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104131002/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import seaborn as sns
# Functions
def check_df(dataframe, head=5):
print("##################### Shape #####################")
print(dataframe.shape)
print("##################### Types #####################")
print(dataframe.dtypes)
print("##################### Head #####################")
print(dataframe.head(head))
print("##################### Tail #####################")
print(dataframe.tail(head))
print("##################### NA #####################")
print(dataframe.isnull().sum())
print("##################### Quantiles #####################")
print(dataframe.quantile([0, 0.05, 0.50, 0.95, 0.99, 1]).T)
def cat_summary(dataframe, col_name, plot=False):
print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),
"Ratio": 100 * dataframe[col_name].value_counts() / len(dataframe)}))
print("##########################################")
if plot:
sns.countplot(x=dataframe[col_name], data=dataframe)
plt.show(block=True)
def num_summary(dataframe, numerical_col, plot=False):
quantiles = [0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95, 0.99]
print(dataframe[numerical_col].describe(quantiles).T)
if plot:
dataframe[numerical_col].hist(bins=20)
plt.xlabel(numerical_col)
plt.title(numerical_col)
plt.show(block=True)
def target_summary_with_num(dataframe, target, numerical_col):
print(dataframe.groupby(target).agg({numerical_col: "mean"}), end="\n\n\n")
def target_summary_with_cat(dataframe, target, categorical_col):
print(pd.DataFrame({"TARGET_MEAN": dataframe.groupby(categorical_col)[target].mean()}), end="\n\n\n")
def correlation_matrix(df, cols):
fig = plt.gcf()
fig.set_size_inches(10, 8)
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
fig = sns.heatmap(df[cols].corr(), annot=True, linewidths=0.5, annot_kws={'size': 12}, linecolor='w', cmap='RdBu')
plt.show(block=True)
def grab_col_names(dataframe, cat_th=10, car_th=20):
"""
Veri setindeki kategorik, numerik ve kategorik fakat kardinal değişkenlerin isimlerini verir.
Not: Kategorik değişkenlerin içerisine numerik görünümlü kategorik değişkenler de dahildir.
Parameters
------
dataframe: dataframe
Değişken isimleri alınmak istenilen dataframe
cat_th: int, optional
numerik fakat kategorik olan değişkenler için sınıf eşik değeri
car_th: int, optinal
kategorik fakat kardinal değişkenler için sınıf eşik değeri
Returns
------
cat_cols: list
Kategorik değişken listesi
num_cols: list
Numerik değişken listesi
cat_but_car: list
Kategorik görünümlü kardinal değişken listesi
Examples
------
import seaborn as sns
df = sns.load_dataset("iris")
print(grab_col_names(df))
Notes
------
cat_cols + num_cols + cat_but_car = toplam değişken sayısı
num_but_cat cat_cols'un içerisinde.
Return olan 3 liste toplamı toplam değişken sayısına eşittir: cat_cols + num_cols + cat_but_car = değişken sayısı
"""
# cat_cols, cat_but_car
cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == "O"]
num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and
dataframe[col].dtypes != "O"]
cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and
dataframe[col].dtypes == "O"]
cat_cols = cat_cols + num_but_cat
cat_cols = [col for col in cat_cols if col not in cat_but_car]
# num_cols
num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != "O"]
num_cols = [col for col in num_cols if col not in num_but_cat]
# print(f"Observations: {dataframe.shape[0]}")
# print(f"Variables: {dataframe.shape[1]}")
# print(f'cat_cols: {len(cat_cols)}')
# print(f'num_cols: {len(num_cols)}')
# print(f'cat_but_car: {len(cat_but_car)}')
# print(f'num_but_cat: {len(num_but_cat)}')
return cat_cols, num_cols, cat_but_car
def missing_values_table(dataframe, na_name=False):
na_columns = [col for col in dataframe.columns if dataframe[col].isnull().sum() > 0]
n_miss = dataframe[na_columns].isnull().sum().sort_values(ascending=False)
ratio = (dataframe[na_columns].isnull().sum() / dataframe.shape[0] * 100).sort_values(ascending=False)
missing_df = pd.concat([n_miss, np.round(ratio, 2)], axis=1, keys=['n_miss', 'ratio'])
print(missing_df, end="\n")
if na_name:
return na_columns
def outlier_thresholds(dataframe, col_name, q1=0.25, q3=0.75):
quartile1 = dataframe[col_name].quantile(q1)
quartile3 = dataframe[col_name].quantile(q3)
interquantile_range = quartile3 - quartile1
up_limit = quartile3 + 1.5 * interquantile_range
low_limit = quartile1 - 1.5 * interquantile_range
return low_limit, up_limit
def replace_with_thresholds(dataframe, variable):
low_limit, up_limit = outlier_thresholds(dataframe, variable)
dataframe.loc[(dataframe[variable] < low_limit), variable] = low_limit
dataframe.loc[(dataframe[variable] > up_limit), variable] = up_limit
def check_outlier(dataframe, col_name, q1=0.25, q3=0.75):
low_limit, up_limit = outlier_thresholds(dataframe, col_name, q1, q3)
if dataframe[(dataframe[col_name] > up_limit) | (dataframe[col_name] < low_limit)].any(axis=None):
return True
else:
return False
def one_hot_encoder(dataframe, categorical_cols, drop_first=False):
dataframe = pd.get_dummies(dataframe, columns=categorical_cols, drop_first=drop_first)
return dataframe
df_ = pd.read_csv('../input/customer-segmentation-with-unsupervised-learning/flo_data_20k.csv')
df = df_.copy()
df[num_cols].describe().T | code |
104131002/cell_3 | [
"text_html_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 |
104131002/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import seaborn as sns
# Functions
def check_df(dataframe, head=5):
print("##################### Shape #####################")
print(dataframe.shape)
print("##################### Types #####################")
print(dataframe.dtypes)
print("##################### Head #####################")
print(dataframe.head(head))
print("##################### Tail #####################")
print(dataframe.tail(head))
print("##################### NA #####################")
print(dataframe.isnull().sum())
print("##################### Quantiles #####################")
print(dataframe.quantile([0, 0.05, 0.50, 0.95, 0.99, 1]).T)
def cat_summary(dataframe, col_name, plot=False):
print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),
"Ratio": 100 * dataframe[col_name].value_counts() / len(dataframe)}))
print("##########################################")
if plot:
sns.countplot(x=dataframe[col_name], data=dataframe)
plt.show(block=True)
def num_summary(dataframe, numerical_col, plot=False):
quantiles = [0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95, 0.99]
print(dataframe[numerical_col].describe(quantiles).T)
if plot:
dataframe[numerical_col].hist(bins=20)
plt.xlabel(numerical_col)
plt.title(numerical_col)
plt.show(block=True)
def target_summary_with_num(dataframe, target, numerical_col):
print(dataframe.groupby(target).agg({numerical_col: "mean"}), end="\n\n\n")
def target_summary_with_cat(dataframe, target, categorical_col):
print(pd.DataFrame({"TARGET_MEAN": dataframe.groupby(categorical_col)[target].mean()}), end="\n\n\n")
def correlation_matrix(df, cols):
fig = plt.gcf()
fig.set_size_inches(10, 8)
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
fig = sns.heatmap(df[cols].corr(), annot=True, linewidths=0.5, annot_kws={'size': 12}, linecolor='w', cmap='RdBu')
plt.show(block=True)
def grab_col_names(dataframe, cat_th=10, car_th=20):
"""
Veri setindeki kategorik, numerik ve kategorik fakat kardinal değişkenlerin isimlerini verir.
Not: Kategorik değişkenlerin içerisine numerik görünümlü kategorik değişkenler de dahildir.
Parameters
------
dataframe: dataframe
Değişken isimleri alınmak istenilen dataframe
cat_th: int, optional
numerik fakat kategorik olan değişkenler için sınıf eşik değeri
car_th: int, optinal
kategorik fakat kardinal değişkenler için sınıf eşik değeri
Returns
------
cat_cols: list
Kategorik değişken listesi
num_cols: list
Numerik değişken listesi
cat_but_car: list
Kategorik görünümlü kardinal değişken listesi
Examples
------
import seaborn as sns
df = sns.load_dataset("iris")
print(grab_col_names(df))
Notes
------
cat_cols + num_cols + cat_but_car = toplam değişken sayısı
num_but_cat cat_cols'un içerisinde.
Return olan 3 liste toplamı toplam değişken sayısına eşittir: cat_cols + num_cols + cat_but_car = değişken sayısı
"""
# cat_cols, cat_but_car
cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == "O"]
num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and
dataframe[col].dtypes != "O"]
cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and
dataframe[col].dtypes == "O"]
cat_cols = cat_cols + num_but_cat
cat_cols = [col for col in cat_cols if col not in cat_but_car]
# num_cols
num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != "O"]
num_cols = [col for col in num_cols if col not in num_but_cat]
# print(f"Observations: {dataframe.shape[0]}")
# print(f"Variables: {dataframe.shape[1]}")
# print(f'cat_cols: {len(cat_cols)}')
# print(f'num_cols: {len(num_cols)}')
# print(f'cat_but_car: {len(cat_but_car)}')
# print(f'num_but_cat: {len(num_but_cat)}')
return cat_cols, num_cols, cat_but_car
def missing_values_table(dataframe, na_name=False):
na_columns = [col for col in dataframe.columns if dataframe[col].isnull().sum() > 0]
n_miss = dataframe[na_columns].isnull().sum().sort_values(ascending=False)
ratio = (dataframe[na_columns].isnull().sum() / dataframe.shape[0] * 100).sort_values(ascending=False)
missing_df = pd.concat([n_miss, np.round(ratio, 2)], axis=1, keys=['n_miss', 'ratio'])
print(missing_df, end="\n")
if na_name:
return na_columns
def outlier_thresholds(dataframe, col_name, q1=0.25, q3=0.75):
quartile1 = dataframe[col_name].quantile(q1)
quartile3 = dataframe[col_name].quantile(q3)
interquantile_range = quartile3 - quartile1
up_limit = quartile3 + 1.5 * interquantile_range
low_limit = quartile1 - 1.5 * interquantile_range
return low_limit, up_limit
def replace_with_thresholds(dataframe, variable):
low_limit, up_limit = outlier_thresholds(dataframe, variable)
dataframe.loc[(dataframe[variable] < low_limit), variable] = low_limit
dataframe.loc[(dataframe[variable] > up_limit), variable] = up_limit
def check_outlier(dataframe, col_name, q1=0.25, q3=0.75):
low_limit, up_limit = outlier_thresholds(dataframe, col_name, q1, q3)
if dataframe[(dataframe[col_name] > up_limit) | (dataframe[col_name] < low_limit)].any(axis=None):
return True
else:
return False
def one_hot_encoder(dataframe, categorical_cols, drop_first=False):
dataframe = pd.get_dummies(dataframe, columns=categorical_cols, drop_first=drop_first)
return dataframe
df_ = pd.read_csv('../input/customer-segmentation-with-unsupervised-learning/flo_data_20k.csv')
df = df_.copy()
for col in num_cols:
num_summary(df, col, plot=True) | code |
104131002/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import seaborn as sns
# Functions
def check_df(dataframe, head=5):
print("##################### Shape #####################")
print(dataframe.shape)
print("##################### Types #####################")
print(dataframe.dtypes)
print("##################### Head #####################")
print(dataframe.head(head))
print("##################### Tail #####################")
print(dataframe.tail(head))
print("##################### NA #####################")
print(dataframe.isnull().sum())
print("##################### Quantiles #####################")
print(dataframe.quantile([0, 0.05, 0.50, 0.95, 0.99, 1]).T)
def cat_summary(dataframe, col_name, plot=False):
print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),
"Ratio": 100 * dataframe[col_name].value_counts() / len(dataframe)}))
print("##########################################")
if plot:
sns.countplot(x=dataframe[col_name], data=dataframe)
plt.show(block=True)
def num_summary(dataframe, numerical_col, plot=False):
quantiles = [0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95, 0.99]
print(dataframe[numerical_col].describe(quantiles).T)
if plot:
dataframe[numerical_col].hist(bins=20)
plt.xlabel(numerical_col)
plt.title(numerical_col)
plt.show(block=True)
def target_summary_with_num(dataframe, target, numerical_col):
print(dataframe.groupby(target).agg({numerical_col: "mean"}), end="\n\n\n")
def target_summary_with_cat(dataframe, target, categorical_col):
print(pd.DataFrame({"TARGET_MEAN": dataframe.groupby(categorical_col)[target].mean()}), end="\n\n\n")
def correlation_matrix(df, cols):
fig = plt.gcf()
fig.set_size_inches(10, 8)
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
fig = sns.heatmap(df[cols].corr(), annot=True, linewidths=0.5, annot_kws={'size': 12}, linecolor='w', cmap='RdBu')
plt.show(block=True)
def grab_col_names(dataframe, cat_th=10, car_th=20):
"""
Veri setindeki kategorik, numerik ve kategorik fakat kardinal değişkenlerin isimlerini verir.
Not: Kategorik değişkenlerin içerisine numerik görünümlü kategorik değişkenler de dahildir.
Parameters
------
dataframe: dataframe
Değişken isimleri alınmak istenilen dataframe
cat_th: int, optional
numerik fakat kategorik olan değişkenler için sınıf eşik değeri
car_th: int, optinal
kategorik fakat kardinal değişkenler için sınıf eşik değeri
Returns
------
cat_cols: list
Kategorik değişken listesi
num_cols: list
Numerik değişken listesi
cat_but_car: list
Kategorik görünümlü kardinal değişken listesi
Examples
------
import seaborn as sns
df = sns.load_dataset("iris")
print(grab_col_names(df))
Notes
------
cat_cols + num_cols + cat_but_car = toplam değişken sayısı
num_but_cat cat_cols'un içerisinde.
Return olan 3 liste toplamı toplam değişken sayısına eşittir: cat_cols + num_cols + cat_but_car = değişken sayısı
"""
# cat_cols, cat_but_car
cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == "O"]
num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and
dataframe[col].dtypes != "O"]
cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and
dataframe[col].dtypes == "O"]
cat_cols = cat_cols + num_but_cat
cat_cols = [col for col in cat_cols if col not in cat_but_car]
# num_cols
num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != "O"]
num_cols = [col for col in num_cols if col not in num_but_cat]
# print(f"Observations: {dataframe.shape[0]}")
# print(f"Variables: {dataframe.shape[1]}")
# print(f'cat_cols: {len(cat_cols)}')
# print(f'num_cols: {len(num_cols)}')
# print(f'cat_but_car: {len(cat_but_car)}')
# print(f'num_but_cat: {len(num_but_cat)}')
return cat_cols, num_cols, cat_but_car
def missing_values_table(dataframe, na_name=False):
na_columns = [col for col in dataframe.columns if dataframe[col].isnull().sum() > 0]
n_miss = dataframe[na_columns].isnull().sum().sort_values(ascending=False)
ratio = (dataframe[na_columns].isnull().sum() / dataframe.shape[0] * 100).sort_values(ascending=False)
missing_df = pd.concat([n_miss, np.round(ratio, 2)], axis=1, keys=['n_miss', 'ratio'])
print(missing_df, end="\n")
if na_name:
return na_columns
def outlier_thresholds(dataframe, col_name, q1=0.25, q3=0.75):
quartile1 = dataframe[col_name].quantile(q1)
quartile3 = dataframe[col_name].quantile(q3)
interquantile_range = quartile3 - quartile1
up_limit = quartile3 + 1.5 * interquantile_range
low_limit = quartile1 - 1.5 * interquantile_range
return low_limit, up_limit
def replace_with_thresholds(dataframe, variable):
low_limit, up_limit = outlier_thresholds(dataframe, variable)
dataframe.loc[(dataframe[variable] < low_limit), variable] = low_limit
dataframe.loc[(dataframe[variable] > up_limit), variable] = up_limit
def check_outlier(dataframe, col_name, q1=0.25, q3=0.75):
low_limit, up_limit = outlier_thresholds(dataframe, col_name, q1, q3)
if dataframe[(dataframe[col_name] > up_limit) | (dataframe[col_name] < low_limit)].any(axis=None):
return True
else:
return False
def one_hot_encoder(dataframe, categorical_cols, drop_first=False):
dataframe = pd.get_dummies(dataframe, columns=categorical_cols, drop_first=drop_first)
return dataframe
cat_but_car | code |
104131002/cell_22 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import datetime as dt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import seaborn as sns
import seaborn as sns
# Functions
def check_df(dataframe, head=5):
print("##################### Shape #####################")
print(dataframe.shape)
print("##################### Types #####################")
print(dataframe.dtypes)
print("##################### Head #####################")
print(dataframe.head(head))
print("##################### Tail #####################")
print(dataframe.tail(head))
print("##################### NA #####################")
print(dataframe.isnull().sum())
print("##################### Quantiles #####################")
print(dataframe.quantile([0, 0.05, 0.50, 0.95, 0.99, 1]).T)
def cat_summary(dataframe, col_name, plot=False):
print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),
"Ratio": 100 * dataframe[col_name].value_counts() / len(dataframe)}))
print("##########################################")
if plot:
sns.countplot(x=dataframe[col_name], data=dataframe)
plt.show(block=True)
def num_summary(dataframe, numerical_col, plot=False):
quantiles = [0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95, 0.99]
print(dataframe[numerical_col].describe(quantiles).T)
if plot:
dataframe[numerical_col].hist(bins=20)
plt.xlabel(numerical_col)
plt.title(numerical_col)
plt.show(block=True)
def target_summary_with_num(dataframe, target, numerical_col):
print(dataframe.groupby(target).agg({numerical_col: "mean"}), end="\n\n\n")
def target_summary_with_cat(dataframe, target, categorical_col):
print(pd.DataFrame({"TARGET_MEAN": dataframe.groupby(categorical_col)[target].mean()}), end="\n\n\n")
def correlation_matrix(df, cols):
fig = plt.gcf()
fig.set_size_inches(10, 8)
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
fig = sns.heatmap(df[cols].corr(), annot=True, linewidths=0.5, annot_kws={'size': 12}, linecolor='w', cmap='RdBu')
plt.show(block=True)
def grab_col_names(dataframe, cat_th=10, car_th=20):
"""
Veri setindeki kategorik, numerik ve kategorik fakat kardinal değişkenlerin isimlerini verir.
Not: Kategorik değişkenlerin içerisine numerik görünümlü kategorik değişkenler de dahildir.
Parameters
------
dataframe: dataframe
Değişken isimleri alınmak istenilen dataframe
cat_th: int, optional
numerik fakat kategorik olan değişkenler için sınıf eşik değeri
car_th: int, optinal
kategorik fakat kardinal değişkenler için sınıf eşik değeri
Returns
------
cat_cols: list
Kategorik değişken listesi
num_cols: list
Numerik değişken listesi
cat_but_car: list
Kategorik görünümlü kardinal değişken listesi
Examples
------
import seaborn as sns
df = sns.load_dataset("iris")
print(grab_col_names(df))
Notes
------
cat_cols + num_cols + cat_but_car = toplam değişken sayısı
num_but_cat cat_cols'un içerisinde.
Return olan 3 liste toplamı toplam değişken sayısına eşittir: cat_cols + num_cols + cat_but_car = değişken sayısı
"""
# cat_cols, cat_but_car
cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == "O"]
num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and
dataframe[col].dtypes != "O"]
cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and
dataframe[col].dtypes == "O"]
cat_cols = cat_cols + num_but_cat
cat_cols = [col for col in cat_cols if col not in cat_but_car]
# num_cols
num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != "O"]
num_cols = [col for col in num_cols if col not in num_but_cat]
# print(f"Observations: {dataframe.shape[0]}")
# print(f"Variables: {dataframe.shape[1]}")
# print(f'cat_cols: {len(cat_cols)}')
# print(f'num_cols: {len(num_cols)}')
# print(f'cat_but_car: {len(cat_but_car)}')
# print(f'num_but_cat: {len(num_but_cat)}')
return cat_cols, num_cols, cat_but_car
def missing_values_table(dataframe, na_name=False):
na_columns = [col for col in dataframe.columns if dataframe[col].isnull().sum() > 0]
n_miss = dataframe[na_columns].isnull().sum().sort_values(ascending=False)
ratio = (dataframe[na_columns].isnull().sum() / dataframe.shape[0] * 100).sort_values(ascending=False)
missing_df = pd.concat([n_miss, np.round(ratio, 2)], axis=1, keys=['n_miss', 'ratio'])
print(missing_df, end="\n")
if na_name:
return na_columns
def outlier_thresholds(dataframe, col_name, q1=0.25, q3=0.75):
quartile1 = dataframe[col_name].quantile(q1)
quartile3 = dataframe[col_name].quantile(q3)
interquantile_range = quartile3 - quartile1
up_limit = quartile3 + 1.5 * interquantile_range
low_limit = quartile1 - 1.5 * interquantile_range
return low_limit, up_limit
def replace_with_thresholds(dataframe, variable):
low_limit, up_limit = outlier_thresholds(dataframe, variable)
dataframe.loc[(dataframe[variable] < low_limit), variable] = low_limit
dataframe.loc[(dataframe[variable] > up_limit), variable] = up_limit
def check_outlier(dataframe, col_name, q1=0.25, q3=0.75):
low_limit, up_limit = outlier_thresholds(dataframe, col_name, q1, q3)
if dataframe[(dataframe[col_name] > up_limit) | (dataframe[col_name] < low_limit)].any(axis=None):
return True
else:
return False
def one_hot_encoder(dataframe, categorical_cols, drop_first=False):
dataframe = pd.get_dummies(dataframe, columns=categorical_cols, drop_first=drop_first)
return dataframe
df_ = pd.read_csv('../input/customer-segmentation-with-unsupervised-learning/flo_data_20k.csv')
df = df_.copy()
date_columns = df.columns[df.columns.str.contains('date')]
df[date_columns] = df[date_columns].apply(pd.to_datetime)
df['last_order_date'].max()
analysis_date = dt.datetime(2021, 6, 1)
df['recency'] = (analysis_date - df['last_order_date']).astype('timedelta64[D]')
df['tenure'] = (df['last_order_date'] - df['first_order_date']).astype('timedelta64[D]')
model_df = df[['order_num_total_ever_online', 'order_num_total_ever_offline', 'customer_value_total_ever_offline', 'customer_value_total_ever_online', 'recency', 'tenure']]
model_df.head() | code |
104131002/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import seaborn as sns
# Functions
def check_df(dataframe, head=5):
print("##################### Shape #####################")
print(dataframe.shape)
print("##################### Types #####################")
print(dataframe.dtypes)
print("##################### Head #####################")
print(dataframe.head(head))
print("##################### Tail #####################")
print(dataframe.tail(head))
print("##################### NA #####################")
print(dataframe.isnull().sum())
print("##################### Quantiles #####################")
print(dataframe.quantile([0, 0.05, 0.50, 0.95, 0.99, 1]).T)
def cat_summary(dataframe, col_name, plot=False):
print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),
"Ratio": 100 * dataframe[col_name].value_counts() / len(dataframe)}))
print("##########################################")
if plot:
sns.countplot(x=dataframe[col_name], data=dataframe)
plt.show(block=True)
def num_summary(dataframe, numerical_col, plot=False):
quantiles = [0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95, 0.99]
print(dataframe[numerical_col].describe(quantiles).T)
if plot:
dataframe[numerical_col].hist(bins=20)
plt.xlabel(numerical_col)
plt.title(numerical_col)
plt.show(block=True)
def target_summary_with_num(dataframe, target, numerical_col):
print(dataframe.groupby(target).agg({numerical_col: "mean"}), end="\n\n\n")
def target_summary_with_cat(dataframe, target, categorical_col):
print(pd.DataFrame({"TARGET_MEAN": dataframe.groupby(categorical_col)[target].mean()}), end="\n\n\n")
def correlation_matrix(df, cols):
fig = plt.gcf()
fig.set_size_inches(10, 8)
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
fig = sns.heatmap(df[cols].corr(), annot=True, linewidths=0.5, annot_kws={'size': 12}, linecolor='w', cmap='RdBu')
plt.show(block=True)
def grab_col_names(dataframe, cat_th=10, car_th=20):
"""
Veri setindeki kategorik, numerik ve kategorik fakat kardinal değişkenlerin isimlerini verir.
Not: Kategorik değişkenlerin içerisine numerik görünümlü kategorik değişkenler de dahildir.
Parameters
------
dataframe: dataframe
Değişken isimleri alınmak istenilen dataframe
cat_th: int, optional
numerik fakat kategorik olan değişkenler için sınıf eşik değeri
car_th: int, optinal
kategorik fakat kardinal değişkenler için sınıf eşik değeri
Returns
------
cat_cols: list
Kategorik değişken listesi
num_cols: list
Numerik değişken listesi
cat_but_car: list
Kategorik görünümlü kardinal değişken listesi
Examples
------
import seaborn as sns
df = sns.load_dataset("iris")
print(grab_col_names(df))
Notes
------
cat_cols + num_cols + cat_but_car = toplam değişken sayısı
num_but_cat cat_cols'un içerisinde.
Return olan 3 liste toplamı toplam değişken sayısına eşittir: cat_cols + num_cols + cat_but_car = değişken sayısı
"""
# cat_cols, cat_but_car
cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == "O"]
num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and
dataframe[col].dtypes != "O"]
cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and
dataframe[col].dtypes == "O"]
cat_cols = cat_cols + num_but_cat
cat_cols = [col for col in cat_cols if col not in cat_but_car]
# num_cols
num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != "O"]
num_cols = [col for col in num_cols if col not in num_but_cat]
# print(f"Observations: {dataframe.shape[0]}")
# print(f"Variables: {dataframe.shape[1]}")
# print(f'cat_cols: {len(cat_cols)}')
# print(f'num_cols: {len(num_cols)}')
# print(f'cat_but_car: {len(cat_but_car)}')
# print(f'num_but_cat: {len(num_but_cat)}')
return cat_cols, num_cols, cat_but_car
def missing_values_table(dataframe, na_name=False):
na_columns = [col for col in dataframe.columns if dataframe[col].isnull().sum() > 0]
n_miss = dataframe[na_columns].isnull().sum().sort_values(ascending=False)
ratio = (dataframe[na_columns].isnull().sum() / dataframe.shape[0] * 100).sort_values(ascending=False)
missing_df = pd.concat([n_miss, np.round(ratio, 2)], axis=1, keys=['n_miss', 'ratio'])
print(missing_df, end="\n")
if na_name:
return na_columns
def outlier_thresholds(dataframe, col_name, q1=0.25, q3=0.75):
quartile1 = dataframe[col_name].quantile(q1)
quartile3 = dataframe[col_name].quantile(q3)
interquantile_range = quartile3 - quartile1
up_limit = quartile3 + 1.5 * interquantile_range
low_limit = quartile1 - 1.5 * interquantile_range
return low_limit, up_limit
def replace_with_thresholds(dataframe, variable):
low_limit, up_limit = outlier_thresholds(dataframe, variable)
dataframe.loc[(dataframe[variable] < low_limit), variable] = low_limit
dataframe.loc[(dataframe[variable] > up_limit), variable] = up_limit
def check_outlier(dataframe, col_name, q1=0.25, q3=0.75):
low_limit, up_limit = outlier_thresholds(dataframe, col_name, q1, q3)
if dataframe[(dataframe[col_name] > up_limit) | (dataframe[col_name] < low_limit)].any(axis=None):
return True
else:
return False
def one_hot_encoder(dataframe, categorical_cols, drop_first=False):
dataframe = pd.get_dummies(dataframe, columns=categorical_cols, drop_first=drop_first)
return dataframe
df_ = pd.read_csv('../input/customer-segmentation-with-unsupervised-learning/flo_data_20k.csv')
df = df_.copy()
check_df(df) | code |
104131002/cell_27 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import datetime as dt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import seaborn as sns
import seaborn as sns
# Functions
def check_df(dataframe, head=5):
print("##################### Shape #####################")
print(dataframe.shape)
print("##################### Types #####################")
print(dataframe.dtypes)
print("##################### Head #####################")
print(dataframe.head(head))
print("##################### Tail #####################")
print(dataframe.tail(head))
print("##################### NA #####################")
print(dataframe.isnull().sum())
print("##################### Quantiles #####################")
print(dataframe.quantile([0, 0.05, 0.50, 0.95, 0.99, 1]).T)
def cat_summary(dataframe, col_name, plot=False):
print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),
"Ratio": 100 * dataframe[col_name].value_counts() / len(dataframe)}))
print("##########################################")
if plot:
sns.countplot(x=dataframe[col_name], data=dataframe)
plt.show(block=True)
def num_summary(dataframe, numerical_col, plot=False):
quantiles = [0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95, 0.99]
print(dataframe[numerical_col].describe(quantiles).T)
if plot:
dataframe[numerical_col].hist(bins=20)
plt.xlabel(numerical_col)
plt.title(numerical_col)
plt.show(block=True)
def target_summary_with_num(dataframe, target, numerical_col):
print(dataframe.groupby(target).agg({numerical_col: "mean"}), end="\n\n\n")
def target_summary_with_cat(dataframe, target, categorical_col):
print(pd.DataFrame({"TARGET_MEAN": dataframe.groupby(categorical_col)[target].mean()}), end="\n\n\n")
def correlation_matrix(df, cols):
fig = plt.gcf()
fig.set_size_inches(10, 8)
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
fig = sns.heatmap(df[cols].corr(), annot=True, linewidths=0.5, annot_kws={'size': 12}, linecolor='w', cmap='RdBu')
plt.show(block=True)
def grab_col_names(dataframe, cat_th=10, car_th=20):
"""
Veri setindeki kategorik, numerik ve kategorik fakat kardinal değişkenlerin isimlerini verir.
Not: Kategorik değişkenlerin içerisine numerik görünümlü kategorik değişkenler de dahildir.
Parameters
------
dataframe: dataframe
Değişken isimleri alınmak istenilen dataframe
cat_th: int, optional
numerik fakat kategorik olan değişkenler için sınıf eşik değeri
car_th: int, optinal
kategorik fakat kardinal değişkenler için sınıf eşik değeri
Returns
------
cat_cols: list
Kategorik değişken listesi
num_cols: list
Numerik değişken listesi
cat_but_car: list
Kategorik görünümlü kardinal değişken listesi
Examples
------
import seaborn as sns
df = sns.load_dataset("iris")
print(grab_col_names(df))
Notes
------
cat_cols + num_cols + cat_but_car = toplam değişken sayısı
num_but_cat cat_cols'un içerisinde.
Return olan 3 liste toplamı toplam değişken sayısına eşittir: cat_cols + num_cols + cat_but_car = değişken sayısı
"""
# cat_cols, cat_but_car
cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == "O"]
num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and
dataframe[col].dtypes != "O"]
cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and
dataframe[col].dtypes == "O"]
cat_cols = cat_cols + num_but_cat
cat_cols = [col for col in cat_cols if col not in cat_but_car]
# num_cols
num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != "O"]
num_cols = [col for col in num_cols if col not in num_but_cat]
# print(f"Observations: {dataframe.shape[0]}")
# print(f"Variables: {dataframe.shape[1]}")
# print(f'cat_cols: {len(cat_cols)}')
# print(f'num_cols: {len(num_cols)}')
# print(f'cat_but_car: {len(cat_but_car)}')
# print(f'num_but_cat: {len(num_but_cat)}')
return cat_cols, num_cols, cat_but_car
def missing_values_table(dataframe, na_name=False):
na_columns = [col for col in dataframe.columns if dataframe[col].isnull().sum() > 0]
n_miss = dataframe[na_columns].isnull().sum().sort_values(ascending=False)
ratio = (dataframe[na_columns].isnull().sum() / dataframe.shape[0] * 100).sort_values(ascending=False)
missing_df = pd.concat([n_miss, np.round(ratio, 2)], axis=1, keys=['n_miss', 'ratio'])
print(missing_df, end="\n")
if na_name:
return na_columns
def outlier_thresholds(dataframe, col_name, q1=0.25, q3=0.75):
quartile1 = dataframe[col_name].quantile(q1)
quartile3 = dataframe[col_name].quantile(q3)
interquantile_range = quartile3 - quartile1
up_limit = quartile3 + 1.5 * interquantile_range
low_limit = quartile1 - 1.5 * interquantile_range
return low_limit, up_limit
def replace_with_thresholds(dataframe, variable):
low_limit, up_limit = outlier_thresholds(dataframe, variable)
dataframe.loc[(dataframe[variable] < low_limit), variable] = low_limit
dataframe.loc[(dataframe[variable] > up_limit), variable] = up_limit
def check_outlier(dataframe, col_name, q1=0.25, q3=0.75):
low_limit, up_limit = outlier_thresholds(dataframe, col_name, q1, q3)
if dataframe[(dataframe[col_name] > up_limit) | (dataframe[col_name] < low_limit)].any(axis=None):
return True
else:
return False
def one_hot_encoder(dataframe, categorical_cols, drop_first=False):
dataframe = pd.get_dummies(dataframe, columns=categorical_cols, drop_first=drop_first)
return dataframe
df_ = pd.read_csv('../input/customer-segmentation-with-unsupervised-learning/flo_data_20k.csv')
df = df_.copy()
date_columns = df.columns[df.columns.str.contains('date')]
df[date_columns] = df[date_columns].apply(pd.to_datetime)
def high_correlated_cols(dataframe, plot=False, corr_th=0.9):
corr = dataframe.corr()
cor_matrix = corr.abs()
upper_triangle_matrix = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(bool))
drop_list = [col for col in upper_triangle_matrix.columns if any(upper_triangle_matrix[col] > corr_th)]
if plot:
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(rc={'figure.figsize': (15, 15)})
return drop_list
high_correlated_cols(df, plot=True)
df['last_order_date'].max()
analysis_date = dt.datetime(2021, 6, 1)
df['recency'] = (analysis_date - df['last_order_date']).astype('timedelta64[D]')
df['tenure'] = (df['last_order_date'] - df['first_order_date']).astype('timedelta64[D]')
model_df = df[['order_num_total_ever_online', 'order_num_total_ever_offline', 'customer_value_total_ever_offline', 'customer_value_total_ever_online', 'recency', 'tenure']]
model_df['order_num_total_ever_online'] = np.log1p(model_df['order_num_total_ever_online'])
model_df['order_num_total_ever_offline'] = np.log1p(model_df['order_num_total_ever_offline'])
model_df['customer_value_total_ever_offline'] = np.log1p(model_df['customer_value_total_ever_offline'])
model_df['customer_value_total_ever_online'] = np.log1p(model_df['customer_value_total_ever_online'])
model_df['recency'] = np.log1p(model_df['recency'])
model_df['tenure'] = np.log1p(model_df['tenure'])
sc = MinMaxScaler((0, 1))
model_scaling = sc.fit_transform(model_df)
model_df = pd.DataFrame(model_scaling, columns=model_df.columns)
model_df.head() | code |
104131002/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import seaborn as sns
# Functions
def check_df(dataframe, head=5):
print("##################### Shape #####################")
print(dataframe.shape)
print("##################### Types #####################")
print(dataframe.dtypes)
print("##################### Head #####################")
print(dataframe.head(head))
print("##################### Tail #####################")
print(dataframe.tail(head))
print("##################### NA #####################")
print(dataframe.isnull().sum())
print("##################### Quantiles #####################")
print(dataframe.quantile([0, 0.05, 0.50, 0.95, 0.99, 1]).T)
def cat_summary(dataframe, col_name, plot=False):
print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),
"Ratio": 100 * dataframe[col_name].value_counts() / len(dataframe)}))
print("##########################################")
if plot:
sns.countplot(x=dataframe[col_name], data=dataframe)
plt.show(block=True)
def num_summary(dataframe, numerical_col, plot=False):
quantiles = [0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95, 0.99]
print(dataframe[numerical_col].describe(quantiles).T)
if plot:
dataframe[numerical_col].hist(bins=20)
plt.xlabel(numerical_col)
plt.title(numerical_col)
plt.show(block=True)
def target_summary_with_num(dataframe, target, numerical_col):
print(dataframe.groupby(target).agg({numerical_col: "mean"}), end="\n\n\n")
def target_summary_with_cat(dataframe, target, categorical_col):
print(pd.DataFrame({"TARGET_MEAN": dataframe.groupby(categorical_col)[target].mean()}), end="\n\n\n")
def correlation_matrix(df, cols):
fig = plt.gcf()
fig.set_size_inches(10, 8)
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
fig = sns.heatmap(df[cols].corr(), annot=True, linewidths=0.5, annot_kws={'size': 12}, linecolor='w', cmap='RdBu')
plt.show(block=True)
def grab_col_names(dataframe, cat_th=10, car_th=20):
"""
Veri setindeki kategorik, numerik ve kategorik fakat kardinal değişkenlerin isimlerini verir.
Not: Kategorik değişkenlerin içerisine numerik görünümlü kategorik değişkenler de dahildir.
Parameters
------
dataframe: dataframe
Değişken isimleri alınmak istenilen dataframe
cat_th: int, optional
numerik fakat kategorik olan değişkenler için sınıf eşik değeri
car_th: int, optinal
kategorik fakat kardinal değişkenler için sınıf eşik değeri
Returns
------
cat_cols: list
Kategorik değişken listesi
num_cols: list
Numerik değişken listesi
cat_but_car: list
Kategorik görünümlü kardinal değişken listesi
Examples
------
import seaborn as sns
df = sns.load_dataset("iris")
print(grab_col_names(df))
Notes
------
cat_cols + num_cols + cat_but_car = toplam değişken sayısı
num_but_cat cat_cols'un içerisinde.
Return olan 3 liste toplamı toplam değişken sayısına eşittir: cat_cols + num_cols + cat_but_car = değişken sayısı
"""
# cat_cols, cat_but_car
cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == "O"]
num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and
dataframe[col].dtypes != "O"]
cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and
dataframe[col].dtypes == "O"]
cat_cols = cat_cols + num_but_cat
cat_cols = [col for col in cat_cols if col not in cat_but_car]
# num_cols
num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != "O"]
num_cols = [col for col in num_cols if col not in num_but_cat]
# print(f"Observations: {dataframe.shape[0]}")
# print(f"Variables: {dataframe.shape[1]}")
# print(f'cat_cols: {len(cat_cols)}')
# print(f'num_cols: {len(num_cols)}')
# print(f'cat_but_car: {len(cat_but_car)}')
# print(f'num_but_cat: {len(num_but_cat)}')
return cat_cols, num_cols, cat_but_car
def missing_values_table(dataframe, na_name=False):
na_columns = [col for col in dataframe.columns if dataframe[col].isnull().sum() > 0]
n_miss = dataframe[na_columns].isnull().sum().sort_values(ascending=False)
ratio = (dataframe[na_columns].isnull().sum() / dataframe.shape[0] * 100).sort_values(ascending=False)
missing_df = pd.concat([n_miss, np.round(ratio, 2)], axis=1, keys=['n_miss', 'ratio'])
print(missing_df, end="\n")
if na_name:
return na_columns
def outlier_thresholds(dataframe, col_name, q1=0.25, q3=0.75):
quartile1 = dataframe[col_name].quantile(q1)
quartile3 = dataframe[col_name].quantile(q3)
interquantile_range = quartile3 - quartile1
up_limit = quartile3 + 1.5 * interquantile_range
low_limit = quartile1 - 1.5 * interquantile_range
return low_limit, up_limit
def replace_with_thresholds(dataframe, variable):
low_limit, up_limit = outlier_thresholds(dataframe, variable)
dataframe.loc[(dataframe[variable] < low_limit), variable] = low_limit
dataframe.loc[(dataframe[variable] > up_limit), variable] = up_limit
def check_outlier(dataframe, col_name, q1=0.25, q3=0.75):
low_limit, up_limit = outlier_thresholds(dataframe, col_name, q1, q3)
if dataframe[(dataframe[col_name] > up_limit) | (dataframe[col_name] < low_limit)].any(axis=None):
return True
else:
return False
def one_hot_encoder(dataframe, categorical_cols, drop_first=False):
dataframe = pd.get_dummies(dataframe, columns=categorical_cols, drop_first=drop_first)
return dataframe
cat_cols | code |
18115772/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/Train.csv')
df_test = pd.read_csv('../input/Test.csv')
df_train.drop_duplicates(keep='first', subset=['date_time'], inplace=True)
df_train.shape
df_train.drop(['date_time'], inplace=True, axis=1)
df_train.info() | code |
18115772/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/Train.csv')
df_test = pd.read_csv('../input/Test.csv')
df_train.describe() | code |
18115772/cell_11 | [
"text_html_output_1.png"
] | from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import m | code |
18115772/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18115772/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/Train.csv')
df_test = pd.read_csv('../input/Test.csv')
df_train.drop_duplicates(keep='first', subset=['date_time'], inplace=True)
df_train.shape
df_train.drop(['date_time'], inplace=True, axis=1)
df_train.head(5) | code |
18115772/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/Train.csv')
df_test = pd.read_csv('../input/Test.csv')
print('The shape of the train dataset is' + str(df_train.shape))
print('The shape of the test dataset is' + str(df_test.shape)) | code |
18115772/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/Train.csv')
df_test = pd.read_csv('../input/Test.csv')
df_train.drop_duplicates(keep='first', subset=['date_time'], inplace=True)
df_train.shape
df_train.drop(['date_time'], inplace=True, axis=1)
df_train.head(10) | code |
18115772/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/Train.csv')
df_test = pd.read_csv('../input/Test.csv')
df_test.describe() | code |
74049349/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
print(f'Train Shape: {train_df.shape}')
print(f'Test Shape : {test_df.shape}') | code |
74049349/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
y = train_df['Survived']
features = train_df.drop(['Survived'], axis=1)
percent_survived = train_df.Survived.sum() / train_df.Survived.count()
num_cols = train_df.select_dtypes(include=['int64', 'float64']).columns
cat_cols = train_df.select_dtypes(include=['object', 'bool']).columns
pd.crosstab(train_df.Sex, train_df.Survived) | code |
74049349/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
y = train_df['Survived']
features = train_df.drop(['Survived'], axis=1)
percent_survived = train_df.Survived.sum() / train_df.Survived.count()
num_cols = train_df.select_dtypes(include=['int64', 'float64']).columns
cat_cols = train_df.select_dtypes(include=['object', 'bool']).columns
num_cols | code |
74049349/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
train_df.describe().transpose() | code |
74049349/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
y = train_df['Survived']
features = train_df.drop(['Survived'], axis=1)
percent_survived = train_df.Survived.sum() / train_df.Survived.count()
print(f'Class: 0 = {1 - percent_survived:.2f} 1 = {percent_survived:.2f}') | code |
74049349/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
train_df.head() | code |
74049349/cell_7 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
plt.style.available | code |
74049349/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
y = train_df['Survived']
features = train_df.drop(['Survived'], axis=1)
percent_survived = train_df.Survived.sum() / train_df.Survived.count()
num_cols = train_df.select_dtypes(include=['int64', 'float64']).columns
cat_cols = train_df.select_dtypes(include=['object', 'bool']).columns
cat_cols | code |
74049349/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
train_df.info() | code |
74049349/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
test_df.info() | code |
74049349/cell_38 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
y = train_df['Survived']
features = train_df.drop(['Survived'], axis=1)
percent_survived = train_df.Survived.sum() / train_df.Survived.count()
num_cols = train_df.select_dtypes(include=['int64', 'float64']).columns
cat_cols = train_df.select_dtypes(include=['object', 'bool']).columns
pd.crosstab(train_df.Sex, train_df.Survived)
survive_percent_male = train_df[train_df.Sex == 'male'].Survived.sum() / train_df[train_df.Sex == 'male'].Survived.count()
survive_percent_female = train_df[train_df.Sex == 'female'].Survived.sum() / train_df[train_df.Sex == 'female'].Survived.count()
pd.crosstab(train_df.Pclass, train_df.Survived)
pd.crosstab(train_df.Embarked, train_df.Survived, margins=True) | code |
74049349/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
y = train_df['Survived']
features = train_df.drop(['Survived'], axis=1)
sns.countplot('Survived', data=train_df) | code |
74049349/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
y = train_df['Survived']
features = train_df.drop(['Survived'], axis=1)
features.head() | code |
74049349/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
y = train_df['Survived']
features = train_df.drop(['Survived'], axis=1)
percent_survived = train_df.Survived.sum() / train_df.Survived.count()
num_cols = train_df.select_dtypes(include=['int64', 'float64']).columns
cat_cols = train_df.select_dtypes(include=['object', 'bool']).columns
pd.crosstab(train_df.Sex, train_df.Survived)
survive_percent_male = train_df[train_df.Sex == 'male'].Survived.sum() / train_df[train_df.Sex == 'male'].Survived.count()
survive_percent_female = train_df[train_df.Sex == 'female'].Survived.sum() / train_df[train_df.Sex == 'female'].Survived.count()
pd.crosstab(train_df.Pclass, train_df.Survived) | code |
74049349/cell_36 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
sample_sub = pd.read_csv('../input/titanic/gender_submission.csv')
y = train_df['Survived']
features = train_df.drop(['Survived'], axis=1)
percent_survived = train_df.Survived.sum() / train_df.Survived.count()
num_cols = train_df.select_dtypes(include=['int64', 'float64']).columns
cat_cols = train_df.select_dtypes(include=['object', 'bool']).columns
pd.crosstab(train_df.Sex, train_df.Survived)
survive_percent_male = train_df[train_df.Sex == 'male'].Survived.sum() / train_df[train_df.Sex == 'male'].Survived.count()
survive_percent_female = train_df[train_df.Sex == 'female'].Survived.sum() / train_df[train_df.Sex == 'female'].Survived.count()
print(f'Male Survivors : {survive_percent_male:.3}')
print(f'Female Survivors: {survive_percent_female:.3}') | code |
2006313/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
price = df.SalePrice
columns_of_interest = ['HouseStyle', 'SaleCondition']
two_columns_of_data = df[columns_of_interest]
y = price
columns_of_interest = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']
X = df[columns_of_interest]
from sklearn.tree import DecisionTreeRegressor
myModel = DecisionTreeRegressor()
myModel.fit(X, y)
from sklearn.metrics import mean_absolute_error
predicted_home_prices = myModel.predict(X)
mean_absolute_error(y, predicted_home_prices) | code |
2006313/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
price = df.SalePrice
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
forest_model = RandomForestRegressor()
forest_model.fit(train_X, train_y)
preds = forest_model.predict(val_X)
cols_with_missing = [col for col in df.columns if df[col].isnull().any()]
cols_with_missing
def score_dataset(Xtrain, Xtest, ytrain, ytest):
forest_model = RandomForestRegressor()
forest_model.fit(Xtrain, ytrain)
preds = forest_model.predict(Xtest)
return mean_absolute_error(ytest, preds)
cols_with_missing = [col for col in X_train.columns if X_train[col].isnull().any()]
reduced_X_train = X_train.drop(cols_with_missing, axis=1)
reduced_X_test = X_test.drop(cols_with_missing, axis=1)
print('Mean Absolute Error from dropping columns with Missing Values:')
print(score_dataset(reduced_X_train, reduced_X_test, y_train, y_test)) | code |
2006313/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
price = df.SalePrice
print(price.head()) | code |
2006313/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
price = df.SalePrice
cols_with_missing = [col for col in df.columns if df[col].isnull().any()]
cols_with_missing | code |
2006313/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
price = df.SalePrice
columns_of_interest = ['HouseStyle', 'SaleCondition']
two_columns_of_data = df[columns_of_interest]
two_columns_of_data.describe() | code |
2006313/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
price = df.SalePrice
cols_with_missing = [col for col in df.columns if df[col].isnull().any()]
cols_with_missing
df = pd.read_csv(main_file_path)
target = df.SalePrice
predictors = df.drop(['SalePrice'], axis=1)
numeric_predictors = df.select_dtypes(exclude=['object'])
predictors.dtypes.sample(10) | code |
2006313/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
print(df.describe()) | code |
2006313/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
price = df.SalePrice
columns_of_interest = ['HouseStyle', 'SaleCondition']
two_columns_of_data = df[columns_of_interest]
y = price
columns_of_interest = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']
X = df[columns_of_interest]
from sklearn.tree import DecisionTreeRegressor
myModel = DecisionTreeRegressor()
myModel.fit(X, y)
print('Making predictions for the following 5 houses:')
print(X.head())
print('The predictions are')
print(myModel.predict(X.head())) | code |
2006313/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
forest_model = RandomForestRegressor()
forest_model.fit(train_X, train_y)
preds = forest_model.predict(val_X)
print(mean_absolute_error(val_y, preds)) | code |
2006313/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.preprocessing import Imputer
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
price = df.SalePrice
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
forest_model = RandomForestRegressor()
forest_model.fit(train_X, train_y)
preds = forest_model.predict(val_X)
cols_with_missing = [col for col in df.columns if df[col].isnull().any()]
cols_with_missing
def score_dataset(Xtrain, Xtest, ytrain, ytest):
forest_model = RandomForestRegressor()
forest_model.fit(Xtrain, ytrain)
preds = forest_model.predict(Xtest)
return mean_absolute_error(ytest, preds)
cols_with_missing = [col for col in X_train.columns if X_train[col].isnull().any()]
reduced_X_train = X_train.drop(cols_with_missing, axis=1)
reduced_X_test = X_test.drop(cols_with_missing, axis=1)
from sklearn.preprocessing import Imputer
my_imputer = Imputer()
imputed_X_train = my_imputer.fit_transform(X_train)
imputed_X_test = my_imputer.transform(X_test)
imputed_X_train_plus = X_train.copy()
imputed_X_test_plus = X_test.copy()
cols_with_missing = (col for col in X_train.columns if X_train[col].isnull().any())
for col in cols_with_missing:
imputed_X_train_plus[col + '_was_missing'] = imputed_X_train_plus[col].isnull()
imputed_X_test_plus[col + '_was_missing'] = imputed_X_test_plus[col].isnull()
my_imputer = Imputer()
imputed_X_train_plus = my_imputer.fit_transform(imputed_X_train_plus)
imputed_X_test_plus = my_imputer.transform(imputed_X_test_plus)
print('Mean Absolute Error from Imputation while Track What Was Imputed:')
print(score_dataset(imputed_X_train_plus, imputed_X_test_plus, y_train, y_test)) | code |
2006313/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
print(df.columns) | code |
2006313/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
def get_mae(max_leaf_nodes, predictors_train, predictors_val, targ_train, targ_val):
model = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes, random_state=0)
model.fit(predictors_train, targ_train)
preds_val = model.predict(predictors_val)
mae = mean_absolute_error(targ_val, preds_val)
return mae
for max_leaf_nodes in [5, 50, 500, 5000]:
my_mae = get_mae(max_leaf_nodes, train_X, val_X, train_y, val_y)
print('Max leaf nodes: %d \t\t Mean Absolute Error: %d' % (max_leaf_nodes, my_mae)) | code |
2006313/cell_14 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
price = df.SalePrice
columns_of_interest = ['HouseStyle', 'SaleCondition']
two_columns_of_data = df[columns_of_interest]
y = price
columns_of_interest = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']
X = df[columns_of_interest]
from sklearn.tree import DecisionTreeRegressor
myModel = DecisionTreeRegressor()
myModel.fit(X, y)
from sklearn.metrics import mean_absolute_error
predicted_home_prices = myModel.predict(X)
mean_absolute_error(y, predicted_home_prices)
from sklearn.model_selection import train_test_split
train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=0)
myModel = DecisionTreeRegressor()
myModel.fit(train_X, train_y)
val_predictions = myModel.predict(val_X)
print(mean_absolute_error(val_y, val_predictions)) | code |
2006313/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
price = df.SalePrice
columns_of_interest = ['HouseStyle', 'SaleCondition']
two_columns_of_data = df[columns_of_interest]
y = price
columns_of_interest = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']
X = df[columns_of_interest]
from sklearn.tree import DecisionTreeRegressor
myModel = DecisionTreeRegressor()
myModel.fit(X, y) | code |
2006313/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.preprocessing import Imputer
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
price = df.SalePrice
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
forest_model = RandomForestRegressor()
forest_model.fit(train_X, train_y)
preds = forest_model.predict(val_X)
cols_with_missing = [col for col in df.columns if df[col].isnull().any()]
cols_with_missing
def score_dataset(Xtrain, Xtest, ytrain, ytest):
forest_model = RandomForestRegressor()
forest_model.fit(Xtrain, ytrain)
preds = forest_model.predict(Xtest)
return mean_absolute_error(ytest, preds)
cols_with_missing = [col for col in X_train.columns if X_train[col].isnull().any()]
reduced_X_train = X_train.drop(cols_with_missing, axis=1)
reduced_X_test = X_test.drop(cols_with_missing, axis=1)
from sklearn.preprocessing import Imputer
my_imputer = Imputer()
imputed_X_train = my_imputer.fit_transform(X_train)
imputed_X_test = my_imputer.transform(X_test)
print('Mean Absolute Error from Imputation:')
print(score_dataset(imputed_X_train, imputed_X_test, y_train, y_test)) | code |
2006313/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import Imputer
from sklearn.preprocessing import Imputer
from sklearn.preprocessing import Imputer
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
from xgboost import XGBRegressor
import pandas as pd
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
price = df.SalePrice
columns_of_interest = ['HouseStyle', 'SaleCondition']
two_columns_of_data = df[columns_of_interest]
y = price
columns_of_interest = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']
X = df[columns_of_interest]
from sklearn.tree import DecisionTreeRegressor
myModel = DecisionTreeRegressor()
myModel.fit(X, y)
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
forest_model = RandomForestRegressor()
forest_model.fit(train_X, train_y)
preds = forest_model.predict(val_X)
cols_with_missing = [col for col in df.columns if df[col].isnull().any()]
cols_with_missing
def score_dataset(Xtrain, Xtest, ytrain, ytest):
forest_model = RandomForestRegressor()
forest_model.fit(Xtrain, ytrain)
preds = forest_model.predict(Xtest)
return mean_absolute_error(ytest, preds)
df = pd.read_csv(main_file_path)
target = df.SalePrice
predictors = df.drop(['SalePrice'], axis=1)
numeric_predictors = df.select_dtypes(exclude=['object'])
cols_with_missing = [col for col in X_train.columns if X_train[col].isnull().any()]
reduced_X_train = X_train.drop(cols_with_missing, axis=1)
reduced_X_test = X_test.drop(cols_with_missing, axis=1)
from sklearn.preprocessing import Imputer
my_imputer = Imputer()
imputed_X_train = my_imputer.fit_transform(X_train)
imputed_X_test = my_imputer.transform(X_test)
imputed_X_train_plus = X_train.copy()
imputed_X_test_plus = X_test.copy()
cols_with_missing = (col for col in X_train.columns if X_train[col].isnull().any())
for col in cols_with_missing:
imputed_X_train_plus[col + '_was_missing'] = imputed_X_train_plus[col].isnull()
imputed_X_test_plus[col + '_was_missing'] = imputed_X_test_plus[col].isnull()
my_imputer = Imputer()
imputed_X_train_plus = my_imputer.fit_transform(imputed_X_train_plus)
imputed_X_test_plus = my_imputer.transform(imputed_X_test_plus)
from sklearn.ensemble import RandomForestRegressor
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import Imputer
my_pipeline = make_pipeline(Imputer(), RandomForestRegressor())
my_pipeline.fit(X_train, y_train)
predictions = my_pipeline.predict(X_test)
my_imputer = Imputer()
my_model = RandomForestRegressor()
imputed_train_X = my_imputer.fit_transform(X_train)
imputed_test_X = my_imputer.transform(X_test)
my_model.fit(imputed_train_X, y_train)
predictions = my_model.predict(imputed_test_X)
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import Imputer
data = pd.read_csv('../input/train.csv')
data.dropna(axis=0, subset=['SalePrice'], inplace=True)
y = data.SalePrice
X = data.drop(['SalePrice'], axis=1).select_dtypes(exclude=['object'])
train_X, test_X, train_y, test_y = train_test_split(X.as_matrix(), y.as_matrix(), test_size=0.25)
my_imputer = Imputer()
train_X = my_imputer.fit_transform(train_X)
test_X = my_imputer.transform(test_X)
from xgboost import XGBRegressor
my_model = XGBRegressor()
my_model.fit(train_X, train_y, verbose=False)
predictions = my_model.predict(test_X)
from sklearn.metrics import mean_absolute_error
my_model = XGBRegressor(n_estimators=1000)
my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], verbose=False)
predictions = my_model.predict(test_X)
from sklearn.metrics import mean_absolute_error
print('Mean Absolute Error : ' + str(mean_absolute_error(predictions, test_y))) | code |
2006313/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import Imputer
from sklearn.preprocessing import Imputer
from sklearn.preprocessing import Imputer
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
from xgboost import XGBRegressor
import pandas as pd
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
df = pd.read_csv(main_file_path)
price = df.SalePrice
columns_of_interest = ['HouseStyle', 'SaleCondition']
two_columns_of_data = df[columns_of_interest]
y = price
columns_of_interest = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']
X = df[columns_of_interest]
from sklearn.tree import DecisionTreeRegressor
myModel = DecisionTreeRegressor()
myModel.fit(X, y)
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
forest_model = RandomForestRegressor()
forest_model.fit(train_X, train_y)
preds = forest_model.predict(val_X)
cols_with_missing = [col for col in df.columns if df[col].isnull().any()]
cols_with_missing
def score_dataset(Xtrain, Xtest, ytrain, ytest):
forest_model = RandomForestRegressor()
forest_model.fit(Xtrain, ytrain)
preds = forest_model.predict(Xtest)
return mean_absolute_error(ytest, preds)
df = pd.read_csv(main_file_path)
target = df.SalePrice
predictors = df.drop(['SalePrice'], axis=1)
numeric_predictors = df.select_dtypes(exclude=['object'])
cols_with_missing = [col for col in X_train.columns if X_train[col].isnull().any()]
reduced_X_train = X_train.drop(cols_with_missing, axis=1)
reduced_X_test = X_test.drop(cols_with_missing, axis=1)
from sklearn.preprocessing import Imputer
my_imputer = Imputer()
imputed_X_train = my_imputer.fit_transform(X_train)
imputed_X_test = my_imputer.transform(X_test)
imputed_X_train_plus = X_train.copy()
imputed_X_test_plus = X_test.copy()
cols_with_missing = (col for col in X_train.columns if X_train[col].isnull().any())
for col in cols_with_missing:
imputed_X_train_plus[col + '_was_missing'] = imputed_X_train_plus[col].isnull()
imputed_X_test_plus[col + '_was_missing'] = imputed_X_test_plus[col].isnull()
my_imputer = Imputer()
imputed_X_train_plus = my_imputer.fit_transform(imputed_X_train_plus)
imputed_X_test_plus = my_imputer.transform(imputed_X_test_plus)
from sklearn.ensemble import RandomForestRegressor
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import Imputer
my_pipeline = make_pipeline(Imputer(), RandomForestRegressor())
my_pipeline.fit(X_train, y_train)
predictions = my_pipeline.predict(X_test)
my_imputer = Imputer()
my_model = RandomForestRegressor()
imputed_train_X = my_imputer.fit_transform(X_train)
imputed_test_X = my_imputer.transform(X_test)
my_model.fit(imputed_train_X, y_train)
predictions = my_model.predict(imputed_test_X)
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import Imputer
data = pd.read_csv('../input/train.csv')
data.dropna(axis=0, subset=['SalePrice'], inplace=True)
y = data.SalePrice
X = data.drop(['SalePrice'], axis=1).select_dtypes(exclude=['object'])
train_X, test_X, train_y, test_y = train_test_split(X.as_matrix(), y.as_matrix(), test_size=0.25)
my_imputer = Imputer()
train_X = my_imputer.fit_transform(train_X)
test_X = my_imputer.transform(test_X)
from xgboost import XGBRegressor
my_model = XGBRegressor()
my_model.fit(train_X, train_y, verbose=False)
predictions = my_model.predict(test_X)
from sklearn.metrics import mean_absolute_error
print('Mean Absolute Error : ' + str(mean_absolute_error(predictions, test_y))) | code |
128018979/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
np.random.seed(0)
x = np.random.rand(100, 1)
y = 2 + 3 * x + np.random.rand(100, 1)
model = LinearRegression()
model.fit(x, y)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
np.random.seed(123)
x = np.linspace(0, 10, 20)
y = 3 * x + 2 + np.random.normal(scale=2, size=len(x))
model = LinearRegression()
model.fit(x.reshape(-1, 1), y)
y_pred = model.predict(x.reshape(-1, 1))
residuals = y - y_pred
SSR = np.sum(residuals ** 2)
print('Sum of squared residuals (SSR): {:.2f}'.format(SSR)) | code |
128018979/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
np.random.seed(0)
x = np.random.rand(100, 1)
y = 2 + 3 * x + np.random.rand(100, 1)
model = LinearRegression()
model.fit(x, y)
plt.scatter(x, y, s=10)
plt.plot(x, model.predict(x), color='r')
plt.xlabel('X')
plt.ylabel('Y')
plt.show() | code |
128018979/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
np.random.seed(0)
x = np.random.rand(100, 1)
y = 2 + 3 * x + np.random.rand(100, 1)
model = LinearRegression()
model.fit(x, y)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
np.random.seed(123)
x = np.linspace(0, 10, 20)
y = 3 * x + 2 + np.random.normal(scale=2, size=len(x))
model = LinearRegression()
model.fit(x.reshape(-1, 1), y)
y_pred = model.predict(x.reshape(-1, 1))
residuals = y - y_pred
plt.scatter(x, y, s=10)
plt.plot(x, y_pred, color='red')
plt.legend(['Regression line', 'Data'])
plt.xlabel('X')
plt.ylabel('Y')
print('Residuals:')
for i in range(10):
print('Data point {}: {:.2f}'.format(i + 1, residuals[i])) | code |
128018979/cell_5 | [
"image_output_2.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
np.random.seed(0)
x = np.random.rand(100, 1)
y = 2 + 3 * x + np.random.rand(100, 1)
model = LinearRegression()
model.fit(x, y)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
np.random.seed(123)
x = np.linspace(0, 10, 20)
y = 3 * x + 2 + np.random.normal(scale=2, size=len(x))
model = LinearRegression()
model.fit(x.reshape(-1, 1), y)
y_pred = model.predict(x.reshape(-1, 1))
residuals = y - y_pred
SSR = np.sum(residuals ** 2)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
np.random.seed(0)
x = np.random.rand(100, 1)
y = 2 + 3 * x + np.random.rand(100, 1)
model = LinearRegression()
model.fit(x, y)
sse = np.sum((y - model.predict(x)) ** 2)
residuals = y - model.predict(x)
fig, axs = plt.subplots(2, 2, figsize=(10, 8))
axs[0, 0].scatter(x, y, s=10)
axs[0, 0].plot(x, model.predict(x), color='r')
axs[0, 0].set_xlabel('X')
axs[0, 0].set_ylabel('Y')
axs[0, 0].set_title('Original Regression Line')
model_high_slope = LinearRegression()
model_high_slope.fit(x, y + 0.5)
sse_high_slope = np.sum((y - model_high_slope.predict(x)) ** 2)
residuals_high_slope = y - model_high_slope.predict(x)
axs[0, 1].scatter(x, y, s=10)
axs[0, 1].plot(x, model_high_slope.predict(x), color='r')
axs[0, 1].set_xlabel('X')
axs[0, 1].set_ylabel('Y')
axs[0, 1].set_title('Line with Higher Slope')
model_low_slope = LinearRegression()
model_low_slope.fit(x, y - 0.5)
sse_low_slope = np.sum((y - model_low_slope.predict(x)) ** 2)
residuals_low_slope = y - model_low_slope.predict(x)
axs[1, 0].scatter(x, y, s=10)
axs[1, 0].plot(x, model_low_slope.predict(x), color='r')
axs[1, 0].set_xlabel('X')
axs[1, 0].set_ylabel('Y')
axs[1, 0].set_title('Line with Lower Slope')
model_diff_intercept = LinearRegression()
model_diff_intercept.fit(x, y + 1)
sse_diff_intercept = np.sum((y - model_diff_intercept.predict(x)) ** 2)
residuals_diff_intercept = y - model_diff_intercept.predict(x)
axs[1, 1].scatter(x, y, s=10)
axs[1, 1].plot(x, model_diff_intercept.predict(x), color='r')
axs[1, 1].set_xlabel('X')
axs[1, 1].set_ylabel('Y')
axs[1, 1].set_title('Line with Different Intercept')
fig, axs = plt.subplots(2, 2, figsize=(10, 8))
axs[0, 0].scatter(model.predict(x), residuals, s=10)
axs[0, 0].set_xlabel('Y Predicted')
axs[0, 0].set_ylabel('Residuals')
axs[0, 0].set_title('Residuals for Original Line')
axs[0, 1].scatter(model_high_slope.predict(x), residuals_high_slope, s=10)
axs[0, 1].set_xlabel('Y Predicted')
axs[0, 1].set_ylabel('Residuals')
axs[0, 1].set_title('Residuals for Line with Higher Slope')
axs[1, 0].scatter(model_low_slope.predict(x), residuals_low_slope, s=10)
axs[1, 0].set_xlabel('Y Predicted')
axs[1, 0].set_ylabel('Residuals')
axs[1, 0].set_title('Residuals for Line with Lower Slope')
axs[1, 1].scatter(model_diff_intercept.predict(x), residuals_diff_intercept, s=10)
axs[1, 1].set_xlabel('Y Predicted')
axs[1, 1].set_ylabel('Residuals')
axs[1, 1].set_title('Residuals for Line with Different Intercept')
plt.tight_layout()
plt.show() | code |
128048760/cell_9 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, BertModel
from transformers import AutoTokenizer, GPT2Model
text = 'Hello World!'
from transformers import AutoTokenizer, BertModel
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
text = 'i love cryptography, mathematics, and cybersecurity.'
tokenized_text = tokenizer.tokenize(text)
from transformers import AutoTokenizer, GPT2Model
tokenizer = AutoTokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained('gpt2')
text = 'i love cryptography, mathematics, and cybersecurity.'
tokenized_text = tokenizer.tokenize(text)
text = 'I love cryptography, mathematics, and cybersecurity.'
tokens = tokenizer.tokenize(text)
print(tokens) | code |
128048760/cell_4 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, BertModel
text = 'Hello World!'
from transformers import AutoTokenizer, BertModel
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
text = 'i love cryptography, mathematics, and cybersecurity.'
tokenized_text = tokenizer.tokenize(text)
print(tokenized_text) | code |
128048760/cell_6 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, BertModel
from transformers import AutoTokenizer, GPT2Model
text = 'Hello World!'
from transformers import AutoTokenizer, BertModel
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
text = 'i love cryptography, mathematics, and cybersecurity.'
tokenized_text = tokenizer.tokenize(text)
from transformers import AutoTokenizer, GPT2Model
tokenizer = AutoTokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained('gpt2')
text = 'i love cryptography, mathematics, and cybersecurity.'
tokenized_text = tokenizer.tokenize(text)
print(tokenized_text) | code |
128048760/cell_11 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, BertModel
from transformers import AutoTokenizer, GPT2Model
text = 'Hello World!'
from transformers import AutoTokenizer, BertModel
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
text = 'i love cryptography, mathematics, and cybersecurity.'
tokenized_text = tokenizer.tokenize(text)
from transformers import AutoTokenizer, GPT2Model
tokenizer = AutoTokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained('gpt2')
text = 'i love cryptography, mathematics, and cybersecurity.'
tokenized_text = tokenizer.tokenize(text)
text = 'I love cryptography, mathematics, and cybersecurity.'
tokens = tokenizer.tokenize(text)
token_ids = tokenizer.convert_tokens_to_ids(tokens)
print(tokenizer.convert_ids_to_tokens(token_ids)) | code |
128048760/cell_19 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, BertModel
from transformers import AutoTokenizer, GPT2Model
import torch
import torch
text = 'Hello World!'
from transformers import AutoTokenizer, BertModel
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
text = 'i love cryptography, mathematics, and cybersecurity.'
tokenized_text = tokenizer.tokenize(text)
from transformers import AutoTokenizer, GPT2Model
tokenizer = AutoTokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained('gpt2')
text = 'i love cryptography, mathematics, and cybersecurity.'
tokenized_text = tokenizer.tokenize(text)
text = 'I love cryptography, mathematics, and cybersecurity.'
tokens = tokenizer.tokenize(text)
token_ids = tokenizer.convert_tokens_to_ids(tokens)
token_ids = tokenizer.encode(text)
tokens_from_encode = tokenizer.convert_ids_to_tokens(token_ids)
example_word = 'cyber'
example_token_id = tokenizer.convert_tokens_to_ids([example_word])[0]
example_embedding = model.embeddings.word_embeddings(torch.tensor([example_token_id]))
word_one = 'king'
word_two = 'queen'
one_token_id = tokenizer.convert_tokens_to_ids([word_one])[0]
one_embedding = model.embeddings.word_embeddings(torch.tensor([one_token_id]))
two_token_id = tokenizer.convert_tokens_to_ids([word_two])[0]
two_embedding = model.embeddings.word_embeddings(torch.tensor([two_token_id]))
cos = torch.nn.CosineSimilarity(dim=1)
cosine_similarity = cos(one_embedding, two_embedding)
euclidean_distance = torch.cdist(one_embedding, two_embedding)
word_three = 'man'
word_four = 'woman'
three_token_id = tokenizer.convert_tokens_to_ids([word_three])[0]
three_embedding = model.embeddings.word_embeddings(torch.tensor([three_token_id]))
four_token_id = tokenizer.convert_tokens_to_ids([word_four])[0]
four_embedding = model.embeddings.word_embeddings(torch.tensor([four_token_id]))
cos = torch.nn.CosineSimilarity(dim=1)
cosine_similarity = cos(three_embedding, four_embedding)
euclidean_distance = torch.cdist(three_embedding, four_embedding)
print(f"Cosine Similarity between '{word_three}' and '{word_four}': {cosine_similarity[0]}")
print(f"Euclidean Distance between '{word_three}' and '{word_four}': {euclidean_distance[0][0]}") | code |
128048760/cell_18 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, BertModel
from transformers import AutoTokenizer, GPT2Model
import torch
import torch
text = 'Hello World!'
from transformers import AutoTokenizer, BertModel
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
text = 'i love cryptography, mathematics, and cybersecurity.'
tokenized_text = tokenizer.tokenize(text)
from transformers import AutoTokenizer, GPT2Model
tokenizer = AutoTokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained('gpt2')
text = 'i love cryptography, mathematics, and cybersecurity.'
tokenized_text = tokenizer.tokenize(text)
text = 'I love cryptography, mathematics, and cybersecurity.'
tokens = tokenizer.tokenize(text)
token_ids = tokenizer.convert_tokens_to_ids(tokens)
token_ids = tokenizer.encode(text)
tokens_from_encode = tokenizer.convert_ids_to_tokens(token_ids)
example_word = 'cyber'
example_token_id = tokenizer.convert_tokens_to_ids([example_word])[0]
example_embedding = model.embeddings.word_embeddings(torch.tensor([example_token_id]))
word_one = 'king'
word_two = 'queen'
one_token_id = tokenizer.convert_tokens_to_ids([word_one])[0]
one_embedding = model.embeddings.word_embeddings(torch.tensor([one_token_id]))
two_token_id = tokenizer.convert_tokens_to_ids([word_two])[0]
two_embedding = model.embeddings.word_embeddings(torch.tensor([two_token_id]))
cos = torch.nn.CosineSimilarity(dim=1)
cosine_similarity = cos(one_embedding, two_embedding)
euclidean_distance = torch.cdist(one_embedding, two_embedding)
print(f"Cosine Similarity between '{word_one}' and '{word_two}': {cosine_similarity[0]}")
print(f"Euclidean Distance between '{word_one}' and '{word_two}': {euclidean_distance[0][0]}") | code |
128048760/cell_15 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, BertModel
from transformers import AutoTokenizer, GPT2Model
import torch
text = 'Hello World!'
from transformers import AutoTokenizer, BertModel
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
text = 'i love cryptography, mathematics, and cybersecurity.'
tokenized_text = tokenizer.tokenize(text)
from transformers import AutoTokenizer, GPT2Model
tokenizer = AutoTokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained('gpt2')
text = 'i love cryptography, mathematics, and cybersecurity.'
tokenized_text = tokenizer.tokenize(text)
text = 'I love cryptography, mathematics, and cybersecurity.'
tokens = tokenizer.tokenize(text)
token_ids = tokenizer.convert_tokens_to_ids(tokens)
token_ids = tokenizer.encode(text)
tokens_from_encode = tokenizer.convert_ids_to_tokens(token_ids)
example_word = 'cyber'
example_token_id = tokenizer.convert_tokens_to_ids([example_word])[0]
example_embedding = model.embeddings.word_embeddings(torch.tensor([example_token_id]))
print(example_embedding.shape)
print(example_embedding) | code |
128048760/cell_3 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, BertModel
from transformers import AutoTokenizer, BertModel
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased') | code |
128048760/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from transformers import AutoTokenizer, BertModel
from transformers import AutoTokenizer, GPT2Model
text = 'Hello World!'
from transformers import AutoTokenizer, BertModel
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
text = 'i love cryptography, mathematics, and cybersecurity.'
tokenized_text = tokenizer.tokenize(text)
from transformers import AutoTokenizer, GPT2Model
tokenizer = AutoTokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained('gpt2')
text = 'i love cryptography, mathematics, and cybersecurity.'
tokenized_text = tokenizer.tokenize(text)
text = 'I love cryptography, mathematics, and cybersecurity.'
tokens = tokenizer.tokenize(text)
token_ids = tokenizer.convert_tokens_to_ids(tokens)
print(token_ids) | code |
128048760/cell_12 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, BertModel
from transformers import AutoTokenizer, GPT2Model
text = 'Hello World!'
from transformers import AutoTokenizer, BertModel
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
text = 'i love cryptography, mathematics, and cybersecurity.'
tokenized_text = tokenizer.tokenize(text)
from transformers import AutoTokenizer, GPT2Model
tokenizer = AutoTokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained('gpt2')
text = 'i love cryptography, mathematics, and cybersecurity.'
tokenized_text = tokenizer.tokenize(text)
text = 'I love cryptography, mathematics, and cybersecurity.'
tokens = tokenizer.tokenize(text)
token_ids = tokenizer.convert_tokens_to_ids(tokens)
token_ids = tokenizer.encode(text)
print(token_ids)
tokens_from_encode = tokenizer.convert_ids_to_tokens(token_ids)
print(tokens_from_encode) | code |
128048760/cell_5 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, BertModel
from transformers import AutoTokenizer, GPT2Model
from transformers import AutoTokenizer, BertModel
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
from transformers import AutoTokenizer, GPT2Model
tokenizer = AutoTokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained('gpt2') | code |
89141968/cell_42 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data = data.drop(['Timestamp', 'Email address', 'Name', 'Employment Status', 'Prediction'], axis=1)
data = data.drop(['City'], axis=1)
{column: len(data[column].unique()) for column in data.select_dtypes('object').columns}
{column: list(data[column].unique()) for column in data.select_dtypes('object').columns}
{column: len(data[column].unique()) for column in data.select_dtypes('float64').columns}
{column: len(data[column].unique()) for column in data.select_dtypes('int64').columns}
target = 'Prediction_status'
ordinal_features = ['How are you feeling today?', 'Is your sadness momentarily or has it been constant for a long time?', 'At what time of the day are you extremely low?', 'How frequently have you had little pleasure or interest in the activities you usually enjoy?', 'Describe how ‘supported’ you feel by others around you – your friends, family, or otherwise.', 'How frequently have you been doing things that mean something to you or your life?', 'How easy is it for you to take medical leave for a mental health condition?', 'How often do you make use of substance abuse(e.g. smoking, alcohol)?', 'How many hours do you spend per day on watching mobile phone, laptop, computer, television, etc.?', 'How often do you get offended or angry or start crying ?']
nominal_features = ['eating and sleeping', '(If sad)have you been in the same mental state for the past few days?', 'Has there been a sudden and huge change in your life?', 'Your stress is related to which of the following areas?', 'If you have a mental health condition, do you feel that it interferes with your work?', 'Have you taken any therapy or medication in the near past for mental health?', 'Having trouble concentrating on things, such as reading the newspaper or watching television, or studying?', 'Do you feel bad about yourself — or that you are a failure or have let yourself or your family down?', 'Has the COVID-19 pandemic affected your mental well being?']
def binary_encode(df, columns, positive_values):
df = df.copy()
for column, positive_value in zip(columns, positive_values):
df[column] = df[column].apply(lambda x: 1 if x == positive_value else 0)
return df
def ordinal_encode(df, columns, orderings):
df = df.copy()
for column, ordering in zip(columns, orderings):
df[column] = df[column].apply(lambda x: ordering.index(x))
return df
def nominal_encode(df, columns, prefixes):
df = df.copy()
for column, prefix in zip(columns, prefixes):
dummies = pd.get_dummies(df[column], prefix)
df = pd.concat([df, dummies], axis=1)
df = df.drop(column, axis=1)
return df
ordinal_orderings = [['Good', 'Fine', 'Sad', 'Depressed'], ['Not sad', 'For some time', 'Significant time', 'Long time'], ['Morning', 'Afternoon', 'Evening'], ['Never', 'Sometimes', 'Often', 'Very Often'], ['Highly supportive', 'Satisfactory', 'Little bit', 'Not at all'], ['Very Often', 'Often', 'Sometimes', 'Never'], ['Very easy', 'Easy', 'Not so easy', 'Difficult'], ['Never', 'Sometimes', 'Often', 'Very Often'], ['1-2 hours', '2-5 hours', '5-10 hours', 'More than 10 hours'], ['Never', 'Sometimes', 'Often', 'Very often']]
nominal_prefixes = ['es', 'smen', 'change', 'stress', 'inter', 'ther', 'conc', 'fbad', 'cov']
data = nominal_encode(data, columns=nominal_features, prefixes=nominal_prefixes)
data = ordinal_encode(data, columns=ordinal_features, orderings=ordinal_orderings)
data | code |
89141968/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data['How likely do you feel yourself vulnerable or lonely?'].mode() | code |
89141968/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data['How often do you get offended or angry or start crying ?'].mode() | code |
89141968/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data['How comfortable are you in talking about your mental health?'].mode() | code |
89141968/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data['Have you taken any therapy or medication in the near past for mental health?'].mode() | code |
89141968/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data | code |
89141968/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data = data.drop(['Timestamp', 'Email address', 'Name', 'Employment Status', 'Prediction'], axis=1)
data = data.drop(['City'], axis=1)
{column: len(data[column].unique()) for column in data.select_dtypes('object').columns}
{column: list(data[column].unique()) for column in data.select_dtypes('object').columns}
{column: len(data[column].unique()) for column in data.select_dtypes('float64').columns}
{column: len(data[column].unique()) for column in data.select_dtypes('int64').columns} | code |
89141968/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data['Do you feel bad about yourself — or that you are a failure or have let yourself or your family down?'].mode() | code |
89141968/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data = data.drop(['Timestamp', 'Email address', 'Name', 'Employment Status', 'Prediction'], axis=1)
data = data.drop(['City'], axis=1)
{column: len(data[column].unique()) for column in data.select_dtypes('object').columns}
{column: list(data[column].unique()) for column in data.select_dtypes('object').columns}
{column: len(data[column].unique()) for column in data.select_dtypes('float64').columns} | code |
89141968/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum() | code |
89141968/cell_48 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data = data.drop(['Timestamp', 'Email address', 'Name', 'Employment Status', 'Prediction'], axis=1)
data = data.drop(['City'], axis=1)
{column: len(data[column].unique()) for column in data.select_dtypes('object').columns}
{column: list(data[column].unique()) for column in data.select_dtypes('object').columns}
{column: len(data[column].unique()) for column in data.select_dtypes('float64').columns}
{column: len(data[column].unique()) for column in data.select_dtypes('int64').columns}
target = 'Prediction_status'
ordinal_features = ['How are you feeling today?', 'Is your sadness momentarily or has it been constant for a long time?', 'At what time of the day are you extremely low?', 'How frequently have you had little pleasure or interest in the activities you usually enjoy?', 'Describe how ‘supported’ you feel by others around you – your friends, family, or otherwise.', 'How frequently have you been doing things that mean something to you or your life?', 'How easy is it for you to take medical leave for a mental health condition?', 'How often do you make use of substance abuse(e.g. smoking, alcohol)?', 'How many hours do you spend per day on watching mobile phone, laptop, computer, television, etc.?', 'How often do you get offended or angry or start crying ?']
nominal_features = ['eating and sleeping', '(If sad)have you been in the same mental state for the past few days?', 'Has there been a sudden and huge change in your life?', 'Your stress is related to which of the following areas?', 'If you have a mental health condition, do you feel that it interferes with your work?', 'Have you taken any therapy or medication in the near past for mental health?', 'Having trouble concentrating on things, such as reading the newspaper or watching television, or studying?', 'Do you feel bad about yourself — or that you are a failure or have let yourself or your family down?', 'Has the COVID-19 pandemic affected your mental well being?']
def binary_encode(df, columns, positive_values):
df = df.copy()
for column, positive_value in zip(columns, positive_values):
df[column] = df[column].apply(lambda x: 1 if x == positive_value else 0)
return df
def ordinal_encode(df, columns, orderings):
df = df.copy()
for column, ordering in zip(columns, orderings):
df[column] = df[column].apply(lambda x: ordering.index(x))
return df
def nominal_encode(df, columns, prefixes):
df = df.copy()
for column, prefix in zip(columns, prefixes):
dummies = pd.get_dummies(df[column], prefix)
df = pd.concat([df, dummies], axis=1)
df = df.drop(column, axis=1)
return df
ordinal_orderings = [['Good', 'Fine', 'Sad', 'Depressed'], ['Not sad', 'For some time', 'Significant time', 'Long time'], ['Morning', 'Afternoon', 'Evening'], ['Never', 'Sometimes', 'Often', 'Very Often'], ['Highly supportive', 'Satisfactory', 'Little bit', 'Not at all'], ['Very Often', 'Often', 'Sometimes', 'Never'], ['Very easy', 'Easy', 'Not so easy', 'Difficult'], ['Never', 'Sometimes', 'Often', 'Very Often'], ['1-2 hours', '2-5 hours', '5-10 hours', 'More than 10 hours'], ['Never', 'Sometimes', 'Often', 'Very often']]
nominal_prefixes = ['es', 'smen', 'change', 'stress', 'inter', 'ther', 'conc', 'fbad', 'cov']
data = nominal_encode(data, columns=nominal_features, prefixes=nominal_prefixes)
data = ordinal_encode(data, columns=ordinal_features, orderings=ordinal_orderings)
data.select_dtypes('object')
data = binary_encode(data, columns=['Are you above 30 years of age?'], positive_values=['Yes'])
data = binary_encode(data, columns=['Prediction_status'], positive_values=['True'])
data.select_dtypes('object')
print('Remaining non-numeric columns:', len(data.select_dtypes('object').columns)) | code |
89141968/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data['How likely do you feel yourself vulnerable or lonely?'].mode() | code |
89141968/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data['How many hours do you spend per day on watching mobile phone, laptop, computer, television, etc.?'].mode() | code |
89141968/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean() | code |
89141968/cell_49 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data = data.drop(['Timestamp', 'Email address', 'Name', 'Employment Status', 'Prediction'], axis=1)
data = data.drop(['City'], axis=1)
{column: len(data[column].unique()) for column in data.select_dtypes('object').columns}
{column: list(data[column].unique()) for column in data.select_dtypes('object').columns}
{column: len(data[column].unique()) for column in data.select_dtypes('float64').columns}
{column: len(data[column].unique()) for column in data.select_dtypes('int64').columns}
target = 'Prediction_status'
ordinal_features = ['How are you feeling today?', 'Is your sadness momentarily or has it been constant for a long time?', 'At what time of the day are you extremely low?', 'How frequently have you had little pleasure or interest in the activities you usually enjoy?', 'Describe how ‘supported’ you feel by others around you – your friends, family, or otherwise.', 'How frequently have you been doing things that mean something to you or your life?', 'How easy is it for you to take medical leave for a mental health condition?', 'How often do you make use of substance abuse(e.g. smoking, alcohol)?', 'How many hours do you spend per day on watching mobile phone, laptop, computer, television, etc.?', 'How often do you get offended or angry or start crying ?']
nominal_features = ['eating and sleeping', '(If sad)have you been in the same mental state for the past few days?', 'Has there been a sudden and huge change in your life?', 'Your stress is related to which of the following areas?', 'If you have a mental health condition, do you feel that it interferes with your work?', 'Have you taken any therapy or medication in the near past for mental health?', 'Having trouble concentrating on things, such as reading the newspaper or watching television, or studying?', 'Do you feel bad about yourself — or that you are a failure or have let yourself or your family down?', 'Has the COVID-19 pandemic affected your mental well being?']
def binary_encode(df, columns, positive_values):
df = df.copy()
for column, positive_value in zip(columns, positive_values):
df[column] = df[column].apply(lambda x: 1 if x == positive_value else 0)
return df
def ordinal_encode(df, columns, orderings):
df = df.copy()
for column, ordering in zip(columns, orderings):
df[column] = df[column].apply(lambda x: ordering.index(x))
return df
def nominal_encode(df, columns, prefixes):
df = df.copy()
for column, prefix in zip(columns, prefixes):
dummies = pd.get_dummies(df[column], prefix)
df = pd.concat([df, dummies], axis=1)
df = df.drop(column, axis=1)
return df
ordinal_orderings = [['Good', 'Fine', 'Sad', 'Depressed'], ['Not sad', 'For some time', 'Significant time', 'Long time'], ['Morning', 'Afternoon', 'Evening'], ['Never', 'Sometimes', 'Often', 'Very Often'], ['Highly supportive', 'Satisfactory', 'Little bit', 'Not at all'], ['Very Often', 'Often', 'Sometimes', 'Never'], ['Very easy', 'Easy', 'Not so easy', 'Difficult'], ['Never', 'Sometimes', 'Often', 'Very Often'], ['1-2 hours', '2-5 hours', '5-10 hours', 'More than 10 hours'], ['Never', 'Sometimes', 'Often', 'Very often']]
nominal_prefixes = ['es', 'smen', 'change', 'stress', 'inter', 'ther', 'conc', 'fbad', 'cov']
data = nominal_encode(data, columns=nominal_features, prefixes=nominal_prefixes)
data = ordinal_encode(data, columns=ordinal_features, orderings=ordinal_orderings)
data.select_dtypes('object')
data = binary_encode(data, columns=['Are you above 30 years of age?'], positive_values=['Yes'])
data = binary_encode(data, columns=['Prediction_status'], positive_values=['True'])
data.select_dtypes('object')
data.tail() | code |
89141968/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data = data.drop(['Timestamp', 'Email address', 'Name', 'Employment Status', 'Prediction'], axis=1)
data = data.drop(['City'], axis=1)
{column: len(data[column].unique()) for column in data.select_dtypes('object').columns}
{column: list(data[column].unique()) for column in data.select_dtypes('object').columns} | code |
89141968/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data['How comfortable are you in talking about your mental health?'].unique() | code |
89141968/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data['Has the COVID-19 pandemic affected your mental well being?'].mode() | code |
89141968/cell_47 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data = data.drop(['Timestamp', 'Email address', 'Name', 'Employment Status', 'Prediction'], axis=1)
data = data.drop(['City'], axis=1)
{column: len(data[column].unique()) for column in data.select_dtypes('object').columns}
{column: list(data[column].unique()) for column in data.select_dtypes('object').columns}
{column: len(data[column].unique()) for column in data.select_dtypes('float64').columns}
{column: len(data[column].unique()) for column in data.select_dtypes('int64').columns}
target = 'Prediction_status'
ordinal_features = ['How are you feeling today?', 'Is your sadness momentarily or has it been constant for a long time?', 'At what time of the day are you extremely low?', 'How frequently have you had little pleasure or interest in the activities you usually enjoy?', 'Describe how ‘supported’ you feel by others around you – your friends, family, or otherwise.', 'How frequently have you been doing things that mean something to you or your life?', 'How easy is it for you to take medical leave for a mental health condition?', 'How often do you make use of substance abuse(e.g. smoking, alcohol)?', 'How many hours do you spend per day on watching mobile phone, laptop, computer, television, etc.?', 'How often do you get offended or angry or start crying ?']
nominal_features = ['eating and sleeping', '(If sad)have you been in the same mental state for the past few days?', 'Has there been a sudden and huge change in your life?', 'Your stress is related to which of the following areas?', 'If you have a mental health condition, do you feel that it interferes with your work?', 'Have you taken any therapy or medication in the near past for mental health?', 'Having trouble concentrating on things, such as reading the newspaper or watching television, or studying?', 'Do you feel bad about yourself — or that you are a failure or have let yourself or your family down?', 'Has the COVID-19 pandemic affected your mental well being?']
def binary_encode(df, columns, positive_values):
df = df.copy()
for column, positive_value in zip(columns, positive_values):
df[column] = df[column].apply(lambda x: 1 if x == positive_value else 0)
return df
def ordinal_encode(df, columns, orderings):
df = df.copy()
for column, ordering in zip(columns, orderings):
df[column] = df[column].apply(lambda x: ordering.index(x))
return df
def nominal_encode(df, columns, prefixes):
df = df.copy()
for column, prefix in zip(columns, prefixes):
dummies = pd.get_dummies(df[column], prefix)
df = pd.concat([df, dummies], axis=1)
df = df.drop(column, axis=1)
return df
ordinal_orderings = [['Good', 'Fine', 'Sad', 'Depressed'], ['Not sad', 'For some time', 'Significant time', 'Long time'], ['Morning', 'Afternoon', 'Evening'], ['Never', 'Sometimes', 'Often', 'Very Often'], ['Highly supportive', 'Satisfactory', 'Little bit', 'Not at all'], ['Very Often', 'Often', 'Sometimes', 'Never'], ['Very easy', 'Easy', 'Not so easy', 'Difficult'], ['Never', 'Sometimes', 'Often', 'Very Often'], ['1-2 hours', '2-5 hours', '5-10 hours', 'More than 10 hours'], ['Never', 'Sometimes', 'Often', 'Very often']]
nominal_prefixes = ['es', 'smen', 'change', 'stress', 'inter', 'ther', 'conc', 'fbad', 'cov']
data = nominal_encode(data, columns=nominal_features, prefixes=nominal_prefixes)
data = ordinal_encode(data, columns=ordinal_features, orderings=ordinal_orderings)
data.select_dtypes('object')
data = binary_encode(data, columns=['Are you above 30 years of age?'], positive_values=['Yes'])
data = binary_encode(data, columns=['Prediction_status'], positive_values=['True'])
data.select_dtypes('object') | code |
89141968/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data['If sad, how likely are you to take an appointment with a psychologist or a counsellor for your current mental state?'].mode() | code |
89141968/cell_43 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data = data.drop(['Timestamp', 'Email address', 'Name', 'Employment Status', 'Prediction'], axis=1)
data = data.drop(['City'], axis=1)
{column: len(data[column].unique()) for column in data.select_dtypes('object').columns}
{column: list(data[column].unique()) for column in data.select_dtypes('object').columns}
{column: len(data[column].unique()) for column in data.select_dtypes('float64').columns}
{column: len(data[column].unique()) for column in data.select_dtypes('int64').columns}
target = 'Prediction_status'
ordinal_features = ['How are you feeling today?', 'Is your sadness momentarily or has it been constant for a long time?', 'At what time of the day are you extremely low?', 'How frequently have you had little pleasure or interest in the activities you usually enjoy?', 'Describe how ‘supported’ you feel by others around you – your friends, family, or otherwise.', 'How frequently have you been doing things that mean something to you or your life?', 'How easy is it for you to take medical leave for a mental health condition?', 'How often do you make use of substance abuse(e.g. smoking, alcohol)?', 'How many hours do you spend per day on watching mobile phone, laptop, computer, television, etc.?', 'How often do you get offended or angry or start crying ?']
nominal_features = ['eating and sleeping', '(If sad)have you been in the same mental state for the past few days?', 'Has there been a sudden and huge change in your life?', 'Your stress is related to which of the following areas?', 'If you have a mental health condition, do you feel that it interferes with your work?', 'Have you taken any therapy or medication in the near past for mental health?', 'Having trouble concentrating on things, such as reading the newspaper or watching television, or studying?', 'Do you feel bad about yourself — or that you are a failure or have let yourself or your family down?', 'Has the COVID-19 pandemic affected your mental well being?']
def binary_encode(df, columns, positive_values):
df = df.copy()
for column, positive_value in zip(columns, positive_values):
df[column] = df[column].apply(lambda x: 1 if x == positive_value else 0)
return df
def ordinal_encode(df, columns, orderings):
df = df.copy()
for column, ordering in zip(columns, orderings):
df[column] = df[column].apply(lambda x: ordering.index(x))
return df
def nominal_encode(df, columns, prefixes):
df = df.copy()
for column, prefix in zip(columns, prefixes):
dummies = pd.get_dummies(df[column], prefix)
df = pd.concat([df, dummies], axis=1)
df = df.drop(column, axis=1)
return df
ordinal_orderings = [['Good', 'Fine', 'Sad', 'Depressed'], ['Not sad', 'For some time', 'Significant time', 'Long time'], ['Morning', 'Afternoon', 'Evening'], ['Never', 'Sometimes', 'Often', 'Very Often'], ['Highly supportive', 'Satisfactory', 'Little bit', 'Not at all'], ['Very Often', 'Often', 'Sometimes', 'Never'], ['Very easy', 'Easy', 'Not so easy', 'Difficult'], ['Never', 'Sometimes', 'Often', 'Very Often'], ['1-2 hours', '2-5 hours', '5-10 hours', 'More than 10 hours'], ['Never', 'Sometimes', 'Often', 'Very often']]
nominal_prefixes = ['es', 'smen', 'change', 'stress', 'inter', 'ther', 'conc', 'fbad', 'cov']
data = nominal_encode(data, columns=nominal_features, prefixes=nominal_prefixes)
data = ordinal_encode(data, columns=ordinal_features, orderings=ordinal_orderings)
data.select_dtypes('object') | code |
89141968/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data = data.drop(['Timestamp', 'Email address', 'Name', 'Employment Status', 'Prediction'], axis=1)
data = data.drop(['City'], axis=1)
{column: len(data[column].unique()) for column in data.select_dtypes('object').columns} | code |
89141968/cell_53 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data = data.drop(['Timestamp', 'Email address', 'Name', 'Employment Status', 'Prediction'], axis=1)
data = data.drop(['City'], axis=1)
{column: len(data[column].unique()) for column in data.select_dtypes('object').columns}
{column: list(data[column].unique()) for column in data.select_dtypes('object').columns}
{column: len(data[column].unique()) for column in data.select_dtypes('float64').columns}
{column: len(data[column].unique()) for column in data.select_dtypes('int64').columns}
target = 'Prediction_status'
ordinal_features = ['How are you feeling today?', 'Is your sadness momentarily or has it been constant for a long time?', 'At what time of the day are you extremely low?', 'How frequently have you had little pleasure or interest in the activities you usually enjoy?', 'Describe how ‘supported’ you feel by others around you – your friends, family, or otherwise.', 'How frequently have you been doing things that mean something to you or your life?', 'How easy is it for you to take medical leave for a mental health condition?', 'How often do you make use of substance abuse(e.g. smoking, alcohol)?', 'How many hours do you spend per day on watching mobile phone, laptop, computer, television, etc.?', 'How often do you get offended or angry or start crying ?']
nominal_features = ['eating and sleeping', '(If sad)have you been in the same mental state for the past few days?', 'Has there been a sudden and huge change in your life?', 'Your stress is related to which of the following areas?', 'If you have a mental health condition, do you feel that it interferes with your work?', 'Have you taken any therapy or medication in the near past for mental health?', 'Having trouble concentrating on things, such as reading the newspaper or watching television, or studying?', 'Do you feel bad about yourself — or that you are a failure or have let yourself or your family down?', 'Has the COVID-19 pandemic affected your mental well being?']
def binary_encode(df, columns, positive_values):
df = df.copy()
for column, positive_value in zip(columns, positive_values):
df[column] = df[column].apply(lambda x: 1 if x == positive_value else 0)
return df
def ordinal_encode(df, columns, orderings):
df = df.copy()
for column, ordering in zip(columns, orderings):
df[column] = df[column].apply(lambda x: ordering.index(x))
return df
def nominal_encode(df, columns, prefixes):
df = df.copy()
for column, prefix in zip(columns, prefixes):
dummies = pd.get_dummies(df[column], prefix)
df = pd.concat([df, dummies], axis=1)
df = df.drop(column, axis=1)
return df
ordinal_orderings = [['Good', 'Fine', 'Sad', 'Depressed'], ['Not sad', 'For some time', 'Significant time', 'Long time'], ['Morning', 'Afternoon', 'Evening'], ['Never', 'Sometimes', 'Often', 'Very Often'], ['Highly supportive', 'Satisfactory', 'Little bit', 'Not at all'], ['Very Often', 'Often', 'Sometimes', 'Never'], ['Very easy', 'Easy', 'Not so easy', 'Difficult'], ['Never', 'Sometimes', 'Often', 'Very Often'], ['1-2 hours', '2-5 hours', '5-10 hours', 'More than 10 hours'], ['Never', 'Sometimes', 'Often', 'Very often']]
nominal_prefixes = ['es', 'smen', 'change', 'stress', 'inter', 'ther', 'conc', 'fbad', 'cov']
data = nominal_encode(data, columns=nominal_features, prefixes=nominal_prefixes)
data = ordinal_encode(data, columns=ordinal_features, orderings=ordinal_orderings)
data.select_dtypes('object')
data = binary_encode(data, columns=['Are you above 30 years of age?'], positive_values=['Yes'])
data = binary_encode(data, columns=['Prediction_status'], positive_values=['True'])
data.select_dtypes('object')
print('Remaining missing values:', data.isna().sum().sum()) | code |
89141968/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/mental-health-dataset/Mental Health Questionnaire 2.0.csv')
data.isna().sum()
data.isna().mean()
data['(If sad)have you been in the same mental state for the past few days?'].mode() | code |
17117770/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.svm import SVC
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
model = SVC(gamma='auto')
features = list(train.columns)
features.remove('label')
model.fit(train_df[features], train_df['label'])
model.score(valid_df[features], valid_df['label']) | code |
17117770/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17117770/cell_10 | [
"text_html_output_1.png"
] | from sklearn.svm import SVC
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
model = SVC(gamma='auto')
features = list(train.columns)
features.remove('label')
model.fit(train_df[features], train_df['label']) | code |
17117770/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
train.head() | code |
34121580/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df1 = pd.read_csv('../input/indiastate/data state.csv')
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
X = df1[['Confirmed', 'Active', 'Recovered', 'Deceased']]
y = target['Deceased']
df2 = pd.read_csv('../input/covid-19-india/hotspot.csv')
df2.Red
df2.State
df2.dropna | code |
34121580/cell_25 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
df1 = pd.read_csv('../input/indiastate/data state.csv')
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
X = df1[['Confirmed', 'Active', 'Recovered', 'Deceased']]
y = target['Deceased']
labels = list(df1.State)
decease = list(df1.Deceased)
explode = []
for i in labels:
explode.append(0.05)
centre_circle = plt.Circle((0, 0), 0.7, fc='white')
fig = plt.gcf()
fig.gca().add_artist(centre_circle)
plt.tight_layout()
df2 = pd.read_csv('../input/covid-19-india/hotspot.csv')
df2.Red
df2.State
df2.dropna
df3 = df2.drop(37)
plt.figure(figsize=(10, 10))
plt.hlines(y=df3['State'], xmin=0, xmax=40, color='grey', alpha=0.4)
plt.scatter(df3['Red'], df3['State'], color='skyblue', label='Red Zone')
plt.scatter(df3['Orange'], df3['State'], color='Green', label='Orange Zone')
plt.scatter(df3['Green'], df3['State'], color='Red', label='Green Zone') | code |
34121580/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/indiastate/data state.csv')
print(df1) | code |
34121580/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df1 = pd.read_csv('../input/indiastate/data state.csv')
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
X = df1[['Confirmed', 'Active', 'Recovered', 'Deceased']]
y = target['Deceased']
df2 = pd.read_csv('../input/covid-19-india/hotspot.csv')
df2.Red
df2.State
df2.dropna
df3 = df2.drop(37)
df3 | code |
34121580/cell_30 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import seaborn as sns
df1 = pd.read_csv('../input/indiastate/data state.csv')
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
X = df1[['Confirmed', 'Active', 'Recovered', 'Deceased']]
y = target['Deceased']
labels = list(df1.State)
decease = list(df1.Deceased)
explode = []
for i in labels:
explode.append(0.05)
centre_circle = plt.Circle((0, 0), 0.7, fc='white')
fig = plt.gcf()
fig.gca().add_artist(centre_circle)
plt.tight_layout()
df2 = pd.read_csv('../input/covid-19-india/hotspot.csv')
df2.Red
df2.State
df2.dropna
df3 = df2.drop(37)
plt.hlines(y=df3['State'], xmin=0, xmax=40, color='grey', alpha=0.4)
df4 = pd.read_csv('../input/covid-19-india/hotty.csv')
df4.columns
hotty = pd.pivot_table(df4, values=['Red', 'Orange', 'Green', 'Total'], index='State', aggfunc='max')
state_names = list(hotty.index)
hotty['State'] = state_names
sns.set_color_codes('pastel')
plt.figure(figsize=(15, 10))
sns.barplot(x='Total', y='State', data=df4, label='Total', color='#9370db')
sns.barplot(x='Red', y='State', data=df4, label='Total', color='Blue')
sns.barplot(x='Orange', y='State', data=df4, label='Total', color='Red')
sns.barplot(x='Green', y='State', data=df4, label='Total', color='Green') | code |
34121580/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
df1 = pd.read_csv('../input/indiastate/data state.csv')
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
X = df1[['Confirmed', 'Active', 'Recovered', 'Deceased']]
y = target['Deceased']
df2 = pd.read_csv('../input/covid-19-india/hotspot.csv')
df2.Red
df2.State | code |
34121580/cell_29 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import seaborn as sns
df1 = pd.read_csv('../input/indiastate/data state.csv')
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
X = df1[['Confirmed', 'Active', 'Recovered', 'Deceased']]
y = target['Deceased']
labels = list(df1.State)
decease = list(df1.Deceased)
explode = []
for i in labels:
explode.append(0.05)
centre_circle = plt.Circle((0, 0), 0.7, fc='white')
fig = plt.gcf()
fig.gca().add_artist(centre_circle)
plt.tight_layout()
df2 = pd.read_csv('../input/covid-19-india/hotspot.csv')
df2.Red
df2.State
df2.dropna
df3 = df2.drop(37)
plt.hlines(y=df3['State'], xmin=0, xmax=40, color='grey', alpha=0.4)
df4 = pd.read_csv('../input/covid-19-india/hotty.csv')
df4.columns
hotty = pd.pivot_table(df4, values=['Red', 'Orange', 'Green', 'Total'], index='State', aggfunc='max')
state_names = list(hotty.index)
hotty['State'] = state_names
plt.figure(figsize=(25, 10))
sns.set_color_codes('pastel') | code |
34121580/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
df1 = pd.read_csv('../input/indiastate/data state.csv')
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
X = df1[['Confirmed', 'Active', 'Recovered', 'Deceased']]
y = target['Deceased']
labels = list(df1.State)
decease = list(df1.Deceased)
df1['Active'] = df1['Confirmed'] - (df1['Deceased'] + df1['Recovered'])
df1['Deceased Rate (per 100)'] = np.round(100 * df1['Deceased'] / df1['Confirmed'], 2)
df1['Recovered Rate (per 100)'] = np.round(100 * df1['Recovered'] / df1['Confirmed'], 2)
df1.sort_values('Confirmed', ascending=False).fillna(0).style.background_gradient(cmap='Blues', subset=['Confirmed']).background_gradient(cmap='Blues', subset=['Deceased']).background_gradient(cmap='Blues', subset=['Recovered']).background_gradient(cmap='Blues', subset=['Active']).background_gradient(cmap='Blues', subset=['Deceased Rate (per 100)']).background_gradient(cmap='Blues', subset=['Recovered Rate (per 100)'])
df1.columns | code |
34121580/cell_11 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import pandas as pd
import statsmodels.api as sm
df1 = pd.read_csv('../input/indiastate/data state.csv')
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
x = df1['Active']
y = target['Deceased']
model = sm.OLS(y, x).fit()
predictions = model.predict(x)
model.summary()
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
X = df1[['Confirmed', 'Active', 'Recovered', 'Deceased']]
y = target['Deceased']
from sklearn import linear_model
lm = linear_model.LinearRegression()
model = lm.fit(X, y)
predictions = lm.predict(X)
print(predictions) | code |
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