path
stringlengths 13
17
| screenshot_names
listlengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
88102865/cell_26
|
[
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_7.png",
"text_plain_output_8.png",
"application_vnd.jupyter.stderr_output_1.png"
] |
import pandas as pd
df = pd.read_csv('../input/misoimprovedta/ta-misogyny-train (3).csv', header=None, sep='\t')
df_eval = pd.read_csv('../input/misoimprovedta/ta-misogyny-dev (2).csv', header=None, sep='\t')
df_test = pd.read_csv('../input/misoimprovedta/ta-misogyny-test (2).csv', header=None)
def create_labels(sentence):
splits = sentence.split('\t')
return splits[0]
def change_sentence(sentence):
splits = sentence.split('\t')
return splits[1]
df_eval.rename(columns={0: 'Labels', 1: 'Text'}, inplace=True)
df_eval = df_eval[['Text', 'Labels']]
df.rename(columns={0: 'Labels', 1: 'Text'}, inplace=True)
df = df[['Text', 'Labels']]
df_test.rename(columns={0: 'Text'}, inplace=True)
num_labels = len(df['Labels'].unique())
keys = list(df['Labels'].unique())
values = list(range(0, num_labels))
label_dict = dict(zip(keys, values))
df['Labels'] = df['Labels'].apply(lambda x: label_dict[x])
df_eval['Labels'] = df_eval['Labels'].apply(lambda x: label_dict[x])
df_final = df_test.copy()
reverse_label_dict = {v: u for u, v in label_dict.items()}
reverse_label_dict
df_final['Predicted_Labels'] = predictions
df_final['Predicted_Labels'] = df_final['Predicted_Labels'].apply(lambda x: reverse_label_dict[x])
df_final['pid'] = df_final.index
df_final = df_final[['pid', 'Predicted_Labels']]
df_final
|
code
|
88102865/cell_19
|
[
"text_html_output_1.png"
] |
for i in range(0,5):
!rm -rf /kaggle/working/outputs
model.train_model(df,eval_data=df_dev,acc=sklearn.metrics.classification_report)
result, model_outputs, preds_list = model.eval_model(df_test_,acc=sklearn.metrics.classification_report)
for j in result.values():
print(j)
|
code
|
88102865/cell_18
|
[
"text_html_output_1.png"
] |
from simpletransformers.classification import ClassificationModel, ClassificationArgs
model_args = ClassificationArgs()
model_args.overwrite_output_dir = True
model_args.eval_batch_size = 8
model_args.train_batch_size = 8
model_args.learning_rate = 4e-05
model = ClassificationModel('bert', 'google/muril-base-cased', num_labels=9, args=model_args, tokenizer_type='bert', tokenizer_name='google/muril-base-cased')
|
code
|
88102865/cell_8
|
[
"text_html_output_1.png"
] |
!pip install simpletransformers
|
code
|
88102865/cell_24
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
from simpletransformers.classification import ClassificationModel, ClassificationArgs
from sklearn.model_selection import train_test_split
import pandas as pd
df = pd.read_csv('../input/misoimprovedta/ta-misogyny-train (3).csv', header=None, sep='\t')
df_eval = pd.read_csv('../input/misoimprovedta/ta-misogyny-dev (2).csv', header=None, sep='\t')
df_test = pd.read_csv('../input/misoimprovedta/ta-misogyny-test (2).csv', header=None)
def create_labels(sentence):
splits = sentence.split('\t')
return splits[0]
def change_sentence(sentence):
splits = sentence.split('\t')
return splits[1]
df_eval.rename(columns={0: 'Labels', 1: 'Text'}, inplace=True)
df_eval = df_eval[['Text', 'Labels']]
df.rename(columns={0: 'Labels', 1: 'Text'}, inplace=True)
df = df[['Text', 'Labels']]
df_test.rename(columns={0: 'Text'}, inplace=True)
from sklearn.model_selection import train_test_split
X_test, X_dev, y_test, y_dev = train_test_split(df_eval['Text'], df_eval['Labels'], random_state=0)
df_test_ = pd.concat([X_test, y_test], axis=1)
df_dev = pd.concat([X_dev, y_dev], axis=1)
df_dev
model_args = ClassificationArgs()
model_args.overwrite_output_dir = True
model_args.eval_batch_size = 8
model_args.train_batch_size = 8
model_args.learning_rate = 4e-05
model = ClassificationModel('bert', 'google/muril-base-cased', num_labels=9, args=model_args, tokenizer_type='bert', tokenizer_name='google/muril-base-cased')
predictions, raw_outputs = model.predict(df_test_['Text'].to_list())
predictions, raw_outputs = model.predict(df_test['Text'].to_list())
|
code
|
88102865/cell_22
|
[
"text_plain_output_1.png"
] |
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
import pandas as pd
df = pd.read_csv('../input/misoimprovedta/ta-misogyny-train (3).csv', header=None, sep='\t')
df_eval = pd.read_csv('../input/misoimprovedta/ta-misogyny-dev (2).csv', header=None, sep='\t')
df_test = pd.read_csv('../input/misoimprovedta/ta-misogyny-test (2).csv', header=None)
def create_labels(sentence):
splits = sentence.split('\t')
return splits[0]
def change_sentence(sentence):
splits = sentence.split('\t')
return splits[1]
df_eval.rename(columns={0: 'Labels', 1: 'Text'}, inplace=True)
df_eval = df_eval[['Text', 'Labels']]
df.rename(columns={0: 'Labels', 1: 'Text'}, inplace=True)
df = df[['Text', 'Labels']]
df_test.rename(columns={0: 'Text'}, inplace=True)
from sklearn.model_selection import train_test_split
X_test, X_dev, y_test, y_dev = train_test_split(df_eval['Text'], df_eval['Labels'], random_state=0)
df_test_ = pd.concat([X_test, y_test], axis=1)
df_dev = pd.concat([X_dev, y_dev], axis=1)
df_dev
def oversample(df):
classes = df['Labels'].value_counts().to_dict()
most = max(classes.values())
classes_list = []
for key in classes:
classes_list.append(df[df['Labels'] == key])
classes_sample = []
for i in range(1, len(classes_list)):
classes_sample.append(classes_list[i].sample(most, replace=True))
df_maybe = pd.concat(classes_sample)
final_df = pd.concat([df_maybe, classes_list[0]], axis=0)
final_df = final_df.reset_index(drop=True)
return pd.DataFrame({'Text': final_df['Text'].tolist(), 'Labels': final_df['Labels'].tolist()})
def over_under_sample(df):
unq_labels = list(set(df['Labels'].tolist()))
texts = df['Text'].tolist()
labels = df['Labels'].tolist()
data_dict = dict()
for l in unq_labels:
data_dict[l] = []
for i in range(len(texts)):
data_dict[labels[i]].append(texts[i])
req_len = len(labels) // len(unq_labels)
for label in data_dict:
if len(data_dict[label]) > req_len:
data_dict[label] = data_dict[label][:req_len]
new_texts = []
new_labels = []
for l in data_dict:
new_texts += data_dict[l]
new_labels += [l] * len(data_dict[l])
return oversample(pd.DataFrame({'Text': new_texts, 'Labels': new_labels}))
from sklearn.metrics import classification_report
result_dict = classification_report(df_test_['Labels'], predictions, output_dict=True)
report = pd.DataFrame(result_dict).transpose()
report
|
code
|
88102865/cell_10
|
[
"text_html_output_1.png"
] |
from sklearn.model_selection import train_test_split
import pandas as pd
df = pd.read_csv('../input/misoimprovedta/ta-misogyny-train (3).csv', header=None, sep='\t')
df_eval = pd.read_csv('../input/misoimprovedta/ta-misogyny-dev (2).csv', header=None, sep='\t')
df_test = pd.read_csv('../input/misoimprovedta/ta-misogyny-test (2).csv', header=None)
def create_labels(sentence):
splits = sentence.split('\t')
return splits[0]
def change_sentence(sentence):
splits = sentence.split('\t')
return splits[1]
df_eval.rename(columns={0: 'Labels', 1: 'Text'}, inplace=True)
df_eval = df_eval[['Text', 'Labels']]
df.rename(columns={0: 'Labels', 1: 'Text'}, inplace=True)
df = df[['Text', 'Labels']]
df_test.rename(columns={0: 'Text'}, inplace=True)
from sklearn.model_selection import train_test_split
X_test, X_dev, y_test, y_dev = train_test_split(df_eval['Text'], df_eval['Labels'], random_state=0)
df_test_ = pd.concat([X_test, y_test], axis=1)
df_dev = pd.concat([X_dev, y_dev], axis=1)
df_dev
|
code
|
88102865/cell_5
|
[
"text_html_output_1.png"
] |
import pandas as pd
df = pd.read_csv('../input/misoimprovedta/ta-misogyny-train (3).csv', header=None, sep='\t')
df_eval = pd.read_csv('../input/misoimprovedta/ta-misogyny-dev (2).csv', header=None, sep='\t')
df_test = pd.read_csv('../input/misoimprovedta/ta-misogyny-test (2).csv', header=None)
df_test
|
code
|
88099646/cell_9
|
[
"text_plain_output_1.png"
] |
from matplotlib.pyplot import figure
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
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 seaborn as sn
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
big_dance = pd.read_csv('../input/mm-data-prediction/MM_score_predictionv2.csv')
big_dance.columns
import seaborn as sn
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
corrMatrix = big_dance.corr()
y = big_dance['Total_Score_March_Madness']
features = ['FG', 'FGA', 'Fgper', 'FT', 'FTA', 'PF', 'PTS']
X = big_dance[features]
from sklearn.tree import DecisionTreeRegressor
bd_model = DecisionTreeRegressor(random_state=1)
bd_model.fit(X, y)
from sklearn.model_selection import train_test_split
train_X, val_X, train_y, val_y = train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)
bd_model.fit(train_X, train_y)
from sklearn.metrics import mean_absolute_error
val_predictions = bd_model.predict(val_X)
val_mae = mean_absolute_error(val_predictions, val_y)
print(val_mae)
|
code
|
88099646/cell_4
|
[
"text_html_output_1.png"
] |
from matplotlib.pyplot import figure
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 seaborn as sn
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
big_dance = pd.read_csv('../input/mm-data-prediction/MM_score_predictionv2.csv')
big_dance.columns
import seaborn as sn
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
figure(figsize=(8, 6), dpi=80)
corrMatrix = big_dance.corr()
sn.heatmap(corrMatrix, annot=False)
plt.show()
|
code
|
88099646/cell_6
|
[
"text_plain_output_1.png"
] |
from matplotlib.pyplot import figure
from sklearn.tree import DecisionTreeRegressor
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 seaborn as sn
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
big_dance = pd.read_csv('../input/mm-data-prediction/MM_score_predictionv2.csv')
big_dance.columns
import seaborn as sn
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
corrMatrix = big_dance.corr()
y = big_dance['Total_Score_March_Madness']
features = ['FG', 'FGA', 'Fgper', 'FT', 'FTA', 'PF', 'PTS']
X = big_dance[features]
from sklearn.tree import DecisionTreeRegressor
bd_model = DecisionTreeRegressor(random_state=1)
bd_model.fit(X, y)
|
code
|
88099646/cell_2
|
[
"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 pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
big_dance = pd.read_csv('../input/mm-data-prediction/MM_score_predictionv2.csv')
big_dance.head()
|
code
|
88099646/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
|
88099646/cell_7
|
[
"text_plain_output_1.png"
] |
from matplotlib.pyplot import figure
from sklearn.tree import DecisionTreeRegressor
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 seaborn as sn
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
big_dance = pd.read_csv('../input/mm-data-prediction/MM_score_predictionv2.csv')
big_dance.columns
import seaborn as sn
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
corrMatrix = big_dance.corr()
y = big_dance['Total_Score_March_Madness']
features = ['FG', 'FGA', 'Fgper', 'FT', 'FTA', 'PF', 'PTS']
X = big_dance[features]
from sklearn.tree import DecisionTreeRegressor
bd_model = DecisionTreeRegressor(random_state=1)
bd_model.fit(X, y)
print("Making predictions for the following 5 March Madness's:")
print(X.head())
print('The predictions are')
print(bd_model.predict(X.head()))
|
code
|
88099646/cell_8
|
[
"text_plain_output_1.png"
] |
from matplotlib.pyplot import figure
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
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 seaborn as sn
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
big_dance = pd.read_csv('../input/mm-data-prediction/MM_score_predictionv2.csv')
big_dance.columns
import seaborn as sn
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
corrMatrix = big_dance.corr()
y = big_dance['Total_Score_March_Madness']
features = ['FG', 'FGA', 'Fgper', 'FT', 'FTA', 'PF', 'PTS']
X = big_dance[features]
from sklearn.tree import DecisionTreeRegressor
bd_model = DecisionTreeRegressor(random_state=1)
bd_model.fit(X, y)
from sklearn.model_selection import train_test_split
train_X, val_X, train_y, val_y = train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)
bd_model.fit(train_X, train_y)
|
code
|
88099646/cell_3
|
[
"image_output_1.png"
] |
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
big_dance = pd.read_csv('../input/mm-data-prediction/MM_score_predictionv2.csv')
big_dance.columns
|
code
|
88099646/cell_10
|
[
"text_html_output_1.png"
] |
from matplotlib.pyplot import figure
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
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 seaborn as sn
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
big_dance = pd.read_csv('../input/mm-data-prediction/MM_score_predictionv2.csv')
big_dance.columns
import seaborn as sn
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
corrMatrix = big_dance.corr()
y = big_dance['Total_Score_March_Madness']
features = ['FG', 'FGA', 'Fgper', 'FT', 'FTA', 'PF', 'PTS']
X = big_dance[features]
from sklearn.tree import DecisionTreeRegressor
bd_model = DecisionTreeRegressor(random_state=1)
bd_model.fit(X, y)
from sklearn.model_selection import train_test_split
train_X, val_X, train_y, val_y = train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)
bd_model.fit(train_X, train_y)
from sklearn.metrics import mean_absolute_error
val_predictions = bd_model.predict(val_X)
val_mae = mean_absolute_error(val_predictions, val_y)
set_up = [[25.7, 58.3, 44.1, 12.5, 17.5, 16.7, 71.4]]
bd_model.predict(set_up)
|
code
|
88099646/cell_5
|
[
"text_plain_output_1.png"
] |
from matplotlib.pyplot import figure
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 seaborn as sn
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
big_dance = pd.read_csv('../input/mm-data-prediction/MM_score_predictionv2.csv')
big_dance.columns
import seaborn as sn
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
corrMatrix = big_dance.corr()
y = big_dance['Total_Score_March_Madness']
features = ['FG', 'FGA', 'Fgper', 'FT', 'FTA', 'PF', 'PTS']
X = big_dance[features]
X.describe()
|
code
|
122260145/cell_42
|
[
"text_plain_output_1.png"
] |
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull().sum()
books.isnull().sum()
ratings_name = ratings.merge(books, on='ISBN')
ratings_name
no_of_rating = ratings_name.groupby('Book-Title').count()['Book-Rating'].reset_index()
no_of_rating.rename(columns={'Book-Rating': 'num_of_rating'}, inplace=True)
no_of_rating
average_rating = ratings_name.groupby('Book-Title').mean()['Book-Rating'].reset_index()
average_rating.rename(columns={'Book-Rating': 'avg_rating'}, inplace=True)
average_rating
a = ratings_name.groupby('User-ID').count()['Book-Title'] > 300
geninue_users = a[a].index
geninue_users
geniune_user = ratings_name[ratings_name['User-ID'].isin(geninue_users)]
geniune_user
b = geniune_user.groupby('Book-Title').count()['Book-Rating'] >= 50
filtered_rat = b[b].index
filtered_rat
final_df = geniune_user[geniune_user['Book-Title'].isin(filtered_rat)]
final_df
piv_tbl = final_df.pivot_table(index='Book-Title', columns='User-ID')
piv_tbl.fillna(0, inplace=True)
cos_simscore = cosine_similarity(piv_tbl)
cos_simscore
def recommend(books_name):
index = np.where(piv_tbl.index == books_name)[0][0]
similar_books = sorted(list(enumerate(cos_simscore[index])), key=lambda x: x[1], reverse=True)[1:6]
recommend('The Fellowship of the Ring (The Lord of the Rings, Part 1)')
|
code
|
122260145/cell_13
|
[
"text_html_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull().sum()
books.isnull().sum()
ratings_name = ratings.merge(books, on='ISBN')
ratings_name
no_of_rating = ratings_name.groupby('Book-Title').count()['Book-Rating'].reset_index()
no_of_rating.rename(columns={'Book-Rating': 'num_of_rating'}, inplace=True)
no_of_rating
|
code
|
122260145/cell_9
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
users.isnull().sum()
|
code
|
122260145/cell_4
|
[
"text_html_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
users.head(5)
|
code
|
122260145/cell_30
|
[
"text_html_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull().sum()
books.isnull().sum()
ratings_name = ratings.merge(books, on='ISBN')
ratings_name
no_of_rating = ratings_name.groupby('Book-Title').count()['Book-Rating'].reset_index()
no_of_rating.rename(columns={'Book-Rating': 'num_of_rating'}, inplace=True)
no_of_rating
average_rating = ratings_name.groupby('Book-Title').mean()['Book-Rating'].reset_index()
average_rating.rename(columns={'Book-Rating': 'avg_rating'}, inplace=True)
average_rating
a = ratings_name.groupby('User-ID').count()['Book-Title'] > 300
geninue_users = a[a].index
geninue_users
geniune_user = ratings_name[ratings_name['User-ID'].isin(geninue_users)]
geniune_user
b = geniune_user.groupby('Book-Title').count()['Book-Rating'] >= 50
filtered_rat = b[b].index
filtered_rat
final_df = geniune_user[geniune_user['Book-Title'].isin(filtered_rat)]
final_df
|
code
|
122260145/cell_20
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull().sum()
books.isnull().sum()
ratings_name = ratings.merge(books, on='ISBN')
ratings_name
no_of_rating = ratings_name.groupby('Book-Title').count()['Book-Rating'].reset_index()
no_of_rating.rename(columns={'Book-Rating': 'num_of_rating'}, inplace=True)
no_of_rating
average_rating = ratings_name.groupby('Book-Title').mean()['Book-Rating'].reset_index()
average_rating.rename(columns={'Book-Rating': 'avg_rating'}, inplace=True)
average_rating
popular_books = no_of_rating.merge(average_rating, on='Book-Title')
popular_books
popular_books_df = popular_books[popular_books['num_of_rating'] >= 500].sort_values('avg_rating', ascending=False)
popular_books_df
pop_books = popular_books_df.merge(books, on='Book-Title').drop_duplicates('Book-Title')[['Book-Title', 'Book-Author', 'Year-Of-Publication']]
pop_books.shape
pop_books
|
code
|
122260145/cell_6
|
[
"text_html_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
books.describe()
|
code
|
122260145/cell_29
|
[
"text_html_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull().sum()
books.isnull().sum()
ratings_name = ratings.merge(books, on='ISBN')
ratings_name
no_of_rating = ratings_name.groupby('Book-Title').count()['Book-Rating'].reset_index()
no_of_rating.rename(columns={'Book-Rating': 'num_of_rating'}, inplace=True)
no_of_rating
average_rating = ratings_name.groupby('Book-Title').mean()['Book-Rating'].reset_index()
average_rating.rename(columns={'Book-Rating': 'avg_rating'}, inplace=True)
average_rating
a = ratings_name.groupby('User-ID').count()['Book-Title'] > 300
geninue_users = a[a].index
geninue_users
geniune_user = ratings_name[ratings_name['User-ID'].isin(geninue_users)]
geniune_user
b = geniune_user.groupby('Book-Title').count()['Book-Rating'] >= 50
filtered_rat = b[b].index
filtered_rat
|
code
|
122260145/cell_39
|
[
"text_plain_output_2.png",
"text_plain_output_1.png"
] |
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull().sum()
books.isnull().sum()
ratings_name = ratings.merge(books, on='ISBN')
ratings_name
no_of_rating = ratings_name.groupby('Book-Title').count()['Book-Rating'].reset_index()
no_of_rating.rename(columns={'Book-Rating': 'num_of_rating'}, inplace=True)
no_of_rating
average_rating = ratings_name.groupby('Book-Title').mean()['Book-Rating'].reset_index()
average_rating.rename(columns={'Book-Rating': 'avg_rating'}, inplace=True)
average_rating
a = ratings_name.groupby('User-ID').count()['Book-Title'] > 300
geninue_users = a[a].index
geninue_users
geniune_user = ratings_name[ratings_name['User-ID'].isin(geninue_users)]
geniune_user
b = geniune_user.groupby('Book-Title').count()['Book-Rating'] >= 50
filtered_rat = b[b].index
filtered_rat
final_df = geniune_user[geniune_user['Book-Title'].isin(filtered_rat)]
final_df
piv_tbl = final_df.pivot_table(index='Book-Title', columns='User-ID')
piv_tbl.fillna(0, inplace=True)
cos_simscore = cosine_similarity(piv_tbl)
cos_simscore
def recommend(books_name):
index = np.where(piv_tbl.index == books_name)[0][0]
similar_books = sorted(list(enumerate(cos_simscore[index])), key=lambda x: x[1], reverse=True)[1:6]
recommend('The Da Vinci Code')
|
code
|
122260145/cell_26
|
[
"text_html_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull().sum()
books.isnull().sum()
ratings_name = ratings.merge(books, on='ISBN')
ratings_name
no_of_rating = ratings_name.groupby('Book-Title').count()['Book-Rating'].reset_index()
no_of_rating.rename(columns={'Book-Rating': 'num_of_rating'}, inplace=True)
no_of_rating
average_rating = ratings_name.groupby('Book-Title').mean()['Book-Rating'].reset_index()
average_rating.rename(columns={'Book-Rating': 'avg_rating'}, inplace=True)
average_rating
ratings_name.head()
|
code
|
122260145/cell_7
|
[
"text_plain_output_2.png",
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.describe()
|
code
|
122260145/cell_18
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull().sum()
books.isnull().sum()
ratings_name = ratings.merge(books, on='ISBN')
ratings_name
no_of_rating = ratings_name.groupby('Book-Title').count()['Book-Rating'].reset_index()
no_of_rating.rename(columns={'Book-Rating': 'num_of_rating'}, inplace=True)
no_of_rating
average_rating = ratings_name.groupby('Book-Title').mean()['Book-Rating'].reset_index()
average_rating.rename(columns={'Book-Rating': 'avg_rating'}, inplace=True)
average_rating
popular_books = no_of_rating.merge(average_rating, on='Book-Title')
popular_books
popular_books_df = popular_books[popular_books['num_of_rating'] >= 500].sort_values('avg_rating', ascending=False)
popular_books_df
|
code
|
122260145/cell_32
|
[
"text_html_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull().sum()
books.isnull().sum()
ratings_name = ratings.merge(books, on='ISBN')
ratings_name
no_of_rating = ratings_name.groupby('Book-Title').count()['Book-Rating'].reset_index()
no_of_rating.rename(columns={'Book-Rating': 'num_of_rating'}, inplace=True)
no_of_rating
average_rating = ratings_name.groupby('Book-Title').mean()['Book-Rating'].reset_index()
average_rating.rename(columns={'Book-Rating': 'avg_rating'}, inplace=True)
average_rating
a = ratings_name.groupby('User-ID').count()['Book-Title'] > 300
geninue_users = a[a].index
geninue_users
geniune_user = ratings_name[ratings_name['User-ID'].isin(geninue_users)]
geniune_user
b = geniune_user.groupby('Book-Title').count()['Book-Rating'] >= 50
filtered_rat = b[b].index
filtered_rat
final_df = geniune_user[geniune_user['Book-Title'].isin(filtered_rat)]
final_df
piv_tbl = final_df.pivot_table(index='Book-Title', columns='User-ID')
piv_tbl.fillna(0, inplace=True)
piv_tbl.head(500)
|
code
|
122260145/cell_28
|
[
"text_html_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull().sum()
books.isnull().sum()
ratings_name = ratings.merge(books, on='ISBN')
ratings_name
no_of_rating = ratings_name.groupby('Book-Title').count()['Book-Rating'].reset_index()
no_of_rating.rename(columns={'Book-Rating': 'num_of_rating'}, inplace=True)
no_of_rating
average_rating = ratings_name.groupby('Book-Title').mean()['Book-Rating'].reset_index()
average_rating.rename(columns={'Book-Rating': 'avg_rating'}, inplace=True)
average_rating
a = ratings_name.groupby('User-ID').count()['Book-Title'] > 300
geninue_users = a[a].index
geninue_users
geniune_user = ratings_name[ratings_name['User-ID'].isin(geninue_users)]
geniune_user
|
code
|
122260145/cell_8
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull().sum()
|
code
|
122260145/cell_15
|
[
"text_html_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull().sum()
books.isnull().sum()
ratings_name = ratings.merge(books, on='ISBN')
ratings_name
no_of_rating = ratings_name.groupby('Book-Title').count()['Book-Rating'].reset_index()
no_of_rating.rename(columns={'Book-Rating': 'num_of_rating'}, inplace=True)
no_of_rating
average_rating = ratings_name.groupby('Book-Title').mean()['Book-Rating'].reset_index()
average_rating.rename(columns={'Book-Rating': 'avg_rating'}, inplace=True)
average_rating
popular_books = no_of_rating.merge(average_rating, on='Book-Title')
popular_books
|
code
|
122260145/cell_16
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull().sum()
books.isnull().sum()
ratings_name = ratings.merge(books, on='ISBN')
ratings_name
no_of_rating = ratings_name.groupby('Book-Title').count()['Book-Rating'].reset_index()
no_of_rating.rename(columns={'Book-Rating': 'num_of_rating'}, inplace=True)
no_of_rating
average_rating = ratings_name.groupby('Book-Title').mean()['Book-Rating'].reset_index()
average_rating.rename(columns={'Book-Rating': 'avg_rating'}, inplace=True)
average_rating
popular_books = no_of_rating.merge(average_rating, on='Book-Title')
popular_books
popular_books[popular_books['num_of_rating'] >= 500].sort_values('num_of_rating', ascending=False)
|
code
|
122260145/cell_3
|
[
"text_html_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
books.head(5)
|
code
|
122260145/cell_35
|
[
"text_html_output_1.png"
] |
from sklearn.metrics.pairwise import cosine_similarity
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull().sum()
books.isnull().sum()
ratings_name = ratings.merge(books, on='ISBN')
ratings_name
no_of_rating = ratings_name.groupby('Book-Title').count()['Book-Rating'].reset_index()
no_of_rating.rename(columns={'Book-Rating': 'num_of_rating'}, inplace=True)
no_of_rating
average_rating = ratings_name.groupby('Book-Title').mean()['Book-Rating'].reset_index()
average_rating.rename(columns={'Book-Rating': 'avg_rating'}, inplace=True)
average_rating
a = ratings_name.groupby('User-ID').count()['Book-Title'] > 300
geninue_users = a[a].index
geninue_users
geniune_user = ratings_name[ratings_name['User-ID'].isin(geninue_users)]
geniune_user
b = geniune_user.groupby('Book-Title').count()['Book-Rating'] >= 50
filtered_rat = b[b].index
filtered_rat
final_df = geniune_user[geniune_user['Book-Title'].isin(filtered_rat)]
final_df
piv_tbl = final_df.pivot_table(index='Book-Title', columns='User-ID')
piv_tbl.fillna(0, inplace=True)
cos_simscore = cosine_similarity(piv_tbl)
print(cos_simscore.shape)
cos_simscore
|
code
|
122260145/cell_14
|
[
"text_html_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull().sum()
books.isnull().sum()
ratings_name = ratings.merge(books, on='ISBN')
ratings_name
no_of_rating = ratings_name.groupby('Book-Title').count()['Book-Rating'].reset_index()
no_of_rating.rename(columns={'Book-Rating': 'num_of_rating'}, inplace=True)
no_of_rating
average_rating = ratings_name.groupby('Book-Title').mean()['Book-Rating'].reset_index()
average_rating.rename(columns={'Book-Rating': 'avg_rating'}, inplace=True)
average_rating
|
code
|
122260145/cell_10
|
[
"text_html_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
books.isnull().sum()
|
code
|
122260145/cell_27
|
[
"text_html_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull().sum()
books.isnull().sum()
ratings_name = ratings.merge(books, on='ISBN')
ratings_name
no_of_rating = ratings_name.groupby('Book-Title').count()['Book-Rating'].reset_index()
no_of_rating.rename(columns={'Book-Rating': 'num_of_rating'}, inplace=True)
no_of_rating
average_rating = ratings_name.groupby('Book-Title').mean()['Book-Rating'].reset_index()
average_rating.rename(columns={'Book-Rating': 'avg_rating'}, inplace=True)
average_rating
a = ratings_name.groupby('User-ID').count()['Book-Title'] > 300
geninue_users = a[a].index
print(type(geninue_users))
geninue_users
|
code
|
122260145/cell_12
|
[
"text_html_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull().sum()
books.isnull().sum()
ratings_name = ratings.merge(books, on='ISBN')
ratings_name
|
code
|
122260145/cell_5
|
[
"text_html_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.head(5)
|
code
|
129020570/cell_21
|
[
"text_html_output_1.png"
] |
from sklearn.metrics import mean_absolute_error, mean_squared_error
import math
|
code
|
129020570/cell_6
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/time-series/JohnsonJohnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
df.time.unique()
df.head()
|
code
|
129020570/cell_11
|
[
"text_html_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/time-series/JohnsonJohnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
df.time.unique()
train = df[df['time'] < 1980]
test = df[df['time'] >= 1980]
def arithmetic_mean(train, test):
train_mean = train['value'].mean()
test_df = test.copy()
test_df['value'] = train_mean
return test_df
arithmetic_mean_df = arithmetic_mean(train, test)
arithmetic_mean_df
|
code
|
129020570/cell_19
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/time-series/JohnsonJohnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
df.time.unique()
train = df[df['time'] < 1980]
test = df[df['time'] >= 1980]
def arithmetic_mean(train, test):
train_mean = train['value'].mean()
test_df = test.copy()
test_df['value'] = train_mean
return test_df
def last_record(train,test):
test_df = test.copy()
test_df['value'] = train.tail(1)['value'].unique()[0]
return test_df
def seasonal(train,test):
test_df = test.copy()
test_df['value'] = train.tail(4)['value'].unique()
return test_df
seasonal_df = seasonal(train, test)
seasonal_df
|
code
|
129020570/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
|
129020570/cell_8
|
[
"image_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/time-series/JohnsonJohnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
df.time.unique()
train = df[df['time'] < 1980]
test = df[df['time'] >= 1980]
print('train_time', train.time.unique())
print('test_time', test.time.unique())
|
code
|
129020570/cell_15
|
[
"text_html_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/time-series/JohnsonJohnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
df.time.unique()
train = df[df['time'] < 1980]
test = df[df['time'] >= 1980]
def arithmetic_mean(train, test):
train_mean = train['value'].mean()
test_df = test.copy()
test_df['value'] = train_mean
return test_df
def last_record(train,test):
test_df = test.copy()
test_df['value'] = train.tail(1)['value'].unique()[0]
return test_df
last_record_df = last_record(train, test)
last_record_df
|
code
|
129020570/cell_3
|
[
"text_html_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/time-series/JohnsonJohnson.csv')
df.head()
|
code
|
129020570/cell_12
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/time-series/JohnsonJohnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
df.time.unique()
train = df[df['time'] < 1980]
test = df[df['time'] >= 1980]
def arithmetic_mean(train, test):
train_mean = train['value'].mean()
test_df = test.copy()
test_df['value'] = train_mean
return test_df
test
|
code
|
129020570/cell_5
|
[
"text_html_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/time-series/JohnsonJohnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
df.time.unique()
|
code
|
1006988/cell_21
|
[
"text_html_output_1.png"
] |
import pandas as pd # for data manipulation/CSV I/O
deliveries = pd.read_csv('../input/deliveries.csv')
matches = pd.read_csv('../input/matches.csv')
Batsman_score = deliveries.groupby('batsman')['batsman_runs'].agg(sum).reset_index().sort_values(by='batsman_runs', ascending=False).reset_index(drop=True)
Top_batsman_score = Batsman_score.iloc[:15, :]
Top_batsman_score
Batsman_Ball_faced = deliveries.groupby(['batsman'])['ball'].count().reset_index().sort_values(by='ball', ascending=False).reset_index(drop=True)
Batsman_Ball_faced_Top = Batsman_Ball_faced.iloc[:15, :]
Batsman_strike_rate = pd.merge(Batsman_score, Batsman_Ball_faced, on='batsman', how='outer')
Batsman_strike_rate = Batsman_strike_rate[Batsman_strike_rate['batsman_runs'] >= 500]
Batsman_strike_rate['strike_rate'] = Batsman_strike_rate['batsman_runs'] / Batsman_strike_rate['ball'] * 100
Batsman_strike_rate = Batsman_strike_rate[['batsman', 'strike_rate']]
Batsman_strike_rate = Batsman_strike_rate.sort_values(by='strike_rate', ascending=False).reset_index(drop=True)
Batsman_strike_rate.iloc[:20, :]
|
code
|
1006988/cell_13
|
[
"text_plain_output_1.png"
] |
import pandas as pd # for data manipulation/CSV I/O
deliveries = pd.read_csv('../input/deliveries.csv')
matches = pd.read_csv('../input/matches.csv')
Batsman_score = deliveries.groupby('batsman')['batsman_runs'].agg(sum).reset_index().sort_values(by='batsman_runs', ascending=False).reset_index(drop=True)
Top_batsman_score = Batsman_score.iloc[:15, :]
Top_batsman_score
|
code
|
1006988/cell_23
|
[
"image_output_1.png"
] |
import matplotlib.pyplot as plt # for plotting Graphs
import numpy as np # for Linear algebra
import pandas as pd # for data manipulation/CSV I/O
deliveries = pd.read_csv('../input/deliveries.csv')
matches = pd.read_csv('../input/matches.csv')
def autolabel(rects):
for rect in rects:
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width() / 2.0, 1.02 * height, '%d' % int(height), ha='center', va='bottom')
Batsman_score = deliveries.groupby('batsman')['batsman_runs'].agg(sum).reset_index().sort_values(by='batsman_runs', ascending=False).reset_index(drop=True)
Top_batsman_score = Batsman_score.iloc[:15, :]
Top_batsman_score
labels = np.array(Top_batsman_score['batsman'])# x axis label of graph
ind = np.arange(len(labels)) # making them as indexes
width = 0.7 # width of rectangle
fig, ax = plt.subplots() # for figure
rects = ax.bar(ind, np.array(Top_batsman_score['batsman_runs']), width=width, color='blue')# here ind is X
#and np.array(Batsman_Ball_faced_Top['ball']) value is height
ax.set_xticks(ind+((width)/2.))# this is to define the postion in x axis
ax.set_xticklabels(labels, rotation='vertical') # this is for label x axis
ax.set_ylabel("Count")
ax.set_title("Top Scorer in IPL")
autolabel(rects)
Batsman_Ball_faced = deliveries.groupby(['batsman'])['ball'].count().reset_index().sort_values(by='ball', ascending=False).reset_index(drop=True)
Batsman_Ball_faced_Top = Batsman_Ball_faced.iloc[:15, :]
labels = np.array(Batsman_Ball_faced_Top['batsman'])# x axis label of graph
ind = np.arange(len(labels)) # making them as indexes
width = 0.7 # width of rectangle
fig, ax = plt.subplots() # for figure
rects = ax.bar(ind, np.array(Batsman_Ball_faced_Top['ball']), width=width, color='blue')# here ind is X
#and np.array(Batsman_Ball_faced_Top['ball']) value is height
ax.set_xticks(ind+((width)/2.))# this is to define the postion in x axis
ax.set_xticklabels(labels, rotation='vertical') # this is for label x axis
ax.set_ylabel("Count")
ax.set_title("Ball faced by Batsman in IPL")
autolabel(rects)
Batsman_strike_rate = pd.merge(Batsman_score, Batsman_Ball_faced, on='batsman', how='outer')
Batsman_strike_rate = Batsman_strike_rate[Batsman_strike_rate['batsman_runs'] >= 500]
Batsman_strike_rate['strike_rate'] = Batsman_strike_rate['batsman_runs'] / Batsman_strike_rate['ball'] * 100
Batsman_strike_rate = Batsman_strike_rate[['batsman', 'strike_rate']]
Batsman_strike_rate = Batsman_strike_rate.sort_values(by='strike_rate', ascending=False).reset_index(drop=True)
Batsman_strike_rate.iloc[:20, :]
Batsman_strike_rate_Top = Batsman_strike_rate.iloc[:15, :]
labels = np.array(Batsman_strike_rate_Top['batsman'])
ind = np.arange(len(labels))
width = 0.5
fig, ax = plt.subplots()
rects = ax.bar(ind, np.array(Batsman_strike_rate_Top['strike_rate']), width=width, color='blue')
ax.set_xticks(ind + width / 2.0)
ax.set_xticklabels(labels, rotation='vertical')
ax.set_ylabel('Strike Rate')
ax.set_title('Most Destructive Player in IPL')
autolabel(rects)
|
code
|
1006988/cell_30
|
[
"text_html_output_1.png"
] |
import matplotlib.pyplot as plt # for plotting Graphs
import numpy as np # for Linear algebra
import pandas as pd # for data manipulation/CSV I/O
deliveries = pd.read_csv('../input/deliveries.csv')
matches = pd.read_csv('../input/matches.csv')
def autolabel(rects):
for rect in rects:
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width() / 2.0, 1.02 * height, '%d' % int(height), ha='center', va='bottom')
Batsman_score = deliveries.groupby('batsman')['batsman_runs'].agg(sum).reset_index().sort_values(by='batsman_runs', ascending=False).reset_index(drop=True)
Top_batsman_score = Batsman_score.iloc[:15, :]
Top_batsman_score
labels = np.array(Top_batsman_score['batsman'])# x axis label of graph
ind = np.arange(len(labels)) # making them as indexes
width = 0.7 # width of rectangle
fig, ax = plt.subplots() # for figure
rects = ax.bar(ind, np.array(Top_batsman_score['batsman_runs']), width=width, color='blue')# here ind is X
#and np.array(Batsman_Ball_faced_Top['ball']) value is height
ax.set_xticks(ind+((width)/2.))# this is to define the postion in x axis
ax.set_xticklabels(labels, rotation='vertical') # this is for label x axis
ax.set_ylabel("Count")
ax.set_title("Top Scorer in IPL")
autolabel(rects)
Batsman_Ball_faced = deliveries.groupby(['batsman'])['ball'].count().reset_index().sort_values(by='ball', ascending=False).reset_index(drop=True)
Batsman_Ball_faced_Top = Batsman_Ball_faced.iloc[:15, :]
labels = np.array(Batsman_Ball_faced_Top['batsman'])# x axis label of graph
ind = np.arange(len(labels)) # making them as indexes
width = 0.7 # width of rectangle
fig, ax = plt.subplots() # for figure
rects = ax.bar(ind, np.array(Batsman_Ball_faced_Top['ball']), width=width, color='blue')# here ind is X
#and np.array(Batsman_Ball_faced_Top['ball']) value is height
ax.set_xticks(ind+((width)/2.))# this is to define the postion in x axis
ax.set_xticklabels(labels, rotation='vertical') # this is for label x axis
ax.set_ylabel("Count")
ax.set_title("Ball faced by Batsman in IPL")
autolabel(rects)
Batsman_strike_rate = pd.merge(Batsman_score, Batsman_Ball_faced, on='batsman', how='outer')
Batsman_strike_rate = Batsman_strike_rate[Batsman_strike_rate['batsman_runs'] >= 500]
Batsman_strike_rate['strike_rate'] = Batsman_strike_rate['batsman_runs'] / Batsman_strike_rate['ball'] * 100
Batsman_strike_rate = Batsman_strike_rate[['batsman', 'strike_rate']]
Batsman_strike_rate = Batsman_strike_rate.sort_values(by='strike_rate', ascending=False).reset_index(drop=True)
Batsman_strike_rate.iloc[:20, :]
Batsman_strike_rate_Top=Batsman_strike_rate.iloc[:15,:]
labels = np.array(Batsman_strike_rate_Top['batsman'])# x axis label of graph
ind = np.arange(len(labels)) # making them as indexes
width = 0.5 # width of rectangle
fig, ax = plt.subplots() # for figure
rects = ax.bar(ind, np.array(Batsman_strike_rate_Top['strike_rate']), width=width, color='blue')# here ind is X
#and np.array(Batsman_Ball_faced_Top['ball']) value is height
ax.set_xticks(ind+((width)/2.))# this is to define the postion in x axis
ax.set_xticklabels(labels, rotation='vertical') # this is for label x axis
ax.set_ylabel("Strike Rate")
ax.set_title("Most Destructive Player in IPL")
autolabel(rects)
Batsman_dotballs = deliveries[deliveries['extra_runs'] == 0].groupby(['batsman'])['batsman_runs'].agg(lambda x: (x == 0).sum()).reset_index().sort_values(by='batsman_runs', ascending=False).reset_index(drop=True)
Batsman_dotballs.columns = ['batsman', 'No_of_Balls']
Batsman_dotballs.iloc[:20, :]
Batsman_dotballs_Top = Batsman_dotballs.iloc[:15,:]
labels = np.array(Batsman_dotballs_Top['batsman'])# x axis label of graph
ind = np.arange(len(labels)) # making them as indexes
width = 0.6 # width of rectangle
fig, ax = plt.subplots() # for figure
rects = ax.bar(ind, np.array(Batsman_dotballs_Top["No_of_Balls"]), width=width, color='blue')# here ind is X
#and np.array(Batsman_Ball_faced_Top['ball']) value is height
ax.set_xticks(ind+((width)/2.))# this is to define the postion in x axis
ax.set_xticklabels(labels, rotation='vertical') # this is for label x axis
ax.set_ylabel("Count")
ax.set_title("No. of Dot Balls")
autolabel(rects)
Percentage_of_dot_balls = pd.merge(Batsman_Ball_faced, Batsman_dotballs, on='batsman', how='outer')
Percentage_of_dot_balls['% of dot balls'] = Percentage_of_dot_balls['No_of_Balls'] / Percentage_of_dot_balls['ball'] * 100
Percentage_of_dot_balls = Percentage_of_dot_balls[Percentage_of_dot_balls['ball'] > 300].reset_index(drop=True)
Percentage_of_dot_balls_top = Percentage_of_dot_balls.sort_values(by='% of dot balls', ascending=False).reset_index(drop=True).iloc[:15, :]
Percentage_of_dot_balls_top.iloc[:20, :]
|
code
|
1006988/cell_26
|
[
"image_output_1.png"
] |
import pandas as pd # for data manipulation/CSV I/O
deliveries = pd.read_csv('../input/deliveries.csv')
matches = pd.read_csv('../input/matches.csv')
Batsman_score = deliveries.groupby('batsman')['batsman_runs'].agg(sum).reset_index().sort_values(by='batsman_runs', ascending=False).reset_index(drop=True)
Top_batsman_score = Batsman_score.iloc[:15, :]
Top_batsman_score
Batsman_Ball_faced = deliveries.groupby(['batsman'])['ball'].count().reset_index().sort_values(by='ball', ascending=False).reset_index(drop=True)
Batsman_Ball_faced_Top = Batsman_Ball_faced.iloc[:15, :]
Batsman_dotballs = deliveries[deliveries['extra_runs'] == 0].groupby(['batsman'])['batsman_runs'].agg(lambda x: (x == 0).sum()).reset_index().sort_values(by='batsman_runs', ascending=False).reset_index(drop=True)
Batsman_dotballs.columns = ['batsman', 'No_of_Balls']
Batsman_dotballs.iloc[:20, :]
|
code
|
1006988/cell_7
|
[
"text_html_output_1.png"
] |
import pandas as pd # for data manipulation/CSV I/O
deliveries = pd.read_csv('../input/deliveries.csv')
matches = pd.read_csv('../input/matches.csv')
matches.head(2)
|
code
|
1006988/cell_18
|
[
"text_html_output_1.png"
] |
import matplotlib.pyplot as plt # for plotting Graphs
import numpy as np # for Linear algebra
import pandas as pd # for data manipulation/CSV I/O
deliveries = pd.read_csv('../input/deliveries.csv')
matches = pd.read_csv('../input/matches.csv')
def autolabel(rects):
for rect in rects:
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width() / 2.0, 1.02 * height, '%d' % int(height), ha='center', va='bottom')
Batsman_score = deliveries.groupby('batsman')['batsman_runs'].agg(sum).reset_index().sort_values(by='batsman_runs', ascending=False).reset_index(drop=True)
Top_batsman_score = Batsman_score.iloc[:15, :]
Top_batsman_score
labels = np.array(Top_batsman_score['batsman'])# x axis label of graph
ind = np.arange(len(labels)) # making them as indexes
width = 0.7 # width of rectangle
fig, ax = plt.subplots() # for figure
rects = ax.bar(ind, np.array(Top_batsman_score['batsman_runs']), width=width, color='blue')# here ind is X
#and np.array(Batsman_Ball_faced_Top['ball']) value is height
ax.set_xticks(ind+((width)/2.))# this is to define the postion in x axis
ax.set_xticklabels(labels, rotation='vertical') # this is for label x axis
ax.set_ylabel("Count")
ax.set_title("Top Scorer in IPL")
autolabel(rects)
Batsman_Ball_faced = deliveries.groupby(['batsman'])['ball'].count().reset_index().sort_values(by='ball', ascending=False).reset_index(drop=True)
Batsman_Ball_faced_Top = Batsman_Ball_faced.iloc[:15, :]
labels = np.array(Batsman_Ball_faced_Top['batsman'])
ind = np.arange(len(labels))
width = 0.7
fig, ax = plt.subplots()
rects = ax.bar(ind, np.array(Batsman_Ball_faced_Top['ball']), width=width, color='blue')
ax.set_xticks(ind + width / 2.0)
ax.set_xticklabels(labels, rotation='vertical')
ax.set_ylabel('Count')
ax.set_title('Ball faced by Batsman in IPL')
autolabel(rects)
|
code
|
1006988/cell_28
|
[
"text_html_output_1.png"
] |
import matplotlib.pyplot as plt # for plotting Graphs
import numpy as np # for Linear algebra
import pandas as pd # for data manipulation/CSV I/O
deliveries = pd.read_csv('../input/deliveries.csv')
matches = pd.read_csv('../input/matches.csv')
def autolabel(rects):
for rect in rects:
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width() / 2.0, 1.02 * height, '%d' % int(height), ha='center', va='bottom')
Batsman_score = deliveries.groupby('batsman')['batsman_runs'].agg(sum).reset_index().sort_values(by='batsman_runs', ascending=False).reset_index(drop=True)
Top_batsman_score = Batsman_score.iloc[:15, :]
Top_batsman_score
labels = np.array(Top_batsman_score['batsman'])# x axis label of graph
ind = np.arange(len(labels)) # making them as indexes
width = 0.7 # width of rectangle
fig, ax = plt.subplots() # for figure
rects = ax.bar(ind, np.array(Top_batsman_score['batsman_runs']), width=width, color='blue')# here ind is X
#and np.array(Batsman_Ball_faced_Top['ball']) value is height
ax.set_xticks(ind+((width)/2.))# this is to define the postion in x axis
ax.set_xticklabels(labels, rotation='vertical') # this is for label x axis
ax.set_ylabel("Count")
ax.set_title("Top Scorer in IPL")
autolabel(rects)
Batsman_Ball_faced = deliveries.groupby(['batsman'])['ball'].count().reset_index().sort_values(by='ball', ascending=False).reset_index(drop=True)
Batsman_Ball_faced_Top = Batsman_Ball_faced.iloc[:15, :]
labels = np.array(Batsman_Ball_faced_Top['batsman'])# x axis label of graph
ind = np.arange(len(labels)) # making them as indexes
width = 0.7 # width of rectangle
fig, ax = plt.subplots() # for figure
rects = ax.bar(ind, np.array(Batsman_Ball_faced_Top['ball']), width=width, color='blue')# here ind is X
#and np.array(Batsman_Ball_faced_Top['ball']) value is height
ax.set_xticks(ind+((width)/2.))# this is to define the postion in x axis
ax.set_xticklabels(labels, rotation='vertical') # this is for label x axis
ax.set_ylabel("Count")
ax.set_title("Ball faced by Batsman in IPL")
autolabel(rects)
Batsman_strike_rate = pd.merge(Batsman_score, Batsman_Ball_faced, on='batsman', how='outer')
Batsman_strike_rate = Batsman_strike_rate[Batsman_strike_rate['batsman_runs'] >= 500]
Batsman_strike_rate['strike_rate'] = Batsman_strike_rate['batsman_runs'] / Batsman_strike_rate['ball'] * 100
Batsman_strike_rate = Batsman_strike_rate[['batsman', 'strike_rate']]
Batsman_strike_rate = Batsman_strike_rate.sort_values(by='strike_rate', ascending=False).reset_index(drop=True)
Batsman_strike_rate.iloc[:20, :]
Batsman_strike_rate_Top=Batsman_strike_rate.iloc[:15,:]
labels = np.array(Batsman_strike_rate_Top['batsman'])# x axis label of graph
ind = np.arange(len(labels)) # making them as indexes
width = 0.5 # width of rectangle
fig, ax = plt.subplots() # for figure
rects = ax.bar(ind, np.array(Batsman_strike_rate_Top['strike_rate']), width=width, color='blue')# here ind is X
#and np.array(Batsman_Ball_faced_Top['ball']) value is height
ax.set_xticks(ind+((width)/2.))# this is to define the postion in x axis
ax.set_xticklabels(labels, rotation='vertical') # this is for label x axis
ax.set_ylabel("Strike Rate")
ax.set_title("Most Destructive Player in IPL")
autolabel(rects)
Batsman_dotballs = deliveries[deliveries['extra_runs'] == 0].groupby(['batsman'])['batsman_runs'].agg(lambda x: (x == 0).sum()).reset_index().sort_values(by='batsman_runs', ascending=False).reset_index(drop=True)
Batsman_dotballs.columns = ['batsman', 'No_of_Balls']
Batsman_dotballs.iloc[:20, :]
Batsman_dotballs_Top = Batsman_dotballs.iloc[:15, :]
labels = np.array(Batsman_dotballs_Top['batsman'])
ind = np.arange(len(labels))
width = 0.6
fig, ax = plt.subplots()
rects = ax.bar(ind, np.array(Batsman_dotballs_Top['No_of_Balls']), width=width, color='blue')
ax.set_xticks(ind + width / 2.0)
ax.set_xticklabels(labels, rotation='vertical')
ax.set_ylabel('Count')
ax.set_title('No. of Dot Balls')
autolabel(rects)
|
code
|
1006988/cell_8
|
[
"image_output_1.png"
] |
import pandas as pd # for data manipulation/CSV I/O
deliveries = pd.read_csv('../input/deliveries.csv')
matches = pd.read_csv('../input/matches.csv')
deliveries.head(2)
|
code
|
1006988/cell_15
|
[
"text_html_output_1.png"
] |
import matplotlib.pyplot as plt # for plotting Graphs
import numpy as np # for Linear algebra
import pandas as pd # for data manipulation/CSV I/O
deliveries = pd.read_csv('../input/deliveries.csv')
matches = pd.read_csv('../input/matches.csv')
def autolabel(rects):
for rect in rects:
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width() / 2.0, 1.02 * height, '%d' % int(height), ha='center', va='bottom')
Batsman_score = deliveries.groupby('batsman')['batsman_runs'].agg(sum).reset_index().sort_values(by='batsman_runs', ascending=False).reset_index(drop=True)
Top_batsman_score = Batsman_score.iloc[:15, :]
Top_batsman_score
labels = np.array(Top_batsman_score['batsman'])
ind = np.arange(len(labels))
width = 0.7
fig, ax = plt.subplots()
rects = ax.bar(ind, np.array(Top_batsman_score['batsman_runs']), width=width, color='blue')
ax.set_xticks(ind + width / 2.0)
ax.set_xticklabels(labels, rotation='vertical')
ax.set_ylabel('Count')
ax.set_title('Top Scorer in IPL')
autolabel(rects)
|
code
|
1006988/cell_3
|
[
"image_output_1.png"
] |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
|
code
|
128021213/cell_4
|
[
"text_plain_output_1.png"
] |
! pip install -q kaggle
|
code
|
128021213/cell_2
|
[
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] |
!pip install scikit-optimize
import numpy as np
import pandas as pd
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score,classification_report
from sklearn.model_selection import train_test_split
import math
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import RandomizedSearchCV, cross_val_score
from scipy.stats import randint
import skopt
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
import torch
import torch.nn as nn
import torch.nn.functional as f
import torch.optim as optim
from torch.utils.data import DataLoader,random_split,TensorDataset
import os
import re
import pandas as pd
import librosa
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.ensemble import AdaBoostClassifier
from sklearn.metrics import accuracy_score,classification_report
from scipy.fft import fft
|
code
|
128021213/cell_5
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
from google.colab import files
from google.colab import files
files.upload()
|
code
|
323429/cell_4
|
[
"text_plain_output_1.png"
] |
from subprocess import check_output
import sqlite3
import numpy as np
import pandas as pd
import sqlite3
import nltk
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
import scipy
from subprocess import check_output
con = sqlite3.connect('../input/database.sqlite')
cur = con.cursor()
sqlString = ' \n SELECT complaint_id, product, consumer_complaint_narrative, company\n FROM consumer_complaints\n WHERE product = "Mortgage" AND \n consumer_complaint_narrative != ""\n '
cur.execute(sqlString)
complaints = cur.fetchall()
con.close()
complaint_list = []
for i in range(len(complaints)):
complaint_list.append(complaints[i][2])
|
code
|
323429/cell_2
|
[
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] |
from subprocess import check_output
import sqlite3
import numpy as np
import pandas as pd
import sqlite3
import nltk
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
import scipy
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
con = sqlite3.connect('../input/database.sqlite')
cur = con.cursor()
sqlString = ' \n SELECT complaint_id, product, consumer_complaint_narrative, company\n FROM consumer_complaints\n WHERE product = "Mortgage" AND \n consumer_complaint_narrative != ""\n '
cur.execute(sqlString)
complaints = cur.fetchall()
con.close()
|
code
|
17112386/cell_13
|
[
"image_output_1.png"
] |
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import os
import torch
batch_size = 32
latent_dim = 256
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class DogDataset(Dataset):
def __init__(self, img_dir, transform1=None, transform2=None):
self.img_dir = img_dir
self.img_names = os.listdir(img_dir)
self.transform1 = transform1
self.transform2 = transform2
self.imgs = []
for img_name in self.img_names:
img = Image.open(os.path.join(img_dir, img_name))
if self.transform1 is not None:
img = self.transform1(img)
self.imgs.append(img)
def __getitem__(self, index):
img = self.imgs[index]
if self.transform2 is not None:
img = self.transform2(img)
return img
def __len__(self):
return len(self.imgs)
transform1 = transforms.Compose([transforms.Resize(64), transforms.CenterCrop(64)])
random_transforms = [transforms.RandomRotation(degrees=10)]
transform2 = transforms.Compose([transforms.RandomHorizontalFlip(p=0.5), transforms.RandomApply(random_transforms, p=0.3), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
train_dataset = DogDataset(img_dir='../input/all-dogs/all-dogs/', transform1=transform1, transform2=transform2)
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)
x = next(iter(train_loader))
fig = plt.figure(figsize=(25, 16))
for ii, img in enumerate(x):
ax = fig.add_subplot(4, 8, ii + 1, xticks=[], yticks=[])
img = img.numpy().transpose(1, 2, 0)
plt.imshow(img)
|
code
|
17112386/cell_20
|
[
"text_plain_output_5.png",
"text_plain_output_15.png",
"text_plain_output_9.png",
"text_plain_output_13.png",
"image_output_5.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_6.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"text_plain_output_11.png"
] |
from PIL import Image
from torch import nn, optim
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import os
import torch
import torch.nn.functional as F
batch_size = 32
latent_dim = 256
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class DogDataset(Dataset):
def __init__(self, img_dir, transform1=None, transform2=None):
self.img_dir = img_dir
self.img_names = os.listdir(img_dir)
self.transform1 = transform1
self.transform2 = transform2
self.imgs = []
for img_name in self.img_names:
img = Image.open(os.path.join(img_dir, img_name))
if self.transform1 is not None:
img = self.transform1(img)
self.imgs.append(img)
def __getitem__(self, index):
img = self.imgs[index]
if self.transform2 is not None:
img = self.transform2(img)
return img
def __len__(self):
return len(self.imgs)
transform1 = transforms.Compose([transforms.Resize(64), transforms.CenterCrop(64)])
random_transforms = [transforms.RandomRotation(degrees=10)]
transform2 = transforms.Compose([transforms.RandomHorizontalFlip(p=0.5), transforms.RandomApply(random_transforms, p=0.3), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
train_dataset = DogDataset(img_dir='../input/all-dogs/all-dogs/', transform1=transform1, transform2=transform2)
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)
x = next(iter(train_loader))
fig = plt.figure(figsize=(25, 16))
for ii, img in enumerate(x):
ax = fig.add_subplot(4, 8, ii + 1, xticks=[], yticks=[])
img = img.numpy().transpose(1, 2, 0)
plt.imshow(img)
class VAE(nn.Module):
def __init__(self, latent_dim=128, no_of_sample=10, batch_size=32, channels=3):
super(VAE, self).__init__()
self.no_of_sample = no_of_sample
self.batch_size = batch_size
self.channels = channels
self.latent_dim = latent_dim
def convlayer_enc(n_input, n_output, k_size=4, stride=2, padding=1, bn=False):
block = [nn.Conv2d(n_input, n_output, kernel_size=k_size, stride=stride, padding=padding, bias=False)]
if bn:
block.append(nn.BatchNorm2d(n_output))
block.append(nn.LeakyReLU(0.2, inplace=True))
return block
self.encoder = nn.Sequential(*convlayer_enc(self.channels, 64, 4, 2, 2), *convlayer_enc(64, 128, 4, 2, 2), *convlayer_enc(128, 256, 4, 2, 2, bn=True), *convlayer_enc(256, 512, 4, 2, 2, bn=True), nn.Conv2d(512, self.latent_dim * 2, 4, 1, 1, bias=False), nn.LeakyReLU(0.2, inplace=True))
def convlayer_dec(n_input, n_output, k_size=4, stride=2, padding=0):
block = [nn.ConvTranspose2d(n_input, n_output, kernel_size=k_size, stride=stride, padding=padding, bias=False), nn.BatchNorm2d(n_output), nn.ReLU(inplace=True)]
return block
self.decoder = nn.Sequential(*convlayer_dec(self.latent_dim, 512, 4, 2, 1), *convlayer_dec(512, 256, 4, 2, 1), *convlayer_dec(256, 128, 4, 2, 1), *convlayer_dec(128, 64, 4, 2, 1), nn.ConvTranspose2d(64, self.channels, 3, 1, 1), nn.Sigmoid())
def encode(self, x):
"""return mu_z and logvar_z"""
x = self.encoder(x)
return (x[:, :self.latent_dim, :, :], x[:, self.latent_dim:, :, :])
def decode(self, z):
z = self.decoder(z)
return z.view(-1, 3 * 64 * 64)
def reparameterize(self, mu, logvar):
if self.training:
sample_z = []
for _ in range(self.no_of_sample):
std = logvar.mul(0.5).exp_()
eps = Variable(std.data.new(std.size()).normal_())
sample_z.append(eps.mul(std).add_(mu))
return sample_z
else:
return mu
def forward(self, x):
mu, logvar = self.encode(x)
z = self.reparameterize(mu, logvar)
if self.training:
return ([self.decode(z) for z in z], mu, logvar)
else:
return (self.decode(z), mu, logvar)
def loss_function(self, recon_x, x, mu, logvar):
if self.training:
BCE = 0
for recon_x_one in recon_x:
BCE += F.binary_cross_entropy(recon_x_one, x.view(-1, 3 * 64 * 64))
BCE /= len(recon_x)
else:
BCE = F.binary_cross_entropy(recon_x, x.view(-1, 3 * 64 * 64))
KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
KLD /= self.batch_size * 3 * 64 * 64
return BCE + 1.5 * KLD
lr = 0.0005
epochs = 30
model = VAE(latent_dim, batch_size=batch_size).to(device)
optimizer = optim.Adam(model.parameters(), lr=lr)
for epoch in range(1, epochs + 1):
model.train()
print(f'Epoch {epoch} start')
for batch_idx, data in tqdm(enumerate(train_loader), total=len(train_loader)):
data = data.to(device)
optimizer.zero_grad()
recon_batch, mu, logvar = model(data)
loss = model.loss_function(recon_batch, data, mu, logvar)
loss.backward()
optimizer.step()
model.eval()
recon_img, _, _ = model(x[:1].to(device))
img = recon_img.view(3, 64, 64).detach().cpu().numpy().transpose(1, 2, 0)
plt.imshow(img)
plt.show()
|
code
|
17112386/cell_18
|
[
"image_output_1.png"
] |
from PIL import Image
from torch import nn, optim
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import os
import torch
import torch.nn.functional as F
batch_size = 32
latent_dim = 256
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class DogDataset(Dataset):
def __init__(self, img_dir, transform1=None, transform2=None):
self.img_dir = img_dir
self.img_names = os.listdir(img_dir)
self.transform1 = transform1
self.transform2 = transform2
self.imgs = []
for img_name in self.img_names:
img = Image.open(os.path.join(img_dir, img_name))
if self.transform1 is not None:
img = self.transform1(img)
self.imgs.append(img)
def __getitem__(self, index):
img = self.imgs[index]
if self.transform2 is not None:
img = self.transform2(img)
return img
def __len__(self):
return len(self.imgs)
transform1 = transforms.Compose([transforms.Resize(64), transforms.CenterCrop(64)])
random_transforms = [transforms.RandomRotation(degrees=10)]
transform2 = transforms.Compose([transforms.RandomHorizontalFlip(p=0.5), transforms.RandomApply(random_transforms, p=0.3), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
train_dataset = DogDataset(img_dir='../input/all-dogs/all-dogs/', transform1=transform1, transform2=transform2)
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)
x = next(iter(train_loader))
fig = plt.figure(figsize=(25, 16))
for ii, img in enumerate(x):
ax = fig.add_subplot(4, 8, ii + 1, xticks=[], yticks=[])
img = img.numpy().transpose(1, 2, 0)
plt.imshow(img)
class VAE(nn.Module):
def __init__(self, latent_dim=128, no_of_sample=10, batch_size=32, channels=3):
super(VAE, self).__init__()
self.no_of_sample = no_of_sample
self.batch_size = batch_size
self.channels = channels
self.latent_dim = latent_dim
def convlayer_enc(n_input, n_output, k_size=4, stride=2, padding=1, bn=False):
block = [nn.Conv2d(n_input, n_output, kernel_size=k_size, stride=stride, padding=padding, bias=False)]
if bn:
block.append(nn.BatchNorm2d(n_output))
block.append(nn.LeakyReLU(0.2, inplace=True))
return block
self.encoder = nn.Sequential(*convlayer_enc(self.channels, 64, 4, 2, 2), *convlayer_enc(64, 128, 4, 2, 2), *convlayer_enc(128, 256, 4, 2, 2, bn=True), *convlayer_enc(256, 512, 4, 2, 2, bn=True), nn.Conv2d(512, self.latent_dim * 2, 4, 1, 1, bias=False), nn.LeakyReLU(0.2, inplace=True))
def convlayer_dec(n_input, n_output, k_size=4, stride=2, padding=0):
block = [nn.ConvTranspose2d(n_input, n_output, kernel_size=k_size, stride=stride, padding=padding, bias=False), nn.BatchNorm2d(n_output), nn.ReLU(inplace=True)]
return block
self.decoder = nn.Sequential(*convlayer_dec(self.latent_dim, 512, 4, 2, 1), *convlayer_dec(512, 256, 4, 2, 1), *convlayer_dec(256, 128, 4, 2, 1), *convlayer_dec(128, 64, 4, 2, 1), nn.ConvTranspose2d(64, self.channels, 3, 1, 1), nn.Sigmoid())
def encode(self, x):
"""return mu_z and logvar_z"""
x = self.encoder(x)
return (x[:, :self.latent_dim, :, :], x[:, self.latent_dim:, :, :])
def decode(self, z):
z = self.decoder(z)
return z.view(-1, 3 * 64 * 64)
def reparameterize(self, mu, logvar):
if self.training:
sample_z = []
for _ in range(self.no_of_sample):
std = logvar.mul(0.5).exp_()
eps = Variable(std.data.new(std.size()).normal_())
sample_z.append(eps.mul(std).add_(mu))
return sample_z
else:
return mu
def forward(self, x):
mu, logvar = self.encode(x)
z = self.reparameterize(mu, logvar)
if self.training:
return ([self.decode(z) for z in z], mu, logvar)
else:
return (self.decode(z), mu, logvar)
def loss_function(self, recon_x, x, mu, logvar):
if self.training:
BCE = 0
for recon_x_one in recon_x:
BCE += F.binary_cross_entropy(recon_x_one, x.view(-1, 3 * 64 * 64))
BCE /= len(recon_x)
else:
BCE = F.binary_cross_entropy(recon_x, x.view(-1, 3 * 64 * 64))
KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
KLD /= self.batch_size * 3 * 64 * 64
return BCE + 1.5 * KLD
plt.imshow(x[0].numpy().transpose(1, 2, 0))
plt.show()
|
code
|
17112386/cell_24
|
[
"image_output_1.png"
] |
from PIL import Image
from torch import nn, optim
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import os
import torch
import torch.nn.functional as F
batch_size = 32
latent_dim = 256
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class DogDataset(Dataset):
def __init__(self, img_dir, transform1=None, transform2=None):
self.img_dir = img_dir
self.img_names = os.listdir(img_dir)
self.transform1 = transform1
self.transform2 = transform2
self.imgs = []
for img_name in self.img_names:
img = Image.open(os.path.join(img_dir, img_name))
if self.transform1 is not None:
img = self.transform1(img)
self.imgs.append(img)
def __getitem__(self, index):
img = self.imgs[index]
if self.transform2 is not None:
img = self.transform2(img)
return img
def __len__(self):
return len(self.imgs)
transform1 = transforms.Compose([transforms.Resize(64), transforms.CenterCrop(64)])
random_transforms = [transforms.RandomRotation(degrees=10)]
transform2 = transforms.Compose([transforms.RandomHorizontalFlip(p=0.5), transforms.RandomApply(random_transforms, p=0.3), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
train_dataset = DogDataset(img_dir='../input/all-dogs/all-dogs/', transform1=transform1, transform2=transform2)
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)
x = next(iter(train_loader))
fig = plt.figure(figsize=(25, 16))
for ii, img in enumerate(x):
ax = fig.add_subplot(4, 8, ii + 1, xticks=[], yticks=[])
img = img.numpy().transpose(1, 2, 0)
plt.imshow(img)
class VAE(nn.Module):
def __init__(self, latent_dim=128, no_of_sample=10, batch_size=32, channels=3):
super(VAE, self).__init__()
self.no_of_sample = no_of_sample
self.batch_size = batch_size
self.channels = channels
self.latent_dim = latent_dim
def convlayer_enc(n_input, n_output, k_size=4, stride=2, padding=1, bn=False):
block = [nn.Conv2d(n_input, n_output, kernel_size=k_size, stride=stride, padding=padding, bias=False)]
if bn:
block.append(nn.BatchNorm2d(n_output))
block.append(nn.LeakyReLU(0.2, inplace=True))
return block
self.encoder = nn.Sequential(*convlayer_enc(self.channels, 64, 4, 2, 2), *convlayer_enc(64, 128, 4, 2, 2), *convlayer_enc(128, 256, 4, 2, 2, bn=True), *convlayer_enc(256, 512, 4, 2, 2, bn=True), nn.Conv2d(512, self.latent_dim * 2, 4, 1, 1, bias=False), nn.LeakyReLU(0.2, inplace=True))
def convlayer_dec(n_input, n_output, k_size=4, stride=2, padding=0):
block = [nn.ConvTranspose2d(n_input, n_output, kernel_size=k_size, stride=stride, padding=padding, bias=False), nn.BatchNorm2d(n_output), nn.ReLU(inplace=True)]
return block
self.decoder = nn.Sequential(*convlayer_dec(self.latent_dim, 512, 4, 2, 1), *convlayer_dec(512, 256, 4, 2, 1), *convlayer_dec(256, 128, 4, 2, 1), *convlayer_dec(128, 64, 4, 2, 1), nn.ConvTranspose2d(64, self.channels, 3, 1, 1), nn.Sigmoid())
def encode(self, x):
"""return mu_z and logvar_z"""
x = self.encoder(x)
return (x[:, :self.latent_dim, :, :], x[:, self.latent_dim:, :, :])
def decode(self, z):
z = self.decoder(z)
return z.view(-1, 3 * 64 * 64)
def reparameterize(self, mu, logvar):
if self.training:
sample_z = []
for _ in range(self.no_of_sample):
std = logvar.mul(0.5).exp_()
eps = Variable(std.data.new(std.size()).normal_())
sample_z.append(eps.mul(std).add_(mu))
return sample_z
else:
return mu
def forward(self, x):
mu, logvar = self.encode(x)
z = self.reparameterize(mu, logvar)
if self.training:
return ([self.decode(z) for z in z], mu, logvar)
else:
return (self.decode(z), mu, logvar)
def loss_function(self, recon_x, x, mu, logvar):
if self.training:
BCE = 0
for recon_x_one in recon_x:
BCE += F.binary_cross_entropy(recon_x_one, x.view(-1, 3 * 64 * 64))
BCE /= len(recon_x)
else:
BCE = F.binary_cross_entropy(recon_x, x.view(-1, 3 * 64 * 64))
KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
KLD /= self.batch_size * 3 * 64 * 64
return BCE + 1.5 * KLD
lr = 0.0005
epochs = 30
model = VAE(latent_dim, batch_size=batch_size).to(device)
optimizer = optim.Adam(model.parameters(), lr=lr)
for epoch in range(1, epochs + 1):
model.train()
for batch_idx, data in tqdm(enumerate(train_loader), total=len(train_loader)):
data = data.to(device)
optimizer.zero_grad()
recon_batch, mu, logvar = model(data)
loss = model.loss_function(recon_batch, data, mu, logvar)
loss.backward()
optimizer.step()
model.eval()
recon_img, _, _ = model(x[:1].to(device))
img = recon_img.view(3, 64, 64).detach().cpu().numpy().transpose(1, 2, 0)
reconstructed, _, _ = model(x.to(device))
reconstructed = reconstructed.view(-1, 3, 64, 64).detach().cpu().numpy().transpose(0, 2, 3, 1)
fig = plt.figure(figsize=(25, 16))
for ii, img in enumerate(reconstructed):
ax = fig.add_subplot(4, 8, ii + 1, xticks=[], yticks=[])
plt.imshow(img)
samples = Variable(torch.randn(32, latent_dim, 4, 4)).to(device)
samples = model.decoder(samples).detach().cpu().numpy().transpose(0, 2, 3, 1)
fig = plt.figure(figsize=(25, 16))
for ii, img in enumerate(samples):
ax = fig.add_subplot(4, 8, ii + 1, xticks=[], yticks=[])
plt.imshow(img)
|
code
|
17112386/cell_22
|
[
"image_output_1.png"
] |
from PIL import Image
from torch import nn, optim
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import os
import torch
import torch.nn.functional as F
batch_size = 32
latent_dim = 256
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class DogDataset(Dataset):
def __init__(self, img_dir, transform1=None, transform2=None):
self.img_dir = img_dir
self.img_names = os.listdir(img_dir)
self.transform1 = transform1
self.transform2 = transform2
self.imgs = []
for img_name in self.img_names:
img = Image.open(os.path.join(img_dir, img_name))
if self.transform1 is not None:
img = self.transform1(img)
self.imgs.append(img)
def __getitem__(self, index):
img = self.imgs[index]
if self.transform2 is not None:
img = self.transform2(img)
return img
def __len__(self):
return len(self.imgs)
transform1 = transforms.Compose([transforms.Resize(64), transforms.CenterCrop(64)])
random_transforms = [transforms.RandomRotation(degrees=10)]
transform2 = transforms.Compose([transforms.RandomHorizontalFlip(p=0.5), transforms.RandomApply(random_transforms, p=0.3), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
train_dataset = DogDataset(img_dir='../input/all-dogs/all-dogs/', transform1=transform1, transform2=transform2)
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)
x = next(iter(train_loader))
fig = plt.figure(figsize=(25, 16))
for ii, img in enumerate(x):
ax = fig.add_subplot(4, 8, ii + 1, xticks=[], yticks=[])
img = img.numpy().transpose(1, 2, 0)
plt.imshow(img)
class VAE(nn.Module):
def __init__(self, latent_dim=128, no_of_sample=10, batch_size=32, channels=3):
super(VAE, self).__init__()
self.no_of_sample = no_of_sample
self.batch_size = batch_size
self.channels = channels
self.latent_dim = latent_dim
def convlayer_enc(n_input, n_output, k_size=4, stride=2, padding=1, bn=False):
block = [nn.Conv2d(n_input, n_output, kernel_size=k_size, stride=stride, padding=padding, bias=False)]
if bn:
block.append(nn.BatchNorm2d(n_output))
block.append(nn.LeakyReLU(0.2, inplace=True))
return block
self.encoder = nn.Sequential(*convlayer_enc(self.channels, 64, 4, 2, 2), *convlayer_enc(64, 128, 4, 2, 2), *convlayer_enc(128, 256, 4, 2, 2, bn=True), *convlayer_enc(256, 512, 4, 2, 2, bn=True), nn.Conv2d(512, self.latent_dim * 2, 4, 1, 1, bias=False), nn.LeakyReLU(0.2, inplace=True))
def convlayer_dec(n_input, n_output, k_size=4, stride=2, padding=0):
block = [nn.ConvTranspose2d(n_input, n_output, kernel_size=k_size, stride=stride, padding=padding, bias=False), nn.BatchNorm2d(n_output), nn.ReLU(inplace=True)]
return block
self.decoder = nn.Sequential(*convlayer_dec(self.latent_dim, 512, 4, 2, 1), *convlayer_dec(512, 256, 4, 2, 1), *convlayer_dec(256, 128, 4, 2, 1), *convlayer_dec(128, 64, 4, 2, 1), nn.ConvTranspose2d(64, self.channels, 3, 1, 1), nn.Sigmoid())
def encode(self, x):
"""return mu_z and logvar_z"""
x = self.encoder(x)
return (x[:, :self.latent_dim, :, :], x[:, self.latent_dim:, :, :])
def decode(self, z):
z = self.decoder(z)
return z.view(-1, 3 * 64 * 64)
def reparameterize(self, mu, logvar):
if self.training:
sample_z = []
for _ in range(self.no_of_sample):
std = logvar.mul(0.5).exp_()
eps = Variable(std.data.new(std.size()).normal_())
sample_z.append(eps.mul(std).add_(mu))
return sample_z
else:
return mu
def forward(self, x):
mu, logvar = self.encode(x)
z = self.reparameterize(mu, logvar)
if self.training:
return ([self.decode(z) for z in z], mu, logvar)
else:
return (self.decode(z), mu, logvar)
def loss_function(self, recon_x, x, mu, logvar):
if self.training:
BCE = 0
for recon_x_one in recon_x:
BCE += F.binary_cross_entropy(recon_x_one, x.view(-1, 3 * 64 * 64))
BCE /= len(recon_x)
else:
BCE = F.binary_cross_entropy(recon_x, x.view(-1, 3 * 64 * 64))
KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
KLD /= self.batch_size * 3 * 64 * 64
return BCE + 1.5 * KLD
lr = 0.0005
epochs = 30
model = VAE(latent_dim, batch_size=batch_size).to(device)
optimizer = optim.Adam(model.parameters(), lr=lr)
for epoch in range(1, epochs + 1):
model.train()
for batch_idx, data in tqdm(enumerate(train_loader), total=len(train_loader)):
data = data.to(device)
optimizer.zero_grad()
recon_batch, mu, logvar = model(data)
loss = model.loss_function(recon_batch, data, mu, logvar)
loss.backward()
optimizer.step()
model.eval()
recon_img, _, _ = model(x[:1].to(device))
img = recon_img.view(3, 64, 64).detach().cpu().numpy().transpose(1, 2, 0)
reconstructed, _, _ = model(x.to(device))
reconstructed = reconstructed.view(-1, 3, 64, 64).detach().cpu().numpy().transpose(0, 2, 3, 1)
fig = plt.figure(figsize=(25, 16))
for ii, img in enumerate(reconstructed):
ax = fig.add_subplot(4, 8, ii + 1, xticks=[], yticks=[])
plt.imshow(img)
|
code
|
128024816/cell_9
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
import pandas as pd
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
idsUnique = len(set(df_train.Id))
idsTotal = df_train.shape[0]
idsDupli = idsTotal - idsUnique
df_train.drop('Id', axis=1, inplace=True)
df_train = df_train[df_train.GrLivArea < 4000]
def find_cat(dataframe):
cat_vars = []
for name in dataframe.columns:
if dataframe[name].dtype.name == 'object':
cat_vars.append(name)
return cat_vars
non_cat_vars = list(df_train.select_dtypes(exclude='object').columns)
cat_vars = find_cat(df_train)
print(cat_vars)
|
code
|
128024816/cell_4
|
[
"image_output_1.png"
] |
import pandas as pd
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
df_train.describe()
|
code
|
128024816/cell_6
|
[
"image_output_1.png"
] |
import pandas as pd
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
print(set(df_train.Id))
idsUnique = len(set(df_train.Id))
idsTotal = df_train.shape[0]
idsDupli = idsTotal - idsUnique
df_train.drop('Id', axis=1, inplace=True)
df_train.head()
|
code
|
128024816/cell_2
|
[
"text_html_output_1.png"
] |
import pandas as pd
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
|
code
|
128024816/cell_11
|
[
"text_plain_output_1.png"
] |
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
idsUnique = len(set(df_train.Id))
idsTotal = df_train.shape[0]
idsDupli = idsTotal - idsUnique
df_train.drop('Id', axis=1, inplace=True)
df_train = df_train[df_train.GrLivArea < 4000]
def find_cat(dataframe):
cat_vars = []
for name in dataframe.columns:
if dataframe[name].dtype.name == 'object':
cat_vars.append(name)
return cat_vars
non_cat_vars = list(df_train.select_dtypes(exclude='object').columns)
df_train.drop(non_cat_vars, axis=1, inplace=True)
import seaborn as sns
sns.distplot(df_train.Saleprice)
|
code
|
128024816/cell_7
|
[
"text_plain_output_1.png"
] |
import pandas as pd
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
idsUnique = len(set(df_train.Id))
idsTotal = df_train.shape[0]
idsDupli = idsTotal - idsUnique
df_train.drop('Id', axis=1, inplace=True)
plt.scatter(df_train.GrLivArea, df_train.SalePrice)
plt.title('Looking for outliers')
plt.xlabel('GrLivArea')
plt.ylabel('SalePrice')
plt.show()
df_train = df_train[df_train.GrLivArea < 4000]
|
code
|
128024816/cell_5
|
[
"text_html_output_1.png",
"text_plain_output_1.png"
] |
import pandas as pd
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
df_train.hist(bins=200, figsize=(20, 15))
|
code
|
34127932/cell_42
|
[
"text_plain_output_1.png"
] |
import pandas as pd #for structuring the data
import seaborn as sns #for visualization
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
sns.countplot(x='parch', hue='survived', data=dataTrain)
|
code
|
34127932/cell_63
|
[
"text_plain_output_1.png"
] |
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler
import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
dataTest = pd.get_dummies(dataTest, columns=['sex'])
dataTrain = pd.get_dummies(dataTrain, columns=['embarked'])
dataTest = pd.get_dummies(dataTest, columns=['embarked'])
y = dataTrain['survived']
x = dataTrain.drop('survived', axis=1)
(x.shape, y.shape)
from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler
stdscale = MinMaxScaler()
x_new = stdscale.fit_transform(x)
testd = stdscale.transform(dataTest)
(x_new.shape, testd.shape)
X = pd.DataFrame(x_new, columns=x.columns)
testData = pd.DataFrame(testd, columns=dataTest.columns)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
(x_train.shape, y_train.shape)
for i in x_train.columns:
x_train[i].fillna(x_train[i].median(), inplace=True)
for i in x_test.columns:
x_test[i].fillna(x_test[i].median(), inplace=True)
for i in testData.columns:
testData[i].fillna(testData[i].median(), inplace=True)
|
code
|
34127932/cell_21
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd #for structuring the data
import seaborn as sns #for visualization
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
sns.countplot(x='sex', hue='survived', data=dataTrain)
|
code
|
34127932/cell_25
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd #for structuring the data
import seaborn as sns #for visualization
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
sns.boxplot(x='age', orient='horizontal', data=dataTrain)
|
code
|
34127932/cell_57
|
[
"text_html_output_1.png"
] |
from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler
import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
dataTest = pd.get_dummies(dataTest, columns=['sex'])
dataTrain = pd.get_dummies(dataTrain, columns=['embarked'])
dataTest = pd.get_dummies(dataTest, columns=['embarked'])
y = dataTrain['survived']
x = dataTrain.drop('survived', axis=1)
(x.shape, y.shape)
from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler
stdscale = MinMaxScaler()
x_new = stdscale.fit_transform(x)
testd = stdscale.transform(dataTest)
(x_new.shape, testd.shape)
X = pd.DataFrame(x_new, columns=x.columns)
testData = pd.DataFrame(testd, columns=dataTest.columns)
testData.head()
|
code
|
34127932/cell_56
|
[
"text_plain_output_1.png"
] |
from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler
import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
dataTest = pd.get_dummies(dataTest, columns=['sex'])
dataTrain = pd.get_dummies(dataTrain, columns=['embarked'])
dataTest = pd.get_dummies(dataTest, columns=['embarked'])
y = dataTrain['survived']
x = dataTrain.drop('survived', axis=1)
(x.shape, y.shape)
from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler
stdscale = MinMaxScaler()
x_new = stdscale.fit_transform(x)
testd = stdscale.transform(dataTest)
(x_new.shape, testd.shape)
X = pd.DataFrame(x_new, columns=x.columns)
testData = pd.DataFrame(testd, columns=dataTest.columns)
X.head()
|
code
|
34127932/cell_34
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd #for structuring the data
import seaborn as sns #for visualization
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
sns.distplot(dataTrain['age'])
|
code
|
34127932/cell_30
|
[
"text_plain_output_1.png"
] |
import pandas as pd #for structuring the data
import seaborn as sns #for visualization
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
sns.boxplot(x='age', orient='horizontal', data=dataTrain)
|
code
|
34127932/cell_33
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd #for structuring the data
import seaborn as sns #for visualization
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
sns.boxplot(x='age', orient='horizontal', data=dataTrain)
|
code
|
34127932/cell_44
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd #for structuring the data
import seaborn as sns #for visualization
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
sns.countplot(x='parch', hue='survived', data=dataTrain)
|
code
|
34127932/cell_20
|
[
"text_plain_output_1.png"
] |
import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain['sex'].value_counts()
|
code
|
34127932/cell_55
|
[
"text_plain_output_1.png"
] |
from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler
import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
dataTest = pd.get_dummies(dataTest, columns=['sex'])
dataTrain = pd.get_dummies(dataTrain, columns=['embarked'])
dataTest = pd.get_dummies(dataTest, columns=['embarked'])
y = dataTrain['survived']
x = dataTrain.drop('survived', axis=1)
(x.shape, y.shape)
from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler
stdscale = MinMaxScaler()
x_new = stdscale.fit_transform(x)
testd = stdscale.transform(dataTest)
(x_new.shape, testd.shape)
|
code
|
34127932/cell_29
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
dataTrain.info()
|
code
|
34127932/cell_39
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd #for structuring the data
import seaborn as sns #for visualization
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
sns.countplot(x='sibsp', hue='survived', data=dataTrain)
|
code
|
34127932/cell_26
|
[
"text_html_output_1.png"
] |
import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
agemean, agemedian, agemode = (dataTrain['age'].mean(), dataTrain['age'].median(), dataTrain['age'].mode()[0])
print(agemean, agemedian, agemode)
|
code
|
34127932/cell_65
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler
import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
dataTest = pd.get_dummies(dataTest, columns=['sex'])
dataTrain = pd.get_dummies(dataTrain, columns=['embarked'])
dataTest = pd.get_dummies(dataTest, columns=['embarked'])
y = dataTrain['survived']
x = dataTrain.drop('survived', axis=1)
(x.shape, y.shape)
from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler
stdscale = MinMaxScaler()
x_new = stdscale.fit_transform(x)
testd = stdscale.transform(dataTest)
(x_new.shape, testd.shape)
X = pd.DataFrame(x_new, columns=x.columns)
testData = pd.DataFrame(testd, columns=dataTest.columns)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
(x_train.shape, y_train.shape)
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from xgboost import XGBClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import accuracy_score, f1_score, classification_report, precision_score, recall_score
lr = LogisticRegression()
rand = RandomForestClassifier()
gbr = GradientBoostingClassifier()
for i in x_train.columns:
x_train[i].fillna(x_train[i].median(), inplace=True)
for i in x_test.columns:
x_test[i].fillna(x_test[i].median(), inplace=True)
for i in testData.columns:
testData[i].fillna(testData[i].median(), inplace=True)
lr.fit(x_train, y_train)
lr.score(x_train, y_train)
|
code
|
34127932/cell_48
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
dataTrain['embarked'].value_counts()
|
code
|
34127932/cell_41
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
dataTrain['parch'].value_counts()
|
code
|
34127932/cell_61
|
[
"text_plain_output_1.png"
] |
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler
import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
dataTest = pd.get_dummies(dataTest, columns=['sex'])
dataTrain = pd.get_dummies(dataTrain, columns=['embarked'])
dataTest = pd.get_dummies(dataTest, columns=['embarked'])
y = dataTrain['survived']
x = dataTrain.drop('survived', axis=1)
(x.shape, y.shape)
from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler
stdscale = MinMaxScaler()
x_new = stdscale.fit_transform(x)
testd = stdscale.transform(dataTest)
(x_new.shape, testd.shape)
X = pd.DataFrame(x_new, columns=x.columns)
testData = pd.DataFrame(testd, columns=dataTest.columns)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
(x_train.shape, y_train.shape)
x_train.describe()
|
code
|
34127932/cell_2
|
[
"text_html_output_1.png"
] |
import os
import os
os.getcwd()
|
code
|
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