path
stringlengths 13
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sequencelengths 1
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stringlengths 0
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stringclasses 1
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|---|---|---|---|
88094115/cell_12 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/years-of-experience-and-salary-dataset/Salary_Data.csv')
x = df[['YearsExperience']]
x
y = df.iloc[:, 1].values
y
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(x, y)
y_pred = model.predict(x)
y_pred
plt.scatter(x, y)
plt.title('Linear Regression using Ordinary Least Square Method')
plt.plot(x, y_pred, color='red', label='Best Fit Line')
plt.legend()
plt.show() | code |
88094115/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/years-of-experience-and-salary-dataset/Salary_Data.csv')
df.info() | code |
2019997/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Input
from keras.layers.core import Dense, Dropout, Activation
from keras.models import Model
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def preprocess_data(test_data=False):
def encode_one_categorical_feature(column):
le = LabelEncoder()
ohe = OneHotEncoder(sparse=False)
num_encoded = le.fit_transform(column.fillna('unk'))
oh_encoded = ohe.fit_transform(num_encoded.reshape(-1, 1))
return oh_encoded
data = pd.read_csv('data/train.csv')
target = ['SalePrice']
features = data.drop(['Id'] + target, axis=1).columns
dataset_types = pd.DataFrame(data[features].dtypes, columns=['datatype'])
dataset_types.reset_index(inplace=True)
numeric_features = dataset_types.rename(columns={'index': 'feature'}).feature[(dataset_types.datatype == 'float64') | (dataset_types.datatype == 'int64')]
num_data = data[numeric_features]
num_features = num_data.fillna(num_data.mean()).values
scaler = StandardScaler()
num_features_scaled = scaler.fit_transform(num_features)
categorical_features = dataset_types.rename(columns={'index': 'feature'}).feature[dataset_types.datatype == 'object']
cat_data = data[categorical_features]
cat_features = np.hstack([encode_one_categorical_feature(data[column]) for column in cat_data.columns])
X = np.hstack((num_features_scaled, cat_features))
if test_data == True:
return X
y = data[target].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=606)
return (X_train, X_test, y_train, y_test)
def plot_history(history):
pass
def keras_model(X_train, X_test, y_train, y_test):
NUM_EPOCHS = 50
BATCH_SIZE = 128
inputs = Input(shape=(304,))
x = Dropout(0.2)(inputs)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.2)(x)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.4)(x)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.4)(x)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.4)(x)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.4)(x)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.4)(x)
predictions = Dense(1)(x)
model = Model(inputs=[inputs], outputs=[predictions])
model.compile(loss='mse', optimizer='adam')
history = model.fit(X_train, y_train, batch_size=BATCH_SIZE, epochs=NUM_EPOCHS, validation_split=0.2, verbose=0)
score = model.evaluate(X_test, y_test, verbose=0)
return (history, model)
test_data = preprocess_data(test_data=True) | code |
2019997/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Input
from keras.layers.core import Dense, Dropout, Activation
from keras.models import Model
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def preprocess_data(test_data=False):
def encode_one_categorical_feature(column):
le = LabelEncoder()
ohe = OneHotEncoder(sparse=False)
num_encoded = le.fit_transform(column.fillna('unk'))
oh_encoded = ohe.fit_transform(num_encoded.reshape(-1, 1))
return oh_encoded
data = pd.read_csv('data/train.csv')
target = ['SalePrice']
features = data.drop(['Id'] + target, axis=1).columns
dataset_types = pd.DataFrame(data[features].dtypes, columns=['datatype'])
dataset_types.reset_index(inplace=True)
numeric_features = dataset_types.rename(columns={'index': 'feature'}).feature[(dataset_types.datatype == 'float64') | (dataset_types.datatype == 'int64')]
num_data = data[numeric_features]
num_features = num_data.fillna(num_data.mean()).values
scaler = StandardScaler()
num_features_scaled = scaler.fit_transform(num_features)
categorical_features = dataset_types.rename(columns={'index': 'feature'}).feature[dataset_types.datatype == 'object']
cat_data = data[categorical_features]
cat_features = np.hstack([encode_one_categorical_feature(data[column]) for column in cat_data.columns])
X = np.hstack((num_features_scaled, cat_features))
if test_data == True:
return X
y = data[target].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=606)
return (X_train, X_test, y_train, y_test)
def plot_history(history):
pass
def keras_model(X_train, X_test, y_train, y_test):
NUM_EPOCHS = 50
BATCH_SIZE = 128
inputs = Input(shape=(304,))
x = Dropout(0.2)(inputs)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.2)(x)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.4)(x)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.4)(x)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.4)(x)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.4)(x)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.4)(x)
predictions = Dense(1)(x)
model = Model(inputs=[inputs], outputs=[predictions])
model.compile(loss='mse', optimizer='adam')
history = model.fit(X_train, y_train, batch_size=BATCH_SIZE, epochs=NUM_EPOCHS, validation_split=0.2, verbose=0)
score = model.evaluate(X_test, y_test, verbose=0)
return (history, model)
X_train, X_test, y_train, y_test = preprocess_data() | code |
2019997/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Input
from keras.layers.core import Dense, Dropout, Activation
from keras.models import Model
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def preprocess_data(test_data=False):
def encode_one_categorical_feature(column):
le = LabelEncoder()
ohe = OneHotEncoder(sparse=False)
num_encoded = le.fit_transform(column.fillna('unk'))
oh_encoded = ohe.fit_transform(num_encoded.reshape(-1, 1))
return oh_encoded
data = pd.read_csv('data/train.csv')
target = ['SalePrice']
features = data.drop(['Id'] + target, axis=1).columns
dataset_types = pd.DataFrame(data[features].dtypes, columns=['datatype'])
dataset_types.reset_index(inplace=True)
numeric_features = dataset_types.rename(columns={'index': 'feature'}).feature[(dataset_types.datatype == 'float64') | (dataset_types.datatype == 'int64')]
num_data = data[numeric_features]
num_features = num_data.fillna(num_data.mean()).values
scaler = StandardScaler()
num_features_scaled = scaler.fit_transform(num_features)
categorical_features = dataset_types.rename(columns={'index': 'feature'}).feature[dataset_types.datatype == 'object']
cat_data = data[categorical_features]
cat_features = np.hstack([encode_one_categorical_feature(data[column]) for column in cat_data.columns])
X = np.hstack((num_features_scaled, cat_features))
if test_data == True:
return X
y = data[target].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=606)
return (X_train, X_test, y_train, y_test)
def plot_history(history):
pass
def keras_model(X_train, X_test, y_train, y_test):
NUM_EPOCHS = 50
BATCH_SIZE = 128
inputs = Input(shape=(304,))
x = Dropout(0.2)(inputs)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.2)(x)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.4)(x)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.4)(x)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.4)(x)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.4)(x)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.4)(x)
predictions = Dense(1)(x)
model = Model(inputs=[inputs], outputs=[predictions])
model.compile(loss='mse', optimizer='adam')
history = model.fit(X_train, y_train, batch_size=BATCH_SIZE, epochs=NUM_EPOCHS, validation_split=0.2, verbose=0)
score = model.evaluate(X_test, y_test, verbose=0)
return (history, model)
model, history = keras_model(X_train, X_test, y_train, y_test) | code |
2019997/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler
from keras.layers import Input
from keras.layers.core import Dense, Dropout, Activation
from keras.models import Model
from hyperopt import Trials, STATUS_OK, tpe
from hyperas import optim
from hyperas.distributions import choice, uniform | code |
2019997/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Input
from keras.layers.core import Dense, Dropout, Activation
from keras.models import Model
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def preprocess_data(test_data=False):
def encode_one_categorical_feature(column):
le = LabelEncoder()
ohe = OneHotEncoder(sparse=False)
num_encoded = le.fit_transform(column.fillna('unk'))
oh_encoded = ohe.fit_transform(num_encoded.reshape(-1, 1))
return oh_encoded
data = pd.read_csv('data/train.csv')
target = ['SalePrice']
features = data.drop(['Id'] + target, axis=1).columns
dataset_types = pd.DataFrame(data[features].dtypes, columns=['datatype'])
dataset_types.reset_index(inplace=True)
numeric_features = dataset_types.rename(columns={'index': 'feature'}).feature[(dataset_types.datatype == 'float64') | (dataset_types.datatype == 'int64')]
num_data = data[numeric_features]
num_features = num_data.fillna(num_data.mean()).values
scaler = StandardScaler()
num_features_scaled = scaler.fit_transform(num_features)
categorical_features = dataset_types.rename(columns={'index': 'feature'}).feature[dataset_types.datatype == 'object']
cat_data = data[categorical_features]
cat_features = np.hstack([encode_one_categorical_feature(data[column]) for column in cat_data.columns])
X = np.hstack((num_features_scaled, cat_features))
if test_data == True:
return X
y = data[target].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=606)
return (X_train, X_test, y_train, y_test)
def plot_history(history):
pass
def keras_model(X_train, X_test, y_train, y_test):
NUM_EPOCHS = 50
BATCH_SIZE = 128
inputs = Input(shape=(304,))
x = Dropout(0.2)(inputs)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.2)(x)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.4)(x)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.4)(x)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.4)(x)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.4)(x)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.4)(x)
predictions = Dense(1)(x)
model = Model(inputs=[inputs], outputs=[predictions])
model.compile(loss='mse', optimizer='adam')
history = model.fit(X_train, y_train, batch_size=BATCH_SIZE, epochs=NUM_EPOCHS, validation_split=0.2, verbose=0)
score = model.evaluate(X_test, y_test, verbose=0)
return (history, model)
predicted = model.model.predict(X_test) | code |
2019997/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Input
from keras.layers.core import Dense, Dropout, Activation
from keras.models import Model
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def preprocess_data(test_data=False):
def encode_one_categorical_feature(column):
le = LabelEncoder()
ohe = OneHotEncoder(sparse=False)
num_encoded = le.fit_transform(column.fillna('unk'))
oh_encoded = ohe.fit_transform(num_encoded.reshape(-1, 1))
return oh_encoded
data = pd.read_csv('data/train.csv')
target = ['SalePrice']
features = data.drop(['Id'] + target, axis=1).columns
dataset_types = pd.DataFrame(data[features].dtypes, columns=['datatype'])
dataset_types.reset_index(inplace=True)
numeric_features = dataset_types.rename(columns={'index': 'feature'}).feature[(dataset_types.datatype == 'float64') | (dataset_types.datatype == 'int64')]
num_data = data[numeric_features]
num_features = num_data.fillna(num_data.mean()).values
scaler = StandardScaler()
num_features_scaled = scaler.fit_transform(num_features)
categorical_features = dataset_types.rename(columns={'index': 'feature'}).feature[dataset_types.datatype == 'object']
cat_data = data[categorical_features]
cat_features = np.hstack([encode_one_categorical_feature(data[column]) for column in cat_data.columns])
X = np.hstack((num_features_scaled, cat_features))
if test_data == True:
return X
y = data[target].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=606)
return (X_train, X_test, y_train, y_test)
def plot_history(history):
pass
def keras_model(X_train, X_test, y_train, y_test):
NUM_EPOCHS = 50
BATCH_SIZE = 128
inputs = Input(shape=(304,))
x = Dropout(0.2)(inputs)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.2)(x)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.4)(x)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.4)(x)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.4)(x)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.4)(x)
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.4)(x)
predictions = Dense(1)(x)
model = Model(inputs=[inputs], outputs=[predictions])
model.compile(loss='mse', optimizer='adam')
history = model.fit(X_train, y_train, batch_size=BATCH_SIZE, epochs=NUM_EPOCHS, validation_split=0.2, verbose=0)
score = model.evaluate(X_test, y_test, verbose=0)
return (history, model)
predicted = model.model.predict(X_test)
plt.plot(y_test - predicted) | code |
106198216/cell_21 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql.functions import format_number
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.columns
from pyspark.sql.functions import format_number
result = df_wmt.describe()
result.select(result['summary'],
format_number(result['Open'].cast('float'),2).alias('Open'),
format_number(result['High'].cast('float'),2).alias('High'),
format_number(result['Low'].cast('float'),2).alias('Low'),
format_number(result['Close'].cast('float'),2).alias('Close'),
result['Volume'].cast('int').alias('Volume')
).show()
df_wmt.orderBy(df_wmt['High'].desc()).show() | code |
106198216/cell_13 | [
"text_html_output_1.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.columns
df_wmt.describe().show() | code |
106198216/cell_9 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.columns | code |
106198216/cell_25 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql.functions import format_number
from pyspark.sql.functions import mean
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.columns
from pyspark.sql.functions import format_number
result = df_wmt.describe()
result.select(result['summary'],
format_number(result['Open'].cast('float'),2).alias('Open'),
format_number(result['High'].cast('float'),2).alias('High'),
format_number(result['Low'].cast('float'),2).alias('Low'),
format_number(result['Close'].cast('float'),2).alias('Close'),
result['Volume'].cast('int').alias('Volume')
).show()
from pyspark.sql.functions import mean
df_wmt.select(mean('Close')).show() | code |
106198216/cell_23 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql.functions import format_number
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.columns
from pyspark.sql.functions import format_number
result = df_wmt.describe()
result.select(result['summary'],
format_number(result['Open'].cast('float'),2).alias('Open'),
format_number(result['High'].cast('float'),2).alias('High'),
format_number(result['Low'].cast('float'),2).alias('Low'),
format_number(result['Close'].cast('float'),2).alias('Close'),
result['Volume'].cast('int').alias('Volume')
).show()
df_wmt.orderBy(df_wmt['High'].desc()).select('Date').show(1) | code |
106198216/cell_2 | [
"text_plain_output_1.png"
] | pip install pyspark | code |
106198216/cell_28 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql.functions import format_number
from pyspark.sql.functions import max,min
from pyspark.sql.functions import mean
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.columns
from pyspark.sql.functions import format_number
result = df_wmt.describe()
result.select(result['summary'],
format_number(result['Open'].cast('float'),2).alias('Open'),
format_number(result['High'].cast('float'),2).alias('High'),
format_number(result['Low'].cast('float'),2).alias('Low'),
format_number(result['Close'].cast('float'),2).alias('Close'),
result['Volume'].cast('int').alias('Volume')
).show()
from pyspark.sql.functions import mean
from pyspark.sql.functions import max, min
df_wmt.select(max('Volume').alias('MAX Volume'), min('Volume').alias('MIN Volume')).show() | code |
106198216/cell_8 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.show(1, vertical=True) | code |
106198216/cell_15 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.columns
df_wmt.describe().printSchema() | code |
106198216/cell_3 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark | code |
106198216/cell_17 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql.functions import format_number
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.columns
from pyspark.sql.functions import format_number
result = df_wmt.describe()
result.select(result['summary'], format_number(result['Open'].cast('float'), 2).alias('Open'), format_number(result['High'].cast('float'), 2).alias('High'), format_number(result['Low'].cast('float'), 2).alias('Low'), format_number(result['Close'].cast('float'), 2).alias('Close'), result['Volume'].cast('int').alias('Volume')).show() | code |
106198216/cell_10 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.columns
df_wmt.printSchema() | code |
106198216/cell_5 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.show() | code |
16152737/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/training.csv')
test = pd.read_csv('../input/test.csv')
uncommon_features = []
for i in train.columns:
if i not in test.columns:
uncommon_features.append(i)
def add_features(data):
df = data.copy()
df['NEW_FD_SUMP'] = df['FlightDistance'] / (df['p0_p'] + df['p1_p'] + df['p2_p'])
df['NEW5_lt'] = df['LifeTime'] * (df['p0_IP'] + df['p1_IP'] + df['p2_IP']) / 3
df['p_track_Chi2Dof_MAX'] = df.loc[:, ['p0_track_Chi2Dof', 'p1_track_Chi2Dof', 'p2_track_Chi2Dof']].max(axis=1)
df['flight_dist_sig2'] = (df['FlightDistance'] / df['FlightDistanceError']) ** 2
df['flight_dist_sig'] = df['FlightDistance'] / df['FlightDistanceError']
df['NEW_IP_dira'] = df['IP'] * df['dira']
df['p0p2_ip_ratio'] = df['IP'] / df['IP_p0p2']
df['p1p2_ip_ratio'] = df['IP'] / df['IP_p1p2']
df['DCA_MAX'] = df.loc[:, ['DOCAone', 'DOCAtwo', 'DOCAthree']].max(axis=1)
df['iso_bdt_min'] = df.loc[:, ['p0_IsoBDT', 'p1_IsoBDT', 'p2_IsoBDT']].min(axis=1)
df['iso_min'] = df.loc[:, ['isolationa', 'isolationb', 'isolationc', 'isolationd', 'isolatione', 'isolationf']].min(axis=1)
df['NEW_iso_abc'] = df['isolationa'] * df['isolationb'] * df['isolationc']
df['NEW_iso_def'] = df['isolationd'] * df['isolatione'] * df['isolationf']
df['NEW_pN_IP'] = df['p0_IP'] + df['p1_IP'] + df['p2_IP']
df['NEW_pN_p'] = df['p0_p'] + df['p1_p'] + df['p2_p']
df['NEW_IP_pNpN'] = df['IP_p0p2'] * df['IP_p1p2']
df['NEW_pN_IPSig'] = df['p0_IPSig'] + df['p1_IPSig'] + df['p2_IPSig']
df['NEW_FD_LT'] = df['FlightDistance'] / df['LifeTime']
return df
train_added = add_features(train)
test_added = add_features(test)
filter_out = ['id', 'min_ANNmuon', 'production', 'mass', 'signal', 'SPDhits', 'CDF1', 'CDF2', 'CDF3', 'isolationb', 'isolationc', 'p0_pt', 'p1_pt', 'p2_pt', 'p0_p', 'p1_p', 'p2_p', 'p0_eta', 'p1_eta', 'p2_eta', 'isolationa', 'isolationb', 'isolationc', 'isolationd', 'isolatione', 'isolationf', 'p0_IsoBDT', 'p1_IsoBDT', 'p2_IsoBDT', 'p0_IP', 'p1_IP', 'p2_IP', 'IP_p0p2', 'IP_p1p2', 'p0_track_Chi2Dof', 'p1_track_Chi2Dof', 'p2_track_Chi2Dof', 'p0_IPSig', 'p1_IPSig', 'p2_IPSig', 'DOCAone', 'DOCAtwo', 'DOCAthree']
features = list((f for f in train_added.columns if f not in filter_out))
scaler = StandardScaler()
X_train = scaler.fit_transform(train_added[features])
X_test = scaler.fit_transform(test_added[features])
y_train = train['signal']
pca = PCA().fit(X_train)
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
def pca_summary(pca, standardized_data, out=True):
names = ['PC' + str(i) for i in range(1, len(pca.explained_variance_ratio_) + 1)]
a = list(np.std(pca.transform(standardized_data), axis=0))
b = list(pca.explained_variance_ratio_)
c = [np.sum(pca.explained_variance_ratio_[:i]) for i in range(1, len(pca.explained_variance_ratio_) + 1)]
columns = pd.MultiIndex.from_tuples([('sdev', 'Standard deviation'), ('varprop', 'Proportion of Variance'), ('cumprop', 'Cumulative Proportion')])
summary = pd.DataFrame(list(zip(a, b, c)), index=names, columns=columns)
return summary
X_train_pca_df = pd.DataFrame(X_train_pca[:, 0:15])
X_train_pca_df.shape | code |
16152737/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/training.csv')
test = pd.read_csv('../input/test.csv')
uncommon_features = []
for i in train.columns:
if i not in test.columns:
uncommon_features.append(i)
def add_features(data):
df = data.copy()
df['NEW_FD_SUMP'] = df['FlightDistance'] / (df['p0_p'] + df['p1_p'] + df['p2_p'])
df['NEW5_lt'] = df['LifeTime'] * (df['p0_IP'] + df['p1_IP'] + df['p2_IP']) / 3
df['p_track_Chi2Dof_MAX'] = df.loc[:, ['p0_track_Chi2Dof', 'p1_track_Chi2Dof', 'p2_track_Chi2Dof']].max(axis=1)
df['flight_dist_sig2'] = (df['FlightDistance'] / df['FlightDistanceError']) ** 2
df['flight_dist_sig'] = df['FlightDistance'] / df['FlightDistanceError']
df['NEW_IP_dira'] = df['IP'] * df['dira']
df['p0p2_ip_ratio'] = df['IP'] / df['IP_p0p2']
df['p1p2_ip_ratio'] = df['IP'] / df['IP_p1p2']
df['DCA_MAX'] = df.loc[:, ['DOCAone', 'DOCAtwo', 'DOCAthree']].max(axis=1)
df['iso_bdt_min'] = df.loc[:, ['p0_IsoBDT', 'p1_IsoBDT', 'p2_IsoBDT']].min(axis=1)
df['iso_min'] = df.loc[:, ['isolationa', 'isolationb', 'isolationc', 'isolationd', 'isolatione', 'isolationf']].min(axis=1)
df['NEW_iso_abc'] = df['isolationa'] * df['isolationb'] * df['isolationc']
df['NEW_iso_def'] = df['isolationd'] * df['isolatione'] * df['isolationf']
df['NEW_pN_IP'] = df['p0_IP'] + df['p1_IP'] + df['p2_IP']
df['NEW_pN_p'] = df['p0_p'] + df['p1_p'] + df['p2_p']
df['NEW_IP_pNpN'] = df['IP_p0p2'] * df['IP_p1p2']
df['NEW_pN_IPSig'] = df['p0_IPSig'] + df['p1_IPSig'] + df['p2_IPSig']
df['NEW_FD_LT'] = df['FlightDistance'] / df['LifeTime']
return df
train_added = add_features(train)
test_added = add_features(test)
filter_out = ['id', 'min_ANNmuon', 'production', 'mass', 'signal', 'SPDhits', 'CDF1', 'CDF2', 'CDF3', 'isolationb', 'isolationc', 'p0_pt', 'p1_pt', 'p2_pt', 'p0_p', 'p1_p', 'p2_p', 'p0_eta', 'p1_eta', 'p2_eta', 'isolationa', 'isolationb', 'isolationc', 'isolationd', 'isolatione', 'isolationf', 'p0_IsoBDT', 'p1_IsoBDT', 'p2_IsoBDT', 'p0_IP', 'p1_IP', 'p2_IP', 'IP_p0p2', 'IP_p1p2', 'p0_track_Chi2Dof', 'p1_track_Chi2Dof', 'p2_track_Chi2Dof', 'p0_IPSig', 'p1_IPSig', 'p2_IPSig', 'DOCAone', 'DOCAtwo', 'DOCAthree']
features = list((f for f in train_added.columns if f not in filter_out))
scaler = StandardScaler()
X_train = scaler.fit_transform(train_added[features])
X_test = scaler.fit_transform(test_added[features])
y_train = train['signal']
print('Shape of Training data: ', X_train.shape, '\nShape of Testing data: ', X_test.shape, '\nShape of Training Labels: ', y_train.shape) | code |
16152737/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | print('Eliminate features')
filter_out = ['id', 'min_ANNmuon', 'production', 'mass', 'signal', 'SPDhits', 'CDF1', 'CDF2', 'CDF3', 'isolationb', 'isolationc', 'p0_pt', 'p1_pt', 'p2_pt', 'p0_p', 'p1_p', 'p2_p', 'p0_eta', 'p1_eta', 'p2_eta', 'isolationa', 'isolationb', 'isolationc', 'isolationd', 'isolatione', 'isolationf', 'p0_IsoBDT', 'p1_IsoBDT', 'p2_IsoBDT', 'p0_IP', 'p1_IP', 'p2_IP', 'IP_p0p2', 'IP_p1p2', 'p0_track_Chi2Dof', 'p1_track_Chi2Dof', 'p2_track_Chi2Dof', 'p0_IPSig', 'p1_IPSig', 'p2_IPSig', 'DOCAone', 'DOCAtwo', 'DOCAthree'] | code |
16152737/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/training.csv')
test = pd.read_csv('../input/test.csv')
print('Missing values in train: ', train.isnull().sum().sum()) | code |
16152737/cell_23 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from keras.layers import Dense, Dropout
from keras.models import Sequential
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/training.csv')
test = pd.read_csv('../input/test.csv')
uncommon_features = []
for i in train.columns:
if i not in test.columns:
uncommon_features.append(i)
def add_features(data):
df = data.copy()
df['NEW_FD_SUMP'] = df['FlightDistance'] / (df['p0_p'] + df['p1_p'] + df['p2_p'])
df['NEW5_lt'] = df['LifeTime'] * (df['p0_IP'] + df['p1_IP'] + df['p2_IP']) / 3
df['p_track_Chi2Dof_MAX'] = df.loc[:, ['p0_track_Chi2Dof', 'p1_track_Chi2Dof', 'p2_track_Chi2Dof']].max(axis=1)
df['flight_dist_sig2'] = (df['FlightDistance'] / df['FlightDistanceError']) ** 2
df['flight_dist_sig'] = df['FlightDistance'] / df['FlightDistanceError']
df['NEW_IP_dira'] = df['IP'] * df['dira']
df['p0p2_ip_ratio'] = df['IP'] / df['IP_p0p2']
df['p1p2_ip_ratio'] = df['IP'] / df['IP_p1p2']
df['DCA_MAX'] = df.loc[:, ['DOCAone', 'DOCAtwo', 'DOCAthree']].max(axis=1)
df['iso_bdt_min'] = df.loc[:, ['p0_IsoBDT', 'p1_IsoBDT', 'p2_IsoBDT']].min(axis=1)
df['iso_min'] = df.loc[:, ['isolationa', 'isolationb', 'isolationc', 'isolationd', 'isolatione', 'isolationf']].min(axis=1)
df['NEW_iso_abc'] = df['isolationa'] * df['isolationb'] * df['isolationc']
df['NEW_iso_def'] = df['isolationd'] * df['isolatione'] * df['isolationf']
df['NEW_pN_IP'] = df['p0_IP'] + df['p1_IP'] + df['p2_IP']
df['NEW_pN_p'] = df['p0_p'] + df['p1_p'] + df['p2_p']
df['NEW_IP_pNpN'] = df['IP_p0p2'] * df['IP_p1p2']
df['NEW_pN_IPSig'] = df['p0_IPSig'] + df['p1_IPSig'] + df['p2_IPSig']
df['NEW_FD_LT'] = df['FlightDistance'] / df['LifeTime']
return df
train_added = add_features(train)
test_added = add_features(test)
filter_out = ['id', 'min_ANNmuon', 'production', 'mass', 'signal', 'SPDhits', 'CDF1', 'CDF2', 'CDF3', 'isolationb', 'isolationc', 'p0_pt', 'p1_pt', 'p2_pt', 'p0_p', 'p1_p', 'p2_p', 'p0_eta', 'p1_eta', 'p2_eta', 'isolationa', 'isolationb', 'isolationc', 'isolationd', 'isolatione', 'isolationf', 'p0_IsoBDT', 'p1_IsoBDT', 'p2_IsoBDT', 'p0_IP', 'p1_IP', 'p2_IP', 'IP_p0p2', 'IP_p1p2', 'p0_track_Chi2Dof', 'p1_track_Chi2Dof', 'p2_track_Chi2Dof', 'p0_IPSig', 'p1_IPSig', 'p2_IPSig', 'DOCAone', 'DOCAtwo', 'DOCAthree']
features = list((f for f in train_added.columns if f not in filter_out))
scaler = StandardScaler()
X_train = scaler.fit_transform(train_added[features])
X_test = scaler.fit_transform(test_added[features])
y_train = train['signal']
pca = PCA().fit(X_train)
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
def pca_summary(pca, standardized_data, out=True):
names = ['PC' + str(i) for i in range(1, len(pca.explained_variance_ratio_) + 1)]
a = list(np.std(pca.transform(standardized_data), axis=0))
b = list(pca.explained_variance_ratio_)
c = [np.sum(pca.explained_variance_ratio_[:i]) for i in range(1, len(pca.explained_variance_ratio_) + 1)]
columns = pd.MultiIndex.from_tuples([('sdev', 'Standard deviation'), ('varprop', 'Proportion of Variance'), ('cumprop', 'Cumulative Proportion')])
summary = pd.DataFrame(list(zip(a, b, c)), index=names, columns=columns)
return summary
X_train_pca_df = pd.DataFrame(X_train_pca[:, 0:15])
X_train_pca_df.shape
y_train_nn = y_train.values.reshape(1, -1)
model = Sequential()
model.add(Dense(128, input_dim=15, kernel_initializer='uniform', activation='relu'))
model.add(Dense(64, kernel_initializer='uniform', activation='relu'))
model.add(Dense(32, kernel_initializer='uniform', activation='elu'))
model.add(Dense(16, kernel_initializer='uniform', activation='relu'))
model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())
model.fit(X_train_pca_df, y_train_nn.T, epochs=10, batch_size=32) | code |
16152737/cell_20 | [
"text_plain_output_1.png"
] | from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.utils import to_categorical
from keras.datasets import mnist
from keras.utils.vis_utils import model_to_dot
from IPython.display import SVG
from keras.utils import np_utils | code |
16152737/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/training.csv')
test = pd.read_csv('../input/test.csv')
uncommon_features = []
for i in train.columns:
if i not in test.columns:
uncommon_features.append(i)
print('Extra features in train: ', uncommon_features) | code |
16152737/cell_2 | [
"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/training.csv')
test = pd.read_csv('../input/test.csv')
print('train.shape:{} test.shape:{}'.format(train.shape, test.shape)) | code |
16152737/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/training.csv')
test = pd.read_csv('../input/test.csv')
uncommon_features = []
for i in train.columns:
if i not in test.columns:
uncommon_features.append(i)
def add_features(data):
df = data.copy()
df['NEW_FD_SUMP'] = df['FlightDistance'] / (df['p0_p'] + df['p1_p'] + df['p2_p'])
df['NEW5_lt'] = df['LifeTime'] * (df['p0_IP'] + df['p1_IP'] + df['p2_IP']) / 3
df['p_track_Chi2Dof_MAX'] = df.loc[:, ['p0_track_Chi2Dof', 'p1_track_Chi2Dof', 'p2_track_Chi2Dof']].max(axis=1)
df['flight_dist_sig2'] = (df['FlightDistance'] / df['FlightDistanceError']) ** 2
df['flight_dist_sig'] = df['FlightDistance'] / df['FlightDistanceError']
df['NEW_IP_dira'] = df['IP'] * df['dira']
df['p0p2_ip_ratio'] = df['IP'] / df['IP_p0p2']
df['p1p2_ip_ratio'] = df['IP'] / df['IP_p1p2']
df['DCA_MAX'] = df.loc[:, ['DOCAone', 'DOCAtwo', 'DOCAthree']].max(axis=1)
df['iso_bdt_min'] = df.loc[:, ['p0_IsoBDT', 'p1_IsoBDT', 'p2_IsoBDT']].min(axis=1)
df['iso_min'] = df.loc[:, ['isolationa', 'isolationb', 'isolationc', 'isolationd', 'isolatione', 'isolationf']].min(axis=1)
df['NEW_iso_abc'] = df['isolationa'] * df['isolationb'] * df['isolationc']
df['NEW_iso_def'] = df['isolationd'] * df['isolatione'] * df['isolationf']
df['NEW_pN_IP'] = df['p0_IP'] + df['p1_IP'] + df['p2_IP']
df['NEW_pN_p'] = df['p0_p'] + df['p1_p'] + df['p2_p']
df['NEW_IP_pNpN'] = df['IP_p0p2'] * df['IP_p1p2']
df['NEW_pN_IPSig'] = df['p0_IPSig'] + df['p1_IPSig'] + df['p2_IPSig']
df['NEW_FD_LT'] = df['FlightDistance'] / df['LifeTime']
return df
train_added = add_features(train)
test_added = add_features(test)
filter_out = ['id', 'min_ANNmuon', 'production', 'mass', 'signal', 'SPDhits', 'CDF1', 'CDF2', 'CDF3', 'isolationb', 'isolationc', 'p0_pt', 'p1_pt', 'p2_pt', 'p0_p', 'p1_p', 'p2_p', 'p0_eta', 'p1_eta', 'p2_eta', 'isolationa', 'isolationb', 'isolationc', 'isolationd', 'isolatione', 'isolationf', 'p0_IsoBDT', 'p1_IsoBDT', 'p2_IsoBDT', 'p0_IP', 'p1_IP', 'p2_IP', 'IP_p0p2', 'IP_p1p2', 'p0_track_Chi2Dof', 'p1_track_Chi2Dof', 'p2_track_Chi2Dof', 'p0_IPSig', 'p1_IPSig', 'p2_IPSig', 'DOCAone', 'DOCAtwo', 'DOCAthree']
features = list((f for f in train_added.columns if f not in filter_out))
scaler = StandardScaler()
X_train = scaler.fit_transform(train_added[features])
X_test = scaler.fit_transform(test_added[features])
y_train = train['signal']
pca = PCA().fit(X_train)
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
def pca_summary(pca, standardized_data, out=True):
names = ['PC' + str(i) for i in range(1, len(pca.explained_variance_ratio_) + 1)]
a = list(np.std(pca.transform(standardized_data), axis=0))
b = list(pca.explained_variance_ratio_)
c = [np.sum(pca.explained_variance_ratio_[:i]) for i in range(1, len(pca.explained_variance_ratio_) + 1)]
columns = pd.MultiIndex.from_tuples([('sdev', 'Standard deviation'), ('varprop', 'Proportion of Variance'), ('cumprop', 'Cumulative Proportion')])
summary = pd.DataFrame(list(zip(a, b, c)), index=names, columns=columns)
return summary
X_train_pca_df = pd.DataFrame(X_train_pca[:, 0:15])
X_test_pca_df = pd.DataFrame(X_test_pca[:, 0:15])
X_test_pca_df.head() | code |
16152737/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import roc_curve, auc
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
print(os.listdir('../input')) | code |
16152737/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/training.csv')
test = pd.read_csv('../input/test.csv')
uncommon_features = []
for i in train.columns:
if i not in test.columns:
uncommon_features.append(i)
def add_features(data):
df = data.copy()
df['NEW_FD_SUMP'] = df['FlightDistance'] / (df['p0_p'] + df['p1_p'] + df['p2_p'])
df['NEW5_lt'] = df['LifeTime'] * (df['p0_IP'] + df['p1_IP'] + df['p2_IP']) / 3
df['p_track_Chi2Dof_MAX'] = df.loc[:, ['p0_track_Chi2Dof', 'p1_track_Chi2Dof', 'p2_track_Chi2Dof']].max(axis=1)
df['flight_dist_sig2'] = (df['FlightDistance'] / df['FlightDistanceError']) ** 2
df['flight_dist_sig'] = df['FlightDistance'] / df['FlightDistanceError']
df['NEW_IP_dira'] = df['IP'] * df['dira']
df['p0p2_ip_ratio'] = df['IP'] / df['IP_p0p2']
df['p1p2_ip_ratio'] = df['IP'] / df['IP_p1p2']
df['DCA_MAX'] = df.loc[:, ['DOCAone', 'DOCAtwo', 'DOCAthree']].max(axis=1)
df['iso_bdt_min'] = df.loc[:, ['p0_IsoBDT', 'p1_IsoBDT', 'p2_IsoBDT']].min(axis=1)
df['iso_min'] = df.loc[:, ['isolationa', 'isolationb', 'isolationc', 'isolationd', 'isolatione', 'isolationf']].min(axis=1)
df['NEW_iso_abc'] = df['isolationa'] * df['isolationb'] * df['isolationc']
df['NEW_iso_def'] = df['isolationd'] * df['isolatione'] * df['isolationf']
df['NEW_pN_IP'] = df['p0_IP'] + df['p1_IP'] + df['p2_IP']
df['NEW_pN_p'] = df['p0_p'] + df['p1_p'] + df['p2_p']
df['NEW_IP_pNpN'] = df['IP_p0p2'] * df['IP_p1p2']
df['NEW_pN_IPSig'] = df['p0_IPSig'] + df['p1_IPSig'] + df['p2_IPSig']
df['NEW_FD_LT'] = df['FlightDistance'] / df['LifeTime']
return df
train_added = add_features(train)
test_added = add_features(test)
filter_out = ['id', 'min_ANNmuon', 'production', 'mass', 'signal', 'SPDhits', 'CDF1', 'CDF2', 'CDF3', 'isolationb', 'isolationc', 'p0_pt', 'p1_pt', 'p2_pt', 'p0_p', 'p1_p', 'p2_p', 'p0_eta', 'p1_eta', 'p2_eta', 'isolationa', 'isolationb', 'isolationc', 'isolationd', 'isolatione', 'isolationf', 'p0_IsoBDT', 'p1_IsoBDT', 'p2_IsoBDT', 'p0_IP', 'p1_IP', 'p2_IP', 'IP_p0p2', 'IP_p1p2', 'p0_track_Chi2Dof', 'p1_track_Chi2Dof', 'p2_track_Chi2Dof', 'p0_IPSig', 'p1_IPSig', 'p2_IPSig', 'DOCAone', 'DOCAtwo', 'DOCAthree']
features = list((f for f in train_added.columns if f not in filter_out))
scaler = StandardScaler()
X_train = scaler.fit_transform(train_added[features])
X_test = scaler.fit_transform(test_added[features])
y_train = train['signal']
pca = PCA().fit(X_train)
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
def pca_summary(pca, standardized_data, out=True):
names = ['PC' + str(i) for i in range(1, len(pca.explained_variance_ratio_) + 1)]
a = list(np.std(pca.transform(standardized_data), axis=0))
b = list(pca.explained_variance_ratio_)
c = [np.sum(pca.explained_variance_ratio_[:i]) for i in range(1, len(pca.explained_variance_ratio_) + 1)]
columns = pd.MultiIndex.from_tuples([('sdev', 'Standard deviation'), ('varprop', 'Proportion of Variance'), ('cumprop', 'Cumulative Proportion')])
summary = pd.DataFrame(list(zip(a, b, c)), index=names, columns=columns)
return summary
X_train_pca_df = pd.DataFrame(X_train_pca[:, 0:15])
X_train_pca_df.head() | code |
16152737/cell_8 | [
"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/training.csv')
test = pd.read_csv('../input/test.csv')
uncommon_features = []
for i in train.columns:
if i not in test.columns:
uncommon_features.append(i)
def add_features(data):
df = data.copy()
df['NEW_FD_SUMP'] = df['FlightDistance'] / (df['p0_p'] + df['p1_p'] + df['p2_p'])
df['NEW5_lt'] = df['LifeTime'] * (df['p0_IP'] + df['p1_IP'] + df['p2_IP']) / 3
df['p_track_Chi2Dof_MAX'] = df.loc[:, ['p0_track_Chi2Dof', 'p1_track_Chi2Dof', 'p2_track_Chi2Dof']].max(axis=1)
df['flight_dist_sig2'] = (df['FlightDistance'] / df['FlightDistanceError']) ** 2
df['flight_dist_sig'] = df['FlightDistance'] / df['FlightDistanceError']
df['NEW_IP_dira'] = df['IP'] * df['dira']
df['p0p2_ip_ratio'] = df['IP'] / df['IP_p0p2']
df['p1p2_ip_ratio'] = df['IP'] / df['IP_p1p2']
df['DCA_MAX'] = df.loc[:, ['DOCAone', 'DOCAtwo', 'DOCAthree']].max(axis=1)
df['iso_bdt_min'] = df.loc[:, ['p0_IsoBDT', 'p1_IsoBDT', 'p2_IsoBDT']].min(axis=1)
df['iso_min'] = df.loc[:, ['isolationa', 'isolationb', 'isolationc', 'isolationd', 'isolatione', 'isolationf']].min(axis=1)
df['NEW_iso_abc'] = df['isolationa'] * df['isolationb'] * df['isolationc']
df['NEW_iso_def'] = df['isolationd'] * df['isolatione'] * df['isolationf']
df['NEW_pN_IP'] = df['p0_IP'] + df['p1_IP'] + df['p2_IP']
df['NEW_pN_p'] = df['p0_p'] + df['p1_p'] + df['p2_p']
df['NEW_IP_pNpN'] = df['IP_p0p2'] * df['IP_p1p2']
df['NEW_pN_IPSig'] = df['p0_IPSig'] + df['p1_IPSig'] + df['p2_IPSig']
df['NEW_FD_LT'] = df['FlightDistance'] / df['LifeTime']
return df
train_added = add_features(train)
test_added = add_features(test)
print('Total Number of Features: ', train_added.shape[1]) | code |
16152737/cell_16 | [
"text_html_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/training.csv')
test = pd.read_csv('../input/test.csv')
uncommon_features = []
for i in train.columns:
if i not in test.columns:
uncommon_features.append(i)
def add_features(data):
df = data.copy()
df['NEW_FD_SUMP'] = df['FlightDistance'] / (df['p0_p'] + df['p1_p'] + df['p2_p'])
df['NEW5_lt'] = df['LifeTime'] * (df['p0_IP'] + df['p1_IP'] + df['p2_IP']) / 3
df['p_track_Chi2Dof_MAX'] = df.loc[:, ['p0_track_Chi2Dof', 'p1_track_Chi2Dof', 'p2_track_Chi2Dof']].max(axis=1)
df['flight_dist_sig2'] = (df['FlightDistance'] / df['FlightDistanceError']) ** 2
df['flight_dist_sig'] = df['FlightDistance'] / df['FlightDistanceError']
df['NEW_IP_dira'] = df['IP'] * df['dira']
df['p0p2_ip_ratio'] = df['IP'] / df['IP_p0p2']
df['p1p2_ip_ratio'] = df['IP'] / df['IP_p1p2']
df['DCA_MAX'] = df.loc[:, ['DOCAone', 'DOCAtwo', 'DOCAthree']].max(axis=1)
df['iso_bdt_min'] = df.loc[:, ['p0_IsoBDT', 'p1_IsoBDT', 'p2_IsoBDT']].min(axis=1)
df['iso_min'] = df.loc[:, ['isolationa', 'isolationb', 'isolationc', 'isolationd', 'isolatione', 'isolationf']].min(axis=1)
df['NEW_iso_abc'] = df['isolationa'] * df['isolationb'] * df['isolationc']
df['NEW_iso_def'] = df['isolationd'] * df['isolatione'] * df['isolationf']
df['NEW_pN_IP'] = df['p0_IP'] + df['p1_IP'] + df['p2_IP']
df['NEW_pN_p'] = df['p0_p'] + df['p1_p'] + df['p2_p']
df['NEW_IP_pNpN'] = df['IP_p0p2'] * df['IP_p1p2']
df['NEW_pN_IPSig'] = df['p0_IPSig'] + df['p1_IPSig'] + df['p2_IPSig']
df['NEW_FD_LT'] = df['FlightDistance'] / df['LifeTime']
return df
train_added = add_features(train)
test_added = add_features(test)
filter_out = ['id', 'min_ANNmuon', 'production', 'mass', 'signal', 'SPDhits', 'CDF1', 'CDF2', 'CDF3', 'isolationb', 'isolationc', 'p0_pt', 'p1_pt', 'p2_pt', 'p0_p', 'p1_p', 'p2_p', 'p0_eta', 'p1_eta', 'p2_eta', 'isolationa', 'isolationb', 'isolationc', 'isolationd', 'isolatione', 'isolationf', 'p0_IsoBDT', 'p1_IsoBDT', 'p2_IsoBDT', 'p0_IP', 'p1_IP', 'p2_IP', 'IP_p0p2', 'IP_p1p2', 'p0_track_Chi2Dof', 'p1_track_Chi2Dof', 'p2_track_Chi2Dof', 'p0_IPSig', 'p1_IPSig', 'p2_IPSig', 'DOCAone', 'DOCAtwo', 'DOCAthree']
features = list((f for f in train_added.columns if f not in filter_out))
scaler = StandardScaler()
X_train = scaler.fit_transform(train_added[features])
X_test = scaler.fit_transform(test_added[features])
y_train = train['signal']
pca = PCA().fit(X_train)
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
def pca_summary(pca, standardized_data, out=True):
names = ['PC' + str(i) for i in range(1, len(pca.explained_variance_ratio_) + 1)]
a = list(np.std(pca.transform(standardized_data), axis=0))
b = list(pca.explained_variance_ratio_)
c = [np.sum(pca.explained_variance_ratio_[:i]) for i in range(1, len(pca.explained_variance_ratio_) + 1)]
columns = pd.MultiIndex.from_tuples([('sdev', 'Standard deviation'), ('varprop', 'Proportion of Variance'), ('cumprop', 'Cumulative Proportion')])
summary = pd.DataFrame(list(zip(a, b, c)), index=names, columns=columns)
return summary
summary = pca_summary(pca, X_train_pca) | code |
16152737/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/training.csv')
test = pd.read_csv('../input/test.csv')
train.head() | code |
16152737/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/training.csv')
test = pd.read_csv('../input/test.csv')
uncommon_features = []
for i in train.columns:
if i not in test.columns:
uncommon_features.append(i)
def add_features(data):
df = data.copy()
df['NEW_FD_SUMP'] = df['FlightDistance'] / (df['p0_p'] + df['p1_p'] + df['p2_p'])
df['NEW5_lt'] = df['LifeTime'] * (df['p0_IP'] + df['p1_IP'] + df['p2_IP']) / 3
df['p_track_Chi2Dof_MAX'] = df.loc[:, ['p0_track_Chi2Dof', 'p1_track_Chi2Dof', 'p2_track_Chi2Dof']].max(axis=1)
df['flight_dist_sig2'] = (df['FlightDistance'] / df['FlightDistanceError']) ** 2
df['flight_dist_sig'] = df['FlightDistance'] / df['FlightDistanceError']
df['NEW_IP_dira'] = df['IP'] * df['dira']
df['p0p2_ip_ratio'] = df['IP'] / df['IP_p0p2']
df['p1p2_ip_ratio'] = df['IP'] / df['IP_p1p2']
df['DCA_MAX'] = df.loc[:, ['DOCAone', 'DOCAtwo', 'DOCAthree']].max(axis=1)
df['iso_bdt_min'] = df.loc[:, ['p0_IsoBDT', 'p1_IsoBDT', 'p2_IsoBDT']].min(axis=1)
df['iso_min'] = df.loc[:, ['isolationa', 'isolationb', 'isolationc', 'isolationd', 'isolatione', 'isolationf']].min(axis=1)
df['NEW_iso_abc'] = df['isolationa'] * df['isolationb'] * df['isolationc']
df['NEW_iso_def'] = df['isolationd'] * df['isolatione'] * df['isolationf']
df['NEW_pN_IP'] = df['p0_IP'] + df['p1_IP'] + df['p2_IP']
df['NEW_pN_p'] = df['p0_p'] + df['p1_p'] + df['p2_p']
df['NEW_IP_pNpN'] = df['IP_p0p2'] * df['IP_p1p2']
df['NEW_pN_IPSig'] = df['p0_IPSig'] + df['p1_IPSig'] + df['p2_IPSig']
df['NEW_FD_LT'] = df['FlightDistance'] / df['LifeTime']
return df
train_added = add_features(train)
test_added = add_features(test)
filter_out = ['id', 'min_ANNmuon', 'production', 'mass', 'signal', 'SPDhits', 'CDF1', 'CDF2', 'CDF3', 'isolationb', 'isolationc', 'p0_pt', 'p1_pt', 'p2_pt', 'p0_p', 'p1_p', 'p2_p', 'p0_eta', 'p1_eta', 'p2_eta', 'isolationa', 'isolationb', 'isolationc', 'isolationd', 'isolatione', 'isolationf', 'p0_IsoBDT', 'p1_IsoBDT', 'p2_IsoBDT', 'p0_IP', 'p1_IP', 'p2_IP', 'IP_p0p2', 'IP_p1p2', 'p0_track_Chi2Dof', 'p1_track_Chi2Dof', 'p2_track_Chi2Dof', 'p0_IPSig', 'p1_IPSig', 'p2_IPSig', 'DOCAone', 'DOCAtwo', 'DOCAthree']
features = list((f for f in train_added.columns if f not in filter_out))
scaler = StandardScaler()
X_train = scaler.fit_transform(train_added[features])
X_test = scaler.fit_transform(test_added[features])
y_train = train['signal']
pca = PCA().fit(X_train)
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
def pca_summary(pca, standardized_data, out=True):
names = ['PC' + str(i) for i in range(1, len(pca.explained_variance_ratio_) + 1)]
a = list(np.std(pca.transform(standardized_data), axis=0))
b = list(pca.explained_variance_ratio_)
c = [np.sum(pca.explained_variance_ratio_[:i]) for i in range(1, len(pca.explained_variance_ratio_) + 1)]
columns = pd.MultiIndex.from_tuples([('sdev', 'Standard deviation'), ('varprop', 'Proportion of Variance'), ('cumprop', 'Cumulative Proportion')])
summary = pd.DataFrame(list(zip(a, b, c)), index=names, columns=columns)
return summary
def screeplot(pca, standardized_values):
y = np.std(pca.transform(standardized_values), axis=0) ** 2
x = np.arange(len(y)) + 1
plt.plot(x, y, 'o-')
plt.xticks(x, ['Comp.' + str(i) for i in x], rotation=60)
plt.ylabel('Variance')
plt.show()
screeplot(pca, X_train) | code |
16152737/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/training.csv')
test = pd.read_csv('../input/test.csv')
print('Missing values in test: ', train.isnull().sum().sum()) | code |
17111990/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from IPython.display import Image
import os
!ls ../input/
Image("../input/images/twitter.png")
Image("../input/images/Trump_New_York_Times_tweet_.jpg") | code |
17111990/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from IPython.display import Image
import os
!ls ../input/
Image("../input/images/history-bigdata.jpg") | code |
17111990/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from IPython.display import Image
import os
!ls ../input/
Image("../input/images/threev.png") | code |
17111990/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from IPython.display import Image
import os
!ls ../input/
Image("../input/images/bda-696x394.jpg") | code |
17111990/cell_11 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from IPython.display import Image
import os
!ls ../input/
Image("../input/images/company.jpg") | code |
17111990/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input'))
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
17111990/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from IPython.display import Image
import os
!ls ../input/
Image("../input/images/Management.png") | code |
104116119/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
print((train_df.isna().sum() / train_df.shape[0])[train_df.isna().sum() / train_df.shape[0] > 0.4]) | code |
104116119/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_df.drop(['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature'], axis=1, inplace=True)
test_df.drop(['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature'], axis=1, inplace=True)
categorical_columns = [c for c in train_df.columns if train_df[c].dtype == 'object']
categorical_columns
len(categorical_columns) | code |
104116119/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_folds_df = pd.read_csv('./train_folds.csv')
train_folds_df = train_folds_df.drop(['Id'], axis=1)
train_folds_df.head(1) | code |
104116119/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_df.drop(['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature'], axis=1, inplace=True)
test_df.drop(['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature'], axis=1, inplace=True)
categorical_columns = [c for c in train_df.columns if train_df[c].dtype == 'object']
categorical_columns
len(categorical_columns)
numerical_columns = [col for col in train_df.columns if train_df[col].dtypes != 'object']
numerical_columns
len(numerical_columns) | code |
104116119/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_df.head(2) | code |
104116119/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_folds_df = pd.read_csv('./train_folds.csv')
train_folds_df.head(1) | code |
33096184/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarked'])
df1.shape
sns.set(style='darkgrid')
sns.countplot(x='Survived', hue='Survived', data=df1) | code |
33096184/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0] | code |
33096184/cell_44 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarked'])
df1.shape
df1_pc3 = df1[df1['Pclass'] == 3]
df1_pc3.shape | code |
33096184/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df.head() | code |
33096184/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarked'])
df1.shape
sns.set(style='darkgrid')
sns.set(style='darkgrid')
sns.set(style='darkgrid')
df1_pc1 = df1[df1['Pclass'] == 1]
df1_pc1.shape
plt.figure(figsize=(8, 5))
sns.boxplot(x='Sex', y='Age', data=df1_pc1, palette='winter') | code |
33096184/cell_39 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarked'])
df1.shape
sns.set(style='darkgrid')
sns.set(style='darkgrid')
sns.set(style='darkgrid')
df1_pc1 = df1[df1['Pclass'] == 1]
df1_pc1.shape
df1_pc2 = df1[df1['Pclass'] == 2]
df1_pc2.shape
plt.figure(figsize=(8, 5))
sns.boxplot(x='Sex', y='Age', data=df1_pc2, palette='winter') | code |
33096184/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarked'])
df1.shape | code |
33096184/cell_50 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarked'])
df1.shape
df1_pc3 = df1[df1['Pclass'] == 3]
df1_pc3.shape
df1_pc3_male = df1_pc3[df1_pc3['Sex'] == 'male']
df1_pc3_male['Age'].fillna(value=df1_pc3_male['Age'].mean(), inplace=True)
df1_pc3_female = df1_pc3[df1_pc3['Sex'] == 'female']
df1_pc3_female['Age'].fillna(value=df1_pc3_female['Age'].mean(), inplace=True)
df1_pc3 = df1_pc3_male.append(df1_pc3_female)
df1_pc3.shape | code |
33096184/cell_52 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarked'])
df1.shape
df1_pc1 = df1[df1['Pclass'] == 1]
df1_pc1.shape
df1_pc1_male = df1_pc1[df1_pc1['Sex'] == 'male']
df1_pc1_female = df1_pc1[df1_pc1['Sex'] == 'female']
df1_pc1 = df1_pc1_male.append(df1_pc1_female)
df1_pc1.shape
df1_pc2 = df1[df1['Pclass'] == 2]
df1_pc2.shape
df1_pc2_male = df1_pc2[df1_pc2['Sex'] == 'male']
df1_pc2_male['Age'].fillna(value=df1_pc2_male['Age'].mean(), inplace=True)
df1_pc2_female = df1_pc2[df1_pc2['Sex'] == 'female']
df1_pc2_female['Age'].fillna(value=df1_pc2_female['Age'].mean(), inplace=True)
df1_pc2 = df1_pc2_male.append(df1_pc2_female)
df1_pc2.shape
df1_pc3 = df1[df1['Pclass'] == 3]
df1_pc3.shape
df1_pc3_male = df1_pc3[df1_pc3['Sex'] == 'male']
df1_pc3_male['Age'].fillna(value=df1_pc3_male['Age'].mean(), inplace=True)
df1_pc3_female = df1_pc3[df1_pc3['Sex'] == 'female']
df1_pc3_female['Age'].fillna(value=df1_pc3_female['Age'].mean(), inplace=True)
df1_pc3 = df1_pc3_male.append(df1_pc3_female)
df1_pc3.shape
df1_pc1 = df1_pc1.append(df1_pc2)
df1 = df1_pc1.append(df1_pc3)
df1.shape | code |
33096184/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('../input/titanic/train.csv')
df.tail() | code |
33096184/cell_45 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarked'])
df1.shape
sns.set(style='darkgrid')
sns.set(style='darkgrid')
sns.set(style='darkgrid')
df1_pc1 = df1[df1['Pclass'] == 1]
df1_pc1.shape
df1_pc2 = df1[df1['Pclass'] == 2]
df1_pc2.shape
df1_pc3 = df1[df1['Pclass'] == 3]
df1_pc3.shape
plt.figure(figsize=(8, 5))
sns.boxplot(x='Sex', y='Age', data=df1_pc3, palette='winter') | code |
33096184/cell_28 | [
"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('../input/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarked'])
df1.shape
df1_pc1 = df1[df1['Pclass'] == 1]
df1_pc1.shape | code |
33096184/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df.shape | code |
33096184/cell_38 | [
"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('../input/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarked'])
df1.shape
df1_pc2 = df1[df1['Pclass'] == 2]
df1_pc2.shape | code |
33096184/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum() | code |
33096184/cell_43 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarked'])
df1.shape
df1_pc2 = df1[df1['Pclass'] == 2]
df1_pc2.shape
df1_pc2_male = df1_pc2[df1_pc2['Sex'] == 'male']
df1_pc2_male['Age'].fillna(value=df1_pc2_male['Age'].mean(), inplace=True)
df1_pc2_female = df1_pc2[df1_pc2['Sex'] == 'female']
df1_pc2_female['Age'].fillna(value=df1_pc2_female['Age'].mean(), inplace=True)
df1_pc2 = df1_pc2_male.append(df1_pc2_female)
df1_pc2.shape | code |
33096184/cell_31 | [
"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('../input/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarked'])
df1.shape
df1_pc1 = df1[df1['Pclass'] == 1]
df1_pc1.shape
df1_pc1.head() | code |
33096184/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarked'])
df1.shape
sns.set(style='darkgrid')
sns.set(style='darkgrid')
sns.set(style='darkgrid')
sns.countplot(x='Survived', hue='Pclass', data=df1) | code |
33096184/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarked'])
df1.shape
sns.set(style='darkgrid')
sns.set(style='darkgrid')
sns.countplot(x='Survived', hue='Sex', data=df1) | code |
33096184/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df.shape
df.info() | code |
33096184/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarked'])
df1.shape
df1_pc1 = df1[df1['Pclass'] == 1]
df1_pc1.shape
df1_pc1_male = df1_pc1[df1_pc1['Sex'] == 'male']
df1_pc1_female = df1_pc1[df1_pc1['Sex'] == 'female']
df1_pc1 = df1_pc1_male.append(df1_pc1_female)
df1_pc1.shape | code |
33096184/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df.shape
df.describe() | code |
1004561/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
os.listdir('../input') | code |
1004561/cell_33 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test_images = test.values.astype('float32')
train_images = train.ix[:, 1:].values.astype('float32')
train_labels = train.ix[:, 0].values.astype('int32')
train_images_reshaped = train_images.reshape(train_images.shape[0], 28, 28)
train_images_reshaped.shape
train_images = train_images / 255
test_images = test_images / 255
np.std(train_images) | code |
1004561/cell_55 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_images = train.ix[:, 1:].values.astype('float32')
train_labels = train.ix[:, 0].values.astype('int32')
train_images_reshaped = train_images.reshape(train_images.shape[0], 28, 28)
train_images_reshaped.shape
history_dict = history.history
history_dict.keys()
loss_values = history_dict['loss']
val_loss_values = history_dict['val_loss']
epochs = range(1, len(loss_values) + 1)
plt.clf()
acc_values = history_dict['acc']
val_acc_values = history_dict['val_acc']
history_dict = history.history
loss_values = history_dict['loss']
val_loss_values = history_dict['val_loss']
epochs = range(1, len(loss_values) + 1)
plt.plot(epochs, loss_values, 'bo')
plt.plot(epochs, val_loss_values, 'r^')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.show() | code |
1004561/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_images = train.ix[:, 1:].values.astype('float32')
train_labels = train.ix[:, 0].values.astype('int32')
train_labels.shape
train_labels[0:10] | code |
1004561/cell_26 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_images = train.ix[:, 1:].values.astype('float32')
train_labels = train.ix[:, 0].values.astype('int32')
train_images_reshaped = train_images.reshape(train_images.shape[0], 28, 28)
train_images_reshaped.shape
for i in range(9):
plt.subplot(330 + (i + 1))
plt.imshow(train_images_reshaped[i])
plt.title(train_labels[i]) | code |
1004561/cell_48 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_images = train.ix[:, 1:].values.astype('float32')
train_labels = train.ix[:, 0].values.astype('int32')
train_images_reshaped = train_images.reshape(train_images.shape[0], 28, 28)
train_images_reshaped.shape
history_dict = history.history
history_dict.keys()
loss_values = history_dict['loss']
val_loss_values = history_dict['val_loss']
epochs = range(1, len(loss_values) + 1)
plt.clf()
acc_values = history_dict['acc']
val_acc_values = history_dict['val_acc']
plt.plot(epochs, acc_values, 'bo')
plt.plot(epochs, val_acc_values, 'r^')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.show() | code |
1004561/cell_45 | [
"text_plain_output_1.png"
] | history = model.fit(train_images, train_labels, validation_split=0.05, nb_epoch=25, batch_size=64) | code |
1004561/cell_28 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_images = train.ix[:, 1:].values.astype('float32')
train_labels = train.ix[:, 0].values.astype('int32')
train_labels.shape | code |
1004561/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.models import Sequential
from keras.layers import Dense, Dropout, Lambda, Flatten | code |
1004561/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
print(train.shape)
train.head() | code |
1004561/cell_38 | [
"text_plain_output_1.png"
] | from keras.utils.np_utils import to_categorical
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_images = train.ix[:, 1:].values.astype('float32')
train_labels = train.ix[:, 0].values.astype('int32')
train_labels.shape
from keras.utils.np_utils import to_categorical
train_labels = to_categorical(train_labels)
train_labels.shape
train_labels[0:10] | code |
1004561/cell_47 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_images = train.ix[:, 1:].values.astype('float32')
train_labels = train.ix[:, 0].values.astype('int32')
train_images_reshaped = train_images.reshape(train_images.shape[0], 28, 28)
train_images_reshaped.shape
history_dict = history.history
history_dict.keys()
loss_values = history_dict['loss']
val_loss_values = history_dict['val_loss']
epochs = range(1, len(loss_values) + 1)
plt.plot(epochs, loss_values, 'bo')
plt.plot(epochs, val_loss_values, 'r^')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.show() | code |
1004561/cell_17 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
print(test.shape)
test.head() | code |
1004561/cell_46 | [
"text_plain_output_1.png"
] | history_dict = history.history
history_dict.keys() | code |
1004561/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_images = train.ix[:, 1:].values.astype('float32')
train_labels = train.ix[:, 0].values.astype('int32')
train_images_reshaped = train_images.reshape(train_images.shape[0], 28, 28)
train_images_reshaped.shape | code |
1004561/cell_22 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_images = train.ix[:, 1:].values.astype('float32')
train_labels = train.ix[:, 0].values.astype('int32')
train_labels[0:10] | code |
1004561/cell_53 | [
"image_output_1.png"
] | model = Sequential()
model.add(Dense(64, activation='relu', input_dim=28 * 28))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.15))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.15))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer=RMSprop(lr=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit(train_images, train_labels, nb_epoch=15, batch_size=64, verbose=0) | code |
1004561/cell_37 | [
"text_plain_output_1.png"
] | from keras.utils.np_utils import to_categorical
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_images = train.ix[:, 1:].values.astype('float32')
train_labels = train.ix[:, 0].values.astype('int32')
train_labels.shape
from keras.utils.np_utils import to_categorical
train_labels = to_categorical(train_labels)
train_labels.shape | code |
90140081/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('E:\\Dockship\\Credict card\\TRAIN.csv')
df.head() | code |
90125749/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
directories = ['../input/csc4851-homework4/birds_400/test', '../input/csc4851-homework4/birds_400/train', '../input//csc4851-homework4/birds_400/valid']
for dir in directories:
label = []
path = []
for dirname, _, filenames in os.walk(dir):
for filename in filenames:
label.append(os.path.split(dirname)[1])
path.append(os.path.join(dirname, filename))
if dir == directories[0]:
df_test = pd.DataFrame(columns=['path', 'label'])
df_test['path'] = path
df_test['label'] = label
elif dir == directories[1]:
df_train = pd.DataFrame(columns=['path', 'label'])
df_train['path'] = path
df_train['label'] = label
elif dir == directories[2]:
df_valid = pd.DataFrame(columns=['path', 'label'])
df_valid['path'] = path
df_valid['label'] = label
fig, axes = plt.subplots(nrows=3, ncols=5, figsize=(15, 7), subplot_kw={'xticks': [], 'yticks': []})
df_sample = df_train.sample(15)
df_sample.reset_index(drop=True, inplace=True)
for i, ax in enumerate(axes.flat):
ax.imshow(plt.imread(df_sample.path[i]))
ax.set_title(df_sample.label[i])
plt.tight_layout()
plt.show() | code |
90125749/cell_2 | [
"text_plain_output_1.png"
] | !ls | code |
90125749/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 |
90125749/cell_5 | [
"text_html_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
directories = ['../input/csc4851-homework4/birds_400/test', '../input/csc4851-homework4/birds_400/train', '../input//csc4851-homework4/birds_400/valid']
for dir in directories:
label = []
path = []
for dirname, _, filenames in os.walk(dir):
for filename in filenames:
label.append(os.path.split(dirname)[1])
path.append(os.path.join(dirname, filename))
if dir == directories[0]:
df_test = pd.DataFrame(columns=['path', 'label'])
df_test['path'] = path
df_test['label'] = label
elif dir == directories[1]:
df_train = pd.DataFrame(columns=['path', 'label'])
df_train['path'] = path
df_train['label'] = label
elif dir == directories[2]:
df_valid = pd.DataFrame(columns=['path', 'label'])
df_valid['path'] = path
df_valid['label'] = label
df_train.head() | code |
16144712/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import decomposition
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/train.csv')
label = data['label']
pixels = data.drop('label', axis=1)
from sklearn import decomposition
pca = decomposition.PCA()
pca.n_components = 2
pca_data = pca.fit_transform(pixels)
label = np.reshape(label.values, (label.shape[0], 1))
data_transformed = np.hstack((pca_data, label))
dframe = pd.DataFrame(data=data_transformed, columns=('pc1', 'pc2', 'label'))
sns.FacetGrid(dframe, hue='label', size=5).map(plt.scatter, 'pc1', 'pc2').add_legend() | code |
16144712/cell_10 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn import decomposition
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/train.csv')
label = data['label']
pixels = data.drop('label', axis=1)
from sklearn import decomposition
pca = decomposition.PCA()
pca.n_components = 2
pca_data = pca.fit_transform(pixels)
label = np.reshape(label.values, (label.shape[0], 1))
data_transformed = np.hstack((pca_data, label))
dframe = pd.DataFrame(data=data_transformed, columns=('pc1', 'pc2', 'label'))
sns.FacetGrid(dframe, hue='label', size=5).map(plt.scatter, 'pc1', 'pc2').add_legend()
from sklearn.manifold import TSNE
model = TSNE(n_components=2, random_state=0)
tsne_transform = model.fit_transform(pixels[:10000])
tsne_trans_data = np.hstack((tsne_transform, label[:10000]))
tsne_dframe = pd.DataFrame(data=tsne_trans_data, columns=('c1', 'c2', 'label'))
sns.FacetGrid(tsne_dframe, hue='label', height=5).map(plt.scatter, 'c1', 'c2').add_legend() | code |
104120795/cell_42 | [
"image_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt # visualization
import numpy as np # linear algebra
import pandas as pd # read and wrangle dataframes
import seaborn as sns # statistical visualizations and aesthetics
df = pd.read_csv('../input/glass/glass.csv')
features = df.columns[:-1].tolist()
df.dtypes
for feat in features:
skew = df[feat].skew()
def outlier_hunt(df):
"""
Takes a dataframe df of features and returns a list of the indices
corresponding to the observations containing more than 2 outliers.
"""
outlier_indices = []
for col in df.columns.tolist():
Q1 = np.percentile(df[col], 25)
Q3 = np.percentile(df[col], 75)
IQR = Q3 - Q1
outlier_step = 1.5 * IQR
outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((k for k, v in outlier_indices.items() if v > 2))
return multiple_outliers
corr = df[features].corr()
outlier_indices = outlier_hunt(df[features])
df = df.drop(outlier_indices).reset_index(drop=True)
for feat in features:
skew = df[feat].skew()
X = df[features]
y = df['Type']
seed = 7
test_size = 0.2
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=seed)
features_boxcox = []
for feature in features:
bc_transformed, _ = boxcox(df[feature] + 1)
features_boxcox.append(bc_transformed)
features_boxcox = np.column_stack(features_boxcox)
df_bc = pd.DataFrame(data=features_boxcox, columns=features)
df_bc['Type'] = df['Type']
for feature in features:
fig, ax = plt.subplots(1,2,figsize=(7,3.5))
ax[0].hist(df[feature], color='blue', bins=30, alpha=0.3, label='Skew = %s' %(str(round(df[feature].skew(),3))) )
ax[0].set_title(str(feature))
ax[0].legend(loc=0)
ax[1].hist(df_bc[feature], color='red', bins=30, alpha=0.3, label='Skew = %s' %(str(round(df_bc[feature].skew(),3))) )
ax[1].set_title(str(feature)+' after a Box-Cox transformation')
ax[1].legend(loc=0)
plt.show()
for feature in features:
delta = np.abs(df_bc[feature].skew() / df[feature].skew())
pca = PCA(random_state=seed)
pca.fit(X_train)
var_exp = pca.explained_variance_ratio_
cum_var_exp = np.cumsum(var_exp)
plt.figure(figsize=(8, 6))
plt.bar(range(1, len(cum_var_exp) + 1), var_exp, align='center', label='individual variance explained', alpha=0.7)
plt.step(range(1, len(cum_var_exp) + 1), cum_var_exp, where='mid', label='cumulative variance explained', color='red')
plt.ylabel('Explained variance ratio')
plt.xlabel('Principal components')
plt.xticks(np.arange(1, len(var_exp) + 1, 1))
plt.legend(loc='center right')
plt.show() | code |
104120795/cell_9 | [
"image_output_1.png"
] | import pandas as pd # read and wrangle dataframes
df = pd.read_csv('../input/glass/glass.csv')
features = df.columns[:-1].tolist()
df.dtypes
df['Type'].value_counts() | code |
104120795/cell_25 | [
"text_plain_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # read and wrangle dataframes
df = pd.read_csv('../input/glass/glass.csv')
features = df.columns[:-1].tolist()
df.dtypes
def outlier_hunt(df):
"""
Takes a dataframe df of features and returns a list of the indices
corresponding to the observations containing more than 2 outliers.
"""
outlier_indices = []
for col in df.columns.tolist():
Q1 = np.percentile(df[col], 25)
Q3 = np.percentile(df[col], 75)
IQR = Q3 - Q1
outlier_step = 1.5 * IQR
outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((k for k, v in outlier_indices.items() if v > 2))
return multiple_outliers
outlier_indices = outlier_hunt(df[features])
df = df.drop(outlier_indices).reset_index(drop=True)
print(df.shape) | code |
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