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stringlengths 13
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sequencelengths 1
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stringclasses 1
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|---|---|---|---|
129023624/cell_50 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
test_df | code |
129023624/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 |
129023624/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.shape | code |
129023624/cell_45 | [
"text_plain_output_1.png"
] | from keras.layers import Dropout, Activation, Flatten, \
from keras.layers import LSTM, GRU, SimpleRNN
from keras.models import Sequential,Model,load_model
from transformers import XLMRobertaTokenizer, TFAutoModel, TFAutoModelForSequenceClassification, TFXLMRobertaModel, XLMRobertaModel
MODEL_TYPE = 'xlm-roberta-large'
xlm_tokenizer = XLMRobertaTokenizer.from_pretrained(MODEL_TYPE)
xlm_model = TFAutoModel.from_pretrained(MODEL_TYPE)
embedding_matrix = xlm_model.get_input_embeddings().weight
def create_rnn_model(input_shape):
model_xlm = Sequential()
model_xlm.add(Embedding(250002, 1024, trainable=False, weights=[embedding_matrix.numpy()]))
model_xlm.add(GRU(512, return_sequences=True))
model_xlm.add(GRU(512, return_sequences=True))
model_xlm.add(GlobalMaxPool1D())
model_xlm.add(Dense(30, activation='relu'))
model_xlm.add(Dropout(0.4))
model_xlm.add(Dense(1, activation='sigmoid'))
return model_xlm
model = create_rnn_model(X_train_xlm.shape[1:])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
batch_size = 64
epochs = 10
history_model_xlm = model.fit(X_train_xlm, y_train_xlm, validation_split=0.2, batch_size=batch_size, epochs=epochs)
y_pred = (model.predict(X_test_xlm) > 0.5).astype(int)
y_pred | code |
129023624/cell_28 | [
"text_plain_output_1.png"
] | !pip install sentencepiece | code |
129023624/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.shape
nan_count = train_df.isna().sum()
nan_count | code |
129023624/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.shape
nan_count = train_df.isna().sum()
nan_count
train_df.drop(['location'], axis=1)
train_df.duplicated(['text', 'target']).sum()
train_df = train_df.drop_duplicates(['text', 'target'])
train_df.shape
sns.set(style='darkgrid')
ax = sns.countplot(x=train_df['target'], data=train_df) | code |
129023624/cell_47 | [
"text_plain_output_1.png"
] | from keras.layers import Dropout, Activation, Flatten, \
from keras.layers import LSTM, GRU, SimpleRNN
from keras.models import Sequential,Model,load_model
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split,cross_val_score, cross_val_predict, KFold, GridSearchCV
from transformers import XLMRobertaTokenizer, TFAutoModel, TFAutoModelForSequenceClassification, TFXLMRobertaModel, XLMRobertaModel
import contractions
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.shape
nan_count = train_df.isna().sum()
nan_count
train_df.drop(['location'], axis=1)
train_df.duplicated(['text', 'target']).sum()
train_df = train_df.drop_duplicates(['text', 'target'])
train_df.shape
def remove_mentions(data_df):
mentions_removed = re.sub('@[A-Za-z0-9_]+', '', data_df)
return mentions_removed
def remove_hashtags(data_df):
hashtags_removed = re.sub('#[A-Za-z0-9_]+', '', data_df)
return hashtags_removed
def remove_urls(data_df):
hashtags_removed = re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[\n]|[$-_@.&+\\]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', '', data_df)
return hashtags_removed
def convert_contractions(data_df):
contractions_converted = contractions.fix(data_df)
return contractions_converted
train_df['text'] = train_df['text'].astype(str)
train_df['mentions_removed'] = train_df['text'].apply(remove_mentions).tolist()
train_df['hashtags_removed'] = train_df['mentions_removed'].apply(remove_hashtags).tolist()
train_df['url_removed'] = train_df['hashtags_removed'].apply(remove_urls).tolist()
train_df['lower_cased'] = train_df['url_removed'].apply(lambda x: x.lower())
train_df['contractions_converted'] = train_df['lower_cased'].apply(convert_contractions).tolist()
MODEL_TYPE = 'xlm-roberta-large'
xlm_tokenizer = XLMRobertaTokenizer.from_pretrained(MODEL_TYPE)
xlm_model = TFAutoModel.from_pretrained(MODEL_TYPE)
embedding_matrix = xlm_model.get_input_embeddings().weight
tokenized_feature_raw = xlm_tokenizer.batch_encode_plus(train_df['contractions_converted'], add_special_tokens=True)
token_sentence_length = [len(x) for x in tokenized_feature_raw['input_ids']]
avg_length = sum(token_sentence_length) / train_df.shape[0]
MAX_LEN = max(token_sentence_length)
import matplotlib.pyplot as plt
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)
max_len = round(max(token_sentence_length))
max_len
tokenized_feature = xlm_tokenizer.batch_encode_plus(train_df['contractions_converted'], add_special_tokens=True, padding='max_length', truncation=True, max_length=max_len, return_attention_mask=True, return_tensors='tf')
padded_inputs = tokenized_feature['input_ids']
train_padded_docs = np.array(padded_inputs)
labels = np.array(train_df['target'])
def create_rnn_model(input_shape):
model_xlm = Sequential()
model_xlm.add(Embedding(250002, 1024, trainable=False, weights=[embedding_matrix.numpy()]))
model_xlm.add(GRU(512, return_sequences=True))
model_xlm.add(GRU(512, return_sequences=True))
model_xlm.add(GlobalMaxPool1D())
model_xlm.add(Dense(30, activation='relu'))
model_xlm.add(Dropout(0.4))
model_xlm.add(Dense(1, activation='sigmoid'))
return model_xlm
kfold = KFold(n_splits=10, shuffle=True, random_state=42)
model = create_rnn_model(X_train_xlm.shape[1:])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
batch_size = 64
epochs = 10
history_model_xlm = model.fit(X_train_xlm, y_train_xlm, validation_split=0.2, batch_size=batch_size, epochs=epochs)
y_pred = (model.predict(X_test_xlm) > 0.5).astype(int)
for train_index, test_index in kfold.split(train_padded_docs):
X_train, X_test = (train_padded_docs[train_index], train_padded_docs[test_index])
y_train, y_test = (labels[train_index], labels[test_index])
model = create_rnn_model(X_train.shape[1:])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10, batch_size=64, verbose=0)
loss, accuracy = model.evaluate(X_test, y_test, verbose=0)
print('Fold accuracy:', accuracy) | code |
129023624/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict, KFold, GridSearchCV
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix, precision_recall_fscore_support
import re
import tensorflow as tf
import keras
from keras import backend as K
from keras.models import Sequential, Model, load_model
from keras.preprocessing.text import Tokenizer
from keras.layers import Dropout, Activation, Flatten, Embedding, Convolution1D, MaxPooling1D, AveragePooling1D, Input, Dense, Add, TimeDistributed, Bidirectional, SpatialDropout1D, GlobalMaxPool1D
from keras.layers import LSTM, GRU, SimpleRNN
from keras.regularizers import l2, l1_l2
from keras.constraints import maxnorm
from keras import callbacks
from keras.callbacks import ModelCheckpoint, EarlyStopping
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import KFold
import contractions | code |
129023624/cell_35 | [
"text_plain_output_1.png"
] | from transformers import XLMRobertaTokenizer, TFAutoModel, TFAutoModelForSequenceClassification, TFXLMRobertaModel, XLMRobertaModel
import contractions
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.shape
nan_count = train_df.isna().sum()
nan_count
train_df.drop(['location'], axis=1)
train_df.duplicated(['text', 'target']).sum()
train_df = train_df.drop_duplicates(['text', 'target'])
train_df.shape
def remove_mentions(data_df):
mentions_removed = re.sub('@[A-Za-z0-9_]+', '', data_df)
return mentions_removed
def remove_hashtags(data_df):
hashtags_removed = re.sub('#[A-Za-z0-9_]+', '', data_df)
return hashtags_removed
def remove_urls(data_df):
hashtags_removed = re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[\n]|[$-_@.&+\\]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', '', data_df)
return hashtags_removed
def convert_contractions(data_df):
contractions_converted = contractions.fix(data_df)
return contractions_converted
train_df['text'] = train_df['text'].astype(str)
train_df['mentions_removed'] = train_df['text'].apply(remove_mentions).tolist()
train_df['hashtags_removed'] = train_df['mentions_removed'].apply(remove_hashtags).tolist()
train_df['url_removed'] = train_df['hashtags_removed'].apply(remove_urls).tolist()
train_df['lower_cased'] = train_df['url_removed'].apply(lambda x: x.lower())
train_df['contractions_converted'] = train_df['lower_cased'].apply(convert_contractions).tolist()
MODEL_TYPE = 'xlm-roberta-large'
xlm_tokenizer = XLMRobertaTokenizer.from_pretrained(MODEL_TYPE)
xlm_model = TFAutoModel.from_pretrained(MODEL_TYPE)
tokenized_feature_raw = xlm_tokenizer.batch_encode_plus(train_df['contractions_converted'], add_special_tokens=True)
token_sentence_length = [len(x) for x in tokenized_feature_raw['input_ids']]
avg_length = sum(token_sentence_length) / train_df.shape[0]
MAX_LEN = max(token_sentence_length)
import matplotlib.pyplot as plt
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)
avg_length = round(avg_length)
avg_length | code |
129023624/cell_43 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.layers import Dropout, Activation, Flatten, \
from keras.layers import LSTM, GRU, SimpleRNN
from keras.models import Sequential,Model,load_model
from transformers import XLMRobertaTokenizer, TFAutoModel, TFAutoModelForSequenceClassification, TFXLMRobertaModel, XLMRobertaModel
MODEL_TYPE = 'xlm-roberta-large'
xlm_tokenizer = XLMRobertaTokenizer.from_pretrained(MODEL_TYPE)
xlm_model = TFAutoModel.from_pretrained(MODEL_TYPE)
embedding_matrix = xlm_model.get_input_embeddings().weight
def create_rnn_model(input_shape):
model_xlm = Sequential()
model_xlm.add(Embedding(250002, 1024, trainable=False, weights=[embedding_matrix.numpy()]))
model_xlm.add(GRU(512, return_sequences=True))
model_xlm.add(GRU(512, return_sequences=True))
model_xlm.add(GlobalMaxPool1D())
model_xlm.add(Dense(30, activation='relu'))
model_xlm.add(Dropout(0.4))
model_xlm.add(Dense(1, activation='sigmoid'))
return model_xlm
model = create_rnn_model(X_train_xlm.shape[1:])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
batch_size = 64
epochs = 10
history_model_xlm = model.fit(X_train_xlm, y_train_xlm, validation_split=0.2, batch_size=batch_size, epochs=epochs) | code |
129023624/cell_31 | [
"text_plain_output_1.png"
] | from transformers import XLMRobertaTokenizer, TFAutoModel, TFAutoModelForSequenceClassification, TFXLMRobertaModel, XLMRobertaModel
MODEL_TYPE = 'xlm-roberta-large'
xlm_tokenizer = XLMRobertaTokenizer.from_pretrained(MODEL_TYPE)
xlm_model = TFAutoModel.from_pretrained(MODEL_TYPE) | code |
129023624/cell_46 | [
"text_plain_output_1.png"
] | from keras.layers import Dropout, Activation, Flatten, \
from keras.layers import LSTM, GRU, SimpleRNN
from keras.models import Sequential,Model,load_model
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix,precision_recall_fscore_support
from transformers import XLMRobertaTokenizer, TFAutoModel, TFAutoModelForSequenceClassification, TFXLMRobertaModel, XLMRobertaModel
MODEL_TYPE = 'xlm-roberta-large'
xlm_tokenizer = XLMRobertaTokenizer.from_pretrained(MODEL_TYPE)
xlm_model = TFAutoModel.from_pretrained(MODEL_TYPE)
embedding_matrix = xlm_model.get_input_embeddings().weight
def create_rnn_model(input_shape):
model_xlm = Sequential()
model_xlm.add(Embedding(250002, 1024, trainable=False, weights=[embedding_matrix.numpy()]))
model_xlm.add(GRU(512, return_sequences=True))
model_xlm.add(GRU(512, return_sequences=True))
model_xlm.add(GlobalMaxPool1D())
model_xlm.add(Dense(30, activation='relu'))
model_xlm.add(Dropout(0.4))
model_xlm.add(Dense(1, activation='sigmoid'))
return model_xlm
model = create_rnn_model(X_train_xlm.shape[1:])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
batch_size = 64
epochs = 10
history_model_xlm = model.fit(X_train_xlm, y_train_xlm, validation_split=0.2, batch_size=batch_size, epochs=epochs)
y_pred = (model.predict(X_test_xlm) > 0.5).astype(int)
print(classification_report(y_test_xlm, y_pred)) | code |
129023624/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.shape
nan_count = train_df.isna().sum()
nan_count
train_df.drop(['location'], axis=1)
train_df.duplicated(['text', 'target']).sum()
train_df = train_df.drop_duplicates(['text', 'target'])
train_df.shape
train_df['lower_cased'] | code |
129023624/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.shape
nan_count = train_df.isna().sum()
nan_count
train_df.drop(['location'], axis=1)
train_df.duplicated(['text', 'target']).sum()
train_df = train_df.drop_duplicates(['text', 'target'])
train_df.shape | code |
129023624/cell_53 | [
"text_plain_output_1.png"
] | from keras.layers import Dropout, Activation, Flatten, \
from keras.layers import LSTM, GRU, SimpleRNN
from keras.models import Sequential,Model,load_model
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split,cross_val_score, cross_val_predict, KFold, GridSearchCV
from transformers import XLMRobertaTokenizer, TFAutoModel, TFAutoModelForSequenceClassification, TFXLMRobertaModel, XLMRobertaModel
import contractions
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.shape
nan_count = train_df.isna().sum()
nan_count
train_df.drop(['location'], axis=1)
train_df.duplicated(['text', 'target']).sum()
train_df = train_df.drop_duplicates(['text', 'target'])
train_df.shape
def remove_mentions(data_df):
mentions_removed = re.sub('@[A-Za-z0-9_]+', '', data_df)
return mentions_removed
def remove_hashtags(data_df):
hashtags_removed = re.sub('#[A-Za-z0-9_]+', '', data_df)
return hashtags_removed
def remove_urls(data_df):
hashtags_removed = re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[\n]|[$-_@.&+\\]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', '', data_df)
return hashtags_removed
def convert_contractions(data_df):
contractions_converted = contractions.fix(data_df)
return contractions_converted
train_df['text'] = train_df['text'].astype(str)
train_df['mentions_removed'] = train_df['text'].apply(remove_mentions).tolist()
train_df['hashtags_removed'] = train_df['mentions_removed'].apply(remove_hashtags).tolist()
train_df['url_removed'] = train_df['hashtags_removed'].apply(remove_urls).tolist()
train_df['lower_cased'] = train_df['url_removed'].apply(lambda x: x.lower())
train_df['contractions_converted'] = train_df['lower_cased'].apply(convert_contractions).tolist()
MODEL_TYPE = 'xlm-roberta-large'
xlm_tokenizer = XLMRobertaTokenizer.from_pretrained(MODEL_TYPE)
xlm_model = TFAutoModel.from_pretrained(MODEL_TYPE)
embedding_matrix = xlm_model.get_input_embeddings().weight
tokenized_feature_raw = xlm_tokenizer.batch_encode_plus(train_df['contractions_converted'], add_special_tokens=True)
token_sentence_length = [len(x) for x in tokenized_feature_raw['input_ids']]
avg_length = sum(token_sentence_length) / train_df.shape[0]
MAX_LEN = max(token_sentence_length)
import matplotlib.pyplot as plt
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)
max_len = round(max(token_sentence_length))
max_len
tokenized_feature = xlm_tokenizer.batch_encode_plus(train_df['contractions_converted'], add_special_tokens=True, padding='max_length', truncation=True, max_length=max_len, return_attention_mask=True, return_tensors='tf')
padded_inputs = tokenized_feature['input_ids']
train_padded_docs = np.array(padded_inputs)
labels = np.array(train_df['target'])
def create_rnn_model(input_shape):
model_xlm = Sequential()
model_xlm.add(Embedding(250002, 1024, trainable=False, weights=[embedding_matrix.numpy()]))
model_xlm.add(GRU(512, return_sequences=True))
model_xlm.add(GRU(512, return_sequences=True))
model_xlm.add(GlobalMaxPool1D())
model_xlm.add(Dense(30, activation='relu'))
model_xlm.add(Dropout(0.4))
model_xlm.add(Dense(1, activation='sigmoid'))
return model_xlm
kfold = KFold(n_splits=10, shuffle=True, random_state=42)
model = create_rnn_model(X_train_xlm.shape[1:])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
batch_size = 64
epochs = 10
history_model_xlm = model.fit(X_train_xlm, y_train_xlm, validation_split=0.2, batch_size=batch_size, epochs=epochs)
y_pred = (model.predict(X_test_xlm) > 0.5).astype(int)
for train_index, test_index in kfold.split(train_padded_docs):
X_train, X_test = (train_padded_docs[train_index], train_padded_docs[test_index])
y_train, y_test = (labels[train_index], labels[test_index])
model = create_rnn_model(X_train.shape[1:])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10, batch_size=64, verbose=0)
loss, accuracy = model.evaluate(X_test, y_test, verbose=0)
tokenized_feature_test_data = xlm_tokenizer.batch_encode_plus(test_df['contractions_converted'], add_special_tokens=True, padding='max_length', truncation=True, max_length=max_len, return_attention_mask=True, return_tensors='tf')
padded_inputs_test = tokenized_feature_test_data['input_ids']
predictions = (model.predict(padded_inputs_test) > 0.5).astype(int) | code |
129023624/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.shape
nan_count = train_df.isna().sum()
nan_count
train_df.drop(['location'], axis=1) | code |
129023624/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.shape
nan_count = train_df.isna().sum()
nan_count
train_df.drop(['location'], axis=1)
train_df.duplicated(['text', 'target']).sum() | code |
129023624/cell_36 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from transformers import XLMRobertaTokenizer, TFAutoModel, TFAutoModelForSequenceClassification, TFXLMRobertaModel, XLMRobertaModel
import contractions
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.shape
nan_count = train_df.isna().sum()
nan_count
train_df.drop(['location'], axis=1)
train_df.duplicated(['text', 'target']).sum()
train_df = train_df.drop_duplicates(['text', 'target'])
train_df.shape
def remove_mentions(data_df):
mentions_removed = re.sub('@[A-Za-z0-9_]+', '', data_df)
return mentions_removed
def remove_hashtags(data_df):
hashtags_removed = re.sub('#[A-Za-z0-9_]+', '', data_df)
return hashtags_removed
def remove_urls(data_df):
hashtags_removed = re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[\n]|[$-_@.&+\\]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', '', data_df)
return hashtags_removed
def convert_contractions(data_df):
contractions_converted = contractions.fix(data_df)
return contractions_converted
train_df['text'] = train_df['text'].astype(str)
train_df['mentions_removed'] = train_df['text'].apply(remove_mentions).tolist()
train_df['hashtags_removed'] = train_df['mentions_removed'].apply(remove_hashtags).tolist()
train_df['url_removed'] = train_df['hashtags_removed'].apply(remove_urls).tolist()
train_df['lower_cased'] = train_df['url_removed'].apply(lambda x: x.lower())
train_df['contractions_converted'] = train_df['lower_cased'].apply(convert_contractions).tolist()
MODEL_TYPE = 'xlm-roberta-large'
xlm_tokenizer = XLMRobertaTokenizer.from_pretrained(MODEL_TYPE)
xlm_model = TFAutoModel.from_pretrained(MODEL_TYPE)
tokenized_feature_raw = xlm_tokenizer.batch_encode_plus(train_df['contractions_converted'], add_special_tokens=True)
token_sentence_length = [len(x) for x in tokenized_feature_raw['input_ids']]
avg_length = sum(token_sentence_length) / train_df.shape[0]
MAX_LEN = max(token_sentence_length)
import matplotlib.pyplot as plt
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)
max_len = round(max(token_sentence_length))
max_len | code |
73099194/cell_9 | [
"text_html_output_1.png"
] | dicom_test = Dicom()
dicom_test.exec('test') | code |
73099194/cell_11 | [
"text_plain_output_1.png"
] | dicom_test = Dicom()
dicom_test.exec('test')
dicom_test.df.head() | code |
73099194/cell_8 | [
"text_html_output_1.png"
] | dicom_train = Dicom()
dicom_train.exec('train') | code |
73099194/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | code |
|
73099194/cell_10 | [
"text_plain_output_1.png"
] | dicom_train = Dicom()
dicom_train.exec('train')
dicom_train.df.head() | code |
73099194/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pydicom import dcmread
from pydicom import dcmread
data_dir = '/kaggle/input/rsna-miccai-brain-tumor-radiogenomic-classification'
fpath = data_dir + '/train/00000/FLAIR/Image-1.dcm'
ds = dcmread(fpath)
print(ds) | code |
50225023/cell_21 | [
"text_plain_output_1.png"
] | from collections import defaultdict
from nltk.corpus import stopwords, wordnet
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
from collections import Counter
from collections import defaultdict
from sklearn.feature_extraction.text import CountVectorizer
from nltk.util import ngrams
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords, wordnet
from nltk.stem import WordNetLemmatizer
stop = set(stopwords.words('english'))
from tqdm.notebook import tqdm
import os
import re
import time
import string
import random
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn import functional as F
from torchtext import data, datasets
from torchtext.vocab import Vectors, GloVe
train = pd.read_csv('../input/nlp-getting-started/train.csv')
test = pd.read_csv('../input/nlp-getting-started/test.csv')
fig, axes = plt.subplots(ncols=2, figsize=(17, 4), dpi=100)
train.groupby('target').count()['id'].plot(kind='pie', ax=axes[0], labels=['Not Disaster (57%)', 'Disaster (43%)'])
sns.countplot(x=train['target'], hue=train['target'], ax=axes[1])
axes[0].set_ylabel('')
axes[1].set_ylabel('')
axes[1].set_xticklabels(['Not Disaster (4342)', 'Disaster (3271)'])
axes[0].tick_params(axis='x', labelsize=15)
axes[0].tick_params(axis='y', labelsize=15)
axes[1].tick_params(axis='x', labelsize=15)
axes[1].tick_params(axis='y', labelsize=15)
axes[0].set_title('Target Distribution in Training Set', fontsize=13)
axes[1].set_title('Target Count in Training Set', fontsize=13)
plt.show()
fig,(ax1,ax2) = plt.subplots(1,2,figsize=(10,5))
# No Disaster Tweets
train_len = train[train['target']==0]['text'].str.len()
ax1.hist(train_len,color='green')
ax1.set_title('Not disaster tweets')
fig.suptitle('Characters in tweets')
# Disaster Tweets
train_len = train[train['target']==1]['text'].str.len()
ax2.hist(train_len,color='red')
ax2.set_title('Disaster tweets')
plt.show()
fig,(ax1,ax2)=plt.subplots(1,2,figsize=(10,5))
train_len = train[train['target']==0]['text'].str.split().map(lambda x: len(x))
ax1.hist(train_len,color='green')
ax1.set_title('Not disaster tweets')
train_len = train[train['target']==1]['text'].str.split().map(lambda x: len(x))
ax2.hist(train_len,color='red')
ax2.set_title('Disaster tweets')
fig.suptitle('Words in a tweet')
plt.show()
def create_corpus(target):
corpus = []
for x in train[train['target'] == target]['text'].str.split():
for i in x:
corpus.append(i)
return corpus
corpus0 = create_corpus(0)
corpus1 = create_corpus(1)
len(corpus0)
dic = defaultdict(int)
for word in corpus0:
if word in stop:
dic[word] += 1
top = sorted(dic.items(), key=lambda x: x[1], reverse=True)[:10]
x, y = zip(*top)
dic = defaultdict(int)
for word in corpus1:
if word in stop:
dic[word] += 1
top = sorted(dic.items(), key=lambda x: x[1], reverse=True)[:10]
x, y = zip(*top)
plt.bar(x, y) | code |
50225023/cell_13 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords, wordnet
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
from collections import Counter
from collections import defaultdict
from sklearn.feature_extraction.text import CountVectorizer
from nltk.util import ngrams
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords, wordnet
from nltk.stem import WordNetLemmatizer
stop = set(stopwords.words('english'))
from tqdm.notebook import tqdm
import os
import re
import time
import string
import random
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn import functional as F
from torchtext import data, datasets
from torchtext.vocab import Vectors, GloVe
train = pd.read_csv('../input/nlp-getting-started/train.csv')
test = pd.read_csv('../input/nlp-getting-started/test.csv')
fig, axes = plt.subplots(ncols=2, figsize=(17, 4), dpi=100)
train.groupby('target').count()['id'].plot(kind='pie', ax=axes[0], labels=['Not Disaster (57%)', 'Disaster (43%)'])
sns.countplot(x=train['target'], hue=train['target'], ax=axes[1])
axes[0].set_ylabel('')
axes[1].set_ylabel('')
axes[1].set_xticklabels(['Not Disaster (4342)', 'Disaster (3271)'])
axes[0].tick_params(axis='x', labelsize=15)
axes[0].tick_params(axis='y', labelsize=15)
axes[1].tick_params(axis='x', labelsize=15)
axes[1].tick_params(axis='y', labelsize=15)
axes[0].set_title('Target Distribution in Training Set', fontsize=13)
axes[1].set_title('Target Count in Training Set', fontsize=13)
plt.show()
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
train_len = train[train['target'] == 0]['text'].str.len()
ax1.hist(train_len, color='green')
ax1.set_title('Not disaster tweets')
fig.suptitle('Characters in tweets')
train_len = train[train['target'] == 1]['text'].str.len()
ax2.hist(train_len, color='red')
ax2.set_title('Disaster tweets')
plt.show() | code |
50225023/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from collections import defaultdict
from nltk.corpus import stopwords, wordnet
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
from collections import Counter
from collections import defaultdict
from sklearn.feature_extraction.text import CountVectorizer
from nltk.util import ngrams
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords, wordnet
from nltk.stem import WordNetLemmatizer
stop = set(stopwords.words('english'))
from tqdm.notebook import tqdm
import os
import re
import time
import string
import random
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn import functional as F
from torchtext import data, datasets
from torchtext.vocab import Vectors, GloVe
train = pd.read_csv('../input/nlp-getting-started/train.csv')
test = pd.read_csv('../input/nlp-getting-started/test.csv')
fig, axes = plt.subplots(ncols=2, figsize=(17, 4), dpi=100)
train.groupby('target').count()['id'].plot(kind='pie', ax=axes[0], labels=['Not Disaster (57%)', 'Disaster (43%)'])
sns.countplot(x=train['target'], hue=train['target'], ax=axes[1])
axes[0].set_ylabel('')
axes[1].set_ylabel('')
axes[1].set_xticklabels(['Not Disaster (4342)', 'Disaster (3271)'])
axes[0].tick_params(axis='x', labelsize=15)
axes[0].tick_params(axis='y', labelsize=15)
axes[1].tick_params(axis='x', labelsize=15)
axes[1].tick_params(axis='y', labelsize=15)
axes[0].set_title('Target Distribution in Training Set', fontsize=13)
axes[1].set_title('Target Count in Training Set', fontsize=13)
plt.show()
fig,(ax1,ax2) = plt.subplots(1,2,figsize=(10,5))
# No Disaster Tweets
train_len = train[train['target']==0]['text'].str.len()
ax1.hist(train_len,color='green')
ax1.set_title('Not disaster tweets')
fig.suptitle('Characters in tweets')
# Disaster Tweets
train_len = train[train['target']==1]['text'].str.len()
ax2.hist(train_len,color='red')
ax2.set_title('Disaster tweets')
plt.show()
fig,(ax1,ax2)=plt.subplots(1,2,figsize=(10,5))
train_len = train[train['target']==0]['text'].str.split().map(lambda x: len(x))
ax1.hist(train_len,color='green')
ax1.set_title('Not disaster tweets')
train_len = train[train['target']==1]['text'].str.split().map(lambda x: len(x))
ax2.hist(train_len,color='red')
ax2.set_title('Disaster tweets')
fig.suptitle('Words in a tweet')
plt.show()
def create_corpus(target):
corpus = []
for x in train[train['target'] == target]['text'].str.split():
for i in x:
corpus.append(i)
return corpus
corpus0 = create_corpus(0)
corpus1 = create_corpus(1)
len(corpus0)
dic = defaultdict(int)
for word in corpus0:
if word in stop:
dic[word] += 1
top = sorted(dic.items(), key=lambda x: x[1], reverse=True)[:10]
x, y = zip(*top)
dic = defaultdict(int)
for word in corpus1:
if word in stop:
dic[word] += 1
top = sorted(dic.items(), key=lambda x: x[1], reverse=True)[:10]
x, y = zip(*top)
plt.figure(figsize=(10, 5))
dic = defaultdict(int)
special = string.punctuation
for i in corpus1:
if i in special:
dic[i] += 1
x, y = zip(*dic.items())
plt.bar(x, y) | code |
50225023/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/nlp-getting-started/train.csv')
test = pd.read_csv('../input/nlp-getting-started/test.csv')
train.head() | code |
50225023/cell_19 | [
"image_output_1.png"
] | from collections import defaultdict
from nltk.corpus import stopwords, wordnet
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
from collections import Counter
from collections import defaultdict
from sklearn.feature_extraction.text import CountVectorizer
from nltk.util import ngrams
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords, wordnet
from nltk.stem import WordNetLemmatizer
stop = set(stopwords.words('english'))
from tqdm.notebook import tqdm
import os
import re
import time
import string
import random
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn import functional as F
from torchtext import data, datasets
from torchtext.vocab import Vectors, GloVe
train = pd.read_csv('../input/nlp-getting-started/train.csv')
test = pd.read_csv('../input/nlp-getting-started/test.csv')
fig, axes = plt.subplots(ncols=2, figsize=(17, 4), dpi=100)
train.groupby('target').count()['id'].plot(kind='pie', ax=axes[0], labels=['Not Disaster (57%)', 'Disaster (43%)'])
sns.countplot(x=train['target'], hue=train['target'], ax=axes[1])
axes[0].set_ylabel('')
axes[1].set_ylabel('')
axes[1].set_xticklabels(['Not Disaster (4342)', 'Disaster (3271)'])
axes[0].tick_params(axis='x', labelsize=15)
axes[0].tick_params(axis='y', labelsize=15)
axes[1].tick_params(axis='x', labelsize=15)
axes[1].tick_params(axis='y', labelsize=15)
axes[0].set_title('Target Distribution in Training Set', fontsize=13)
axes[1].set_title('Target Count in Training Set', fontsize=13)
plt.show()
fig,(ax1,ax2) = plt.subplots(1,2,figsize=(10,5))
# No Disaster Tweets
train_len = train[train['target']==0]['text'].str.len()
ax1.hist(train_len,color='green')
ax1.set_title('Not disaster tweets')
fig.suptitle('Characters in tweets')
# Disaster Tweets
train_len = train[train['target']==1]['text'].str.len()
ax2.hist(train_len,color='red')
ax2.set_title('Disaster tweets')
plt.show()
fig,(ax1,ax2)=plt.subplots(1,2,figsize=(10,5))
train_len = train[train['target']==0]['text'].str.split().map(lambda x: len(x))
ax1.hist(train_len,color='green')
ax1.set_title('Not disaster tweets')
train_len = train[train['target']==1]['text'].str.split().map(lambda x: len(x))
ax2.hist(train_len,color='red')
ax2.set_title('Disaster tweets')
fig.suptitle('Words in a tweet')
plt.show()
def create_corpus(target):
corpus = []
for x in train[train['target'] == target]['text'].str.split():
for i in x:
corpus.append(i)
return corpus
corpus0 = create_corpus(0)
corpus1 = create_corpus(1)
len(corpus0)
dic = defaultdict(int)
for word in corpus0:
if word in stop:
dic[word] += 1
top = sorted(dic.items(), key=lambda x: x[1], reverse=True)[:10]
x, y = zip(*top)
plt.bar(x, y, color='green') | code |
50225023/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/nlp-getting-started/train.csv')
test = pd.read_csv('../input/nlp-getting-started/test.csv')
print('There are {} rows and {} columns in train'.format(train.shape[0], train.shape[1]))
print('There are {} rows and {} columns in test'.format(test.shape[0], test.shape[1])) | code |
50225023/cell_16 | [
"image_output_1.png"
] | from nltk.corpus import stopwords, wordnet
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
from collections import Counter
from collections import defaultdict
from sklearn.feature_extraction.text import CountVectorizer
from nltk.util import ngrams
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords, wordnet
from nltk.stem import WordNetLemmatizer
stop = set(stopwords.words('english'))
from tqdm.notebook import tqdm
import os
import re
import time
import string
import random
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn import functional as F
from torchtext import data, datasets
from torchtext.vocab import Vectors, GloVe
train = pd.read_csv('../input/nlp-getting-started/train.csv')
test = pd.read_csv('../input/nlp-getting-started/test.csv')
fig, axes = plt.subplots(ncols=2, figsize=(17, 4), dpi=100)
train.groupby('target').count()['id'].plot(kind='pie', ax=axes[0], labels=['Not Disaster (57%)', 'Disaster (43%)'])
sns.countplot(x=train['target'], hue=train['target'], ax=axes[1])
axes[0].set_ylabel('')
axes[1].set_ylabel('')
axes[1].set_xticklabels(['Not Disaster (4342)', 'Disaster (3271)'])
axes[0].tick_params(axis='x', labelsize=15)
axes[0].tick_params(axis='y', labelsize=15)
axes[1].tick_params(axis='x', labelsize=15)
axes[1].tick_params(axis='y', labelsize=15)
axes[0].set_title('Target Distribution in Training Set', fontsize=13)
axes[1].set_title('Target Count in Training Set', fontsize=13)
plt.show()
fig,(ax1,ax2) = plt.subplots(1,2,figsize=(10,5))
# No Disaster Tweets
train_len = train[train['target']==0]['text'].str.len()
ax1.hist(train_len,color='green')
ax1.set_title('Not disaster tweets')
fig.suptitle('Characters in tweets')
# Disaster Tweets
train_len = train[train['target']==1]['text'].str.len()
ax2.hist(train_len,color='red')
ax2.set_title('Disaster tweets')
plt.show()
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
train_len = train[train['target'] == 0]['text'].str.split().map(lambda x: len(x))
ax1.hist(train_len, color='green')
ax1.set_title('Not disaster tweets')
train_len = train[train['target'] == 1]['text'].str.split().map(lambda x: len(x))
ax2.hist(train_len, color='red')
ax2.set_title('Disaster tweets')
fig.suptitle('Words in a tweet')
plt.show() | code |
50225023/cell_17 | [
"image_output_1.png"
] | from nltk.corpus import stopwords, wordnet
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
from collections import Counter
from collections import defaultdict
from sklearn.feature_extraction.text import CountVectorizer
from nltk.util import ngrams
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords, wordnet
from nltk.stem import WordNetLemmatizer
stop = set(stopwords.words('english'))
from tqdm.notebook import tqdm
import os
import re
import time
import string
import random
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn import functional as F
from torchtext import data, datasets
from torchtext.vocab import Vectors, GloVe
train = pd.read_csv('../input/nlp-getting-started/train.csv')
test = pd.read_csv('../input/nlp-getting-started/test.csv')
fig, axes = plt.subplots(ncols=2, figsize=(17, 4), dpi=100)
train.groupby('target').count()['id'].plot(kind='pie', ax=axes[0], labels=['Not Disaster (57%)', 'Disaster (43%)'])
sns.countplot(x=train['target'], hue=train['target'], ax=axes[1])
axes[0].set_ylabel('')
axes[1].set_ylabel('')
axes[1].set_xticklabels(['Not Disaster (4342)', 'Disaster (3271)'])
axes[0].tick_params(axis='x', labelsize=15)
axes[0].tick_params(axis='y', labelsize=15)
axes[1].tick_params(axis='x', labelsize=15)
axes[1].tick_params(axis='y', labelsize=15)
axes[0].set_title('Target Distribution in Training Set', fontsize=13)
axes[1].set_title('Target Count in Training Set', fontsize=13)
plt.show()
def create_corpus(target):
corpus = []
for x in train[train['target'] == target]['text'].str.split():
for i in x:
corpus.append(i)
return corpus
corpus0 = create_corpus(0)
corpus1 = create_corpus(1)
len(corpus0) | code |
50225023/cell_10 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords, wordnet
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
from collections import Counter
from collections import defaultdict
from sklearn.feature_extraction.text import CountVectorizer
from nltk.util import ngrams
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords, wordnet
from nltk.stem import WordNetLemmatizer
stop = set(stopwords.words('english'))
from tqdm.notebook import tqdm
import os
import re
import time
import string
import random
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn import functional as F
from torchtext import data, datasets
from torchtext.vocab import Vectors, GloVe
train = pd.read_csv('../input/nlp-getting-started/train.csv')
test = pd.read_csv('../input/nlp-getting-started/test.csv')
fig, axes = plt.subplots(ncols=2, figsize=(17, 4), dpi=100)
train.groupby('target').count()['id'].plot(kind='pie', ax=axes[0], labels=['Not Disaster (57%)', 'Disaster (43%)'])
sns.countplot(x=train['target'], hue=train['target'], ax=axes[1])
axes[0].set_ylabel('')
axes[1].set_ylabel('')
axes[1].set_xticklabels(['Not Disaster (4342)', 'Disaster (3271)'])
axes[0].tick_params(axis='x', labelsize=15)
axes[0].tick_params(axis='y', labelsize=15)
axes[1].tick_params(axis='x', labelsize=15)
axes[1].tick_params(axis='y', labelsize=15)
axes[0].set_title('Target Distribution in Training Set', fontsize=13)
axes[1].set_title('Target Count in Training Set', fontsize=13)
plt.show() | code |
90126740/cell_9 | [
"image_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/student-marks-dataset/Student_Marks.csv')
dataset
dataset.describe(include='all') | code |
90126740/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/student-marks-dataset/Student_Marks.csv')
dataset
dataset.isnull().sum()
dataset.nunique() | code |
90126740/cell_19 | [
"image_output_1.png"
] | from sklearn.cluster import AgglomerativeClustering
import matplotlib.pyplot as plt
import pandas as pd
import scipy.cluster.hierarchy as sch
dataset = pd.read_csv('/kaggle/input/student-marks-dataset/Student_Marks.csv')
dataset
dataset.isnull().sum()
dataset.nunique()
X = dataset.iloc[:, 1:3].values.round(2)
import scipy.cluster.hierarchy as sch
den = sch.dendrogram(sch.linkage(X, method='ward'))
from sklearn.cluster import AgglomerativeClustering
hc = AgglomerativeClustering(n_clusters=5, affinity='euclidean', linkage='ward')
y_hc = hc.fit_predict(X)
plt.figure(figsize=(10, 7))
plt.scatter(X[y_hc == 0, 0], X[y_hc == 0, 1], s=100, c='red', label='Cluster 1')
plt.scatter(X[y_hc == 1, 0], X[y_hc == 1, 1], s=100, c='blue', label='Cluster 2')
plt.scatter(X[y_hc == 2, 0], X[y_hc == 2, 1], s=100, c='green', label='Cluster 3')
plt.scatter(X[y_hc == 3, 0], X[y_hc == 3, 1], s=100, c='cyan', label='Cluster 4')
plt.scatter(X[y_hc == 4, 0], X[y_hc == 4, 1], s=100, c='magenta', label='Cluster 5')
plt.title('Clusters of students')
plt.xlabel('Number of courses')
plt.ylabel('Time spent to study')
plt.legend()
plt.show() | code |
90126740/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/student-marks-dataset/Student_Marks.csv')
dataset | code |
90126740/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import scipy.cluster.hierarchy as sch
dataset = pd.read_csv('/kaggle/input/student-marks-dataset/Student_Marks.csv')
dataset
dataset.isnull().sum()
dataset.nunique()
X = dataset.iloc[:, 1:3].values.round(2)
import scipy.cluster.hierarchy as sch
den = sch.dendrogram(sch.linkage(X, method='ward'))
plt.title('Dendrogram')
plt.xlabel('Number of courses')
plt.ylabel('Time spent to study')
plt.show() | code |
90126740/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.cluster import AgglomerativeClustering
import pandas as pd
dataset = pd.read_csv('/kaggle/input/student-marks-dataset/Student_Marks.csv')
dataset
dataset.isnull().sum()
dataset.nunique()
X = dataset.iloc[:, 1:3].values.round(2)
from sklearn.cluster import AgglomerativeClustering
hc = AgglomerativeClustering(n_clusters=5, affinity='euclidean', linkage='ward')
y_hc = hc.fit_predict(X)
print('setup complete') | code |
90126740/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/student-marks-dataset/Student_Marks.csv')
dataset
dataset.isnull().sum() | code |
105204136/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import mutual_info_classif
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, f1_score, make_scorer
from sklearn.model_selection import cross_val_score, GridSearchCV, RandomizedSearchCV
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
rs = 42
mi = 10000
sns.set_style('whitegrid')
models = [DecisionTreeClassifier(random_state=rs), KNeighborsClassifier(), GaussianNB(), LinearSVC(random_state=rs, max_iter=mi), SVC(random_state=rs, max_iter=mi), LogisticRegression(random_state=rs, max_iter=mi)]
train_path = '../input/cs-3110-mini-project/train.csv'
test_path = '../input/cs-3110-mini-project/test.csv'
def plt_distribution(dataset):
data = dataset.copy()
fig, axes = plt.subplots(5, 3)
fig.set_figwidth(16)
fig.set_figheight(20)
sns.histplot(data=data, x='account_length', kde=True, ax=axes[0,0])
sns.histplot(data=data, x='number_vm_messages', kde=True, ax=axes[0,1])
sns.histplot(data=data, x='total_day_min', kde=True, ax=axes[0,2])
sns.histplot(data=data, x='total_day_calls', kde=True, ax=axes[1,0])
sns.histplot(data=data, x='total_day_charge', kde=True, ax=axes[1,1])
sns.histplot(data=data, x='total_eve_min', kde=True, ax=axes[1,2])
sns.histplot(data=data, x='total_eve_calls', kde=True, ax=axes[2,0])
sns.histplot(data=data, x='total_eve_charge', kde=True, ax=axes[2,1])
sns.histplot(data=data, x='total_night_minutes', kde=True, ax=axes[2,2])
sns.histplot(data=data, x='total_night_calls', kde=True, ax=axes[3,0])
sns.histplot(data=data, x='total_night_charge', kde=True, ax=axes[3,1])
sns.histplot(data=data, x='total_intl_minutes', kde=True, ax=axes[3,2])
sns.histplot(data=data, x='total_intl_calls', kde=True, ax=axes[4,0])
sns.histplot(data=data, x='total_intl_charge', kde=True, ax=axes[4,1])
sns.histplot(data=data, x='customer_service_calls', kde=True, ax=axes[4,2])
plt.show()
def make_mi_scores(X, y):
discrete_features = X.dtypes == int
mi_scores = mutual_info_classif(X, y, discrete_features=discrete_features, random_state=rs)
mi_scores = pd.Series(mi_scores, name="MI Scores", index=X.columns)
mi_scores = mi_scores.sort_values(ascending=False)
return mi_scores
def plot_mi_scores(scores):
plt.figure(dpi=100, figsize=(8, 10))
scores = scores.sort_values(ascending=True)
width = np.arange(len(scores))
ticks = list(scores.index)
plt.barh(width, scores)
plt.yticks(width, ticks)
plt.title("Mutual Information Scores")
plt.show()
def evaluate_for_models(models, X, y):
results = pd.DataFrame({'Model': [],
'ScoreMean(F1)': [], 'Score Standard Deviation(F1)': [],
'ScoreMean': [], 'Score Standard Deviation': []})
for model in models:
score_f1 = cross_val_score(model, X, y,
scoring='f1')
score = cross_val_score(model, X, y)
new_result = {'Model': model.__class__.__name__,
'ScoreMean(F1)': score_f1.mean(), 'Score Standard Deviation(F1)': score_f1.std(),
'ScoreMean': score.mean(), 'Score Standard Deviation': score.std()}
results = results.append(new_result, ignore_index=True)
return results.sort_values(by=['ScoreMean(F1)', 'Score Standard Deviation(F1)',
'ScoreMean', 'Score Standard Deviation'], ascending=False)
def classification_report_with_accuracy_score(y_true, y_pred):
print(classification_report(y_true, y_pred))
return f1_score(y_true, y_pred)
def encode(dataframe, is_train=True):
data = dataframe.copy()
encoded_data = pd.get_dummies(data, columns=['location_code'])
if is_train:
encoded_data['Churn'] = encoded_data['Churn'].map({'Yes': 1, 'No': 0})
for col in ['intertiol_plan', 'voice_mail_plan']:
encoded_data[col] = encoded_data[col].map({'yes': 1, 'no': 0})
return encoded_data
def add_features(dataframe):
global lr_day
global lr_eve
global lr_nyt
data = dataframe.copy()
data['total_min'] = data['total_day_min'] + data['total_eve_min'] + data['total_night_minutes'] + data['total_intl_minutes']
try:
data['expected_total_day_charge'] = lr_day.predict(data[['total_day_min']])
data['expected_total_eve_charge'] = lr_eve.predict(data[['total_eve_min']])
data['expected_total_nyt_charge'] = lr_nyt.predict(data[['total_night_minutes']])
except NameError:
lr_day = LinearRegression()
lr_eve = LinearRegression()
lr_nyt = LinearRegression()
lr_day.fit(data[['total_day_min']], data['total_day_charge'])
lr_eve.fit(data[['total_eve_min']], data['total_eve_charge'])
lr_nyt.fit(data[['total_night_minutes']], data['total_night_charge'])
data['expected_total_day_charge'] = lr_day.predict(data[['total_day_min']])
data['expected_total_eve_charge'] = lr_eve.predict(data[['total_eve_min']])
data['expected_total_nyt_charge'] = lr_nyt.predict(data[['total_night_minutes']])
data['error_total_day_charge'] = abs(data['expected_total_day_charge'] - data['total_day_charge'])
data['error_total_eve_charge'] = abs(data['expected_total_eve_charge'] - data['total_eve_charge'])
data['error_total_nyt_charge'] = abs(data['expected_total_nyt_charge'] - data['total_night_charge'])
return data
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
d_droped_train = train_df.drop_duplicates(train_df.columns.drop(['customer_id']))
d_droped_train = d_droped_train.drop(columns=['Unnamed: 20'])
cols = d_droped_train.select_dtypes([np.number]).columns
d_droped_train[cols] = d_droped_train[cols].abs()
d_droped_train['account_length'].fillna(d_droped_train.account_length.median(), inplace=True)
d_droped_train['intertiol_plan'].fillna('no', inplace=True)
d_droped_train['voice_mail_plan'].fillna('no', inplace=True)
d_droped_train.loc[d_droped_train['voice_mail_plan'] == 'no', 'number_vm_messages'] = 0
d_droped_train.loc[(d_droped_train['voice_mail_plan'] == 'yes') & d_droped_train['number_vm_messages'].isnull(), 'number_vm_messages'] = d_droped_train[d_droped_train.voice_mail_plan == 'yes'].number_vm_messages.median()
d_droped_train.loc[d_droped_train['total_day_min'] > 500, 'total_day_min'] = np.nan
d_droped_train['total_day_min'] = d_droped_train.sort_values(['total_day_charge']).total_day_min.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train.loc[d_droped_train['total_day_calls'] > 350, 'total_day_calls'] = np.nan
d_droped_train['total_day_calls'] = d_droped_train.sort_values(['total_day_min']).total_day_calls.ffill().sort_index()
d_droped_train['total_day_charge'] = d_droped_train.sort_values(['total_day_min']).total_day_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train.loc[d_droped_train['total_eve_min'] > 500, 'total_eve_min'] = np.nan
d_droped_train['total_eve_min'] = d_droped_train.sort_values(['total_eve_charge']).total_eve_min.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train['total_eve_calls'] = d_droped_train.sort_values(['total_eve_min']).total_eve_calls.ffill().sort_index()
d_droped_train['total_eve_charge'] = d_droped_train.sort_values(['total_eve_min']).total_eve_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train.loc[d_droped_train['total_night_minutes'] > 500, 'total_night_minutes'] = np.nan
d_droped_train['total_night_minutes'] = d_droped_train.sort_values(['total_night_charge']).total_night_minutes.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train['total_night_calls'] = d_droped_train.sort_values(['total_night_minutes']).total_night_calls.ffill().sort_index()
d_droped_train.loc[d_droped_train['total_night_charge'] > 150, 'total_night_charge'] = np.nan
d_droped_train['total_night_charge'] = d_droped_train.sort_values(['total_night_minutes']).total_night_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train['total_intl_minutes'] = d_droped_train.sort_values(['total_intl_charge']).total_intl_minutes.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train.loc[(d_droped_train['total_intl_minutes'] > 0) & (d_droped_train['total_intl_charge'] > 0) & (d_droped_train['total_intl_calls'] < 1), 'total_intl_calls'] = np.nan
d_droped_train['total_intl_calls'] = d_droped_train.sort_values(['total_intl_minutes']).total_intl_calls.ffill().sort_index()
d_droped_train['total_intl_charge'] = d_droped_train.sort_values(['total_intl_minutes']).total_intl_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train['customer_service_calls'].fillna(1, inplace=True)
odm_handled_train = d_droped_train.dropna(subset=['Churn'])
test_df = test_df.drop(columns=['Unnamed: 19', 'Unnamed: 20'])
cols = test_df.select_dtypes([np.number]).columns
test_df[cols] = test_df[cols].abs()
test_df['location_code'] = test_df['location_code'].ffill()
test_df['intertiol_plan'].fillna('no', inplace=True)
test_df['voice_mail_plan'].fillna('no', inplace=True)
test_df['number_vm_messages'].fillna(0, inplace=True)
test_df['total_day_min'] = test_df.sort_values(['total_day_charge']).total_day_min.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df['total_day_calls'] = test_df.sort_values(['total_day_min']).total_day_calls.ffill().sort_index()
test_df['total_day_charge'] = test_df.sort_values(['total_day_min']).total_day_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df['total_eve_min'] = test_df.sort_values(['total_eve_charge']).total_eve_min.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df['total_eve_charge'] = test_df.sort_values(['total_eve_min']).total_eve_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df['total_night_minutes'] = test_df.sort_values(['total_night_charge']).total_night_minutes.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df['total_night_calls'] = test_df.sort_values(['total_night_minutes']).total_night_calls.ffill().sort_index()
test_df['total_night_charge'] = test_df.sort_values(['total_night_minutes']).total_night_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df['total_intl_minutes'] = test_df.sort_values(['total_intl_charge']).total_intl_minutes.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df.loc[(test_df['total_intl_minutes'] > 0) & (test_df['total_intl_charge'] > 0) & (test_df['total_intl_calls'] < 1), 'total_intl_calls'] = np.nan
test_df['total_intl_calls'] = test_df.sort_values(['total_intl_minutes']).total_intl_calls.ffill().sort_index()
test_df['customer_service_calls'].fillna(1, inplace=True)
train = odm_handled_train.copy()
test = test_df.copy()
train.info() | code |
105204136/cell_9 | [
"image_output_1.png"
] | from sklearn.feature_selection import mutual_info_classif
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, f1_score, make_scorer
from sklearn.model_selection import cross_val_score, GridSearchCV, RandomizedSearchCV
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
rs = 42
mi = 10000
sns.set_style('whitegrid')
models = [DecisionTreeClassifier(random_state=rs), KNeighborsClassifier(), GaussianNB(), LinearSVC(random_state=rs, max_iter=mi), SVC(random_state=rs, max_iter=mi), LogisticRegression(random_state=rs, max_iter=mi)]
train_path = '../input/cs-3110-mini-project/train.csv'
test_path = '../input/cs-3110-mini-project/test.csv'
def plt_distribution(dataset):
data = dataset.copy()
fig, axes = plt.subplots(5, 3)
fig.set_figwidth(16)
fig.set_figheight(20)
sns.histplot(data=data, x='account_length', kde=True, ax=axes[0,0])
sns.histplot(data=data, x='number_vm_messages', kde=True, ax=axes[0,1])
sns.histplot(data=data, x='total_day_min', kde=True, ax=axes[0,2])
sns.histplot(data=data, x='total_day_calls', kde=True, ax=axes[1,0])
sns.histplot(data=data, x='total_day_charge', kde=True, ax=axes[1,1])
sns.histplot(data=data, x='total_eve_min', kde=True, ax=axes[1,2])
sns.histplot(data=data, x='total_eve_calls', kde=True, ax=axes[2,0])
sns.histplot(data=data, x='total_eve_charge', kde=True, ax=axes[2,1])
sns.histplot(data=data, x='total_night_minutes', kde=True, ax=axes[2,2])
sns.histplot(data=data, x='total_night_calls', kde=True, ax=axes[3,0])
sns.histplot(data=data, x='total_night_charge', kde=True, ax=axes[3,1])
sns.histplot(data=data, x='total_intl_minutes', kde=True, ax=axes[3,2])
sns.histplot(data=data, x='total_intl_calls', kde=True, ax=axes[4,0])
sns.histplot(data=data, x='total_intl_charge', kde=True, ax=axes[4,1])
sns.histplot(data=data, x='customer_service_calls', kde=True, ax=axes[4,2])
plt.show()
def make_mi_scores(X, y):
discrete_features = X.dtypes == int
mi_scores = mutual_info_classif(X, y, discrete_features=discrete_features, random_state=rs)
mi_scores = pd.Series(mi_scores, name="MI Scores", index=X.columns)
mi_scores = mi_scores.sort_values(ascending=False)
return mi_scores
def plot_mi_scores(scores):
plt.figure(dpi=100, figsize=(8, 10))
scores = scores.sort_values(ascending=True)
width = np.arange(len(scores))
ticks = list(scores.index)
plt.barh(width, scores)
plt.yticks(width, ticks)
plt.title("Mutual Information Scores")
plt.show()
def evaluate_for_models(models, X, y):
results = pd.DataFrame({'Model': [],
'ScoreMean(F1)': [], 'Score Standard Deviation(F1)': [],
'ScoreMean': [], 'Score Standard Deviation': []})
for model in models:
score_f1 = cross_val_score(model, X, y,
scoring='f1')
score = cross_val_score(model, X, y)
new_result = {'Model': model.__class__.__name__,
'ScoreMean(F1)': score_f1.mean(), 'Score Standard Deviation(F1)': score_f1.std(),
'ScoreMean': score.mean(), 'Score Standard Deviation': score.std()}
results = results.append(new_result, ignore_index=True)
return results.sort_values(by=['ScoreMean(F1)', 'Score Standard Deviation(F1)',
'ScoreMean', 'Score Standard Deviation'], ascending=False)
def classification_report_with_accuracy_score(y_true, y_pred):
print(classification_report(y_true, y_pred))
return f1_score(y_true, y_pred)
def encode(dataframe, is_train=True):
data = dataframe.copy()
encoded_data = pd.get_dummies(data, columns=['location_code'])
if is_train:
encoded_data['Churn'] = encoded_data['Churn'].map({'Yes': 1, 'No': 0})
for col in ['intertiol_plan', 'voice_mail_plan']:
encoded_data[col] = encoded_data[col].map({'yes': 1, 'no': 0})
return encoded_data
def add_features(dataframe):
global lr_day
global lr_eve
global lr_nyt
data = dataframe.copy()
data['total_min'] = data['total_day_min'] + data['total_eve_min'] + data['total_night_minutes'] + data['total_intl_minutes']
try:
data['expected_total_day_charge'] = lr_day.predict(data[['total_day_min']])
data['expected_total_eve_charge'] = lr_eve.predict(data[['total_eve_min']])
data['expected_total_nyt_charge'] = lr_nyt.predict(data[['total_night_minutes']])
except NameError:
lr_day = LinearRegression()
lr_eve = LinearRegression()
lr_nyt = LinearRegression()
lr_day.fit(data[['total_day_min']], data['total_day_charge'])
lr_eve.fit(data[['total_eve_min']], data['total_eve_charge'])
lr_nyt.fit(data[['total_night_minutes']], data['total_night_charge'])
data['expected_total_day_charge'] = lr_day.predict(data[['total_day_min']])
data['expected_total_eve_charge'] = lr_eve.predict(data[['total_eve_min']])
data['expected_total_nyt_charge'] = lr_nyt.predict(data[['total_night_minutes']])
data['error_total_day_charge'] = abs(data['expected_total_day_charge'] - data['total_day_charge'])
data['error_total_eve_charge'] = abs(data['expected_total_eve_charge'] - data['total_eve_charge'])
data['error_total_nyt_charge'] = abs(data['expected_total_nyt_charge'] - data['total_night_charge'])
return data
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
plt_distribution(test_df) | code |
105204136/cell_6 | [
"text_html_output_1.png"
] | from sklearn.feature_selection import mutual_info_classif
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, f1_score, make_scorer
from sklearn.model_selection import cross_val_score, GridSearchCV, RandomizedSearchCV
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
rs = 42
mi = 10000
sns.set_style('whitegrid')
models = [DecisionTreeClassifier(random_state=rs), KNeighborsClassifier(), GaussianNB(), LinearSVC(random_state=rs, max_iter=mi), SVC(random_state=rs, max_iter=mi), LogisticRegression(random_state=rs, max_iter=mi)]
train_path = '../input/cs-3110-mini-project/train.csv'
test_path = '../input/cs-3110-mini-project/test.csv'
def plt_distribution(dataset):
data = dataset.copy()
fig, axes = plt.subplots(5, 3)
fig.set_figwidth(16)
fig.set_figheight(20)
sns.histplot(data=data, x='account_length', kde=True, ax=axes[0,0])
sns.histplot(data=data, x='number_vm_messages', kde=True, ax=axes[0,1])
sns.histplot(data=data, x='total_day_min', kde=True, ax=axes[0,2])
sns.histplot(data=data, x='total_day_calls', kde=True, ax=axes[1,0])
sns.histplot(data=data, x='total_day_charge', kde=True, ax=axes[1,1])
sns.histplot(data=data, x='total_eve_min', kde=True, ax=axes[1,2])
sns.histplot(data=data, x='total_eve_calls', kde=True, ax=axes[2,0])
sns.histplot(data=data, x='total_eve_charge', kde=True, ax=axes[2,1])
sns.histplot(data=data, x='total_night_minutes', kde=True, ax=axes[2,2])
sns.histplot(data=data, x='total_night_calls', kde=True, ax=axes[3,0])
sns.histplot(data=data, x='total_night_charge', kde=True, ax=axes[3,1])
sns.histplot(data=data, x='total_intl_minutes', kde=True, ax=axes[3,2])
sns.histplot(data=data, x='total_intl_calls', kde=True, ax=axes[4,0])
sns.histplot(data=data, x='total_intl_charge', kde=True, ax=axes[4,1])
sns.histplot(data=data, x='customer_service_calls', kde=True, ax=axes[4,2])
plt.show()
def make_mi_scores(X, y):
discrete_features = X.dtypes == int
mi_scores = mutual_info_classif(X, y, discrete_features=discrete_features, random_state=rs)
mi_scores = pd.Series(mi_scores, name="MI Scores", index=X.columns)
mi_scores = mi_scores.sort_values(ascending=False)
return mi_scores
def plot_mi_scores(scores):
plt.figure(dpi=100, figsize=(8, 10))
scores = scores.sort_values(ascending=True)
width = np.arange(len(scores))
ticks = list(scores.index)
plt.barh(width, scores)
plt.yticks(width, ticks)
plt.title("Mutual Information Scores")
plt.show()
def evaluate_for_models(models, X, y):
results = pd.DataFrame({'Model': [],
'ScoreMean(F1)': [], 'Score Standard Deviation(F1)': [],
'ScoreMean': [], 'Score Standard Deviation': []})
for model in models:
score_f1 = cross_val_score(model, X, y,
scoring='f1')
score = cross_val_score(model, X, y)
new_result = {'Model': model.__class__.__name__,
'ScoreMean(F1)': score_f1.mean(), 'Score Standard Deviation(F1)': score_f1.std(),
'ScoreMean': score.mean(), 'Score Standard Deviation': score.std()}
results = results.append(new_result, ignore_index=True)
return results.sort_values(by=['ScoreMean(F1)', 'Score Standard Deviation(F1)',
'ScoreMean', 'Score Standard Deviation'], ascending=False)
def classification_report_with_accuracy_score(y_true, y_pred):
print(classification_report(y_true, y_pred))
return f1_score(y_true, y_pred)
def encode(dataframe, is_train=True):
data = dataframe.copy()
encoded_data = pd.get_dummies(data, columns=['location_code'])
if is_train:
encoded_data['Churn'] = encoded_data['Churn'].map({'Yes': 1, 'No': 0})
for col in ['intertiol_plan', 'voice_mail_plan']:
encoded_data[col] = encoded_data[col].map({'yes': 1, 'no': 0})
return encoded_data
def add_features(dataframe):
global lr_day
global lr_eve
global lr_nyt
data = dataframe.copy()
data['total_min'] = data['total_day_min'] + data['total_eve_min'] + data['total_night_minutes'] + data['total_intl_minutes']
try:
data['expected_total_day_charge'] = lr_day.predict(data[['total_day_min']])
data['expected_total_eve_charge'] = lr_eve.predict(data[['total_eve_min']])
data['expected_total_nyt_charge'] = lr_nyt.predict(data[['total_night_minutes']])
except NameError:
lr_day = LinearRegression()
lr_eve = LinearRegression()
lr_nyt = LinearRegression()
lr_day.fit(data[['total_day_min']], data['total_day_charge'])
lr_eve.fit(data[['total_eve_min']], data['total_eve_charge'])
lr_nyt.fit(data[['total_night_minutes']], data['total_night_charge'])
data['expected_total_day_charge'] = lr_day.predict(data[['total_day_min']])
data['expected_total_eve_charge'] = lr_eve.predict(data[['total_eve_min']])
data['expected_total_nyt_charge'] = lr_nyt.predict(data[['total_night_minutes']])
data['error_total_day_charge'] = abs(data['expected_total_day_charge'] - data['total_day_charge'])
data['error_total_eve_charge'] = abs(data['expected_total_eve_charge'] - data['total_eve_charge'])
data['error_total_nyt_charge'] = abs(data['expected_total_nyt_charge'] - data['total_night_charge'])
return data
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
train_df.info() | code |
105204136/cell_7 | [
"image_output_1.png"
] | from sklearn.feature_selection import mutual_info_classif
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, f1_score, make_scorer
from sklearn.model_selection import cross_val_score, GridSearchCV, RandomizedSearchCV
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
rs = 42
mi = 10000
sns.set_style('whitegrid')
models = [DecisionTreeClassifier(random_state=rs), KNeighborsClassifier(), GaussianNB(), LinearSVC(random_state=rs, max_iter=mi), SVC(random_state=rs, max_iter=mi), LogisticRegression(random_state=rs, max_iter=mi)]
train_path = '../input/cs-3110-mini-project/train.csv'
test_path = '../input/cs-3110-mini-project/test.csv'
def plt_distribution(dataset):
data = dataset.copy()
fig, axes = plt.subplots(5, 3)
fig.set_figwidth(16)
fig.set_figheight(20)
sns.histplot(data=data, x='account_length', kde=True, ax=axes[0,0])
sns.histplot(data=data, x='number_vm_messages', kde=True, ax=axes[0,1])
sns.histplot(data=data, x='total_day_min', kde=True, ax=axes[0,2])
sns.histplot(data=data, x='total_day_calls', kde=True, ax=axes[1,0])
sns.histplot(data=data, x='total_day_charge', kde=True, ax=axes[1,1])
sns.histplot(data=data, x='total_eve_min', kde=True, ax=axes[1,2])
sns.histplot(data=data, x='total_eve_calls', kde=True, ax=axes[2,0])
sns.histplot(data=data, x='total_eve_charge', kde=True, ax=axes[2,1])
sns.histplot(data=data, x='total_night_minutes', kde=True, ax=axes[2,2])
sns.histplot(data=data, x='total_night_calls', kde=True, ax=axes[3,0])
sns.histplot(data=data, x='total_night_charge', kde=True, ax=axes[3,1])
sns.histplot(data=data, x='total_intl_minutes', kde=True, ax=axes[3,2])
sns.histplot(data=data, x='total_intl_calls', kde=True, ax=axes[4,0])
sns.histplot(data=data, x='total_intl_charge', kde=True, ax=axes[4,1])
sns.histplot(data=data, x='customer_service_calls', kde=True, ax=axes[4,2])
plt.show()
def make_mi_scores(X, y):
discrete_features = X.dtypes == int
mi_scores = mutual_info_classif(X, y, discrete_features=discrete_features, random_state=rs)
mi_scores = pd.Series(mi_scores, name="MI Scores", index=X.columns)
mi_scores = mi_scores.sort_values(ascending=False)
return mi_scores
def plot_mi_scores(scores):
plt.figure(dpi=100, figsize=(8, 10))
scores = scores.sort_values(ascending=True)
width = np.arange(len(scores))
ticks = list(scores.index)
plt.barh(width, scores)
plt.yticks(width, ticks)
plt.title("Mutual Information Scores")
plt.show()
def evaluate_for_models(models, X, y):
results = pd.DataFrame({'Model': [],
'ScoreMean(F1)': [], 'Score Standard Deviation(F1)': [],
'ScoreMean': [], 'Score Standard Deviation': []})
for model in models:
score_f1 = cross_val_score(model, X, y,
scoring='f1')
score = cross_val_score(model, X, y)
new_result = {'Model': model.__class__.__name__,
'ScoreMean(F1)': score_f1.mean(), 'Score Standard Deviation(F1)': score_f1.std(),
'ScoreMean': score.mean(), 'Score Standard Deviation': score.std()}
results = results.append(new_result, ignore_index=True)
return results.sort_values(by=['ScoreMean(F1)', 'Score Standard Deviation(F1)',
'ScoreMean', 'Score Standard Deviation'], ascending=False)
def classification_report_with_accuracy_score(y_true, y_pred):
print(classification_report(y_true, y_pred))
return f1_score(y_true, y_pred)
def encode(dataframe, is_train=True):
data = dataframe.copy()
encoded_data = pd.get_dummies(data, columns=['location_code'])
if is_train:
encoded_data['Churn'] = encoded_data['Churn'].map({'Yes': 1, 'No': 0})
for col in ['intertiol_plan', 'voice_mail_plan']:
encoded_data[col] = encoded_data[col].map({'yes': 1, 'no': 0})
return encoded_data
def add_features(dataframe):
global lr_day
global lr_eve
global lr_nyt
data = dataframe.copy()
data['total_min'] = data['total_day_min'] + data['total_eve_min'] + data['total_night_minutes'] + data['total_intl_minutes']
try:
data['expected_total_day_charge'] = lr_day.predict(data[['total_day_min']])
data['expected_total_eve_charge'] = lr_eve.predict(data[['total_eve_min']])
data['expected_total_nyt_charge'] = lr_nyt.predict(data[['total_night_minutes']])
except NameError:
lr_day = LinearRegression()
lr_eve = LinearRegression()
lr_nyt = LinearRegression()
lr_day.fit(data[['total_day_min']], data['total_day_charge'])
lr_eve.fit(data[['total_eve_min']], data['total_eve_charge'])
lr_nyt.fit(data[['total_night_minutes']], data['total_night_charge'])
data['expected_total_day_charge'] = lr_day.predict(data[['total_day_min']])
data['expected_total_eve_charge'] = lr_eve.predict(data[['total_eve_min']])
data['expected_total_nyt_charge'] = lr_nyt.predict(data[['total_night_minutes']])
data['error_total_day_charge'] = abs(data['expected_total_day_charge'] - data['total_day_charge'])
data['error_total_eve_charge'] = abs(data['expected_total_eve_charge'] - data['total_eve_charge'])
data['error_total_nyt_charge'] = abs(data['expected_total_nyt_charge'] - data['total_night_charge'])
return data
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
plt_distribution(train_df) | code |
105204136/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.feature_selection import mutual_info_classif
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, f1_score, make_scorer
from sklearn.model_selection import cross_val_score, GridSearchCV, RandomizedSearchCV
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
rs = 42
mi = 10000
sns.set_style('whitegrid')
models = [DecisionTreeClassifier(random_state=rs), KNeighborsClassifier(), GaussianNB(), LinearSVC(random_state=rs, max_iter=mi), SVC(random_state=rs, max_iter=mi), LogisticRegression(random_state=rs, max_iter=mi)]
train_path = '../input/cs-3110-mini-project/train.csv'
test_path = '../input/cs-3110-mini-project/test.csv'
def plt_distribution(dataset):
data = dataset.copy()
fig, axes = plt.subplots(5, 3)
fig.set_figwidth(16)
fig.set_figheight(20)
sns.histplot(data=data, x='account_length', kde=True, ax=axes[0,0])
sns.histplot(data=data, x='number_vm_messages', kde=True, ax=axes[0,1])
sns.histplot(data=data, x='total_day_min', kde=True, ax=axes[0,2])
sns.histplot(data=data, x='total_day_calls', kde=True, ax=axes[1,0])
sns.histplot(data=data, x='total_day_charge', kde=True, ax=axes[1,1])
sns.histplot(data=data, x='total_eve_min', kde=True, ax=axes[1,2])
sns.histplot(data=data, x='total_eve_calls', kde=True, ax=axes[2,0])
sns.histplot(data=data, x='total_eve_charge', kde=True, ax=axes[2,1])
sns.histplot(data=data, x='total_night_minutes', kde=True, ax=axes[2,2])
sns.histplot(data=data, x='total_night_calls', kde=True, ax=axes[3,0])
sns.histplot(data=data, x='total_night_charge', kde=True, ax=axes[3,1])
sns.histplot(data=data, x='total_intl_minutes', kde=True, ax=axes[3,2])
sns.histplot(data=data, x='total_intl_calls', kde=True, ax=axes[4,0])
sns.histplot(data=data, x='total_intl_charge', kde=True, ax=axes[4,1])
sns.histplot(data=data, x='customer_service_calls', kde=True, ax=axes[4,2])
plt.show()
def make_mi_scores(X, y):
discrete_features = X.dtypes == int
mi_scores = mutual_info_classif(X, y, discrete_features=discrete_features, random_state=rs)
mi_scores = pd.Series(mi_scores, name="MI Scores", index=X.columns)
mi_scores = mi_scores.sort_values(ascending=False)
return mi_scores
def plot_mi_scores(scores):
plt.figure(dpi=100, figsize=(8, 10))
scores = scores.sort_values(ascending=True)
width = np.arange(len(scores))
ticks = list(scores.index)
plt.barh(width, scores)
plt.yticks(width, ticks)
plt.title("Mutual Information Scores")
plt.show()
def evaluate_for_models(models, X, y):
results = pd.DataFrame({'Model': [],
'ScoreMean(F1)': [], 'Score Standard Deviation(F1)': [],
'ScoreMean': [], 'Score Standard Deviation': []})
for model in models:
score_f1 = cross_val_score(model, X, y,
scoring='f1')
score = cross_val_score(model, X, y)
new_result = {'Model': model.__class__.__name__,
'ScoreMean(F1)': score_f1.mean(), 'Score Standard Deviation(F1)': score_f1.std(),
'ScoreMean': score.mean(), 'Score Standard Deviation': score.std()}
results = results.append(new_result, ignore_index=True)
return results.sort_values(by=['ScoreMean(F1)', 'Score Standard Deviation(F1)',
'ScoreMean', 'Score Standard Deviation'], ascending=False)
def classification_report_with_accuracy_score(y_true, y_pred):
print(classification_report(y_true, y_pred))
return f1_score(y_true, y_pred)
def encode(dataframe, is_train=True):
data = dataframe.copy()
encoded_data = pd.get_dummies(data, columns=['location_code'])
if is_train:
encoded_data['Churn'] = encoded_data['Churn'].map({'Yes': 1, 'No': 0})
for col in ['intertiol_plan', 'voice_mail_plan']:
encoded_data[col] = encoded_data[col].map({'yes': 1, 'no': 0})
return encoded_data
def add_features(dataframe):
global lr_day
global lr_eve
global lr_nyt
data = dataframe.copy()
data['total_min'] = data['total_day_min'] + data['total_eve_min'] + data['total_night_minutes'] + data['total_intl_minutes']
try:
data['expected_total_day_charge'] = lr_day.predict(data[['total_day_min']])
data['expected_total_eve_charge'] = lr_eve.predict(data[['total_eve_min']])
data['expected_total_nyt_charge'] = lr_nyt.predict(data[['total_night_minutes']])
except NameError:
lr_day = LinearRegression()
lr_eve = LinearRegression()
lr_nyt = LinearRegression()
lr_day.fit(data[['total_day_min']], data['total_day_charge'])
lr_eve.fit(data[['total_eve_min']], data['total_eve_charge'])
lr_nyt.fit(data[['total_night_minutes']], data['total_night_charge'])
data['expected_total_day_charge'] = lr_day.predict(data[['total_day_min']])
data['expected_total_eve_charge'] = lr_eve.predict(data[['total_eve_min']])
data['expected_total_nyt_charge'] = lr_nyt.predict(data[['total_night_minutes']])
data['error_total_day_charge'] = abs(data['expected_total_day_charge'] - data['total_day_charge'])
data['error_total_eve_charge'] = abs(data['expected_total_eve_charge'] - data['total_eve_charge'])
data['error_total_nyt_charge'] = abs(data['expected_total_nyt_charge'] - data['total_night_charge'])
return data
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
test_df.info() | code |
105204136/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import mutual_info_classif
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, f1_score, make_scorer
from sklearn.model_selection import cross_val_score, GridSearchCV, RandomizedSearchCV
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
rs = 42
mi = 10000
sns.set_style('whitegrid')
models = [DecisionTreeClassifier(random_state=rs), KNeighborsClassifier(), GaussianNB(), LinearSVC(random_state=rs, max_iter=mi), SVC(random_state=rs, max_iter=mi), LogisticRegression(random_state=rs, max_iter=mi)]
train_path = '../input/cs-3110-mini-project/train.csv'
test_path = '../input/cs-3110-mini-project/test.csv'
def plt_distribution(dataset):
data = dataset.copy()
fig, axes = plt.subplots(5, 3)
fig.set_figwidth(16)
fig.set_figheight(20)
sns.histplot(data=data, x='account_length', kde=True, ax=axes[0,0])
sns.histplot(data=data, x='number_vm_messages', kde=True, ax=axes[0,1])
sns.histplot(data=data, x='total_day_min', kde=True, ax=axes[0,2])
sns.histplot(data=data, x='total_day_calls', kde=True, ax=axes[1,0])
sns.histplot(data=data, x='total_day_charge', kde=True, ax=axes[1,1])
sns.histplot(data=data, x='total_eve_min', kde=True, ax=axes[1,2])
sns.histplot(data=data, x='total_eve_calls', kde=True, ax=axes[2,0])
sns.histplot(data=data, x='total_eve_charge', kde=True, ax=axes[2,1])
sns.histplot(data=data, x='total_night_minutes', kde=True, ax=axes[2,2])
sns.histplot(data=data, x='total_night_calls', kde=True, ax=axes[3,0])
sns.histplot(data=data, x='total_night_charge', kde=True, ax=axes[3,1])
sns.histplot(data=data, x='total_intl_minutes', kde=True, ax=axes[3,2])
sns.histplot(data=data, x='total_intl_calls', kde=True, ax=axes[4,0])
sns.histplot(data=data, x='total_intl_charge', kde=True, ax=axes[4,1])
sns.histplot(data=data, x='customer_service_calls', kde=True, ax=axes[4,2])
plt.show()
def make_mi_scores(X, y):
discrete_features = X.dtypes == int
mi_scores = mutual_info_classif(X, y, discrete_features=discrete_features, random_state=rs)
mi_scores = pd.Series(mi_scores, name="MI Scores", index=X.columns)
mi_scores = mi_scores.sort_values(ascending=False)
return mi_scores
def plot_mi_scores(scores):
plt.figure(dpi=100, figsize=(8, 10))
scores = scores.sort_values(ascending=True)
width = np.arange(len(scores))
ticks = list(scores.index)
plt.barh(width, scores)
plt.yticks(width, ticks)
plt.title("Mutual Information Scores")
plt.show()
def evaluate_for_models(models, X, y):
results = pd.DataFrame({'Model': [],
'ScoreMean(F1)': [], 'Score Standard Deviation(F1)': [],
'ScoreMean': [], 'Score Standard Deviation': []})
for model in models:
score_f1 = cross_val_score(model, X, y,
scoring='f1')
score = cross_val_score(model, X, y)
new_result = {'Model': model.__class__.__name__,
'ScoreMean(F1)': score_f1.mean(), 'Score Standard Deviation(F1)': score_f1.std(),
'ScoreMean': score.mean(), 'Score Standard Deviation': score.std()}
results = results.append(new_result, ignore_index=True)
return results.sort_values(by=['ScoreMean(F1)', 'Score Standard Deviation(F1)',
'ScoreMean', 'Score Standard Deviation'], ascending=False)
def classification_report_with_accuracy_score(y_true, y_pred):
print(classification_report(y_true, y_pred))
return f1_score(y_true, y_pred)
def encode(dataframe, is_train=True):
data = dataframe.copy()
encoded_data = pd.get_dummies(data, columns=['location_code'])
if is_train:
encoded_data['Churn'] = encoded_data['Churn'].map({'Yes': 1, 'No': 0})
for col in ['intertiol_plan', 'voice_mail_plan']:
encoded_data[col] = encoded_data[col].map({'yes': 1, 'no': 0})
return encoded_data
def add_features(dataframe):
global lr_day
global lr_eve
global lr_nyt
data = dataframe.copy()
data['total_min'] = data['total_day_min'] + data['total_eve_min'] + data['total_night_minutes'] + data['total_intl_minutes']
try:
data['expected_total_day_charge'] = lr_day.predict(data[['total_day_min']])
data['expected_total_eve_charge'] = lr_eve.predict(data[['total_eve_min']])
data['expected_total_nyt_charge'] = lr_nyt.predict(data[['total_night_minutes']])
except NameError:
lr_day = LinearRegression()
lr_eve = LinearRegression()
lr_nyt = LinearRegression()
lr_day.fit(data[['total_day_min']], data['total_day_charge'])
lr_eve.fit(data[['total_eve_min']], data['total_eve_charge'])
lr_nyt.fit(data[['total_night_minutes']], data['total_night_charge'])
data['expected_total_day_charge'] = lr_day.predict(data[['total_day_min']])
data['expected_total_eve_charge'] = lr_eve.predict(data[['total_eve_min']])
data['expected_total_nyt_charge'] = lr_nyt.predict(data[['total_night_minutes']])
data['error_total_day_charge'] = abs(data['expected_total_day_charge'] - data['total_day_charge'])
data['error_total_eve_charge'] = abs(data['expected_total_eve_charge'] - data['total_eve_charge'])
data['error_total_nyt_charge'] = abs(data['expected_total_nyt_charge'] - data['total_night_charge'])
return data
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
d_droped_train = train_df.drop_duplicates(train_df.columns.drop(['customer_id']))
d_droped_train = d_droped_train.drop(columns=['Unnamed: 20'])
cols = d_droped_train.select_dtypes([np.number]).columns
d_droped_train[cols] = d_droped_train[cols].abs()
d_droped_train['account_length'].fillna(d_droped_train.account_length.median(), inplace=True)
d_droped_train['intertiol_plan'].fillna('no', inplace=True)
d_droped_train['voice_mail_plan'].fillna('no', inplace=True)
d_droped_train.loc[d_droped_train['voice_mail_plan'] == 'no', 'number_vm_messages'] = 0
d_droped_train.loc[(d_droped_train['voice_mail_plan'] == 'yes') & d_droped_train['number_vm_messages'].isnull(), 'number_vm_messages'] = d_droped_train[d_droped_train.voice_mail_plan == 'yes'].number_vm_messages.median()
d_droped_train.loc[d_droped_train['total_day_min'] > 500, 'total_day_min'] = np.nan
d_droped_train['total_day_min'] = d_droped_train.sort_values(['total_day_charge']).total_day_min.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train.loc[d_droped_train['total_day_calls'] > 350, 'total_day_calls'] = np.nan
d_droped_train['total_day_calls'] = d_droped_train.sort_values(['total_day_min']).total_day_calls.ffill().sort_index()
d_droped_train['total_day_charge'] = d_droped_train.sort_values(['total_day_min']).total_day_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train.loc[d_droped_train['total_eve_min'] > 500, 'total_eve_min'] = np.nan
d_droped_train['total_eve_min'] = d_droped_train.sort_values(['total_eve_charge']).total_eve_min.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train['total_eve_calls'] = d_droped_train.sort_values(['total_eve_min']).total_eve_calls.ffill().sort_index()
d_droped_train['total_eve_charge'] = d_droped_train.sort_values(['total_eve_min']).total_eve_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train.loc[d_droped_train['total_night_minutes'] > 500, 'total_night_minutes'] = np.nan
d_droped_train['total_night_minutes'] = d_droped_train.sort_values(['total_night_charge']).total_night_minutes.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train['total_night_calls'] = d_droped_train.sort_values(['total_night_minutes']).total_night_calls.ffill().sort_index()
d_droped_train.loc[d_droped_train['total_night_charge'] > 150, 'total_night_charge'] = np.nan
d_droped_train['total_night_charge'] = d_droped_train.sort_values(['total_night_minutes']).total_night_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train['total_intl_minutes'] = d_droped_train.sort_values(['total_intl_charge']).total_intl_minutes.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train.loc[(d_droped_train['total_intl_minutes'] > 0) & (d_droped_train['total_intl_charge'] > 0) & (d_droped_train['total_intl_calls'] < 1), 'total_intl_calls'] = np.nan
d_droped_train['total_intl_calls'] = d_droped_train.sort_values(['total_intl_minutes']).total_intl_calls.ffill().sort_index()
d_droped_train['total_intl_charge'] = d_droped_train.sort_values(['total_intl_minutes']).total_intl_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train['customer_service_calls'].fillna(1, inplace=True)
odm_handled_train = d_droped_train.dropna(subset=['Churn'])
test_df = test_df.drop(columns=['Unnamed: 19', 'Unnamed: 20'])
cols = test_df.select_dtypes([np.number]).columns
test_df[cols] = test_df[cols].abs()
test_df['location_code'] = test_df['location_code'].ffill()
test_df['intertiol_plan'].fillna('no', inplace=True)
test_df['voice_mail_plan'].fillna('no', inplace=True)
test_df['number_vm_messages'].fillna(0, inplace=True)
test_df['total_day_min'] = test_df.sort_values(['total_day_charge']).total_day_min.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df['total_day_calls'] = test_df.sort_values(['total_day_min']).total_day_calls.ffill().sort_index()
test_df['total_day_charge'] = test_df.sort_values(['total_day_min']).total_day_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df['total_eve_min'] = test_df.sort_values(['total_eve_charge']).total_eve_min.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df['total_eve_charge'] = test_df.sort_values(['total_eve_min']).total_eve_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df['total_night_minutes'] = test_df.sort_values(['total_night_charge']).total_night_minutes.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df['total_night_calls'] = test_df.sort_values(['total_night_minutes']).total_night_calls.ffill().sort_index()
test_df['total_night_charge'] = test_df.sort_values(['total_night_minutes']).total_night_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df['total_intl_minutes'] = test_df.sort_values(['total_intl_charge']).total_intl_minutes.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df.loc[(test_df['total_intl_minutes'] > 0) & (test_df['total_intl_charge'] > 0) & (test_df['total_intl_calls'] < 1), 'total_intl_calls'] = np.nan
test_df['total_intl_calls'] = test_df.sort_values(['total_intl_minutes']).total_intl_calls.ffill().sort_index()
test_df['customer_service_calls'].fillna(1, inplace=True)
train = odm_handled_train.copy()
test = test_df.copy()
plt_distribution(train) | code |
105204136/cell_16 | [
"image_output_1.png"
] | from sklearn.feature_selection import mutual_info_classif
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, f1_score, make_scorer
from sklearn.model_selection import cross_val_score, GridSearchCV, RandomizedSearchCV
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
rs = 42
mi = 10000
sns.set_style('whitegrid')
models = [DecisionTreeClassifier(random_state=rs), KNeighborsClassifier(), GaussianNB(), LinearSVC(random_state=rs, max_iter=mi), SVC(random_state=rs, max_iter=mi), LogisticRegression(random_state=rs, max_iter=mi)]
train_path = '../input/cs-3110-mini-project/train.csv'
test_path = '../input/cs-3110-mini-project/test.csv'
def plt_distribution(dataset):
data = dataset.copy()
fig, axes = plt.subplots(5, 3)
fig.set_figwidth(16)
fig.set_figheight(20)
sns.histplot(data=data, x='account_length', kde=True, ax=axes[0,0])
sns.histplot(data=data, x='number_vm_messages', kde=True, ax=axes[0,1])
sns.histplot(data=data, x='total_day_min', kde=True, ax=axes[0,2])
sns.histplot(data=data, x='total_day_calls', kde=True, ax=axes[1,0])
sns.histplot(data=data, x='total_day_charge', kde=True, ax=axes[1,1])
sns.histplot(data=data, x='total_eve_min', kde=True, ax=axes[1,2])
sns.histplot(data=data, x='total_eve_calls', kde=True, ax=axes[2,0])
sns.histplot(data=data, x='total_eve_charge', kde=True, ax=axes[2,1])
sns.histplot(data=data, x='total_night_minutes', kde=True, ax=axes[2,2])
sns.histplot(data=data, x='total_night_calls', kde=True, ax=axes[3,0])
sns.histplot(data=data, x='total_night_charge', kde=True, ax=axes[3,1])
sns.histplot(data=data, x='total_intl_minutes', kde=True, ax=axes[3,2])
sns.histplot(data=data, x='total_intl_calls', kde=True, ax=axes[4,0])
sns.histplot(data=data, x='total_intl_charge', kde=True, ax=axes[4,1])
sns.histplot(data=data, x='customer_service_calls', kde=True, ax=axes[4,2])
plt.show()
def make_mi_scores(X, y):
discrete_features = X.dtypes == int
mi_scores = mutual_info_classif(X, y, discrete_features=discrete_features, random_state=rs)
mi_scores = pd.Series(mi_scores, name="MI Scores", index=X.columns)
mi_scores = mi_scores.sort_values(ascending=False)
return mi_scores
def plot_mi_scores(scores):
plt.figure(dpi=100, figsize=(8, 10))
scores = scores.sort_values(ascending=True)
width = np.arange(len(scores))
ticks = list(scores.index)
plt.barh(width, scores)
plt.yticks(width, ticks)
plt.title("Mutual Information Scores")
plt.show()
def evaluate_for_models(models, X, y):
results = pd.DataFrame({'Model': [],
'ScoreMean(F1)': [], 'Score Standard Deviation(F1)': [],
'ScoreMean': [], 'Score Standard Deviation': []})
for model in models:
score_f1 = cross_val_score(model, X, y,
scoring='f1')
score = cross_val_score(model, X, y)
new_result = {'Model': model.__class__.__name__,
'ScoreMean(F1)': score_f1.mean(), 'Score Standard Deviation(F1)': score_f1.std(),
'ScoreMean': score.mean(), 'Score Standard Deviation': score.std()}
results = results.append(new_result, ignore_index=True)
return results.sort_values(by=['ScoreMean(F1)', 'Score Standard Deviation(F1)',
'ScoreMean', 'Score Standard Deviation'], ascending=False)
def classification_report_with_accuracy_score(y_true, y_pred):
print(classification_report(y_true, y_pred))
return f1_score(y_true, y_pred)
def encode(dataframe, is_train=True):
data = dataframe.copy()
encoded_data = pd.get_dummies(data, columns=['location_code'])
if is_train:
encoded_data['Churn'] = encoded_data['Churn'].map({'Yes': 1, 'No': 0})
for col in ['intertiol_plan', 'voice_mail_plan']:
encoded_data[col] = encoded_data[col].map({'yes': 1, 'no': 0})
return encoded_data
def add_features(dataframe):
global lr_day
global lr_eve
global lr_nyt
data = dataframe.copy()
data['total_min'] = data['total_day_min'] + data['total_eve_min'] + data['total_night_minutes'] + data['total_intl_minutes']
try:
data['expected_total_day_charge'] = lr_day.predict(data[['total_day_min']])
data['expected_total_eve_charge'] = lr_eve.predict(data[['total_eve_min']])
data['expected_total_nyt_charge'] = lr_nyt.predict(data[['total_night_minutes']])
except NameError:
lr_day = LinearRegression()
lr_eve = LinearRegression()
lr_nyt = LinearRegression()
lr_day.fit(data[['total_day_min']], data['total_day_charge'])
lr_eve.fit(data[['total_eve_min']], data['total_eve_charge'])
lr_nyt.fit(data[['total_night_minutes']], data['total_night_charge'])
data['expected_total_day_charge'] = lr_day.predict(data[['total_day_min']])
data['expected_total_eve_charge'] = lr_eve.predict(data[['total_eve_min']])
data['expected_total_nyt_charge'] = lr_nyt.predict(data[['total_night_minutes']])
data['error_total_day_charge'] = abs(data['expected_total_day_charge'] - data['total_day_charge'])
data['error_total_eve_charge'] = abs(data['expected_total_eve_charge'] - data['total_eve_charge'])
data['error_total_nyt_charge'] = abs(data['expected_total_nyt_charge'] - data['total_night_charge'])
return data
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
d_droped_train = train_df.drop_duplicates(train_df.columns.drop(['customer_id']))
d_droped_train = d_droped_train.drop(columns=['Unnamed: 20'])
cols = d_droped_train.select_dtypes([np.number]).columns
d_droped_train[cols] = d_droped_train[cols].abs()
d_droped_train['account_length'].fillna(d_droped_train.account_length.median(), inplace=True)
d_droped_train['intertiol_plan'].fillna('no', inplace=True)
d_droped_train['voice_mail_plan'].fillna('no', inplace=True)
d_droped_train.loc[d_droped_train['voice_mail_plan'] == 'no', 'number_vm_messages'] = 0
d_droped_train.loc[(d_droped_train['voice_mail_plan'] == 'yes') & d_droped_train['number_vm_messages'].isnull(), 'number_vm_messages'] = d_droped_train[d_droped_train.voice_mail_plan == 'yes'].number_vm_messages.median()
d_droped_train.loc[d_droped_train['total_day_min'] > 500, 'total_day_min'] = np.nan
d_droped_train['total_day_min'] = d_droped_train.sort_values(['total_day_charge']).total_day_min.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train.loc[d_droped_train['total_day_calls'] > 350, 'total_day_calls'] = np.nan
d_droped_train['total_day_calls'] = d_droped_train.sort_values(['total_day_min']).total_day_calls.ffill().sort_index()
d_droped_train['total_day_charge'] = d_droped_train.sort_values(['total_day_min']).total_day_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train.loc[d_droped_train['total_eve_min'] > 500, 'total_eve_min'] = np.nan
d_droped_train['total_eve_min'] = d_droped_train.sort_values(['total_eve_charge']).total_eve_min.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train['total_eve_calls'] = d_droped_train.sort_values(['total_eve_min']).total_eve_calls.ffill().sort_index()
d_droped_train['total_eve_charge'] = d_droped_train.sort_values(['total_eve_min']).total_eve_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train.loc[d_droped_train['total_night_minutes'] > 500, 'total_night_minutes'] = np.nan
d_droped_train['total_night_minutes'] = d_droped_train.sort_values(['total_night_charge']).total_night_minutes.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train['total_night_calls'] = d_droped_train.sort_values(['total_night_minutes']).total_night_calls.ffill().sort_index()
d_droped_train.loc[d_droped_train['total_night_charge'] > 150, 'total_night_charge'] = np.nan
d_droped_train['total_night_charge'] = d_droped_train.sort_values(['total_night_minutes']).total_night_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train['total_intl_minutes'] = d_droped_train.sort_values(['total_intl_charge']).total_intl_minutes.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train.loc[(d_droped_train['total_intl_minutes'] > 0) & (d_droped_train['total_intl_charge'] > 0) & (d_droped_train['total_intl_calls'] < 1), 'total_intl_calls'] = np.nan
d_droped_train['total_intl_calls'] = d_droped_train.sort_values(['total_intl_minutes']).total_intl_calls.ffill().sort_index()
d_droped_train['total_intl_charge'] = d_droped_train.sort_values(['total_intl_minutes']).total_intl_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train['customer_service_calls'].fillna(1, inplace=True)
odm_handled_train = d_droped_train.dropna(subset=['Churn'])
test_df = test_df.drop(columns=['Unnamed: 19', 'Unnamed: 20'])
cols = test_df.select_dtypes([np.number]).columns
test_df[cols] = test_df[cols].abs()
test_df['location_code'] = test_df['location_code'].ffill()
test_df['intertiol_plan'].fillna('no', inplace=True)
test_df['voice_mail_plan'].fillna('no', inplace=True)
test_df['number_vm_messages'].fillna(0, inplace=True)
test_df['total_day_min'] = test_df.sort_values(['total_day_charge']).total_day_min.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df['total_day_calls'] = test_df.sort_values(['total_day_min']).total_day_calls.ffill().sort_index()
test_df['total_day_charge'] = test_df.sort_values(['total_day_min']).total_day_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df['total_eve_min'] = test_df.sort_values(['total_eve_charge']).total_eve_min.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df['total_eve_charge'] = test_df.sort_values(['total_eve_min']).total_eve_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df['total_night_minutes'] = test_df.sort_values(['total_night_charge']).total_night_minutes.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df['total_night_calls'] = test_df.sort_values(['total_night_minutes']).total_night_calls.ffill().sort_index()
test_df['total_night_charge'] = test_df.sort_values(['total_night_minutes']).total_night_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df['total_intl_minutes'] = test_df.sort_values(['total_intl_charge']).total_intl_minutes.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df.loc[(test_df['total_intl_minutes'] > 0) & (test_df['total_intl_charge'] > 0) & (test_df['total_intl_calls'] < 1), 'total_intl_calls'] = np.nan
test_df['total_intl_calls'] = test_df.sort_values(['total_intl_minutes']).total_intl_calls.ffill().sort_index()
test_df['customer_service_calls'].fillna(1, inplace=True)
train = odm_handled_train.copy()
test = test_df.copy()
plt.figure(figsize=(16, 16))
sns.pairplot(train, hue='Churn')
plt.show() | code |
105204136/cell_14 | [
"image_output_1.png"
] | from sklearn.feature_selection import mutual_info_classif
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, f1_score, make_scorer
from sklearn.model_selection import cross_val_score, GridSearchCV, RandomizedSearchCV
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
rs = 42
mi = 10000
sns.set_style('whitegrid')
models = [DecisionTreeClassifier(random_state=rs), KNeighborsClassifier(), GaussianNB(), LinearSVC(random_state=rs, max_iter=mi), SVC(random_state=rs, max_iter=mi), LogisticRegression(random_state=rs, max_iter=mi)]
train_path = '../input/cs-3110-mini-project/train.csv'
test_path = '../input/cs-3110-mini-project/test.csv'
def plt_distribution(dataset):
data = dataset.copy()
fig, axes = plt.subplots(5, 3)
fig.set_figwidth(16)
fig.set_figheight(20)
sns.histplot(data=data, x='account_length', kde=True, ax=axes[0,0])
sns.histplot(data=data, x='number_vm_messages', kde=True, ax=axes[0,1])
sns.histplot(data=data, x='total_day_min', kde=True, ax=axes[0,2])
sns.histplot(data=data, x='total_day_calls', kde=True, ax=axes[1,0])
sns.histplot(data=data, x='total_day_charge', kde=True, ax=axes[1,1])
sns.histplot(data=data, x='total_eve_min', kde=True, ax=axes[1,2])
sns.histplot(data=data, x='total_eve_calls', kde=True, ax=axes[2,0])
sns.histplot(data=data, x='total_eve_charge', kde=True, ax=axes[2,1])
sns.histplot(data=data, x='total_night_minutes', kde=True, ax=axes[2,2])
sns.histplot(data=data, x='total_night_calls', kde=True, ax=axes[3,0])
sns.histplot(data=data, x='total_night_charge', kde=True, ax=axes[3,1])
sns.histplot(data=data, x='total_intl_minutes', kde=True, ax=axes[3,2])
sns.histplot(data=data, x='total_intl_calls', kde=True, ax=axes[4,0])
sns.histplot(data=data, x='total_intl_charge', kde=True, ax=axes[4,1])
sns.histplot(data=data, x='customer_service_calls', kde=True, ax=axes[4,2])
plt.show()
def make_mi_scores(X, y):
discrete_features = X.dtypes == int
mi_scores = mutual_info_classif(X, y, discrete_features=discrete_features, random_state=rs)
mi_scores = pd.Series(mi_scores, name="MI Scores", index=X.columns)
mi_scores = mi_scores.sort_values(ascending=False)
return mi_scores
def plot_mi_scores(scores):
plt.figure(dpi=100, figsize=(8, 10))
scores = scores.sort_values(ascending=True)
width = np.arange(len(scores))
ticks = list(scores.index)
plt.barh(width, scores)
plt.yticks(width, ticks)
plt.title("Mutual Information Scores")
plt.show()
def evaluate_for_models(models, X, y):
results = pd.DataFrame({'Model': [],
'ScoreMean(F1)': [], 'Score Standard Deviation(F1)': [],
'ScoreMean': [], 'Score Standard Deviation': []})
for model in models:
score_f1 = cross_val_score(model, X, y,
scoring='f1')
score = cross_val_score(model, X, y)
new_result = {'Model': model.__class__.__name__,
'ScoreMean(F1)': score_f1.mean(), 'Score Standard Deviation(F1)': score_f1.std(),
'ScoreMean': score.mean(), 'Score Standard Deviation': score.std()}
results = results.append(new_result, ignore_index=True)
return results.sort_values(by=['ScoreMean(F1)', 'Score Standard Deviation(F1)',
'ScoreMean', 'Score Standard Deviation'], ascending=False)
def classification_report_with_accuracy_score(y_true, y_pred):
print(classification_report(y_true, y_pred))
return f1_score(y_true, y_pred)
def encode(dataframe, is_train=True):
data = dataframe.copy()
encoded_data = pd.get_dummies(data, columns=['location_code'])
if is_train:
encoded_data['Churn'] = encoded_data['Churn'].map({'Yes': 1, 'No': 0})
for col in ['intertiol_plan', 'voice_mail_plan']:
encoded_data[col] = encoded_data[col].map({'yes': 1, 'no': 0})
return encoded_data
def add_features(dataframe):
global lr_day
global lr_eve
global lr_nyt
data = dataframe.copy()
data['total_min'] = data['total_day_min'] + data['total_eve_min'] + data['total_night_minutes'] + data['total_intl_minutes']
try:
data['expected_total_day_charge'] = lr_day.predict(data[['total_day_min']])
data['expected_total_eve_charge'] = lr_eve.predict(data[['total_eve_min']])
data['expected_total_nyt_charge'] = lr_nyt.predict(data[['total_night_minutes']])
except NameError:
lr_day = LinearRegression()
lr_eve = LinearRegression()
lr_nyt = LinearRegression()
lr_day.fit(data[['total_day_min']], data['total_day_charge'])
lr_eve.fit(data[['total_eve_min']], data['total_eve_charge'])
lr_nyt.fit(data[['total_night_minutes']], data['total_night_charge'])
data['expected_total_day_charge'] = lr_day.predict(data[['total_day_min']])
data['expected_total_eve_charge'] = lr_eve.predict(data[['total_eve_min']])
data['expected_total_nyt_charge'] = lr_nyt.predict(data[['total_night_minutes']])
data['error_total_day_charge'] = abs(data['expected_total_day_charge'] - data['total_day_charge'])
data['error_total_eve_charge'] = abs(data['expected_total_eve_charge'] - data['total_eve_charge'])
data['error_total_nyt_charge'] = abs(data['expected_total_nyt_charge'] - data['total_night_charge'])
return data
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
d_droped_train = train_df.drop_duplicates(train_df.columns.drop(['customer_id']))
d_droped_train = d_droped_train.drop(columns=['Unnamed: 20'])
cols = d_droped_train.select_dtypes([np.number]).columns
d_droped_train[cols] = d_droped_train[cols].abs()
d_droped_train['account_length'].fillna(d_droped_train.account_length.median(), inplace=True)
d_droped_train['intertiol_plan'].fillna('no', inplace=True)
d_droped_train['voice_mail_plan'].fillna('no', inplace=True)
d_droped_train.loc[d_droped_train['voice_mail_plan'] == 'no', 'number_vm_messages'] = 0
d_droped_train.loc[(d_droped_train['voice_mail_plan'] == 'yes') & d_droped_train['number_vm_messages'].isnull(), 'number_vm_messages'] = d_droped_train[d_droped_train.voice_mail_plan == 'yes'].number_vm_messages.median()
d_droped_train.loc[d_droped_train['total_day_min'] > 500, 'total_day_min'] = np.nan
d_droped_train['total_day_min'] = d_droped_train.sort_values(['total_day_charge']).total_day_min.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train.loc[d_droped_train['total_day_calls'] > 350, 'total_day_calls'] = np.nan
d_droped_train['total_day_calls'] = d_droped_train.sort_values(['total_day_min']).total_day_calls.ffill().sort_index()
d_droped_train['total_day_charge'] = d_droped_train.sort_values(['total_day_min']).total_day_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train.loc[d_droped_train['total_eve_min'] > 500, 'total_eve_min'] = np.nan
d_droped_train['total_eve_min'] = d_droped_train.sort_values(['total_eve_charge']).total_eve_min.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train['total_eve_calls'] = d_droped_train.sort_values(['total_eve_min']).total_eve_calls.ffill().sort_index()
d_droped_train['total_eve_charge'] = d_droped_train.sort_values(['total_eve_min']).total_eve_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train.loc[d_droped_train['total_night_minutes'] > 500, 'total_night_minutes'] = np.nan
d_droped_train['total_night_minutes'] = d_droped_train.sort_values(['total_night_charge']).total_night_minutes.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train['total_night_calls'] = d_droped_train.sort_values(['total_night_minutes']).total_night_calls.ffill().sort_index()
d_droped_train.loc[d_droped_train['total_night_charge'] > 150, 'total_night_charge'] = np.nan
d_droped_train['total_night_charge'] = d_droped_train.sort_values(['total_night_minutes']).total_night_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train['total_intl_minutes'] = d_droped_train.sort_values(['total_intl_charge']).total_intl_minutes.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train.loc[(d_droped_train['total_intl_minutes'] > 0) & (d_droped_train['total_intl_charge'] > 0) & (d_droped_train['total_intl_calls'] < 1), 'total_intl_calls'] = np.nan
d_droped_train['total_intl_calls'] = d_droped_train.sort_values(['total_intl_minutes']).total_intl_calls.ffill().sort_index()
d_droped_train['total_intl_charge'] = d_droped_train.sort_values(['total_intl_minutes']).total_intl_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
d_droped_train['customer_service_calls'].fillna(1, inplace=True)
odm_handled_train = d_droped_train.dropna(subset=['Churn'])
test_df = test_df.drop(columns=['Unnamed: 19', 'Unnamed: 20'])
cols = test_df.select_dtypes([np.number]).columns
test_df[cols] = test_df[cols].abs()
test_df['location_code'] = test_df['location_code'].ffill()
test_df['intertiol_plan'].fillna('no', inplace=True)
test_df['voice_mail_plan'].fillna('no', inplace=True)
test_df['number_vm_messages'].fillna(0, inplace=True)
test_df['total_day_min'] = test_df.sort_values(['total_day_charge']).total_day_min.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df['total_day_calls'] = test_df.sort_values(['total_day_min']).total_day_calls.ffill().sort_index()
test_df['total_day_charge'] = test_df.sort_values(['total_day_min']).total_day_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df['total_eve_min'] = test_df.sort_values(['total_eve_charge']).total_eve_min.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df['total_eve_charge'] = test_df.sort_values(['total_eve_min']).total_eve_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df['total_night_minutes'] = test_df.sort_values(['total_night_charge']).total_night_minutes.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df['total_night_calls'] = test_df.sort_values(['total_night_minutes']).total_night_calls.ffill().sort_index()
test_df['total_night_charge'] = test_df.sort_values(['total_night_minutes']).total_night_charge.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df['total_intl_minutes'] = test_df.sort_values(['total_intl_charge']).total_intl_minutes.interpolate(method='linear', limit_direction='forward', axis=0).sort_index()
test_df.loc[(test_df['total_intl_minutes'] > 0) & (test_df['total_intl_charge'] > 0) & (test_df['total_intl_calls'] < 1), 'total_intl_calls'] = np.nan
test_df['total_intl_calls'] = test_df.sort_values(['total_intl_minutes']).total_intl_calls.ffill().sort_index()
test_df['customer_service_calls'].fillna(1, inplace=True)
train = odm_handled_train.copy()
test = test_df.copy()
train.head() | code |
89136081/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_medal = pd.read_csv('../input/beijing-2022-olympics/medals.csv')
df_total_medals = pd.read_csv('../input/beijing-2022-olympics/medals_total.csv')
df_events = pd.read_csv('../input/beijing-2022-olympics/events.csv')
df_athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
s_athletes = pd.read_excel('../input/2021-olympics-in-tokyo/Athletes.xlsx')
d_start = pd.to_datetime(df_events['time'][0])
d_end = pd.to_datetime(df_events['time'][len(df_events) - 1])
days = d_end - d_start
df_og = pd.DataFrame()
competition = ['Summer', 'Winter']
nb_discipline = []
nb_country = []
nb_athlete = []
nb_discipline.append(len(s_athletes['Discipline'].unique()))
nb_discipline.append(len(df_medal['discipline'].unique()))
nb_country.append(len(s_athletes['NOC'].unique()))
nb_country.append(len(df_athletes['country'].unique()))
nb_athlete.append(len(s_athletes['Name'].unique()))
nb_athlete.append(len(df_athletes['name'].unique()))
df_og['competition'] = competition
df_og['disciplines'] = nb_discipline
df_og['countries'] = nb_country
df_og['athletes'] = nb_athlete
df_og
df_og.style.set_caption('Summer vs Winter')
cols = ['medal_type', 'event', 'country', 'discipline']
df_medal = df_medal[cols]
countries = df_total_medals['Country']
nb_d = []
df_medal.drop_duplicates()
for country in countries:
temp = df_medal.drop_duplicates()[df_medal.drop_duplicates()['country'] == country]
temp_2 = temp['discipline']
temp_2 = temp_2.drop_duplicates()
nb_d.append(len(temp_2))
df_total_medals['Discipline'] = nb_d
columns = ['Order', 'Country', 'Bronze', 'Silver', 'Gold', 'Total', 'Order by Total', 'Country Code', 'Discipline']
df_total_medals = df_total_medals.reindex(columns=columns)
# Medals barplot
df_total_medals = df_total_medals.sort_index(ascending=False)
# figure prep
plt.rcParams['figure.dpi'] = 200 # figure dots per inch
fig = plt.figure(figsize=(3,30), facecolor='#f6f5f5')
gs = fig.add_gridspec(1, 1)
gs.update(wspace=1.5, hspace=0.05)
background_color = "#f6f5f5"
sns.set_palette(['#D8392B','#CD7F32','#C0C0C0','#FFD700'])
ax0 = fig.add_subplot(gs[0, 0])
for s in ["right", "top"]:
ax0.spines[s].set_visible(False)
ax0.set_facecolor(background_color)
#things to plot on the figure
ax0_sns = df_total_medals.plot(x='Country',y=['Discipline','Bronze','Silver','Gold'],kind='barh',ax=ax0,zorder=2,width=0.8)
ax0_sns.set_xlabel('Medals Count',fontsize=4, weight='bold')
ax0_sns.set_ylabel('Team Name',fontsize=4, weight='bold')
ax0_sns.grid(which='major', axis='x', zorder=0, color='#EEEEEE', linewidth=0.4)
ax0_sns.grid(which='major', axis='y', zorder=0, color='#EEEEEE', linewidth=0.4)
ax0_sns.tick_params(labelsize=3, width=0.5, length=1.5)
ax0_sns.legend(['Discipline','Bronze', 'Silver','Gold'], ncol=4, facecolor='#D8D8D8'\
,edgecolor=background_color, fontsize=3, bbox_to_anchor=(1, 1.005), loc='upper right')
for p in ax0_sns.patches:
value = f'{p.get_width():.0f}'
if value == '0':
pass
else:
x = p.get_x() + p.get_width() + 1
y = p.get_y() + p.get_height() / 2
ax0.text(x, y, value, ha='left', va='center', fontsize=3)
Xstart, Xend = ax0.get_xlim()
Ystart, Yend = ax0.get_ylim()
ax0_sns.text(Xend-1, Yend+0.3, f'Medals Table', fontsize=6, weight='bold',ha='right')
plt.show()
athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
athletes = athletes.rename(columns={'discipline': 'Discipline', 'country': 'Country'})
cols = ['gender', 'Discipline']
gender = athletes[cols]
gender['M'] = 0
gender['F'] = 0
i = 0
while i < len(gender['gender']):
if gender['gender'][i] == 'Male':
gender['M'][i] = 1
else:
gender['F'][i] = 1
i += 1
gender['Male'] = gender.groupby('Discipline')['M'].transform('sum')
gender['Female'] = gender.groupby('Discipline')['F'].transform('sum')
del gender['gender']
del gender['M']
del gender['F']
gender = gender.drop_duplicates()
gender = gender.iloc[:-1, :]
gender['Total'] = gender['Male'] + gender['Female']
total_male = int(gender['Male'].sum())
total_female = int(gender['Female'].sum())
gender.sort_values(by='Total', inplace=True)
plt.rcParams['figure.dpi'] = 300
fig = plt.figure(figsize=(2, 5), facecolor='#f6f5f5')
gs = fig.add_gridspec(1, 1)
gs.update(wspace=1.5, hspace=0.05)
background_color = '#f6f5f5'
sns.set_palette(['#87ceeb', '#ff355d'])
ax0 = fig.add_subplot(gs[0, 0])
for s in ['right', 'top']:
ax0.spines[s].set_visible(False)
ax0.set_facecolor(background_color)
ax0_sns = gender.plot(x='Discipline', y=['Male', 'Female'], kind='barh', ax=ax0, zorder=2, width=0.8)
ax0_sns.set_xlabel('Genders Count', fontsize=4, weight='bold')
ax0_sns.set_ylabel('Discipline', fontsize=4, weight='bold')
ax0_sns.grid(which='major', axis='x', zorder=0, color='#EEEEEE', linewidth=0.4)
ax0_sns.grid(which='major', axis='y', zorder=0, color='#EEEEEE', linewidth=0.4)
ax0_sns.tick_params(labelsize=3, width=0.5, length=1.5)
ax0_sns.legend(['Male', 'Female'], ncol=2, facecolor='#D8D8D8', edgecolor=background_color, fontsize=3, bbox_to_anchor=(1, 1.03), loc='upper right')
for p in ax0_sns.patches:
value = f'{p.get_width():.0f}'
x = p.get_x() + p.get_width() + 20
y = p.get_y() + p.get_height() / 2
ax0.text(x, y, value, ha='left', va='center', fontsize=3, bbox=dict(facecolor='none', edgecolor='black', boxstyle='round', linewidth=0.3))
ax0_sns.text(300, 16, f'Gender Plot', fontsize=6, weight='bold', ha='right')
ax0.text(480, 15.5, f'Entries by Discipline and number of females and males taking part in it', fontsize=3, ha='right')
plt.show() | code |
89136081/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df_medal = pd.read_csv('../input/beijing-2022-olympics/medals.csv')
df_total_medals = pd.read_csv('../input/beijing-2022-olympics/medals_total.csv')
df_events = pd.read_csv('../input/beijing-2022-olympics/events.csv')
df_athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
s_athletes = pd.read_excel('../input/2021-olympics-in-tokyo/Athletes.xlsx')
columns = ['Order', 'Country', 'Bronze', 'Silver', 'Gold', 'Total', 'Order by Total', 'Country Code', 'Discipline']
df_total_medals = df_total_medals.reindex(columns=columns)
for i in range(len(df_total_medals)):
df_total_medals['Country'][i] = '#' + str(df_total_medals['Order'][i]) + ' ' + df_total_medals['Country'][i] | code |
89136081/cell_25 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_medal = pd.read_csv('../input/beijing-2022-olympics/medals.csv')
df_total_medals = pd.read_csv('../input/beijing-2022-olympics/medals_total.csv')
df_events = pd.read_csv('../input/beijing-2022-olympics/events.csv')
df_athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
s_athletes = pd.read_excel('../input/2021-olympics-in-tokyo/Athletes.xlsx')
d_start = pd.to_datetime(df_events['time'][0])
d_end = pd.to_datetime(df_events['time'][len(df_events) - 1])
days = d_end - d_start
df_og = pd.DataFrame()
competition = ['Summer', 'Winter']
nb_discipline = []
nb_country = []
nb_athlete = []
nb_discipline.append(len(s_athletes['Discipline'].unique()))
nb_discipline.append(len(df_medal['discipline'].unique()))
nb_country.append(len(s_athletes['NOC'].unique()))
nb_country.append(len(df_athletes['country'].unique()))
nb_athlete.append(len(s_athletes['Name'].unique()))
nb_athlete.append(len(df_athletes['name'].unique()))
df_og['competition'] = competition
df_og['disciplines'] = nb_discipline
df_og['countries'] = nb_country
df_og['athletes'] = nb_athlete
df_og
df_og.style.set_caption('Summer vs Winter')
cols = ['medal_type', 'event', 'country', 'discipline']
df_medal = df_medal[cols]
countries = df_total_medals['Country']
nb_d = []
df_medal.drop_duplicates()
for country in countries:
temp = df_medal.drop_duplicates()[df_medal.drop_duplicates()['country'] == country]
temp_2 = temp['discipline']
temp_2 = temp_2.drop_duplicates()
nb_d.append(len(temp_2))
df_total_medals['Discipline'] = nb_d
columns = ['Order', 'Country', 'Bronze', 'Silver', 'Gold', 'Total', 'Order by Total', 'Country Code', 'Discipline']
df_total_medals = df_total_medals.reindex(columns=columns)
# Medals barplot
df_total_medals = df_total_medals.sort_index(ascending=False)
# figure prep
plt.rcParams['figure.dpi'] = 200 # figure dots per inch
fig = plt.figure(figsize=(3,30), facecolor='#f6f5f5')
gs = fig.add_gridspec(1, 1)
gs.update(wspace=1.5, hspace=0.05)
background_color = "#f6f5f5"
sns.set_palette(['#D8392B','#CD7F32','#C0C0C0','#FFD700'])
ax0 = fig.add_subplot(gs[0, 0])
for s in ["right", "top"]:
ax0.spines[s].set_visible(False)
ax0.set_facecolor(background_color)
#things to plot on the figure
ax0_sns = df_total_medals.plot(x='Country',y=['Discipline','Bronze','Silver','Gold'],kind='barh',ax=ax0,zorder=2,width=0.8)
ax0_sns.set_xlabel('Medals Count',fontsize=4, weight='bold')
ax0_sns.set_ylabel('Team Name',fontsize=4, weight='bold')
ax0_sns.grid(which='major', axis='x', zorder=0, color='#EEEEEE', linewidth=0.4)
ax0_sns.grid(which='major', axis='y', zorder=0, color='#EEEEEE', linewidth=0.4)
ax0_sns.tick_params(labelsize=3, width=0.5, length=1.5)
ax0_sns.legend(['Discipline','Bronze', 'Silver','Gold'], ncol=4, facecolor='#D8D8D8'\
,edgecolor=background_color, fontsize=3, bbox_to_anchor=(1, 1.005), loc='upper right')
for p in ax0_sns.patches:
value = f'{p.get_width():.0f}'
if value == '0':
pass
else:
x = p.get_x() + p.get_width() + 1
y = p.get_y() + p.get_height() / 2
ax0.text(x, y, value, ha='left', va='center', fontsize=3)
Xstart, Xend = ax0.get_xlim()
Ystart, Yend = ax0.get_ylim()
ax0_sns.text(Xend-1, Yend+0.3, f'Medals Table', fontsize=6, weight='bold',ha='right')
plt.show()
athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
athletes = athletes.rename(columns={'discipline': 'Discipline', 'country': 'Country'})
cols = ['gender', 'Discipline']
gender = athletes[cols]
gender['M'] = 0
gender['F'] = 0
i = 0
while i < len(gender['gender']):
if gender['gender'][i] == 'Male':
gender['M'][i] = 1
else:
gender['F'][i] = 1
i += 1
gender['Male'] = gender.groupby('Discipline')['M'].transform('sum')
gender['Female'] = gender.groupby('Discipline')['F'].transform('sum')
del gender['gender']
del gender['M']
del gender['F']
gender = gender.drop_duplicates()
gender = gender.iloc[:-1, :]
gender['Total'] = gender['Male'] + gender['Female']
total_male = int(gender['Male'].sum())
total_female = int(gender['Female'].sum())
gender.sort_values(by='Total',inplace=True)
# Gender barplot
plt.rcParams['figure.dpi'] = 300
fig = plt.figure(figsize=(2,5), facecolor='#f6f5f5')
gs = fig.add_gridspec(1, 1)
gs.update(wspace=1.5, hspace=0.05)
background_color = "#f6f5f5"
sns.set_palette(['#87ceeb','#ff355d'])
ax0 = fig.add_subplot(gs[0, 0])
for s in ["right", "top"]:
ax0.spines[s].set_visible(False)
ax0.set_facecolor(background_color)
ax0_sns = gender.plot(x='Discipline',y=['Male','Female'],kind='barh',ax=ax0,zorder=2,width=0.8) ##plotttt of bars
ax0_sns.set_xlabel('Genders Count',fontsize=4, weight='bold',)
ax0_sns.set_ylabel('Discipline',fontsize=4, weight='bold')
ax0_sns.grid(which='major', axis='x', zorder=0, color='#EEEEEE', linewidth=0.4)
ax0_sns.grid(which='major', axis='y', zorder=0, color='#EEEEEE', linewidth=0.4)
ax0_sns.tick_params(labelsize=3, width=0.5, length=1.5) # w and l of petit trait de mesure de l'axe x et y
ax0_sns.legend(['Male', 'Female'], ncol=2, facecolor='#D8D8D8', edgecolor=background_color, fontsize=3, bbox_to_anchor=(1, 1.03), loc='upper right')
for p in ax0_sns.patches:
value = f'{p.get_width():.0f}'
if value == '0':
pass
else:
x = p.get_x() + p.get_width() + 20
y = p.get_y() + p.get_height() / 2
ax0.text(x, y, value, ha='left', va='center', fontsize=3,
bbox=dict(facecolor='none', edgecolor='black', boxstyle='round', linewidth=0.3))
ax0_sns.text(300,16, f'Gender Plot', fontsize=6, weight='bold',ha='right')
ax0.text(480, 15.5,f'Entries by Discipline and number of females and males taking part in it',fontsize=3,ha='right')
plt.show()
cols = ['Country', 'Discipline']
athletes = athletes[cols]
y = athletes.Country.value_counts().index
x = athletes.Country.value_counts().values
plt.rcParams['figure.dpi'] = 300
fig = plt.figure(figsize=(2, 48), facecolor='#f6f5f5')
gs = fig.add_gridspec(1, 1)
gs.update(wspace=1.5, hspace=0.05)
background_color = '#f6f5f5'
sns.set_palette(['#bca6cf'] * 1200)
ax0 = fig.add_subplot(gs[0, 0])
for s in ['right', 'top']:
ax0.spines[s].set_visible(False)
ax0.set_facecolor(background_color)
ax0_sns = sns.barplot(data=athletes, y=y, x=x, zorder=2)
ax0_sns.set_xlabel('No of Athletes', fontsize=4, weight='bold')
ax0_sns.set_ylabel('Countries', fontsize=4, weight='bold')
ax0_sns.grid(which='major', axis='x', zorder=0, color='#EEEEEE', linewidth=0.4)
ax0_sns.grid(which='major', axis='y', zorder=0, color='#EEEEEE', linewidth=0.4)
ax0_sns.tick_params(labelsize=3, width=0.5, length=1.5)
for p in ax0_sns.patches:
value = f'{p.get_width():.0f}'
x = p.get_x() + p.get_width() + 20
y = p.get_y() + p.get_height() / 2
ax0.text(x, y, value, ha='left', va='center', fontsize=3, bbox=dict(facecolor='none', edgecolor='black', boxstyle='round', linewidth=0.3))
ax0_sns.text(150, -1.6, f'Athletes Plot', fontsize=6, weight='bold', ha='right')
ax0.text(175, -1.3, f'Contains details about the participating Athletes', fontsize=3, ha='right')
plt.show() | code |
89136081/cell_4 | [
"image_output_1.png"
] | pip install openpyxl | code |
89136081/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
df_medal = pd.read_csv('../input/beijing-2022-olympics/medals.csv')
df_total_medals = pd.read_csv('../input/beijing-2022-olympics/medals_total.csv')
df_events = pd.read_csv('../input/beijing-2022-olympics/events.csv')
df_athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
s_athletes = pd.read_excel('../input/2021-olympics-in-tokyo/Athletes.xlsx')
d_start = pd.to_datetime(df_events['time'][0])
d_end = pd.to_datetime(df_events['time'][len(df_events) - 1])
days = d_end - d_start
df_og = pd.DataFrame()
competition = ['Summer', 'Winter']
nb_discipline = []
nb_country = []
nb_athlete = []
nb_discipline.append(len(s_athletes['Discipline'].unique()))
nb_discipline.append(len(df_medal['discipline'].unique()))
nb_country.append(len(s_athletes['NOC'].unique()))
nb_country.append(len(df_athletes['country'].unique()))
nb_athlete.append(len(s_athletes['Name'].unique()))
nb_athlete.append(len(df_athletes['name'].unique()))
df_og['competition'] = competition
df_og['disciplines'] = nb_discipline
df_og['countries'] = nb_country
df_og['athletes'] = nb_athlete
df_og
df_og.style.set_caption('Summer vs Winter')
cols = ['medal_type', 'event', 'country', 'discipline']
df_medal = df_medal[cols]
countries = df_total_medals['Country']
nb_d = []
df_medal.drop_duplicates()
for country in countries:
temp = df_medal.drop_duplicates()[df_medal.drop_duplicates()['country'] == country]
temp_2 = temp['discipline']
temp_2 = temp_2.drop_duplicates()
nb_d.append(len(temp_2))
df_total_medals['Discipline'] = nb_d
athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
athletes = athletes.rename(columns={'discipline': 'Discipline', 'country': 'Country'})
cols = ['gender', 'Discipline']
gender = athletes[cols]
athletes.head() | code |
89136081/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
df_medal = pd.read_csv('../input/beijing-2022-olympics/medals.csv')
df_total_medals = pd.read_csv('../input/beijing-2022-olympics/medals_total.csv')
df_events = pd.read_csv('../input/beijing-2022-olympics/events.csv')
df_athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
s_athletes = pd.read_excel('../input/2021-olympics-in-tokyo/Athletes.xlsx')
d_start = pd.to_datetime(df_events['time'][0])
d_end = pd.to_datetime(df_events['time'][len(df_events) - 1])
days = d_end - d_start
df_og = pd.DataFrame()
competition = ['Summer', 'Winter']
nb_discipline = []
nb_country = []
nb_athlete = []
nb_discipline.append(len(s_athletes['Discipline'].unique()))
nb_discipline.append(len(df_medal['discipline'].unique()))
nb_country.append(len(s_athletes['NOC'].unique()))
nb_country.append(len(df_athletes['country'].unique()))
nb_athlete.append(len(s_athletes['Name'].unique()))
nb_athlete.append(len(df_athletes['name'].unique()))
df_og['competition'] = competition
df_og['disciplines'] = nb_discipline
df_og['countries'] = nb_country
df_og['athletes'] = nb_athlete
df_og
df_og.style.set_caption('Summer vs Winter')
cols = ['medal_type', 'event', 'country', 'discipline']
df_medal = df_medal[cols]
countries = df_total_medals['Country']
nb_d = []
df_medal.drop_duplicates()
for country in countries:
temp = df_medal.drop_duplicates()[df_medal.drop_duplicates()['country'] == country]
temp_2 = temp['discipline']
temp_2 = temp_2.drop_duplicates()
nb_d.append(len(temp_2))
df_total_medals['Discipline'] = nb_d
athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
athletes = athletes.rename(columns={'discipline': 'Discipline', 'country': 'Country'})
cols = ['gender', 'Discipline']
gender = athletes[cols]
gender['M'] = 0
gender['F'] = 0
i = 0
while i < len(gender['gender']):
if gender['gender'][i] == 'Male':
gender['M'][i] = 1
else:
gender['F'][i] = 1
i += 1
gender['Male'] = gender.groupby('Discipline')['M'].transform('sum')
gender['Female'] = gender.groupby('Discipline')['F'].transform('sum')
del gender['gender']
del gender['M']
del gender['F']
gender = gender.drop_duplicates()
gender = gender.iloc[:-1, :]
gender['Total'] = gender['Male'] + gender['Female']
total_male = int(gender['Male'].sum())
total_female = int(gender['Female'].sum())
print('There is a total of', total_male, 'male and', total_female, 'female for the Beijing 2022 Olympic Games') | code |
89136081/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
df_medal = pd.read_csv('../input/beijing-2022-olympics/medals.csv')
df_total_medals = pd.read_csv('../input/beijing-2022-olympics/medals_total.csv')
df_events = pd.read_csv('../input/beijing-2022-olympics/events.csv')
df_athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
s_athletes = pd.read_excel('../input/2021-olympics-in-tokyo/Athletes.xlsx')
d_start = pd.to_datetime(df_events['time'][0])
d_end = pd.to_datetime(df_events['time'][len(df_events) - 1])
days = d_end - d_start
df_og = pd.DataFrame()
competition = ['Summer', 'Winter']
nb_discipline = []
nb_country = []
nb_athlete = []
nb_discipline.append(len(s_athletes['Discipline'].unique()))
nb_discipline.append(len(df_medal['discipline'].unique()))
nb_country.append(len(s_athletes['NOC'].unique()))
nb_country.append(len(df_athletes['country'].unique()))
nb_athlete.append(len(s_athletes['Name'].unique()))
nb_athlete.append(len(df_athletes['name'].unique()))
df_og['competition'] = competition
df_og['disciplines'] = nb_discipline
df_og['countries'] = nb_country
df_og['athletes'] = nb_athlete
df_og
df_og.style.set_caption('Summer vs Winter')
cols = ['medal_type', 'event', 'country', 'discipline']
df_medal = df_medal[cols]
countries = df_total_medals['Country']
nb_d = []
df_medal.drop_duplicates()
for country in countries:
temp = df_medal.drop_duplicates()[df_medal.drop_duplicates()['country'] == country]
temp_2 = temp['discipline']
temp_2 = temp_2.drop_duplicates()
nb_d.append(len(temp_2))
df_total_medals['Discipline'] = nb_d
athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
athletes = athletes.rename(columns={'discipline': 'Discipline', 'country': 'Country'})
cols = ['gender', 'Discipline']
gender = athletes[cols]
gender['M'] = 0
gender['F'] = 0
i = 0
while i < len(gender['gender']):
if gender['gender'][i] == 'Male':
gender['M'][i] = 1
else:
gender['F'][i] = 1
i += 1
gender['Male'] = gender.groupby('Discipline')['M'].transform('sum')
gender['Female'] = gender.groupby('Discipline')['F'].transform('sum')
del gender['gender']
del gender['M']
del gender['F']
gender = gender.drop_duplicates()
gender = gender.iloc[:-1, :]
gender['Total'] = gender['Male'] + gender['Female']
total_male = int(gender['Male'].sum())
total_female = int(gender['Female'].sum()) | code |
89136081/cell_7 | [
"image_output_1.png"
] | import pandas as pd
df_medal = pd.read_csv('../input/beijing-2022-olympics/medals.csv')
df_total_medals = pd.read_csv('../input/beijing-2022-olympics/medals_total.csv')
df_events = pd.read_csv('../input/beijing-2022-olympics/events.csv')
df_athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
s_athletes = pd.read_excel('../input/2021-olympics-in-tokyo/Athletes.xlsx')
d_start = pd.to_datetime(df_events['time'][0])
d_end = pd.to_datetime(df_events['time'][len(df_events) - 1])
days = d_end - d_start
df_og = pd.DataFrame()
competition = ['Summer', 'Winter']
nb_discipline = []
nb_country = []
nb_athlete = []
nb_discipline.append(len(s_athletes['Discipline'].unique()))
nb_discipline.append(len(df_medal['discipline'].unique()))
nb_country.append(len(s_athletes['NOC'].unique()))
nb_country.append(len(df_athletes['country'].unique()))
nb_athlete.append(len(s_athletes['Name'].unique()))
nb_athlete.append(len(df_athletes['name'].unique()))
df_og['competition'] = competition
df_og['disciplines'] = nb_discipline
df_og['countries'] = nb_country
df_og['athletes'] = nb_athlete
df_og
df_og.style.set_caption('Summer vs Winter') | code |
89136081/cell_18 | [
"image_output_1.png"
] | import pandas as pd
df_medal = pd.read_csv('../input/beijing-2022-olympics/medals.csv')
df_total_medals = pd.read_csv('../input/beijing-2022-olympics/medals_total.csv')
df_events = pd.read_csv('../input/beijing-2022-olympics/events.csv')
df_athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
s_athletes = pd.read_excel('../input/2021-olympics-in-tokyo/Athletes.xlsx')
d_start = pd.to_datetime(df_events['time'][0])
d_end = pd.to_datetime(df_events['time'][len(df_events) - 1])
days = d_end - d_start
df_og = pd.DataFrame()
competition = ['Summer', 'Winter']
nb_discipline = []
nb_country = []
nb_athlete = []
nb_discipline.append(len(s_athletes['Discipline'].unique()))
nb_discipline.append(len(df_medal['discipline'].unique()))
nb_country.append(len(s_athletes['NOC'].unique()))
nb_country.append(len(df_athletes['country'].unique()))
nb_athlete.append(len(s_athletes['Name'].unique()))
nb_athlete.append(len(df_athletes['name'].unique()))
df_og['competition'] = competition
df_og['disciplines'] = nb_discipline
df_og['countries'] = nb_country
df_og['athletes'] = nb_athlete
df_og
df_og.style.set_caption('Summer vs Winter')
cols = ['medal_type', 'event', 'country', 'discipline']
df_medal = df_medal[cols]
countries = df_total_medals['Country']
nb_d = []
df_medal.drop_duplicates()
for country in countries:
temp = df_medal.drop_duplicates()[df_medal.drop_duplicates()['country'] == country]
temp_2 = temp['discipline']
temp_2 = temp_2.drop_duplicates()
nb_d.append(len(temp_2))
df_total_medals['Discipline'] = nb_d
athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
athletes = athletes.rename(columns={'discipline': 'Discipline', 'country': 'Country'})
cols = ['gender', 'Discipline']
gender = athletes[cols]
gender['Discipline'].value_counts() | code |
89136081/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_medal = pd.read_csv('../input/beijing-2022-olympics/medals.csv')
df_total_medals = pd.read_csv('../input/beijing-2022-olympics/medals_total.csv')
df_events = pd.read_csv('../input/beijing-2022-olympics/events.csv')
df_athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
s_athletes = pd.read_excel('../input/2021-olympics-in-tokyo/Athletes.xlsx')
d_start = pd.to_datetime(df_events['time'][0])
d_end = pd.to_datetime(df_events['time'][len(df_events) - 1])
days = d_end - d_start
df_og = pd.DataFrame()
competition = ['Summer', 'Winter']
nb_discipline = []
nb_country = []
nb_athlete = []
nb_discipline.append(len(s_athletes['Discipline'].unique()))
nb_discipline.append(len(df_medal['discipline'].unique()))
nb_country.append(len(s_athletes['NOC'].unique()))
nb_country.append(len(df_athletes['country'].unique()))
nb_athlete.append(len(s_athletes['Name'].unique()))
nb_athlete.append(len(df_athletes['name'].unique()))
df_og['competition'] = competition
df_og['disciplines'] = nb_discipline
df_og['countries'] = nb_country
df_og['athletes'] = nb_athlete
df_og
df_og.style.set_caption('Summer vs Winter')
cols = ['medal_type', 'event', 'country', 'discipline']
df_medal = df_medal[cols]
countries = df_total_medals['Country']
nb_d = []
df_medal.drop_duplicates()
for country in countries:
temp = df_medal.drop_duplicates()[df_medal.drop_duplicates()['country'] == country]
temp_2 = temp['discipline']
temp_2 = temp_2.drop_duplicates()
nb_d.append(len(temp_2))
df_total_medals['Discipline'] = nb_d
athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
athletes = athletes.rename(columns={'discipline': 'Discipline', 'country': 'Country'})
cols = ['gender', 'Discipline']
gender = athletes[cols]
gender.info() | code |
89136081/cell_24 | [
"image_output_1.png"
] | import pandas as pd
df_medal = pd.read_csv('../input/beijing-2022-olympics/medals.csv')
df_total_medals = pd.read_csv('../input/beijing-2022-olympics/medals_total.csv')
df_events = pd.read_csv('../input/beijing-2022-olympics/events.csv')
df_athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
s_athletes = pd.read_excel('../input/2021-olympics-in-tokyo/Athletes.xlsx')
d_start = pd.to_datetime(df_events['time'][0])
d_end = pd.to_datetime(df_events['time'][len(df_events) - 1])
days = d_end - d_start
df_og = pd.DataFrame()
competition = ['Summer', 'Winter']
nb_discipline = []
nb_country = []
nb_athlete = []
nb_discipline.append(len(s_athletes['Discipline'].unique()))
nb_discipline.append(len(df_medal['discipline'].unique()))
nb_country.append(len(s_athletes['NOC'].unique()))
nb_country.append(len(df_athletes['country'].unique()))
nb_athlete.append(len(s_athletes['Name'].unique()))
nb_athlete.append(len(df_athletes['name'].unique()))
df_og['competition'] = competition
df_og['disciplines'] = nb_discipline
df_og['countries'] = nb_country
df_og['athletes'] = nb_athlete
df_og
df_og.style.set_caption('Summer vs Winter')
cols = ['medal_type', 'event', 'country', 'discipline']
df_medal = df_medal[cols]
countries = df_total_medals['Country']
nb_d = []
df_medal.drop_duplicates()
for country in countries:
temp = df_medal.drop_duplicates()[df_medal.drop_duplicates()['country'] == country]
temp_2 = temp['discipline']
temp_2 = temp_2.drop_duplicates()
nb_d.append(len(temp_2))
df_total_medals['Discipline'] = nb_d
athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
athletes = athletes.rename(columns={'discipline': 'Discipline', 'country': 'Country'})
cols = ['gender', 'Discipline']
gender = athletes[cols]
cols = ['Country', 'Discipline']
athletes = athletes[cols]
athletes.head() | code |
89136081/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_medal = pd.read_csv('../input/beijing-2022-olympics/medals.csv')
df_total_medals = pd.read_csv('../input/beijing-2022-olympics/medals_total.csv')
df_events = pd.read_csv('../input/beijing-2022-olympics/events.csv')
df_athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
s_athletes = pd.read_excel('../input/2021-olympics-in-tokyo/Athletes.xlsx')
columns = ['Order', 'Country', 'Bronze', 'Silver', 'Gold', 'Total', 'Order by Total', 'Country Code', 'Discipline']
df_total_medals = df_total_medals.reindex(columns=columns)
df_total_medals = df_total_medals.sort_index(ascending=False)
plt.rcParams['figure.dpi'] = 200
fig = plt.figure(figsize=(3, 30), facecolor='#f6f5f5')
gs = fig.add_gridspec(1, 1)
gs.update(wspace=1.5, hspace=0.05)
background_color = '#f6f5f5'
sns.set_palette(['#D8392B', '#CD7F32', '#C0C0C0', '#FFD700'])
ax0 = fig.add_subplot(gs[0, 0])
for s in ['right', 'top']:
ax0.spines[s].set_visible(False)
ax0.set_facecolor(background_color)
ax0_sns = df_total_medals.plot(x='Country', y=['Discipline', 'Bronze', 'Silver', 'Gold'], kind='barh', ax=ax0, zorder=2, width=0.8)
ax0_sns.set_xlabel('Medals Count', fontsize=4, weight='bold')
ax0_sns.set_ylabel('Team Name', fontsize=4, weight='bold')
ax0_sns.grid(which='major', axis='x', zorder=0, color='#EEEEEE', linewidth=0.4)
ax0_sns.grid(which='major', axis='y', zorder=0, color='#EEEEEE', linewidth=0.4)
ax0_sns.tick_params(labelsize=3, width=0.5, length=1.5)
ax0_sns.legend(['Discipline', 'Bronze', 'Silver', 'Gold'], ncol=4, facecolor='#D8D8D8', edgecolor=background_color, fontsize=3, bbox_to_anchor=(1, 1.005), loc='upper right')
for p in ax0_sns.patches:
value = f'{p.get_width():.0f}'
x = p.get_x() + p.get_width() + 1
y = p.get_y() + p.get_height() / 2
ax0.text(x, y, value, ha='left', va='center', fontsize=3)
Xstart, Xend = ax0.get_xlim()
Ystart, Yend = ax0.get_ylim()
ax0_sns.text(Xend - 1, Yend + 0.3, f'Medals Table', fontsize=6, weight='bold', ha='right')
plt.show() | code |
89136081/cell_10 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
df_medal = pd.read_csv('../input/beijing-2022-olympics/medals.csv')
df_total_medals = pd.read_csv('../input/beijing-2022-olympics/medals_total.csv')
df_events = pd.read_csv('../input/beijing-2022-olympics/events.csv')
df_athletes = pd.read_csv('../input/beijing-2022-olympics/athletes.csv')
s_athletes = pd.read_excel('../input/2021-olympics-in-tokyo/Athletes.xlsx')
df_medal.head() | code |
34143777/cell_13 | [
"text_plain_output_1.png"
] | from keras import optimizers
from keras.layers import Activation, Convolution2D, Dropout, Conv2D,MaxPool2D
from keras.layers import Dense, Conv2D , MaxPooling2D , Flatten , Dropout
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.models import Sequential
from keras.models import Sequential
from sklearn.preprocessing import LabelBinarizer
import keras
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('/kaggle/input/sign-language-mnist/sign_mnist_train.csv')
test = pd.read_csv('/kaggle/input/sign-language-mnist/sign_mnist_test.csv')
X_train = train.iloc[:, 1:]
Y_train = train.iloc[:, 0]
X_test = test.iloc[:, 1:]
Y_test = test.iloc[:, 0]
(X_train.shape, Y_train.shape)
X_train = np.array(X_train)
X_test = np.array(X_test)
X_train = X_train / 255
X_test = X_test / 255
X_train = X_train.reshape(-1, 28, 28, 1)
X_test = X_test.reshape(-1, 28, 28, 1)
X_train.shape
from sklearn.preprocessing import LabelBinarizer
encoder = LabelBinarizer()
Y_train = encoder.fit_transform(Y_train)
Y_test = encoder.fit_transform(Y_test)
model = Sequential()
model.add(Conv2D(input_shape=(28, 28, 1), filters=20, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(keras.layers.Dropout(0.1))
model.add(MaxPool2D(2, 2))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPool2D(2, 2))
model.add(keras.layers.Dropout(0.1))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(keras.layers.Dropout(0.1))
model.add(Conv2D(128, (1, 1), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(keras.layers.Dropout(0.1))
model.add(Flatten())
model.add(Dense(units=64, activation='relu'))
model.add(keras.layers.Dropout(0.1))
model.add(Dense(units=24, activation='softmax'))
learning_rate = 0.001
lr_decay = 1e-06
sgd = optimizers.SGD(lr=learning_rate, decay=lr_decay, momentum=0.9, nesterov=True)
adam = optimizers.Adam(lr=0.002)
model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])
history = model.fit(X_train, Y_train, epochs=60, batch_size=524, verbose=1, validation_data=(X_valid, Y_valid))
testModel = model.evaluate(X_test, Y_test)
print('Acuarcy = %.2f%%' % (testModel[1] * 100))
print('Loss = %.2f%%' % (testModel[0] * 100))
print(history.history.keys())
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show() | code |
34143777/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 |
34143777/cell_7 | [
"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)
train = pd.read_csv('/kaggle/input/sign-language-mnist/sign_mnist_train.csv')
test = pd.read_csv('/kaggle/input/sign-language-mnist/sign_mnist_test.csv')
X_train = train.iloc[:, 1:]
Y_train = train.iloc[:, 0]
X_test = test.iloc[:, 1:]
Y_test = test.iloc[:, 0]
(X_train.shape, Y_train.shape)
X_train = np.array(X_train)
X_test = np.array(X_test)
X_train = X_train / 255
X_test = X_test / 255
X_train = X_train.reshape(-1, 28, 28, 1)
X_test = X_test.reshape(-1, 28, 28, 1)
X_train.shape | code |
34143777/cell_15 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from keras import optimizers
from keras.layers import Activation, Convolution2D, Dropout, Conv2D,MaxPool2D
from keras.layers import Dense, Conv2D , MaxPooling2D , Flatten , Dropout
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.models import Sequential
from keras.models import Sequential
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.preprocessing import LabelBinarizer
import keras
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)
import seaborn as sns
train = pd.read_csv('/kaggle/input/sign-language-mnist/sign_mnist_train.csv')
test = pd.read_csv('/kaggle/input/sign-language-mnist/sign_mnist_test.csv')
X_train = train.iloc[:, 1:]
Y_train = train.iloc[:, 0]
X_test = test.iloc[:, 1:]
Y_test = test.iloc[:, 0]
(X_train.shape, Y_train.shape)
X_train = np.array(X_train)
X_test = np.array(X_test)
X_train = X_train / 255
X_test = X_test / 255
X_train = X_train.reshape(-1, 28, 28, 1)
X_test = X_test.reshape(-1, 28, 28, 1)
X_train.shape
from sklearn.preprocessing import LabelBinarizer
encoder = LabelBinarizer()
Y_train = encoder.fit_transform(Y_train)
Y_test = encoder.fit_transform(Y_test)
model = Sequential()
model.add(Conv2D(input_shape=(28, 28, 1), filters=20, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(keras.layers.Dropout(0.1))
model.add(MaxPool2D(2, 2))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPool2D(2, 2))
model.add(keras.layers.Dropout(0.1))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(keras.layers.Dropout(0.1))
model.add(Conv2D(128, (1, 1), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(keras.layers.Dropout(0.1))
model.add(Flatten())
model.add(Dense(units=64, activation='relu'))
model.add(keras.layers.Dropout(0.1))
model.add(Dense(units=24, activation='softmax'))
learning_rate = 0.001
lr_decay = 1e-06
sgd = optimizers.SGD(lr=learning_rate, decay=lr_decay, momentum=0.9, nesterov=True)
adam = optimizers.Adam(lr=0.002)
model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])
history = model.fit(X_train, Y_train, epochs=60, batch_size=524, verbose=1, validation_data=(X_valid, Y_valid))
testModel = model.evaluate(X_test, Y_test)
predicted_classes = model.predict_classes(X_test)
rounded_labels = np.argmax(Y_test, axis=1)
confusionMatrix = confusion_matrix(rounded_labels, predicted_classes)
confusionMatrix = pd.DataFrame(confusionMatrix, index=[i for i in range(25) if i != 9], columns=[i for i in range(25) if i != 9])
classes = ['Class ' + str(i) for i in range(25) if i != 9]
print(classification_report(rounded_labels, predicted_classes, target_names=classes)) | code |
34143777/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/sign-language-mnist/sign_mnist_train.csv')
test = pd.read_csv('/kaggle/input/sign-language-mnist/sign_mnist_test.csv')
X_train = train.iloc[:, 1:]
Y_train = train.iloc[:, 0]
X_test = test.iloc[:, 1:]
Y_test = test.iloc[:, 0]
(X_train.shape, Y_train.shape) | code |
34143777/cell_14 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras import optimizers
from keras.layers import Activation, Convolution2D, Dropout, Conv2D,MaxPool2D
from keras.layers import Dense, Conv2D , MaxPooling2D , Flatten , Dropout
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.models import Sequential
from keras.models import Sequential
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.preprocessing import LabelBinarizer
import keras
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)
import seaborn as sns
train = pd.read_csv('/kaggle/input/sign-language-mnist/sign_mnist_train.csv')
test = pd.read_csv('/kaggle/input/sign-language-mnist/sign_mnist_test.csv')
X_train = train.iloc[:, 1:]
Y_train = train.iloc[:, 0]
X_test = test.iloc[:, 1:]
Y_test = test.iloc[:, 0]
(X_train.shape, Y_train.shape)
X_train = np.array(X_train)
X_test = np.array(X_test)
X_train = X_train / 255
X_test = X_test / 255
X_train = X_train.reshape(-1, 28, 28, 1)
X_test = X_test.reshape(-1, 28, 28, 1)
X_train.shape
from sklearn.preprocessing import LabelBinarizer
encoder = LabelBinarizer()
Y_train = encoder.fit_transform(Y_train)
Y_test = encoder.fit_transform(Y_test)
model = Sequential()
model.add(Conv2D(input_shape=(28, 28, 1), filters=20, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(keras.layers.Dropout(0.1))
model.add(MaxPool2D(2, 2))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPool2D(2, 2))
model.add(keras.layers.Dropout(0.1))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(keras.layers.Dropout(0.1))
model.add(Conv2D(128, (1, 1), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(keras.layers.Dropout(0.1))
model.add(Flatten())
model.add(Dense(units=64, activation='relu'))
model.add(keras.layers.Dropout(0.1))
model.add(Dense(units=24, activation='softmax'))
learning_rate = 0.001
lr_decay = 1e-06
sgd = optimizers.SGD(lr=learning_rate, decay=lr_decay, momentum=0.9, nesterov=True)
adam = optimizers.Adam(lr=0.002)
model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])
history = model.fit(X_train, Y_train, epochs=60, batch_size=524, verbose=1, validation_data=(X_valid, Y_valid))
testModel = model.evaluate(X_test, Y_test)
predicted_classes = model.predict_classes(X_test)
rounded_labels = np.argmax(Y_test, axis=1)
confusionMatrix = confusion_matrix(rounded_labels, predicted_classes)
confusionMatrix = pd.DataFrame(confusionMatrix, index=[i for i in range(25) if i != 9], columns=[i for i in range(25) if i != 9])
plt.figure(figsize=(15, 15))
sns.heatmap(confusionMatrix, cmap='OrRd_r', linecolor='black', linewidth=1, annot=True, fmt='') | code |
34143777/cell_12 | [
"text_plain_output_1.png"
] | from keras import optimizers
from keras.layers import Activation, Convolution2D, Dropout, Conv2D,MaxPool2D
from keras.layers import Dense, Conv2D , MaxPooling2D , Flatten , Dropout
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.models import Sequential
from keras.models import Sequential
from sklearn.preprocessing import LabelBinarizer
import keras
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('/kaggle/input/sign-language-mnist/sign_mnist_train.csv')
test = pd.read_csv('/kaggle/input/sign-language-mnist/sign_mnist_test.csv')
X_train = train.iloc[:, 1:]
Y_train = train.iloc[:, 0]
X_test = test.iloc[:, 1:]
Y_test = test.iloc[:, 0]
(X_train.shape, Y_train.shape)
X_train = np.array(X_train)
X_test = np.array(X_test)
X_train = X_train / 255
X_test = X_test / 255
X_train = X_train.reshape(-1, 28, 28, 1)
X_test = X_test.reshape(-1, 28, 28, 1)
X_train.shape
from sklearn.preprocessing import LabelBinarizer
encoder = LabelBinarizer()
Y_train = encoder.fit_transform(Y_train)
Y_test = encoder.fit_transform(Y_test)
model = Sequential()
model.add(Conv2D(input_shape=(28, 28, 1), filters=20, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(keras.layers.Dropout(0.1))
model.add(MaxPool2D(2, 2))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPool2D(2, 2))
model.add(keras.layers.Dropout(0.1))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(keras.layers.Dropout(0.1))
model.add(Conv2D(128, (1, 1), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(keras.layers.Dropout(0.1))
model.add(Flatten())
model.add(Dense(units=64, activation='relu'))
model.add(keras.layers.Dropout(0.1))
model.add(Dense(units=24, activation='softmax'))
learning_rate = 0.001
lr_decay = 1e-06
sgd = optimizers.SGD(lr=learning_rate, decay=lr_decay, momentum=0.9, nesterov=True)
adam = optimizers.Adam(lr=0.002)
model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])
history = model.fit(X_train, Y_train, epochs=60, batch_size=524, verbose=1, validation_data=(X_valid, Y_valid)) | code |
105203731/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/random-linear-regression/train.csv')
df.shape
df.corr()
sns.heatmap(df.corr(), annot=True) | code |
105203731/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.metrics import r2_score
import statsmodels.api as sm
x_train.shape
x_train_sm = sm.add_constant(x_train)
x_train_sm
model = sm.OLS(y_train, x_train_sm)
lr_model = model.fit()
lr_model.params
lr_model.summary()
y_train_pred = lr_model.predict(x_train_sm)
x_test_sm = sm.add_constant(x_test)
y_test_pred = lr_model.predict(x_test_sm)
r2 = r2_score(y_test, y_test_pred)
r2 | code |
105203731/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/random-linear-regression/train.csv')
df.shape | code |
105203731/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import statsmodels.api as sm
df = pd.read_csv('../input/random-linear-regression/train.csv')
df.shape
x_train.shape
x_train_sm = sm.add_constant(x_train)
x_train_sm
model = sm.OLS(y_train, x_train_sm)
lr_model = model.fit()
lr_model.params
lr_model.summary()
y_train_pred = lr_model.predict(x_train_sm)
res = y_train - y_train_pred
x_test_sm = sm.add_constant(x_test)
y_test_pred = lr_model.predict(x_test_sm)
plt.figure()
plt.scatter(x_test, y_test)
plt.plot(x_test, y_test_pred, 'y') | code |
105203731/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('../input/random-linear-regression/train.csv')
df.shape
df.corr()
x_train.shape
x_train_sm = sm.add_constant(x_train)
x_train_sm
model = sm.OLS(y_train, x_train_sm)
lr_model = model.fit()
lr_model.params
lr_model.summary()
y_train_pred = lr_model.predict(x_train_sm)
res = y_train - y_train_pred
sns.distplot(res) | code |
105203731/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/random-linear-regression/train.csv')
df.shape
plt.scatter(df['x'], df['y'])
plt.show() | code |
105203731/cell_11 | [
"text_plain_output_1.png"
] | x_train.shape | code |
105203731/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import statsmodels.api as sm
df = pd.read_csv('../input/random-linear-regression/train.csv')
df.shape
x_train.shape
x_train_sm = sm.add_constant(x_train)
x_train_sm
model = sm.OLS(y_train, x_train_sm)
lr_model = model.fit()
lr_model.params
lr_model.summary()
y_train_pred = lr_model.predict(x_train_sm)
res = y_train - y_train_pred
plt.scatter(x_train, res) | code |
105203731/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/random-linear-regression/train.csv')
df.shape
sns.regplot(x='x', y='y', data=df) | code |
105203731/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/random-linear-regression/train.csv')
df.shape
df.corr() | code |
105203731/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import statsmodels.api as sm
x_train.shape
x_train_sm = sm.add_constant(x_train)
x_train_sm
model = sm.OLS(y_train, x_train_sm)
lr_model = model.fit()
lr_model.params
lr_model.summary() | code |
105203731/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import statsmodels.api as sm
df = pd.read_csv('../input/random-linear-regression/train.csv')
df.shape
x_train.shape
x_train_sm = sm.add_constant(x_train)
x_train_sm
model = sm.OLS(y_train, x_train_sm)
lr_model = model.fit()
lr_model.params
lr_model.summary()
y_train_pred = lr_model.predict(x_train_sm)
plt.scatter(x_train, y_train)
plt.plot(x_train, y_train_pred)
plt.show() | code |
105203731/cell_14 | [
"image_output_1.png"
] | import statsmodels.api as sm
x_train.shape
x_train_sm = sm.add_constant(x_train)
x_train_sm
model = sm.OLS(y_train, x_train_sm)
lr_model = model.fit()
lr_model.params | code |
105203731/cell_12 | [
"text_html_output_1.png"
] | import statsmodels.api as sm
x_train.shape
x_train_sm = sm.add_constant(x_train)
x_train_sm | code |
105203731/cell_5 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/random-linear-regression/train.csv')
df.shape
df.head() | code |
33098715/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
plt.tight_layout()
plt.xticks(rotation=90)
plt.tight_layout()
plt.xticks(rotation=90)
plt.xticks(rotation=90)
plt.tight_layout()
plt.xticks(rotation=90)
plt.tight_layout()
df = data.agg({'price': ['sum', 'min', 'max', 'median'], 'mileage': ['sum', 'min', 'max', 'median']})
print('Max Year: ', data['year'].max())
print('Min Year: ', data['year'].min()) | code |
33098715/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
plt.tight_layout()
plt.xticks(rotation=90)
plt.tight_layout()
plt.xticks(rotation=90)
plt.xticks(rotation=90)
plt.tight_layout()
data['brand'].value_counts().head() | code |
33098715/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
plt.figure(figsize=(18, 8))
sns.countplot(data['brand'])
plt.tight_layout()
plt.xticks(rotation=90)
plt.xlabel('Car Brands')
plt.show() | code |
33098715/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.describe().transpose() | code |
33098715/cell_20 | [
"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
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
plt.tight_layout()
plt.xticks(rotation=90)
plt.tight_layout()
plt.xticks(rotation=90)
plt.xticks(rotation=90)
plt.tight_layout()
plt.xticks(rotation=90)
plt.tight_layout()
df = data.agg({'price': ['sum', 'min', 'max', 'median'], 'mileage': ['sum', 'min', 'max', 'median']})
df | code |
33098715/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum() | code |
33098715/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
data['model'].value_counts().head() | code |
33098715/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 |
33098715/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
data.head() | code |
33098715/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
plt.tight_layout()
plt.xticks(rotation=90)
plt.tight_layout()
plt.xticks(rotation=90)
plt.xticks(rotation=90)
plt.tight_layout()
plt.xticks(rotation=90)
plt.tight_layout()
plt.xticks(rotation=90)
plt.figure(figsize=(15, 8))
sns.distplot(data['price'])
plt.show() | code |
33098715/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
print('Unique car brands: ', data['brand'].nunique())
print('Unique car models: ', data['model'].nunique()) | code |
33098715/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
plt.tight_layout()
plt.xticks(rotation=90)
plt.tight_layout()
plt.xticks(rotation=90)
plt.xticks(rotation=90)
plt.tight_layout()
plt.xticks(rotation=90)
plt.tight_layout()
data.head() | code |
33098715/cell_16 | [
"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
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
plt.tight_layout()
plt.xticks(rotation=90)
plt.tight_layout()
plt.xticks(rotation=90)
plt.xticks(rotation=90)
plt.tight_layout()
plt.xticks(rotation=90)
plt.tight_layout()
plt.figure(figsize=(18, 7))
sns.countplot(data['color'])
plt.xticks(rotation=90)
plt.title('Most used colors on cars')
plt.show() | code |
33098715/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.info() | code |
33098715/cell_17 | [
"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
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
plt.tight_layout()
plt.xticks(rotation=90)
plt.tight_layout()
plt.xticks(rotation=90)
plt.xticks(rotation=90)
plt.tight_layout()
plt.xticks(rotation=90)
plt.tight_layout()
print('Mean price for a car: ', round(data['price'].mean(), 2)) | code |
33098715/cell_14 | [
"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
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
data.isnull().sum()
plt.tight_layout()
plt.xticks(rotation=90)
plt.tight_layout()
plt.xticks(rotation=90)
plt.xticks(rotation=90)
plt.tight_layout()
plt.figure(figsize=(20, 8))
data.groupby('model')['price'].mean().sort_values(ascending=False).plot.bar()
plt.xticks(rotation=90)
plt.ylabel('Mean Price')
plt.xlabel('Car Models')
plt.tight_layout()
plt.show() | code |
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