path
stringlengths 13
17
| screenshot_names
listlengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
106212685/cell_15
|
[
"text_plain_output_1.png"
] |
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
abnb['name'].head(5)
|
code
|
106212685/cell_16
|
[
"text_html_output_1.png"
] |
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = abnb.name[0] + abnb.name[1] + abnb.name[2] + abnb.name[4] + abnb.name[5]
word_cloud = WordCloud(background_color='white').generate(text)
plt.imshow(word_cloud, interpolation='bilinear')
plt.axis('off')
plt.show()
|
code
|
106212685/cell_3
|
[
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] |
from IPython.core.interactiveshell import InteractiveShell
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from wordcloud import WordCloud
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = 'all'
import os, time, sys, gc
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
|
code
|
106212685/cell_35
|
[
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] |
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = abnb.name[0] + abnb.name[1] + abnb.name[2] + abnb.name[4] + abnb.name[5]
word_cloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
abnb.columns
abnb['service_fee'] = abnb['service_fee'].str.replace('$', '').str.replace(' ', '').str.replace(',', '').astype(float)
abnb['service_fee'].head(5)
|
code
|
106212685/cell_43
|
[
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] |
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = abnb.name[0] + abnb.name[1] + abnb.name[2] + abnb.name[4] + abnb.name[5]
word_cloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
abnb.columns
abnb.columns
abnb.columns
|
code
|
106212685/cell_31
|
[
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] |
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = abnb.name[0] + abnb.name[1] + abnb.name[2] + abnb.name[4] + abnb.name[5]
word_cloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
abnb.columns
abnb['host_identity_verified'].unique()
print('\n')
abnb['host_identity_verified'].value_counts()
|
code
|
106212685/cell_46
|
[
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] |
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = abnb.name[0] + abnb.name[1] + abnb.name[2] + abnb.name[4] + abnb.name[5]
word_cloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
abnb.columns
abnb.columns
abnb.columns
'Present memory: {} '.format(abnb.memory_usage().sum())
gc.collect()
abnb.drop(columns=['lat', 'long', 'cancellation_policy', 'room_type', 'license'], inplace=True)
'Current memory usage: {} '.format(abnb.memory_usage().sum())
|
code
|
106212685/cell_10
|
[
"text_plain_output_1.png"
] |
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.head(3)
|
code
|
106212685/cell_27
|
[
"text_plain_output_1.png"
] |
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = abnb.name[0] + abnb.name[1] + abnb.name[2] + abnb.name[4] + abnb.name[5]
word_cloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
abnb.columns
abnb['room_type'].unique()
print('\n')
abnb['room_type'].value_counts()
|
code
|
106212685/cell_37
|
[
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] |
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = abnb.name[0] + abnb.name[1] + abnb.name[2] + abnb.name[4] + abnb.name[5]
word_cloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
abnb.columns
abnb['construction_year'].head(5)
abnb['constructed_year'] = abnb['construction_year'].dt.year
abnb['constructed_month'] = abnb['construction_year'].dt.month
abnb['constructed_day'] = abnb['construction_year'].dt.day
print('\n')
abnb[['constructed_year', 'constructed_month', 'constructed_day']].head(5)
|
code
|
106212685/cell_12
|
[
"text_plain_output_1.png"
] |
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.info()
|
code
|
106212685/cell_5
|
[
"text_plain_output_3.png",
"text_plain_output_1.png"
] |
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
|
code
|
130014262/cell_21
|
[
"text_plain_output_1.png"
] |
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import OrdinalEncoder
import numpy as np # linear algebra
obj_cols = x_train.select_dtypes(include='object').columns
obj_cols
float_cols = x_train.select_dtypes(include='int64').columns
float_cols
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
le.fit(y_train)
y_train_processed = le.transform(y_train)
y_test_processed = le.transform(y_test)
from sklearn.preprocessing import OrdinalEncoder
oe = OrdinalEncoder(categories=[x_train[i].unique() for i in obj_cols])
oe.fit(x_train[obj_cols])
x_train_cat_encoded = oe.transform(x_train[obj_cols])
x_test_cat_encoded = oe.transform(x_test[obj_cols])
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaler.fit(x_train[float_cols])
x_train_float_encoded = scaler.transform(x_train[float_cols])
x_test_float_encoded = scaler.transform(x_test[float_cols])
x_train_processed = np.hstack((x_train_cat_encoded, x_train_float_encoded))
x_test_processed = np.hstack((x_test_cat_encoded, x_test_float_encoded))
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(x_train_processed, y_train_processed)
y_pred = lr.predict(x_test_processed)
print(accuracy_score(y_test_processed, y_pred))
print(confusion_matrix(y_test_processed, y_pred))
|
code
|
130014262/cell_13
|
[
"text_plain_output_1.png"
] |
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
le.fit(y_train)
y_train_processed = le.transform(y_train)
y_test_processed = le.transform(y_test)
y_train_processed
|
code
|
130014262/cell_9
|
[
"image_output_1.png"
] |
obj_cols = x_train.select_dtypes(include='object').columns
obj_cols
|
code
|
130014262/cell_25
|
[
"text_plain_output_1.png"
] |
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import OrdinalEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/autism-screening-for-toddlers/Toddler Autism dataset July 2018.csv')
df.shape
df.sample(5)
df = df.rename(columns={'Class/ASD Traits ': 'ASD'})
x = df.drop('Case_No', axis=1)
x = x.drop('Ethnicity', axis=1)
x = x.drop('ASD', axis=1)
y = df['ASD']
obj_cols = x_train.select_dtypes(include='object').columns
obj_cols
float_cols = x_train.select_dtypes(include='int64').columns
float_cols
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
le.fit(y_train)
y_train_processed = le.transform(y_train)
y_test_processed = le.transform(y_test)
from sklearn.preprocessing import OrdinalEncoder
oe = OrdinalEncoder(categories=[x_train[i].unique() for i in obj_cols])
oe.fit(x_train[obj_cols])
x_train_cat_encoded = oe.transform(x_train[obj_cols])
x_test_cat_encoded = oe.transform(x_test[obj_cols])
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaler.fit(x_train[float_cols])
x_train_float_encoded = scaler.transform(x_train[float_cols])
x_test_float_encoded = scaler.transform(x_test[float_cols])
x_train_processed = np.hstack((x_train_cat_encoded, x_train_float_encoded))
x_test_processed = np.hstack((x_test_cat_encoded, x_test_float_encoded))
feature_names = np.concatenate([obj_cols, float_cols])
x_train_final = pd.DataFrame(x_train_processed, columns=feature_names)
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(x_train_processed, y_train_processed)
y_pred = lr.predict(x_test_processed)
def pretty_confusion_matrix(y_test, y_pred, labels=['Not_Diagnosed_with_ASD', 'ASD_Diagnosed']):
cm = confusion_matrix(y_test, y_pred)
pred_labels = ['Predicted ' + i for i in labels]
df = pd.DataFrame(cm, columns=pred_labels, index=labels)
return df
results_plot = pretty_confusion_matrix(y_test_processed, y_pred, ['Not_Diagnosed_with_ASD', 'ASD_Diagnosed'])
results_plot
import seaborn as sns
sns.heatmap(results_plot)
|
code
|
130014262/cell_4
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/autism-screening-for-toddlers/Toddler Autism dataset July 2018.csv')
df.shape
df.sample(5)
|
code
|
130014262/cell_20
|
[
"text_plain_output_1.png"
] |
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import OrdinalEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/autism-screening-for-toddlers/Toddler Autism dataset July 2018.csv')
obj_cols = x_train.select_dtypes(include='object').columns
obj_cols
float_cols = x_train.select_dtypes(include='int64').columns
float_cols
from sklearn.preprocessing import OrdinalEncoder
oe = OrdinalEncoder(categories=[x_train[i].unique() for i in obj_cols])
oe.fit(x_train[obj_cols])
x_train_cat_encoded = oe.transform(x_train[obj_cols])
x_test_cat_encoded = oe.transform(x_test[obj_cols])
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaler.fit(x_train[float_cols])
x_train_float_encoded = scaler.transform(x_train[float_cols])
x_test_float_encoded = scaler.transform(x_test[float_cols])
x_train_processed = np.hstack((x_train_cat_encoded, x_train_float_encoded))
x_test_processed = np.hstack((x_test_cat_encoded, x_test_float_encoded))
feature_names = np.concatenate([obj_cols, float_cols])
x_train_final = pd.DataFrame(x_train_processed, columns=feature_names)
x_train_final
|
code
|
130014262/cell_26
|
[
"text_html_output_1.png"
] |
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import OrdinalEncoder
import numpy as np # linear algebra
obj_cols = x_train.select_dtypes(include='object').columns
obj_cols
float_cols = x_train.select_dtypes(include='int64').columns
float_cols
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
le.fit(y_train)
y_train_processed = le.transform(y_train)
y_test_processed = le.transform(y_test)
from sklearn.preprocessing import OrdinalEncoder
oe = OrdinalEncoder(categories=[x_train[i].unique() for i in obj_cols])
oe.fit(x_train[obj_cols])
x_train_cat_encoded = oe.transform(x_train[obj_cols])
x_test_cat_encoded = oe.transform(x_test[obj_cols])
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaler.fit(x_train[float_cols])
x_train_float_encoded = scaler.transform(x_train[float_cols])
x_test_float_encoded = scaler.transform(x_test[float_cols])
x_train_processed = np.hstack((x_train_cat_encoded, x_train_float_encoded))
x_test_processed = np.hstack((x_test_cat_encoded, x_test_float_encoded))
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(x_train_processed, y_train_processed)
y_pred = lr.predict(x_test_processed)
lr.coef_
|
code
|
130014262/cell_11
|
[
"text_html_output_1.png"
] |
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
le.fit(y_train)
|
code
|
130014262/cell_1
|
[
"text_plain_output_1.png"
] |
import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
|
code
|
130014262/cell_7
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/autism-screening-for-toddlers/Toddler Autism dataset July 2018.csv')
df.shape
df.sample(5)
df = df.rename(columns={'Class/ASD Traits ': 'ASD'})
x = df.drop('Case_No', axis=1)
x = x.drop('Ethnicity', axis=1)
x = x.drop('ASD', axis=1)
y = df['ASD']
(x.shape, y.shape)
|
code
|
130014262/cell_28
|
[
"text_plain_output_1.png"
] |
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import OrdinalEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/autism-screening-for-toddlers/Toddler Autism dataset July 2018.csv')
df.shape
df.sample(5)
df = df.rename(columns={'Class/ASD Traits ': 'ASD'})
x = df.drop('Case_No', axis=1)
x = x.drop('Ethnicity', axis=1)
x = x.drop('ASD', axis=1)
y = df['ASD']
obj_cols = x_train.select_dtypes(include='object').columns
obj_cols
float_cols = x_train.select_dtypes(include='int64').columns
float_cols
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
le.fit(y_train)
y_train_processed = le.transform(y_train)
y_test_processed = le.transform(y_test)
from sklearn.preprocessing import OrdinalEncoder
oe = OrdinalEncoder(categories=[x_train[i].unique() for i in obj_cols])
oe.fit(x_train[obj_cols])
x_train_cat_encoded = oe.transform(x_train[obj_cols])
x_test_cat_encoded = oe.transform(x_test[obj_cols])
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaler.fit(x_train[float_cols])
x_train_float_encoded = scaler.transform(x_train[float_cols])
x_test_float_encoded = scaler.transform(x_test[float_cols])
x_train_processed = np.hstack((x_train_cat_encoded, x_train_float_encoded))
x_test_processed = np.hstack((x_test_cat_encoded, x_test_float_encoded))
feature_names = np.concatenate([obj_cols, float_cols])
x_train_final = pd.DataFrame(x_train_processed, columns=feature_names)
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(x_train_processed, y_train_processed)
y_pred = lr.predict(x_test_processed)
def pretty_confusion_matrix(y_test, y_pred, labels=['Not_Diagnosed_with_ASD', 'ASD_Diagnosed']):
cm = confusion_matrix(y_test, y_pred)
pred_labels = ['Predicted ' + i for i in labels]
df = pd.DataFrame(cm, columns=pred_labels, index=labels)
return df
lr.coef_
feature_dict = dict(zip(df.columns, list(lr.coef_[0])))
feature_dict
feature_df = pd.DataFrame(feature_dict, index=[0])
feature_df.T.plot.bar(title='Feature Importance', legend=False)
|
code
|
130014262/cell_8
|
[
"text_plain_output_1.png"
] |
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/autism-screening-for-toddlers/Toddler Autism dataset July 2018.csv')
df.shape
df.sample(5)
df = df.rename(columns={'Class/ASD Traits ': 'ASD'})
x = df.drop('Case_No', axis=1)
x = x.drop('Ethnicity', axis=1)
x = x.drop('ASD', axis=1)
y = df['ASD']
(x.shape, y.shape)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=36)
|
code
|
130014262/cell_15
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
from sklearn.preprocessing import OrdinalEncoder
obj_cols = x_train.select_dtypes(include='object').columns
obj_cols
float_cols = x_train.select_dtypes(include='int64').columns
float_cols
from sklearn.preprocessing import OrdinalEncoder
oe = OrdinalEncoder(categories=[x_train[i].unique() for i in obj_cols])
oe.fit(x_train[obj_cols])
x_train_cat_encoded = oe.transform(x_train[obj_cols])
x_test_cat_encoded = oe.transform(x_test[obj_cols])
x_train_cat_encoded
|
code
|
130014262/cell_3
|
[
"text_html_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/autism-screening-for-toddlers/Toddler Autism dataset July 2018.csv')
df.shape
|
code
|
130014262/cell_24
|
[
"text_plain_output_1.png"
] |
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import OrdinalEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/autism-screening-for-toddlers/Toddler Autism dataset July 2018.csv')
df.shape
df.sample(5)
df = df.rename(columns={'Class/ASD Traits ': 'ASD'})
x = df.drop('Case_No', axis=1)
x = x.drop('Ethnicity', axis=1)
x = x.drop('ASD', axis=1)
y = df['ASD']
obj_cols = x_train.select_dtypes(include='object').columns
obj_cols
float_cols = x_train.select_dtypes(include='int64').columns
float_cols
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
le.fit(y_train)
y_train_processed = le.transform(y_train)
y_test_processed = le.transform(y_test)
from sklearn.preprocessing import OrdinalEncoder
oe = OrdinalEncoder(categories=[x_train[i].unique() for i in obj_cols])
oe.fit(x_train[obj_cols])
x_train_cat_encoded = oe.transform(x_train[obj_cols])
x_test_cat_encoded = oe.transform(x_test[obj_cols])
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaler.fit(x_train[float_cols])
x_train_float_encoded = scaler.transform(x_train[float_cols])
x_test_float_encoded = scaler.transform(x_test[float_cols])
x_train_processed = np.hstack((x_train_cat_encoded, x_train_float_encoded))
x_test_processed = np.hstack((x_test_cat_encoded, x_test_float_encoded))
feature_names = np.concatenate([obj_cols, float_cols])
x_train_final = pd.DataFrame(x_train_processed, columns=feature_names)
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(x_train_processed, y_train_processed)
y_pred = lr.predict(x_test_processed)
def pretty_confusion_matrix(y_test, y_pred, labels=['Not_Diagnosed_with_ASD', 'ASD_Diagnosed']):
cm = confusion_matrix(y_test, y_pred)
pred_labels = ['Predicted ' + i for i in labels]
df = pd.DataFrame(cm, columns=pred_labels, index=labels)
return df
results_plot = pretty_confusion_matrix(y_test_processed, y_pred, ['Not_Diagnosed_with_ASD', 'ASD_Diagnosed'])
results_plot
|
code
|
130014262/cell_22
|
[
"text_html_output_1.png"
] |
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
le.fit(y_train)
y_train_processed = le.transform(y_train)
y_test_processed = le.transform(y_test)
le.inverse_transform([0, 1])
|
code
|
130014262/cell_10
|
[
"text_plain_output_1.png"
] |
obj_cols = x_train.select_dtypes(include='object').columns
obj_cols
float_cols = x_train.select_dtypes(include='int64').columns
float_cols
|
code
|
130014262/cell_27
|
[
"text_plain_output_1.png"
] |
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import OrdinalEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/autism-screening-for-toddlers/Toddler Autism dataset July 2018.csv')
df.shape
df.sample(5)
df = df.rename(columns={'Class/ASD Traits ': 'ASD'})
x = df.drop('Case_No', axis=1)
x = x.drop('Ethnicity', axis=1)
x = x.drop('ASD', axis=1)
y = df['ASD']
obj_cols = x_train.select_dtypes(include='object').columns
obj_cols
float_cols = x_train.select_dtypes(include='int64').columns
float_cols
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
le.fit(y_train)
y_train_processed = le.transform(y_train)
y_test_processed = le.transform(y_test)
from sklearn.preprocessing import OrdinalEncoder
oe = OrdinalEncoder(categories=[x_train[i].unique() for i in obj_cols])
oe.fit(x_train[obj_cols])
x_train_cat_encoded = oe.transform(x_train[obj_cols])
x_test_cat_encoded = oe.transform(x_test[obj_cols])
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaler.fit(x_train[float_cols])
x_train_float_encoded = scaler.transform(x_train[float_cols])
x_test_float_encoded = scaler.transform(x_test[float_cols])
x_train_processed = np.hstack((x_train_cat_encoded, x_train_float_encoded))
x_test_processed = np.hstack((x_test_cat_encoded, x_test_float_encoded))
feature_names = np.concatenate([obj_cols, float_cols])
x_train_final = pd.DataFrame(x_train_processed, columns=feature_names)
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(x_train_processed, y_train_processed)
y_pred = lr.predict(x_test_processed)
def pretty_confusion_matrix(y_test, y_pred, labels=['Not_Diagnosed_with_ASD', 'ASD_Diagnosed']):
cm = confusion_matrix(y_test, y_pred)
pred_labels = ['Predicted ' + i for i in labels]
df = pd.DataFrame(cm, columns=pred_labels, index=labels)
return df
lr.coef_
feature_dict = dict(zip(df.columns, list(lr.coef_[0])))
feature_dict
|
code
|
128005328/cell_42
|
[
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train.replace({'Sex': {'male': 0, 'female': 1}, 'Embarked': {'S': 0, 'C': 1, 'Q': 2}}, inplace=True)
data_train.head()
|
code
|
128005328/cell_21
|
[
"text_html_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
sns.set()
sns.countplot('Survived', data=data_train)
|
code
|
128005328/cell_13
|
[
"text_plain_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
emb = pd.get_dummies(data_train['Embarked'], drop_first=True)
emb
|
code
|
128005328/cell_25
|
[
"text_html_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)
|
code
|
128005328/cell_4
|
[
"text_plain_output_1.png"
] |
import pandas as pd
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.tail()
|
code
|
128005328/cell_34
|
[
"text_html_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train['Embarked'].value_counts()
|
code
|
128005328/cell_23
|
[
"text_plain_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
sns.set()
sns.countplot('Sex', data=data_train)
|
code
|
128005328/cell_30
|
[
"text_html_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train['Age'].value_counts()
|
code
|
128005328/cell_33
|
[
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)
|
code
|
128005328/cell_44
|
[
"text_html_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train.replace({'Sex': {'male': 0, 'female': 1}, 'Embarked': {'S': 0, 'C': 1, 'Q': 2}}, inplace=True)
data_train.drop('Embarked', axis=1, inplace=True)
data_train
X = data_train.drop(columns=['PassengerId', 'Name', 'Ticket', 'Survived', 'Fare'], axis=1)
X.head()
|
code
|
128005328/cell_6
|
[
"text_plain_output_1.png"
] |
import pandas as pd
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.info()
data_test.info()
|
code
|
128005328/cell_40
|
[
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train.replace({'Sex': {'male': 0, 'female': 1}, 'Embarked': {'S': 0, 'C': 1, 'Q': 2}}, inplace=True)
data_train.head()
|
code
|
128005328/cell_29
|
[
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train[['Age', 'Survived']].groupby(['Age'], as_index=False).mean().sort_values(by='Survived', ascending=False)
|
code
|
128005328/cell_39
|
[
"text_html_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
emb = pd.get_dummies(data_train['Embarked'], drop_first=True)
emb
data_train.isnull().sum()
data_train.replace({'Sex': {'male': 0, 'female': 1}, 'Embarked': {'S': 0, 'C': 1, 'Q': 2}}, inplace=True)
emb = pd.get_dummies(data_train['Embarked'], drop_first=True)
emb
|
code
|
128005328/cell_26
|
[
"text_plain_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train['Pclass'].value_counts()
|
code
|
128005328/cell_48
|
[
"text_html_output_1.png"
] |
from sklearn.linear_model import LogisticRegression
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train.replace({'Sex': {'male': 0, 'female': 1}, 'Embarked': {'S': 0, 'C': 1, 'Q': 2}}, inplace=True)
data_train.drop('Embarked', axis=1, inplace=True)
data_train
X = data_train.drop(columns=['PassengerId', 'Name', 'Ticket', 'Survived', 'Fare'], axis=1)
LRmodel = LogisticRegression(max_iter=5000)
LRmodel.fit(X_train, y_train)
print(LRmodel.score(X_train, y_train) * 100)
print(LRmodel.score(X_test, y_test) * 100)
|
code
|
128005328/cell_19
|
[
"text_plain_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train['Survived'].value_counts()
|
code
|
128005328/cell_52
|
[
"text_plain_output_1.png"
] |
from sklearn.linear_model import LogisticRegression
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train.replace({'Sex': {'male': 0, 'female': 1}, 'Embarked': {'S': 0, 'C': 1, 'Q': 2}}, inplace=True)
data_train.drop('Embarked', axis=1, inplace=True)
data_train
X = data_train.drop(columns=['PassengerId', 'Name', 'Ticket', 'Survived', 'Fare'], axis=1)
LRmodel = LogisticRegression(max_iter=5000)
LRmodel.fit(X_train, y_train)
X_train_predict = LRmodel.predict(X_train)
X_train_predict
X_train_predict = LRmodel.predict(X_train)
X_train_predict
|
code
|
128005328/cell_7
|
[
"text_plain_output_1.png"
] |
import pandas as pd
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
|
code
|
128005328/cell_45
|
[
"text_html_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train.replace({'Sex': {'male': 0, 'female': 1}, 'Embarked': {'S': 0, 'C': 1, 'Q': 2}}, inplace=True)
data_train.drop('Embarked', axis=1, inplace=True)
data_train
X = data_train.drop(columns=['PassengerId', 'Name', 'Ticket', 'Survived', 'Fare'], axis=1)
y = data_train['Survived']
y.head()
|
code
|
128005328/cell_49
|
[
"text_html_output_1.png"
] |
from sklearn.linear_model import LogisticRegression
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train.replace({'Sex': {'male': 0, 'female': 1}, 'Embarked': {'S': 0, 'C': 1, 'Q': 2}}, inplace=True)
data_train.drop('Embarked', axis=1, inplace=True)
data_train
X = data_train.drop(columns=['PassengerId', 'Name', 'Ticket', 'Survived', 'Fare'], axis=1)
LRmodel = LogisticRegression(max_iter=5000)
LRmodel.fit(X_train, y_train)
X_train_predict = LRmodel.predict(X_train)
X_train_predict
|
code
|
128005328/cell_18
|
[
"text_html_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)
|
code
|
128005328/cell_32
|
[
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
sns.set()
sns.countplot('Age', hue='Survived', data=data_train)
|
code
|
128005328/cell_51
|
[
"text_plain_output_1.png"
] |
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train.replace({'Sex': {'male': 0, 'female': 1}, 'Embarked': {'S': 0, 'C': 1, 'Q': 2}}, inplace=True)
data_train.drop('Embarked', axis=1, inplace=True)
data_train
X = data_train.drop(columns=['PassengerId', 'Name', 'Ticket', 'Survived', 'Fare'], axis=1)
randommodel = RandomForestClassifier(n_estimators=1000)
randommodel.fit(X_train, y_train)
print(randommodel.score(X_train, y_train) * 100)
print(randommodel.score(X_test, y_test) * 100)
|
code
|
128005328/cell_28
|
[
"text_plain_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
sns.set()
sns.countplot('Pclass', hue='Survived', data=data_train)
|
code
|
128005328/cell_8
|
[
"text_plain_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
sns.heatmap(data_train.isnull())
|
code
|
128005328/cell_16
|
[
"text_plain_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
|
code
|
128005328/cell_38
|
[
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train.replace({'Sex': {'male': 0, 'female': 1}, 'Embarked': {'S': 0, 'C': 1, 'Q': 2}}, inplace=True)
data_train.head()
|
code
|
128005328/cell_47
|
[
"text_html_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train.replace({'Sex': {'male': 0, 'female': 1}, 'Embarked': {'S': 0, 'C': 1, 'Q': 2}}, inplace=True)
data_train.drop('Embarked', axis=1, inplace=True)
data_train
X = data_train.drop(columns=['PassengerId', 'Name', 'Ticket', 'Survived', 'Fare'], axis=1)
print(X.shape, X_train.shape, X_test.shape)
|
code
|
128005328/cell_3
|
[
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] |
import pandas as pd
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.head()
|
code
|
128005328/cell_17
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train.describe()
|
code
|
128005328/cell_35
|
[
"text_plain_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
sns.set()
sns.countplot('Embarked', data=data_train)
|
code
|
128005328/cell_43
|
[
"text_html_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train.replace({'Sex': {'male': 0, 'female': 1}, 'Embarked': {'S': 0, 'C': 1, 'Q': 2}}, inplace=True)
data_train.drop('Embarked', axis=1, inplace=True)
data_train
|
code
|
128005328/cell_31
|
[
"text_plain_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
sns.set()
sns.countplot('Age', data=data_train)
|
code
|
128005328/cell_24
|
[
"text_html_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
sns.set()
sns.countplot('Sex', hue='Survived', data=data_train)
|
code
|
128005328/cell_14
|
[
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
print(data_train['Embarked'].mode()[0])
|
code
|
128005328/cell_22
|
[
"text_plain_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
data_train['Sex'].value_counts()
|
code
|
128005328/cell_10
|
[
"text_html_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.head()
|
code
|
128005328/cell_27
|
[
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
sns.set()
sns.countplot('Pclass', data=data_train)
|
code
|
128005328/cell_12
|
[
"text_html_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
print(data_train['Embarked'].mode())
|
code
|
128005328/cell_5
|
[
"text_plain_output_1.png"
] |
import pandas as pd
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
|
code
|
128005328/cell_36
|
[
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] |
import pandas as pd
import seaborn as sns
data_train = pd.read_csv('D:\\projects ML\\titanic\\train.csv')
data_train.shape
data_train.isnull().sum()
data_train = data_train.drop(columns='Cabin', axis=1)
data_train.isnull().sum()
sns.set()
sns.countplot('Embarked', hue='Survived', data=data_train)
|
code
|
2012676/cell_21
|
[
"text_html_output_1.png"
] |
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
from sklearn import cluster, mixture, metrics # For clustering
import matplotlib.pyplot as plt # For graphics
import pandas as pd # Dataframe manipulation
import plotly.graph_objs as go
whr_data = pd.read_csv('../input/2017.csv', header=0)
whr_data_for_clus = whr_data.iloc[:, 5:]
n_clusters = 2
bandwidth = 0.1
eps = 0.3
damping = 0.9
preference = -200
metric = 'euclidean'
cluster_dist = {'Technique': ['K-means', 'Mean Shift', 'Mini Batch K-Means', 'Spectral', 'DBSCAN', 'Affinity Propagation', 'Birch', 'Gaussian Mixture Modeling'], 'FunctionName': ['Kmeans_Technique', 'MeanShift_Technique', 'MiniKmean_Technique', 'Spectral_Technique', 'Dbscan_Technique', 'AffProp_Technique', 'Birch_Technique', 'Gmm_Technique']}
cluster_df = pd.DataFrame(cluster_dist)
def Kmeans_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#k-means
"""
km = cluster.KMeans(n_clusters=n_clusters)
return km.fit_predict(ds)
def MeanShift_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#mean-shift
"""
ms = cluster.MeanShift(bandwidth=bandwidth)
return ms.fit_predict(ds)
def MiniKmean_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#mini-batch-k-means
"""
mkm = cluster.MiniBatchKMeans(n_clusters=n_clusters)
return mkm.fit_predict(ds)
def Spectral_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#spectral-clustering
"""
spectral = cluster.SpectralClustering(n_clusters=n_clusters)
return spectral.fit_predict(ds)
def Dbscan_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#dbscan
"""
dbscan = cluster.DBSCAN(eps=eps)
return dbscan.fit_predict(ds)
def AffProp_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#affinity-propagation
"""
ap = cluster.AffinityPropagation(damping=damping, preference=preference)
return ap.fit_predict(ds)
def Birch_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#birch
"""
birch = cluster.Birch(n_clusters=n_clusters)
return birch.fit_predict(ds)
def Gmm_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html#sklearn.mixture.GaussianMixture
"""
gmm = mixture.GaussianMixture(n_components=n_clusters, covariance_type='full')
gmm.fit(ds)
return gmm.predict(ds)
def GetSilhouetteCoeff(ds, result):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#silhouette-coefficient
Input -
- ds - The dataset for which the clustering was done
- result - The labels after the clustering
"""
return metrics.silhouette_score(ds, result, metric=metric)
def GetMethodName_Temp(method):
m = str(method)
m = m[1:m.index('\n')]
return m.strip()
for t in cluster_df.Technique:
method = cluster_df[cluster_df.Technique == t].FunctionName
m = GetMethodName_Temp(method)
result = locals()[m](whr_data_for_clus)
whr_data[t] = pd.DataFrame(result)
if t != 'Affinity Propagation':
cluster_df.loc[cluster_df.Technique == t, 'Silhouette.Coeff'] = GetSilhouetteCoeff(whr_data_for_clus, result)
cluster_df.loc[cluster_df.Technique == 'Affinity Propagation', 'SilCoeff'] = 0
whr_data.iloc[:, [0, 12, 13, 14, 15, 16, 17, 18, 19]]
cluster_df.iloc[:, [1, 2]]
rows = 4 # No of rows for the plot
cols = 2 # No of columns for the plot
cdf = cluster_df['Technique']
# 4 X 2 plot
fig,ax = plt.subplots(rows,cols, figsize=(15, 10))
x = 0
y = 0
for i in cdf:
ax[x,y].scatter(whr_data.iloc[:, 6], whr_data.iloc[:, 5], c=whr_data.iloc[:, 12+(x*y)])
# Set the title for each of the plot
ax[x,y].set_title(i + " Cluster Result")
y = y + 1
if y == cols:
x = x + 1
y = 0
plt.subplots_adjust(bottom=-0.5, top=1.5)
plt.show()
x = 0
y = 0
data = dict(type='choropleth', locations=whr_data['Country'], locationmode='country names', z=whr_data['Happiness.Score'], text=whr_data['Country'], colorbar={'title': 'Happiness Score'})
layout = dict(title='Global Happiness Score', geo=dict(showframe=False, projection={'type': 'Mercator'}))
choromap3 = go.Figure(data=[data], layout=layout)
iplot(choromap3)
|
code
|
2012676/cell_23
|
[
"text_html_output_1.png"
] |
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
from sklearn import cluster, mixture, metrics # For clustering
import matplotlib.pyplot as plt # For graphics
import pandas as pd # Dataframe manipulation
import plotly.graph_objs as go
whr_data = pd.read_csv('../input/2017.csv', header=0)
whr_data_for_clus = whr_data.iloc[:, 5:]
n_clusters = 2
bandwidth = 0.1
eps = 0.3
damping = 0.9
preference = -200
metric = 'euclidean'
cluster_dist = {'Technique': ['K-means', 'Mean Shift', 'Mini Batch K-Means', 'Spectral', 'DBSCAN', 'Affinity Propagation', 'Birch', 'Gaussian Mixture Modeling'], 'FunctionName': ['Kmeans_Technique', 'MeanShift_Technique', 'MiniKmean_Technique', 'Spectral_Technique', 'Dbscan_Technique', 'AffProp_Technique', 'Birch_Technique', 'Gmm_Technique']}
cluster_df = pd.DataFrame(cluster_dist)
def Kmeans_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#k-means
"""
km = cluster.KMeans(n_clusters=n_clusters)
return km.fit_predict(ds)
def MeanShift_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#mean-shift
"""
ms = cluster.MeanShift(bandwidth=bandwidth)
return ms.fit_predict(ds)
def MiniKmean_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#mini-batch-k-means
"""
mkm = cluster.MiniBatchKMeans(n_clusters=n_clusters)
return mkm.fit_predict(ds)
def Spectral_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#spectral-clustering
"""
spectral = cluster.SpectralClustering(n_clusters=n_clusters)
return spectral.fit_predict(ds)
def Dbscan_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#dbscan
"""
dbscan = cluster.DBSCAN(eps=eps)
return dbscan.fit_predict(ds)
def AffProp_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#affinity-propagation
"""
ap = cluster.AffinityPropagation(damping=damping, preference=preference)
return ap.fit_predict(ds)
def Birch_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#birch
"""
birch = cluster.Birch(n_clusters=n_clusters)
return birch.fit_predict(ds)
def Gmm_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html#sklearn.mixture.GaussianMixture
"""
gmm = mixture.GaussianMixture(n_components=n_clusters, covariance_type='full')
gmm.fit(ds)
return gmm.predict(ds)
def GetSilhouetteCoeff(ds, result):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#silhouette-coefficient
Input -
- ds - The dataset for which the clustering was done
- result - The labels after the clustering
"""
return metrics.silhouette_score(ds, result, metric=metric)
def GetMethodName_Temp(method):
m = str(method)
m = m[1:m.index('\n')]
return m.strip()
for t in cluster_df.Technique:
method = cluster_df[cluster_df.Technique == t].FunctionName
m = GetMethodName_Temp(method)
result = locals()[m](whr_data_for_clus)
whr_data[t] = pd.DataFrame(result)
if t != 'Affinity Propagation':
cluster_df.loc[cluster_df.Technique == t, 'Silhouette.Coeff'] = GetSilhouetteCoeff(whr_data_for_clus, result)
cluster_df.loc[cluster_df.Technique == 'Affinity Propagation', 'SilCoeff'] = 0
whr_data.iloc[:, [0, 12, 13, 14, 15, 16, 17, 18, 19]]
cluster_df.iloc[:, [1, 2]]
rows = 4 # No of rows for the plot
cols = 2 # No of columns for the plot
cdf = cluster_df['Technique']
# 4 X 2 plot
fig,ax = plt.subplots(rows,cols, figsize=(15, 10))
x = 0
y = 0
for i in cdf:
ax[x,y].scatter(whr_data.iloc[:, 6], whr_data.iloc[:, 5], c=whr_data.iloc[:, 12+(x*y)])
# Set the title for each of the plot
ax[x,y].set_title(i + " Cluster Result")
y = y + 1
if y == cols:
x = x + 1
y = 0
plt.subplots_adjust(bottom=-0.5, top=1.5)
plt.show()
x = 0
y = 0
data = dict(type='choropleth', locations=whr_data['Country'], locationmode='country names', z=whr_data['Happiness.Score'], text=whr_data['Country'], colorbar={'title': 'Happiness Score'})
layout = dict(title='Global Happiness Score', geo=dict(showframe=False, projection={'type': 'Mercator'}))
choromap3 = go.Figure(data=[data], layout=layout)
data = dict(type='choropleth', locations=whr_data['Country'], locationmode='country names', z=whr_data['K-means'], text=whr_data['Country'], colorbar={'title': 'Cluster Group'})
layout = dict(title='K-Means Clustering Visualization', geo=dict(showframe=False, projection={'type': 'Mercator'}))
choromap3 = go.Figure(data=[data], layout=layout)
iplot(choromap3)
|
code
|
2012676/cell_6
|
[
"image_output_1.png"
] |
import pandas as pd # Dataframe manipulation
whr_data = pd.read_csv('../input/2017.csv', header=0)
whr_data.head()
|
code
|
2012676/cell_2
|
[
"text_plain_output_1.png"
] |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import cluster, mixture, metrics
from sklearn.preprocessing import StandardScaler
import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True)
import os
import warnings
warnings.filterwarnings('ignore')
|
code
|
2012676/cell_7
|
[
"text_html_output_1.png"
] |
import pandas as pd # Dataframe manipulation
whr_data = pd.read_csv('../input/2017.csv', header=0)
whr_data_for_clus = whr_data.iloc[:, 5:]
whr_data_for_clus.head(3)
|
code
|
2012676/cell_18
|
[
"text_html_output_1.png"
] |
from sklearn import cluster, mixture, metrics # For clustering
import matplotlib.pyplot as plt # For graphics
import pandas as pd # Dataframe manipulation
whr_data = pd.read_csv('../input/2017.csv', header=0)
whr_data_for_clus = whr_data.iloc[:, 5:]
n_clusters = 2
bandwidth = 0.1
eps = 0.3
damping = 0.9
preference = -200
metric = 'euclidean'
cluster_dist = {'Technique': ['K-means', 'Mean Shift', 'Mini Batch K-Means', 'Spectral', 'DBSCAN', 'Affinity Propagation', 'Birch', 'Gaussian Mixture Modeling'], 'FunctionName': ['Kmeans_Technique', 'MeanShift_Technique', 'MiniKmean_Technique', 'Spectral_Technique', 'Dbscan_Technique', 'AffProp_Technique', 'Birch_Technique', 'Gmm_Technique']}
cluster_df = pd.DataFrame(cluster_dist)
def Kmeans_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#k-means
"""
km = cluster.KMeans(n_clusters=n_clusters)
return km.fit_predict(ds)
def MeanShift_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#mean-shift
"""
ms = cluster.MeanShift(bandwidth=bandwidth)
return ms.fit_predict(ds)
def MiniKmean_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#mini-batch-k-means
"""
mkm = cluster.MiniBatchKMeans(n_clusters=n_clusters)
return mkm.fit_predict(ds)
def Spectral_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#spectral-clustering
"""
spectral = cluster.SpectralClustering(n_clusters=n_clusters)
return spectral.fit_predict(ds)
def Dbscan_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#dbscan
"""
dbscan = cluster.DBSCAN(eps=eps)
return dbscan.fit_predict(ds)
def AffProp_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#affinity-propagation
"""
ap = cluster.AffinityPropagation(damping=damping, preference=preference)
return ap.fit_predict(ds)
def Birch_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#birch
"""
birch = cluster.Birch(n_clusters=n_clusters)
return birch.fit_predict(ds)
def Gmm_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html#sklearn.mixture.GaussianMixture
"""
gmm = mixture.GaussianMixture(n_components=n_clusters, covariance_type='full')
gmm.fit(ds)
return gmm.predict(ds)
def GetSilhouetteCoeff(ds, result):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#silhouette-coefficient
Input -
- ds - The dataset for which the clustering was done
- result - The labels after the clustering
"""
return metrics.silhouette_score(ds, result, metric=metric)
def GetMethodName_Temp(method):
m = str(method)
m = m[1:m.index('\n')]
return m.strip()
for t in cluster_df.Technique:
method = cluster_df[cluster_df.Technique == t].FunctionName
m = GetMethodName_Temp(method)
result = locals()[m](whr_data_for_clus)
whr_data[t] = pd.DataFrame(result)
if t != 'Affinity Propagation':
cluster_df.loc[cluster_df.Technique == t, 'Silhouette.Coeff'] = GetSilhouetteCoeff(whr_data_for_clus, result)
cluster_df.loc[cluster_df.Technique == 'Affinity Propagation', 'SilCoeff'] = 0
whr_data.iloc[:, [0, 12, 13, 14, 15, 16, 17, 18, 19]]
cluster_df.iloc[:, [1, 2]]
rows = 4
cols = 2
cdf = cluster_df['Technique']
fig, ax = plt.subplots(rows, cols, figsize=(15, 10))
x = 0
y = 0
for i in cdf:
ax[x, y].scatter(whr_data.iloc[:, 6], whr_data.iloc[:, 5], c=whr_data.iloc[:, 12 + x * y])
ax[x, y].set_title(i + ' Cluster Result')
y = y + 1
if y == cols:
x = x + 1
y = 0
plt.subplots_adjust(bottom=-0.5, top=1.5)
plt.show()
x = 0
y = 0
|
code
|
2012676/cell_8
|
[
"text_html_output_1.png"
] |
import pandas as pd # Dataframe manipulation
whr_data = pd.read_csv('../input/2017.csv', header=0)
whr_data_for_clus = whr_data.iloc[:, 5:]
ss = StandardScaler()
ss.fit_transform(whr_data_for_clus)
|
code
|
2012676/cell_15
|
[
"text_html_output_1.png"
] |
from sklearn import cluster, mixture, metrics # For clustering
import pandas as pd # Dataframe manipulation
whr_data = pd.read_csv('../input/2017.csv', header=0)
whr_data_for_clus = whr_data.iloc[:, 5:]
n_clusters = 2
bandwidth = 0.1
eps = 0.3
damping = 0.9
preference = -200
metric = 'euclidean'
cluster_dist = {'Technique': ['K-means', 'Mean Shift', 'Mini Batch K-Means', 'Spectral', 'DBSCAN', 'Affinity Propagation', 'Birch', 'Gaussian Mixture Modeling'], 'FunctionName': ['Kmeans_Technique', 'MeanShift_Technique', 'MiniKmean_Technique', 'Spectral_Technique', 'Dbscan_Technique', 'AffProp_Technique', 'Birch_Technique', 'Gmm_Technique']}
cluster_df = pd.DataFrame(cluster_dist)
def Kmeans_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#k-means
"""
km = cluster.KMeans(n_clusters=n_clusters)
return km.fit_predict(ds)
def MeanShift_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#mean-shift
"""
ms = cluster.MeanShift(bandwidth=bandwidth)
return ms.fit_predict(ds)
def MiniKmean_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#mini-batch-k-means
"""
mkm = cluster.MiniBatchKMeans(n_clusters=n_clusters)
return mkm.fit_predict(ds)
def Spectral_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#spectral-clustering
"""
spectral = cluster.SpectralClustering(n_clusters=n_clusters)
return spectral.fit_predict(ds)
def Dbscan_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#dbscan
"""
dbscan = cluster.DBSCAN(eps=eps)
return dbscan.fit_predict(ds)
def AffProp_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#affinity-propagation
"""
ap = cluster.AffinityPropagation(damping=damping, preference=preference)
return ap.fit_predict(ds)
def Birch_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#birch
"""
birch = cluster.Birch(n_clusters=n_clusters)
return birch.fit_predict(ds)
def Gmm_Technique(ds):
"""
Ref: http://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html#sklearn.mixture.GaussianMixture
"""
gmm = mixture.GaussianMixture(n_components=n_clusters, covariance_type='full')
gmm.fit(ds)
return gmm.predict(ds)
def GetSilhouetteCoeff(ds, result):
"""
Ref: http://scikit-learn.org/stable/modules/clustering.html#silhouette-coefficient
Input -
- ds - The dataset for which the clustering was done
- result - The labels after the clustering
"""
return metrics.silhouette_score(ds, result, metric=metric)
def GetMethodName_Temp(method):
m = str(method)
m = m[1:m.index('\n')]
return m.strip()
for t in cluster_df.Technique:
method = cluster_df[cluster_df.Technique == t].FunctionName
m = GetMethodName_Temp(method)
result = locals()[m](whr_data_for_clus)
whr_data[t] = pd.DataFrame(result)
if t != 'Affinity Propagation':
cluster_df.loc[cluster_df.Technique == t, 'Silhouette.Coeff'] = GetSilhouetteCoeff(whr_data_for_clus, result)
cluster_df.loc[cluster_df.Technique == 'Affinity Propagation', 'SilCoeff'] = 0
cluster_df.iloc[:, [1, 2]]
|
code
|
16120163/cell_4
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
from sklearn import datasets
boston = datasets.load_boston()
print(boston.keys())
print(boston.data.shape)
print(boston.feature_names)
print(boston.DESCR)
|
code
|
16120163/cell_20
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
from keras.layers import Input, Dense
from keras.models import Model
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
boston = datasets.load_boston()
bos = pd.DataFrame(boston.data, columns=boston.feature_names)
target = pd.DataFrame(boston.target, columns=['MEDV'])
bos['MEDV'] = target['MEDV']
correlation_matrix = bos.corr().round(2)
X = bos[['RM', 'LSTAT']]
Y = target['MEDV']
model = sm.OLS(Y, X).fit()
predictions = model.predict(X)
model.summary()
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state=5)
scalar = StandardScaler()
X_train = scalar.fit_transform(X_train)
X_test = scalar.transform(X_test)
input_data = Input(shape=(2,))
firstlayer = Dense(2, activation='relu', name='input')(input_data)
ff = Dense(2, activation='relu', name='ff')(firstlayer)
secondlayer = Dense(1, activation='linear', name='prices')(ff)
MLPRegModel = Model(inputs=input_data, outputs=secondlayer)
MLPRegModel.compile(loss='mse', optimizer='rmsprop')
MLPRegModel.fit(X_train, Y_train, epochs=250, batch_size=10)
print('Now making predictions')
predictions = MLPRegModel.predict(X_test)
'""\n#this is for remainder purpose\nseed=3 best for adam\nin adam batch size=1 best result\nMSE: 21.7102 epoch-500 batch 2 adam\nMSE: 22.2704 epoch-250 batch 2 adam\nwithout tensorflow seeding\nMSE: 21.3172 rmsprop "\nMSE: 20.3754 rmsprop with ff layer\nMSE: 20.758 rmsprop epoch- 500 batch 1\nMSE: 20.4079 rmsprop epoch- 500 batch 2\nMSE: 20.3896 rmsprop epoch- 150 batch 2\nMSE: 20.4025 rmsprop epoch- 150 batch 1\nafter tensorflow seed\nMSE: 20.425 rmsprop epoch- 250 batch 2\nMSE: 20.3996 rmsprop epoch- 250 batch 3\nMSE: 20.3558 rmsprop epoch- 250 batch 10\nMSE: 20.4105 rmsprop epoch- 500 batch 10\n#original way to calculate mse\npred=pd.DataFrame(predictions)\npred.columns=["MEDV"]\npred["MEDV"]=pred.MEDV.astype(float)\nprint(pred.head())\nytest=pd.DataFrame(Y_test)\nprint(ytest.head())\npred.index=ytest.index\npred["diff"]=pred.loc[:,"MEDV"] - ytest.loc[:,"MEDV"]\nprint(pred.head())\nmse = (pred["diff"] ** 2).mean()\nprint(\'MSE: {}\'.format(round(mse, 4)))\n'
|
code
|
16120163/cell_6
|
[
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] |
from sklearn import datasets
import pandas as pd
boston = datasets.load_boston()
bos = pd.DataFrame(boston.data, columns=boston.feature_names)
print(bos.head())
target = pd.DataFrame(boston.target, columns=['MEDV'])
bos['MEDV'] = target['MEDV']
|
code
|
16120163/cell_2
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
from sklearn import datasets
import numpy as np
import pandas as pd
import statsmodels.api as sm
from sklearn.model_selection import train_test_split
import seaborn as sns
from keras.layers import Input, Dense
from keras.models import Model
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
from math import sqrt
import matplotlib.pyplot as plt
|
code
|
16120163/cell_18
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
from keras.layers import Input, Dense
from keras.models import Model
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
boston = datasets.load_boston()
bos = pd.DataFrame(boston.data, columns=boston.feature_names)
target = pd.DataFrame(boston.target, columns=['MEDV'])
bos['MEDV'] = target['MEDV']
correlation_matrix = bos.corr().round(2)
X = bos[['RM', 'LSTAT']]
Y = target['MEDV']
model = sm.OLS(Y, X).fit()
predictions = model.predict(X)
model.summary()
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state=5)
scalar = StandardScaler()
X_train = scalar.fit_transform(X_train)
X_test = scalar.transform(X_test)
input_data = Input(shape=(2,))
firstlayer = Dense(2, activation='relu', name='input')(input_data)
ff = Dense(2, activation='relu', name='ff')(firstlayer)
secondlayer = Dense(1, activation='linear', name='prices')(ff)
MLPRegModel = Model(inputs=input_data, outputs=secondlayer)
MLPRegModel.compile(loss='mse', optimizer='rmsprop')
MLPRegModel.fit(X_train, Y_train, epochs=250, batch_size=10)
|
code
|
16120163/cell_8
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
from sklearn import datasets
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
boston = datasets.load_boston()
bos = pd.DataFrame(boston.data, columns=boston.feature_names)
target = pd.DataFrame(boston.target, columns=['MEDV'])
bos['MEDV'] = target['MEDV']
correlation_matrix = bos.corr().round(2)
plt.figure(figsize=(12, 5))
sns.heatmap(data=correlation_matrix, annot=True)
|
code
|
16120163/cell_14
|
[
"text_plain_output_1.png"
] |
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
boston = datasets.load_boston()
bos = pd.DataFrame(boston.data, columns=boston.feature_names)
target = pd.DataFrame(boston.target, columns=['MEDV'])
bos['MEDV'] = target['MEDV']
correlation_matrix = bos.corr().round(2)
X = bos[['RM', 'LSTAT']]
Y = target['MEDV']
model = sm.OLS(Y, X).fit()
predictions = model.predict(X)
model.summary()
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state=5)
scalar = StandardScaler()
X_train = scalar.fit_transform(X_train)
X_test = scalar.transform(X_test)
|
code
|
16120163/cell_22
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
from keras.layers import Input, Dense
from keras.models import Model
from math import sqrt
from sklearn import datasets
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
boston = datasets.load_boston()
bos = pd.DataFrame(boston.data, columns=boston.feature_names)
target = pd.DataFrame(boston.target, columns=['MEDV'])
bos['MEDV'] = target['MEDV']
correlation_matrix = bos.corr().round(2)
X = bos[['RM', 'LSTAT']]
Y = target['MEDV']
model = sm.OLS(Y, X).fit()
predictions = model.predict(X)
model.summary()
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state=5)
scalar = StandardScaler()
X_train = scalar.fit_transform(X_train)
X_test = scalar.transform(X_test)
input_data = Input(shape=(2,))
firstlayer = Dense(2, activation='relu', name='input')(input_data)
ff = Dense(2, activation='relu', name='ff')(firstlayer)
secondlayer = Dense(1, activation='linear', name='prices')(ff)
MLPRegModel = Model(inputs=input_data, outputs=secondlayer)
MLPRegModel.compile(loss='mse', optimizer='rmsprop')
MLPRegModel.fit(X_train, Y_train, epochs=250, batch_size=10)
predictions = MLPRegModel.predict(X_test)
'""\n#this is for remainder purpose\nseed=3 best for adam\nin adam batch size=1 best result\nMSE: 21.7102 epoch-500 batch 2 adam\nMSE: 22.2704 epoch-250 batch 2 adam\n\nwithout tensorflow seeding\nMSE: 21.3172 rmsprop "\nMSE: 20.3754 rmsprop with ff layer\nMSE: 20.758 rmsprop epoch- 500 batch 1\nMSE: 20.4079 rmsprop epoch- 500 batch 2\nMSE: 20.3896 rmsprop epoch- 150 batch 2\nMSE: 20.4025 rmsprop epoch- 150 batch 1\n\nafter tensorflow seed\nMSE: 20.425 rmsprop epoch- 250 batch 2\nMSE: 20.3996 rmsprop epoch- 250 batch 3\nMSE: 20.3558 rmsprop epoch- 250 batch 10\nMSE: 20.4105 rmsprop epoch- 500 batch 10\n\n#original way to calculate mse\n\npred=pd.DataFrame(predictions)\npred.columns=["MEDV"]\npred["MEDV"]=pred.MEDV.astype(float)\nprint(pred.head())\nytest=pd.DataFrame(Y_test)\nprint(ytest.head())\npred.index=ytest.index\npred["diff"]=pred.loc[:,"MEDV"] - ytest.loc[:,"MEDV"]\nprint(pred.head())\nmse = (pred["diff"] ** 2).mean()\nprint(\'MSE: {}\'.format(round(mse, 4)))\n\n'
print('R2 score: {}'.format(round(r2_score(Y_test, predictions), 4)))
print('MSE: {}'.format(round(mean_squared_error(Y_test, predictions), 4)))
print('RMSE: {}'.format(round(sqrt(mean_squared_error(Y_test, predictions)), 4)))
|
code
|
16120163/cell_10
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
from sklearn import datasets
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
boston = datasets.load_boston()
bos = pd.DataFrame(boston.data, columns=boston.feature_names)
target = pd.DataFrame(boston.target, columns=['MEDV'])
bos['MEDV'] = target['MEDV']
correlation_matrix = bos.corr().round(2)
X = bos[['RM', 'LSTAT']]
Y = target['MEDV']
model = sm.OLS(Y, X).fit()
predictions = model.predict(X)
model.summary()
|
code
|
16120163/cell_12
|
[
"text_plain_output_1.png"
] |
from sklearn import datasets
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
boston = datasets.load_boston()
bos = pd.DataFrame(boston.data, columns=boston.feature_names)
target = pd.DataFrame(boston.target, columns=['MEDV'])
bos['MEDV'] = target['MEDV']
correlation_matrix = bos.corr().round(2)
X = bos[['RM', 'LSTAT']]
Y = target['MEDV']
model = sm.OLS(Y, X).fit()
predictions = model.predict(X)
model.summary()
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state=5)
print(X_train.shape)
print(X_test.shape)
print(Y_train.shape)
print(Y_test.shape)
|
code
|
2002376/cell_4
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
from sklearn import svm
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mushrooms.csv')
data = np.array(data)
train, test = (data[0:8000,], data[8000:,])
Xtrain, ytrain = (train[:, 0:-1], train[:, -1])
Xtest, ytest = (test[:, 0:-1], test[:, -1])
from sklearn import svm
model = svm.SVC(kernel='linear', gamma=1)
model.fit(Xtrain, ytrain)
|
code
|
2002376/cell_1
|
[
"text_plain_output_1.png"
] |
from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
|
code
|
2002376/cell_3
|
[
"text_plain_output_1.png"
] |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mushrooms.csv')
print(data.shape)
data = np.array(data)
train, test = (data[0:8000,], data[8000:,])
Xtrain, ytrain = (train[:, 0:-1], train[:, -1])
Xtest, ytest = (test[:, 0:-1], test[:, -1])
print(Xtrain.shape)
print(ytrain.shape)
print(Xtest.shape)
print(ytest.shape)
|
code
|
128003343/cell_42
|
[
"text_plain_output_1.png"
] |
import pandas as pd
SHEET_ID = '10DM_ZqwV6CvOryD6u3pYHb5scjEfiwfkitUGTCG8l7Y'
SHEET_NAME = 'AAPL'
url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
df = pd.read_csv(url, decimal=',')
df_retention_ab = df.groupby('version').agg({'userid': 'count', 'retention_1': 'mean', 'retention_7': 'mean', 'sum_gamerounds': 'sum'})
df_retention_ab
df_c = df[df['version'] == 'gate_30']
df_t = df[df['version'] == 'gate_40']
#calc of difference of retentionin between 2 groups
ret1_dif = (df_c.describe(include='all').loc['mean']['retention_1'] - df_t.describe(include='all').loc['mean']['retention_1']) / df_t.describe(include='all').loc['mean']['retention_1']
ret7_dif = (df_c.describe(include='all').loc['mean']['retention_7'] - df_t.describe(include='all').loc['mean']['retention_7']) / df_t.describe(include='all').loc['mean']['retention_7']
ret1_dif, ret7_dif
n1 = df_t.shape[0]
n2 = df_c.shape[0]
(n1, n2)
n1 = df_t.shape[0]
n2 = df_c.shape[0]
(n1, n2)
|
code
|
128003343/cell_21
|
[
"text_html_output_1.png"
] |
import pandas as pd
import plotly.express as px # Интерактивная библиотека для графиков.
SHEET_ID = '10DM_ZqwV6CvOryD6u3pYHb5scjEfiwfkitUGTCG8l7Y'
SHEET_NAME = 'AAPL'
url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
df = pd.read_csv(url, decimal=',')
df_retention_ab = df.groupby('version').agg({'userid': 'count', 'retention_1': 'mean', 'retention_7': 'mean', 'sum_gamerounds': 'sum'})
df_retention_ab
fig = px.histogram(df['sum_gamerounds'][df['sum_gamerounds'] < 100], marginal='box')
fig.update_layout(xaxis_title='gamerounds per user', yaxis_title='users', title='Distribution of game rounds', showlegend=False)
fig.show(renderer='colab')
|
code
|
128003343/cell_9
|
[
"text_plain_output_1.png"
] |
import pandas as pd
SHEET_ID = '10DM_ZqwV6CvOryD6u3pYHb5scjEfiwfkitUGTCG8l7Y'
SHEET_NAME = 'AAPL'
url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
df = pd.read_csv(url, decimal=',')
df.describe(include='object')
|
code
|
128003343/cell_4
|
[
"text_plain_output_1.png"
] |
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from scipy.stats import ttest_1samp, mannwhitneyu, shapiro, norm, t, kstest, shapiro
from statsmodels.stats.power import TTestIndPower
from statsmodels.stats import proportion
import plotly.express as px
import math
import statsmodels.stats.power as smp
|
code
|
128003343/cell_34
|
[
"text_plain_output_1.png"
] |
if abs(z_pvalue) < 0.05:
print('We may reject the null hypothesis!')
else:
print('We have failed to reject the null hypothesis')
|
code
|
128003343/cell_33
|
[
"text_html_output_1.png"
] |
from statsmodels.stats import proportion
import numpy as np
import pandas as pd
SHEET_ID = '10DM_ZqwV6CvOryD6u3pYHb5scjEfiwfkitUGTCG8l7Y'
SHEET_NAME = 'AAPL'
url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'
df = pd.read_csv(url, decimal=',')
df_retention_ab = df.groupby('version').agg({'userid': 'count', 'retention_1': 'mean', 'retention_7': 'mean', 'sum_gamerounds': 'sum'})
df_retention_ab
df_c = df[df['version'] == 'gate_30']
df_t = df[df['version'] == 'gate_40']
#calc of difference of retentionin between 2 groups
ret1_dif = (df_c.describe(include='all').loc['mean']['retention_1'] - df_t.describe(include='all').loc['mean']['retention_1']) / df_t.describe(include='all').loc['mean']['retention_1']
ret7_dif = (df_c.describe(include='all').loc['mean']['retention_7'] - df_t.describe(include='all').loc['mean']['retention_7']) / df_t.describe(include='all').loc['mean']['retention_7']
ret1_dif, ret7_dif
k1 = df_t['retention_1'].sum()
k2 = df_c['retention_1'].sum()
(k1, k2)
n1 = df_t.shape[0]
n2 = df_c.shape[0]
(n1, n2)
z_score, z_pvalue = proportion.proportions_ztest(np.array([k1, k2]), np.array([n1, n2]))
print('Results are ', 'z_score =%.3f, pvalue = %.3f' % (z_score, z_pvalue))
|
code
|
128003343/cell_44
|
[
"text_plain_output_1.png"
] |
if abs(z_pvalue) < 0.05:
print('We may reject the null hypothesis!')
else:
print('We have failed to reject the null hypothesis')
|
code
|
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