
df_niveau = 100 - df_niveau.xs('M', level=1)
df_niveau = df_niveau.reindex(niveaux_ordonnes)
df_niveau | code |
32062089/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import interpolate
df = pd.read_csv('../input/datasetauteur.csv')
df_maj = pd.read_csv('../input/MJ.csv', delimiter=';', encoding='utf-8')
df = df.append(df_maj, ignore_index=True, verify_integrity=True, sort=False)
df = df.drop(columns=['Source', 'Thématique', 'Questionnement', 'Problématique'])
df.loc[997, 'Nombre de titres'] = 3
nan_index = df[df['Nombre de titres'].isnull()].index
df.loc[nan_index, 'Nombre de titres'] = 1
niveaux_ordonnes = ['sixième', 'cinquième', 'quatrième', 'troisième', 'terminale L', 'classes préparatoires aux grandes écoles scientifiques', "concours A/L de l'ENS", 'agrégation externe de lettres modernes']
niveaux_ordonnes_court = ['6e', '5e', '4e', '3e', 'Tle', 'Prépa Sc', 'Prépa A/L', 'Agreg']
df = df[df['Genre'] != '-']
df_top = df['Autrice - auteur'].value_counts().sort_values(ascending=False)
df_top.head(15) | code |
32062089/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import interpolate
df = pd.read_csv('../input/datasetauteur.csv')
df_maj = pd.read_csv('../input/MJ.csv', delimiter=';', encoding='utf-8')
df = df.append(df_maj, ignore_index=True, verify_integrity=True, sort=False)
df = df.drop(columns=['Source', 'Thématique', 'Questionnement', 'Problématique'])
df.loc[997, 'Nombre de titres'] = 3
nan_index = df[df['Nombre de titres'].isnull()].index
df.loc[nan_index, 'Nombre de titres'] = 1
niveaux_ordonnes = ['sixième', 'cinquième', 'quatrième', 'troisième', 'terminale L', 'classes préparatoires aux grandes écoles scientifiques', "concours A/L de l'ENS", 'agrégation externe de lettres modernes']
niveaux_ordonnes_court = ['6e', '5e', '4e', '3e', 'Tle', 'Prépa Sc', 'Prépa A/L', 'Agreg']
df = df[df['Genre'] != '-']
df_corpus = df[df['Nombre de titres'] >= 3]
df_corpus = df_corpus.sort_values(by='Nombre de titres', ascending=False)
df_corpus['Autrice - auteur'].value_counts() | code |
32062089/cell_40 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import interpolate
df = pd.read_csv('../input/datasetauteur.csv')
df_maj = pd.read_csv('../input/MJ.csv', delimiter=';', encoding='utf-8')
df = df.append(df_maj, ignore_index=True, verify_integrity=True, sort=False)
df = df.drop(columns=['Source', 'Thématique', 'Questionnement', 'Problématique'])
df.loc[997, 'Nombre de titres'] = 3
nan_index = df[df['Nombre de titres'].isnull()].index
df.loc[nan_index, 'Nombre de titres'] = 1
niveaux_ordonnes = ['sixième', 'cinquième', 'quatrième', 'troisième', 'terminale L', 'classes préparatoires aux grandes écoles scientifiques', "concours A/L de l'ENS", 'agrégation externe de lettres modernes']
niveaux_ordonnes_court = ['6e', '5e', '4e', '3e', 'Tle', 'Prépa Sc', 'Prépa A/L', 'Agreg']
df = df[df['Genre'] != '-']
df_niveau = df.groupby(['Programme'])['Genre'].value_counts(normalize=True) * 100
df_niveau = 100 - df_niveau.xs('M', level=1)
df_niveau = df_niveau.reindex(niveaux_ordonnes)
df_niveau
plt.figure(figsize=(14,6))
graph_niveau = sns.barplot(x=niveaux_ordonnes_court, y=df_niveau.values , palette="GnBu_d")
plt.ylabel("Pourcentage des oeuvres d'autrices")
plt.xlabel("Niveau")
df_agreg = df[df['Programme'] == 'agrégation externe de lettres modernes'].groupby(['Année'])['Genre'].value_counts(normalize=True) * 100
df_agreg = 100 - df_agreg.xs('M', level=1)
plt.figure(figsize=(14, 6))
sns.set(style='darkgrid')
sns.barplot(x=df_agreg.index, y=df_agreg.values, palette='GnBu_d')
plt.ylabel("Pourcentage des oeuvres d'autrices")
plt.xlabel("Sessions de l'Agrégation externe de Lettres Modernes") | code |
32062089/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import interpolate
df = pd.read_csv('../input/datasetauteur.csv')
df_maj = pd.read_csv('../input/MJ.csv', delimiter=';', encoding='utf-8')
df = df.append(df_maj, ignore_index=True, verify_integrity=True, sort=False)
df = df.drop(columns=['Source', 'Thématique', 'Questionnement', 'Problématique'])
df.loc[997, 'Nombre de titres'] = 3
nan_index = df[df['Nombre de titres'].isnull()].index
df.loc[nan_index, 'Nombre de titres'] = 1
niveaux_ordonnes = ['sixième', 'cinquième', 'quatrième', 'troisième', 'terminale L', 'classes préparatoires aux grandes écoles scientifiques', "concours A/L de l'ENS", 'agrégation externe de lettres modernes']
niveaux_ordonnes_court = ['6e', '5e', '4e', '3e', 'Tle', 'Prépa Sc', 'Prépa A/L', 'Agreg']
df = df[df['Genre'] != '-']
df_F = df[df['Genre'] == 'F']
df_F = df_F['Autrice - auteur'].value_counts().sort_values(ascending=False)
df_F.head(15) | code |
32062089/cell_45 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import interpolate
df = pd.read_csv('../input/datasetauteur.csv')
df_maj = pd.read_csv('../input/MJ.csv', delimiter=';', encoding='utf-8')
df = df.append(df_maj, ignore_index=True, verify_integrity=True, sort=False)
df = df.drop(columns=['Source', 'Thématique', 'Questionnement', 'Problématique'])
df.loc[997, 'Nombre de titres'] = 3
nan_index = df[df['Nombre de titres'].isnull()].index
df.loc[nan_index, 'Nombre de titres'] = 1
niveaux_ordonnes = ['sixième', 'cinquième', 'quatrième', 'troisième', 'terminale L', 'classes préparatoires aux grandes écoles scientifiques', "concours A/L de l'ENS", 'agrégation externe de lettres modernes']
niveaux_ordonnes_court = ['6e', '5e', '4e', '3e', 'Tle', 'Prépa Sc', 'Prépa A/L', 'Agreg']
df = df[df['Genre'] != '-']
df_niveau = df.groupby(['Programme'])['Genre'].value_counts(normalize=True) * 100
df_niveau = 100 - df_niveau.xs('M', level=1)
df_niveau = df_niveau.reindex(niveaux_ordonnes)
df_niveau
plt.figure(figsize=(14,6))
graph_niveau = sns.barplot(x=niveaux_ordonnes_court, y=df_niveau.values , palette="GnBu_d")
plt.ylabel("Pourcentage des oeuvres d'autrices")
plt.xlabel("Niveau")
df_agreg = df[df['Programme'] == 'agrégation externe de lettres modernes'].groupby(['Année'])['Genre'].value_counts(normalize=True) * 100
df_agreg = 100 - df_agreg.xs('M', level=1)
sns.set(style='darkgrid')
df_obligatoire = df[df["Niveau d'enseignement"] != 'collège'].groupby(['Année'])['Genre'].value_counts(normalize=True) * 100
df_obligatoire = 100 - df_obligatoire.xs('M', level=1)
plt.figure(figsize=(14, 6))
sns.set(style='darkgrid')
sns.lineplot(x=df_obligatoire.index, y=df_obligatoire.values, palette='husl')
plt.ylabel("Pourcentage des oeuvres d'autrices dans les programmes prescriptifs")
plt.xlabel('Années') | code |
32062089/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import interpolate
df = pd.read_csv('../input/datasetauteur.csv')
df_maj = pd.read_csv('../input/MJ.csv', delimiter=';', encoding='utf-8')
df = df.append(df_maj, ignore_index=True, verify_integrity=True, sort=False)
df = df.drop(columns=['Source', 'Thématique', 'Questionnement', 'Problématique'])
df.loc[997, 'Nombre de titres'] = 3
nan_index = df[df['Nombre de titres'].isnull()].index
df.loc[nan_index, 'Nombre de titres'] = 1
niveaux_ordonnes = ['sixième', 'cinquième', 'quatrième', 'troisième', 'terminale L', 'classes préparatoires aux grandes écoles scientifiques', "concours A/L de l'ENS", 'agrégation externe de lettres modernes']
niveaux_ordonnes_court = ['6e', '5e', '4e', '3e', 'Tle', 'Prépa Sc', 'Prépa A/L', 'Agreg']
df['Genre'].value_counts(normalize=True) * 100 | code |
32062089/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import interpolate
df = pd.read_csv('../input/datasetauteur.csv')
df_maj = pd.read_csv('../input/MJ.csv', delimiter=';', encoding='utf-8')
df = df.append(df_maj, ignore_index=True, verify_integrity=True, sort=False)
df = df.drop(columns=['Source', 'Thématique', 'Questionnement', 'Problématique'])
df.loc[997, 'Nombre de titres'] = 3
nan_index = df[df['Nombre de titres'].isnull()].index
df.loc[nan_index, 'Nombre de titres'] = 1
niveaux_ordonnes = ['sixième', 'cinquième', 'quatrième', 'troisième', 'terminale L', 'classes préparatoires aux grandes écoles scientifiques', "concours A/L de l'ENS", 'agrégation externe de lettres modernes']
niveaux_ordonnes_court = ['6e', '5e', '4e', '3e', 'Tle', 'Prépa Sc', 'Prépa A/L', 'Agreg']
df_andro = df[(df['Genre'] == '-') | df['Genre'].isnull()]
df_andro.sample(5) | code |
32062089/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import interpolate
df = pd.read_csv('../input/datasetauteur.csv')
df_maj = pd.read_csv('../input/MJ.csv', delimiter=';', encoding='utf-8')
df = df.append(df_maj, ignore_index=True, verify_integrity=True, sort=False)
df = df.drop(columns=['Source', 'Thématique', 'Questionnement', 'Problématique'])
df.loc[997, 'Nombre de titres'] = 3
nan_index = df[df['Nombre de titres'].isnull()].index
df.loc[nan_index, 'Nombre de titres'] = 1
niveaux_ordonnes = ['sixième', 'cinquième', 'quatrième', 'troisième', 'terminale L', 'classes préparatoires aux grandes écoles scientifiques', "concours A/L de l'ENS", 'agrégation externe de lettres modernes']
niveaux_ordonnes_court = ['6e', '5e', '4e', '3e', 'Tle', 'Prépa Sc', 'Prépa A/L', 'Agreg']
df = df[df['Genre'] != '-']
df_niveau = df.groupby(['Programme'])['Genre'].value_counts(normalize=True) * 100
df_niveau = 100 - df_niveau.xs('M', level=1)
df_niveau = df_niveau.reindex(niveaux_ordonnes)
df_niveau
plt.figure(figsize=(14, 6))
graph_niveau = sns.barplot(x=niveaux_ordonnes_court, y=df_niveau.values, palette='GnBu_d')
plt.ylabel("Pourcentage des oeuvres d'autrices")
plt.xlabel('Niveau') | code |
32062089/cell_43 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import interpolate
df = pd.read_csv('../input/datasetauteur.csv')
df_maj = pd.read_csv('../input/MJ.csv', delimiter=';', encoding='utf-8')
df = df.append(df_maj, ignore_index=True, verify_integrity=True, sort=False)
df = df.drop(columns=['Source', 'Thématique', 'Questionnement', 'Problématique'])
df.loc[997, 'Nombre de titres'] = 3
nan_index = df[df['Nombre de titres'].isnull()].index
df.loc[nan_index, 'Nombre de titres'] = 1
niveaux_ordonnes = ['sixième', 'cinquième', 'quatrième', 'troisième', 'terminale L', 'classes préparatoires aux grandes écoles scientifiques', "concours A/L de l'ENS", 'agrégation externe de lettres modernes']
niveaux_ordonnes_court = ['6e', '5e', '4e', '3e', 'Tle', 'Prépa Sc', 'Prépa A/L', 'Agreg']
df = df[df['Genre'] != '-']
df_niveau = df.groupby(['Programme'])['Genre'].value_counts(normalize=True) * 100
df_niveau = 100 - df_niveau.xs('M', level=1)
df_niveau = df_niveau.reindex(niveaux_ordonnes)
df_niveau
df[(df['Genre'] == 'F') & df['Année'].isin([1996, 2000, 2005, 2006]) & (df['Programme'] == 'agrégation externe de lettres modernes')] | code |
130008239/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
kw = pd.read_csv('/kaggle/input/2023-kaggle-ai-report/kaggle_writeups_20230510.csv', parse_dates=[0, 3])
kw['Title of Competition'].value_counts().tail(20) | code |
130008239/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
kw = pd.read_csv('/kaggle/input/2023-kaggle-ai-report/kaggle_writeups_20230510.csv', parse_dates=[0, 3])
kw.info() | code |
130008239/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
130008239/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
kw = pd.read_csv('/kaggle/input/2023-kaggle-ai-report/kaggle_writeups_20230510.csv', parse_dates=[0, 3])
num_comp = kw['Title of Competition'].nunique()
print(f'The dataset has {num_comp} competitions.') | code |
130008239/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
kw = pd.read_csv('/kaggle/input/2023-kaggle-ai-report/kaggle_writeups_20230510.csv', parse_dates=[0, 3])
past_two_years_kw = kw[(kw['Competition Launch Date'].dt.year > 2020) & (kw['Competition Launch Date'].dt.month > 4)]
num_comp_past_two_years = past_two_years_kw['Title of Competition'].nunique()
print(f'Number of competitions from the past two years: {num_comp_past_two_years}.') | code |
130008239/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
kw = pd.read_csv('/kaggle/input/2023-kaggle-ai-report/kaggle_writeups_20230510.csv', parse_dates=[0, 3])
earliest_comp = kw['Competition Launch Date'].min().strftime('%Y-%m-%d')
latest_comp = kw['Competition Launch Date'].max().strftime('%Y-%m-%d')
print(f'The earliest competition was held at {earliest_comp} and the latest at {latest_comp}.') | code |
130008239/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
kw = pd.read_csv('/kaggle/input/2023-kaggle-ai-report/kaggle_writeups_20230510.csv', parse_dates=[0, 3])
kw['Title of Competition'].value_counts().head(20) | code |
130008239/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
kw = pd.read_csv('/kaggle/input/2023-kaggle-ai-report/kaggle_writeups_20230510.csv', parse_dates=[0, 3])
kw.head() | code |
33096880/cell_21 | [
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
corrMatrix = train.corr()
number = LabelEncoder()
train['status'] = number.fit_transform(train['status'].astype('str'))
number = LabelEncoder()
train['gender'] = number.fit_transform(train['gender'].astype('str'))
number = LabelEncoder()
train['workex'] = number.fit_transform(train['workex'].astype('str'))
corr_numeric = sns.heatmap(train[["status","mba_p","etest_p","hsc_p","degree_p","ssc_p", "gender", "workex"]].corr(),
annot=True, fmt = ".2f", cmap = "summer")
corr_numeric = sns.heatmap(train[["salary","mba_p","etest_p","hsc_p","degree_p","ssc_p", "gender", "workex"]].corr(),
annot=True, fmt = ".2f", cmap = "summer")
sns.barplot(x='specialisation', y='status', data=train) | code |
33096880/cell_13 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
corrMatrix = train.corr()
number = LabelEncoder()
train['status'] = number.fit_transform(train['status'].astype('str'))
train.head() | code |
33096880/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape | code |
33096880/cell_23 | [
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
corrMatrix = train.corr()
number = LabelEncoder()
train['status'] = number.fit_transform(train['status'].astype('str'))
number = LabelEncoder()
train['gender'] = number.fit_transform(train['gender'].astype('str'))
number = LabelEncoder()
train['workex'] = number.fit_transform(train['workex'].astype('str'))
corr_numeric = sns.heatmap(train[["status","mba_p","etest_p","hsc_p","degree_p","ssc_p", "gender", "workex"]].corr(),
annot=True, fmt = ".2f", cmap = "summer")
corr_numeric = sns.heatmap(train[["salary","mba_p","etest_p","hsc_p","degree_p","ssc_p", "gender", "workex"]].corr(),
annot=True, fmt = ".2f", cmap = "summer")
sns.barplot(x='salary', y='specialisation', data=train) | code |
33096880/cell_20 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
corrMatrix = train.corr()
number = LabelEncoder()
train['status'] = number.fit_transform(train['status'].astype('str'))
number = LabelEncoder()
train['gender'] = number.fit_transform(train['gender'].astype('str'))
number = LabelEncoder()
train['workex'] = number.fit_transform(train['workex'].astype('str'))
corr_numeric = sns.heatmap(train[["status","mba_p","etest_p","hsc_p","degree_p","ssc_p", "gender", "workex"]].corr(),
annot=True, fmt = ".2f", cmap = "summer")
corr_numeric = sns.heatmap(train[["salary","mba_p","etest_p","hsc_p","degree_p","ssc_p", "gender", "workex"]].corr(),
annot=True, fmt = ".2f", cmap = "summer")
sns.barplot(x='degree_t', y='salary', data=train) | code |
33096880/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
train.head() | code |
33096880/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
corrMatrix = train.corr()
sns.heatmap(corrMatrix, annot=True)
plt.show() | code |
33096880/cell_19 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
corrMatrix = train.corr()
number = LabelEncoder()
train['status'] = number.fit_transform(train['status'].astype('str'))
number = LabelEncoder()
train['gender'] = number.fit_transform(train['gender'].astype('str'))
number = LabelEncoder()
train['workex'] = number.fit_transform(train['workex'].astype('str'))
corr_numeric = sns.heatmap(train[["status","mba_p","etest_p","hsc_p","degree_p","ssc_p", "gender", "workex"]].corr(),
annot=True, fmt = ".2f", cmap = "summer")
corr_numeric = sns.heatmap(train[["salary","mba_p","etest_p","hsc_p","degree_p","ssc_p", "gender", "workex"]].corr(),
annot=True, fmt = ".2f", cmap = "summer")
sns.barplot(x='degree_t', y='status', data=train) | code |
33096880/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
train.tail() | code |
33096880/cell_18 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
corrMatrix = train.corr()
number = LabelEncoder()
train['status'] = number.fit_transform(train['status'].astype('str'))
number = LabelEncoder()
train['gender'] = number.fit_transform(train['gender'].astype('str'))
number = LabelEncoder()
train['workex'] = number.fit_transform(train['workex'].astype('str'))
corr_numeric = sns.heatmap(train[["status","mba_p","etest_p","hsc_p","degree_p","ssc_p", "gender", "workex"]].corr(),
annot=True, fmt = ".2f", cmap = "summer")
corr_numeric = sns.heatmap(train[["salary","mba_p","etest_p","hsc_p","degree_p","ssc_p", "gender", "workex"]].corr(),
annot=True, fmt = ".2f", cmap = "summer")
sns.barplot(x='gender', y='status', data=train) | code |
33096880/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
train.info() | code |
33096880/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
corrMatrix = train.corr()
number = LabelEncoder()
train['status'] = number.fit_transform(train['status'].astype('str'))
number = LabelEncoder()
train['gender'] = number.fit_transform(train['gender'].astype('str'))
number = LabelEncoder()
train['workex'] = number.fit_transform(train['workex'].astype('str'))
train.head() | code |
33096880/cell_16 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
corrMatrix = train.corr()
number = LabelEncoder()
train['status'] = number.fit_transform(train['status'].astype('str'))
number = LabelEncoder()
train['gender'] = number.fit_transform(train['gender'].astype('str'))
number = LabelEncoder()
train['workex'] = number.fit_transform(train['workex'].astype('str'))
corr_numeric = sns.heatmap(train[['status', 'mba_p', 'etest_p', 'hsc_p', 'degree_p', 'ssc_p', 'gender', 'workex']].corr(), annot=True, fmt='.2f', cmap='summer') | code |
33096880/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
print(train.columns.values) | code |
33096880/cell_17 | [
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
corrMatrix = train.corr()
number = LabelEncoder()
train['status'] = number.fit_transform(train['status'].astype('str'))
number = LabelEncoder()
train['gender'] = number.fit_transform(train['gender'].astype('str'))
number = LabelEncoder()
train['workex'] = number.fit_transform(train['workex'].astype('str'))
corr_numeric = sns.heatmap(train[["status","mba_p","etest_p","hsc_p","degree_p","ssc_p", "gender", "workex"]].corr(),
annot=True, fmt = ".2f", cmap = "summer")
corr_numeric = sns.heatmap(train[['salary', 'mba_p', 'etest_p', 'hsc_p', 'degree_p', 'ssc_p', 'gender', 'workex']].corr(), annot=True, fmt='.2f', cmap='summer') | code |
33096880/cell_14 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
corrMatrix = train.corr()
number = LabelEncoder()
train['status'] = number.fit_transform(train['status'].astype('str'))
number = LabelEncoder()
train['gender'] = number.fit_transform(train['gender'].astype('str'))
train.head() | code |
33096880/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv')
train.shape
def find_missing_data(data):
Total = data.isnull().sum().sort_values(ascending=False)
Percentage = (data.isnull().sum() / data.isnull().count()).sort_values(ascending=False)
return pd.concat([Total, Percentage], axis=1, keys=['Total', 'Percent'])
find_missing_data(train) | code |
90109221/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_train = mnist_train.iloc[:, 0]
lda = LDA(n_components=9)
X_train_r2 = lda.fit(X_train, y_train)
X_test = mnist_test.iloc[:, 1:785]
y_test = mnist_test.iloc[:, 0]
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape
y_test_final = mnist_test.loc[mnist_test['label'].isin([3, 8])]
y_test_final = y_test_final.iloc[:, 0]
X_test_final = mnist_test.loc[mnist_test['label'].isin([3, 8])]
X_test_final = X_test_final.iloc[:, 1:785]
X_test_final1 = X_test_final.to_numpy(dtype='uint8')
print(X_test_final1) | code |
90109221/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_train = mnist_train.iloc[:, 0]
lda = LDA(n_components=9)
X_train_r2 = lda.fit(X_train, y_train)
X_test = mnist_test.iloc[:, 1:785]
y_test = mnist_test.iloc[:, 0]
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape
y_test_final = mnist_test.loc[mnist_test['label'].isin([3, 8])]
y_test_final = y_test_final.iloc[:, 0]
X_test_final = mnist_test.loc[mnist_test['label'].isin([3, 8])]
X_test_final = X_test_final.iloc[:, 1:785]
print(y_test_final)
print(np.shape(y_test_final))
print(X_test_final)
print(np.shape(X_test_final)) | code |
90109221/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import accuracy_score
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_train = mnist_train.iloc[:, 0]
lda = LDA(n_components=9)
X_train_r2 = lda.fit(X_train, y_train)
X_test = mnist_test.iloc[:, 1:785]
y_test = mnist_test.iloc[:, 0]
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape | code |
90109221/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_train = mnist_train.iloc[:, 0]
lda = LDA(n_components=9)
X_train_r2 = lda.fit(X_train, y_train)
X_test = mnist_test.iloc[:, 1:785]
y_test = mnist_test.iloc[:, 0]
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape
y_test_final = mnist_test.loc[mnist_test['label'].isin([3, 8])]
y_test_final = y_test_final.iloc[:, 0]
X_test_final = mnist_test.loc[mnist_test['label'].isin([3, 8])]
X_test_final = X_test_final.iloc[:, 1:785]
y_train_final = mnist_train.loc[mnist_train['label'].isin([3, 8])]
y_train_final = y_train_final.iloc[:, 0]
X_train_final = mnist_train.loc[mnist_train['label'].isin([3, 8])]
X_train_final = X_train_final.iloc[:, 1:785]
lda1 = LDA(n_components=1)
X_train_r21 = lda1.fit(X_train_final, y_train_final)
y_pred1 = lda1.predict(X_test_final)
w = lda1.coef_
w.shape
w0 = lda1.intercept_
np.transpose(w0).shape
X_test_final1 = X_test_final.to_numpy(dtype='uint8')
X_attack = X_test_final1 - (X_test_final1 @ np.transpose(w) + w0) @ w / np.linalg.norm(w)
Y_attack = lda1.predict(X_attack)
print('Accuracy' + ' ' + str(accuracy_score(y_test_final, Y_attack))) | code |
90109221/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_train = mnist_train.iloc[:, 0]
lda = LDA(n_components=9)
X_train_r2 = lda.fit(X_train, y_train)
X_test = mnist_test.iloc[:, 1:785]
y_test = mnist_test.iloc[:, 0]
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape
y_test_final = mnist_test.loc[mnist_test['label'].isin([3, 8])]
y_test_final = y_test_final.iloc[:, 0]
X_test_final = mnist_test.loc[mnist_test['label'].isin([3, 8])]
X_test_final = X_test_final.iloc[:, 1:785]
y_train_final = mnist_train.loc[mnist_train['label'].isin([3, 8])]
y_train_final = y_train_final.iloc[:, 0]
X_train_final = mnist_train.loc[mnist_train['label'].isin([3, 8])]
X_train_final = X_train_final.iloc[:, 1:785]
lda1 = LDA(n_components=1)
X_train_r21 = lda1.fit(X_train_final, y_train_final)
y_pred1 = lda1.predict(X_test_final)
w = lda1.coef_
w.shape
w0 = lda1.intercept_
np.transpose(w0).shape
X_test_final1 = X_test_final.to_numpy(dtype='uint8')
X_attack = X_test_final1 - (X_test_final1 @ np.transpose(w) + w0) @ w / np.linalg.norm(w)
print(X_attack) | code |
90109221/cell_20 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_train = mnist_train.iloc[:, 0]
lda = LDA(n_components=9)
X_train_r2 = lda.fit(X_train, y_train)
X_test = mnist_test.iloc[:, 1:785]
y_test = mnist_test.iloc[:, 0]
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape
disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_pred)
y_test_final = mnist_test.loc[mnist_test['label'].isin([3, 8])]
y_test_final = y_test_final.iloc[:, 0]
X_test_final = mnist_test.loc[mnist_test['label'].isin([3, 8])]
X_test_final = X_test_final.iloc[:, 1:785]
y_train_final = mnist_train.loc[mnist_train['label'].isin([3, 8])]
y_train_final = y_train_final.iloc[:, 0]
X_train_final = mnist_train.loc[mnist_train['label'].isin([3, 8])]
X_train_final = X_train_final.iloc[:, 1:785]
lda1 = LDA(n_components=1)
X_train_r21 = lda1.fit(X_train_final, y_train_final)
y_pred1 = lda1.predict(X_test_final)
w = lda1.coef_
w.shape
w0 = lda1.intercept_
np.transpose(w0).shape
disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test_final, y_pred1)
disp.figure_.suptitle('Confusion Matrix')
print(f'Confusion matrix:\n{disp.confusion_matrix}')
plt.show() | code |
90109221/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_train = mnist_train.iloc[:, 0]
lda = LDA(n_components=9)
X_train_r2 = lda.fit(X_train, y_train)
X_test = mnist_test.iloc[:, 1:785]
y_test = mnist_test.iloc[:, 0]
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape
disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_pred)
y_test_final = mnist_test.loc[mnist_test['label'].isin([3, 8])]
y_test_final = y_test_final.iloc[:, 0]
X_test_final = mnist_test.loc[mnist_test['label'].isin([3, 8])]
X_test_final = X_test_final.iloc[:, 1:785]
y_train_final = mnist_train.loc[mnist_train['label'].isin([3, 8])]
y_train_final = y_train_final.iloc[:, 0]
X_train_final = mnist_train.loc[mnist_train['label'].isin([3, 8])]
X_train_final = X_train_final.iloc[:, 1:785]
lda1 = LDA(n_components=1)
X_train_r21 = lda1.fit(X_train_final, y_train_final)
y_pred1 = lda1.predict(X_test_final)
w = lda1.coef_
w.shape
w0 = lda1.intercept_
np.transpose(w0).shape
disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test_final, y_pred1)
X_test_final1 = X_test_final.to_numpy(dtype='uint8')
X_attack = X_test_final1 - (X_test_final1 @ np.transpose(w) + w0) @ w / np.linalg.norm(w)
Y_attack = lda1.predict(X_attack)
print(f'Classification report for classifier {lda1}:\n{metrics.classification_report(y_test_final, Y_attack)}\n') | code |
90109221/cell_11 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_train = mnist_train.iloc[:, 0]
lda = LDA(n_components=9)
X_train_r2 = lda.fit(X_train, y_train)
X_test = mnist_test.iloc[:, 1:785]
y_test = mnist_test.iloc[:, 0]
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape
disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_pred)
disp.figure_.suptitle('Confusion Matrix')
print(f'Confusion matrix:\n{disp.confusion_matrix}')
plt.show() | code |
90109221/cell_19 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_train = mnist_train.iloc[:, 0]
lda = LDA(n_components=9)
X_train_r2 = lda.fit(X_train, y_train)
X_test = mnist_test.iloc[:, 1:785]
y_test = mnist_test.iloc[:, 0]
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape
disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_pred)
y_test_final = mnist_test.loc[mnist_test['label'].isin([3, 8])]
y_test_final = y_test_final.iloc[:, 0]
X_test_final = mnist_test.loc[mnist_test['label'].isin([3, 8])]
X_test_final = X_test_final.iloc[:, 1:785]
y_train_final = mnist_train.loc[mnist_train['label'].isin([3, 8])]
y_train_final = y_train_final.iloc[:, 0]
X_train_final = mnist_train.loc[mnist_train['label'].isin([3, 8])]
X_train_final = X_train_final.iloc[:, 1:785]
lda1 = LDA(n_components=1)
X_train_r21 = lda1.fit(X_train_final, y_train_final)
y_pred1 = lda1.predict(X_test_final)
w = lda1.coef_
w.shape
w0 = lda1.intercept_
np.transpose(w0).shape
print(f'Classification report for classifier {lda1}:\n{metrics.classification_report(y_test_final, y_pred1)}\n') | code |
90109221/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_train = mnist_train.iloc[:, 0]
lda = LDA(n_components=9)
X_train_r2 = lda.fit(X_train, y_train)
X_test = mnist_test.iloc[:, 1:785]
y_test = mnist_test.iloc[:, 0]
y_pred = lda.predict(X_test)
print(accuracy_score(y_test, y_pred))
print(y_pred.shape) | code |
90109221/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import accuracy_score
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_train = mnist_train.iloc[:, 0]
lda = LDA(n_components=9)
X_train_r2 = lda.fit(X_train, y_train)
X_test = mnist_test.iloc[:, 1:785]
y_test = mnist_test.iloc[:, 0]
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape
y_test_final = mnist_test.loc[mnist_test['label'].isin([3, 8])]
y_test_final = y_test_final.iloc[:, 0]
X_test_final = mnist_test.loc[mnist_test['label'].isin([3, 8])]
X_test_final = X_test_final.iloc[:, 1:785]
y_train_final = mnist_train.loc[mnist_train['label'].isin([3, 8])]
y_train_final = y_train_final.iloc[:, 0]
X_train_final = mnist_train.loc[mnist_train['label'].isin([3, 8])]
X_train_final = X_train_final.iloc[:, 1:785]
lda1 = LDA(n_components=1)
X_train_r21 = lda1.fit(X_train_final, y_train_final)
y_pred1 = lda1.predict(X_test_final)
w = lda1.coef_
w.shape
w0 = lda1.intercept_
np.transpose(w0).shape | code |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.