path
stringlengths 13
17
| screenshot_names
listlengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
89133561/cell_3
|
[
"text_plain_output_1.png"
] |
# Install pycocotools
!pip install pycocotools
|
code
|
89133561/cell_12
|
[
"text_plain_output_1.png"
] |
from joblib import Parallel, delayed
from tqdm.notebook import tqdm
import json
import numpy as np
import os
import pandas as pd
thingClasses = ['Aortic enlargement', 'Atelectasis', 'Calcification', 'Cardiomegaly', 'Consolidation', 'ILD', 'Infiltration', 'Lung Opacity', 'Nodule/Mass', 'Other lesion', 'Pleural effusion', 'Pleural thickening', 'Pneumothorax', 'Pulmonary fibrosis', 'No finding']
cfgDict = {'dicomPath': None, 'orgDataPath': '../input/sartorius-cell-instance-segmentation/', 'newDataPath': None, 'cachePath': './', 'trainDataName': 'vinbigdataTrain', 'validDataName': 'vinbigdataValid', 'sampleSize': 1000, 'imSize': 256, 'modelName': 'COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml', 'debug': False, 'outdir': './results/', 'logFile': 'log.txt', 'splitMode': True, 'seed': 111, 'device': 'cuda', 'iter': 1000, 'ims_per_batch': 16, 'roi_batch_size_per_image': 512, 'eval_period': 20, 'lr_scheduler_name': 'WarmupCosineLR', 'base_lr': 0.001, 'checkpoint_period': 500, 'num_workers': 4, 'score_thresh_test': 0.05, 'augKwargs': {'RandomFlip': {'prob': 0.5}, 'RandomRotation': {'angle': [0, 360]}}}
def rle2mask(rle, h, w):
rleArray = np.fromiter(rle.split(), dtype=np.uint)
rleArray = rleArray.reshape((-1, 2)).T
rleArray[0] = rleArray[0] - 1
starts, lenghts = rleArray
rleArray = np.concatenate([np.arange(s, s + l, dtype=np.uint) for s, l in zip(starts, lenghts)])
mask = np.zeros(h * w, dtype=np.uint8)
mask[rleArray] = 1
mask = mask.reshape((h, w))
mask = np.asfortranarray(mask)
return mask
def mask2annotation(idx, row, catIds):
mask = rle2mask(row['annotation'], row['height'], row['width'])
rle = maskUtils.encode(mask)
rle['counts'] = rle['counts'].decode('utf-8')
area = maskUtils.area(rle).item()
bbox = maskUtils.toBbox(rle).astype(int).tolist()
annotation = {'segmentation': rle, 'bbox': bbox, 'area': area, 'image_id': row['id'], 'category_id': catIds[row['cell_type']], 'iscrowd': 0, 'id': idx}
return annotation
def df2COCO(cfg, df, workers=4):
catIds = {name: id + 1 for id, name in enumerate(df.cell_type.unique())}
cats = [{'name': name, 'id': id} for name, id in catIds.items()]
images = [{'id': id, 'width': row.width, 'height': row.height, 'file_name': f'train/{id}.png'} for id, row in df.groupby('id').agg('first').iterrows()]
annotations = Parallel(n_jobs=workers)((delayed(mask2annotation)(idx, row, catIds) for idx, row in tqdm(df.iterrows(), total=len(df))))
return {'categories': cats, 'images': images, 'annotations': annotations}
df = pd.read_csv(os.path.join(cfgDict['orgDataPath'], 'train.csv'))
df.head()
root = df2COCO(cfgDict, df[:cfgDict['sampleSize']])
with open('annotations_train.json', 'w', encoding='utf-8') as f:
json.dump(root, f, ensure_ascii=True, indent=4)
|
code
|
105185383/cell_13
|
[
"text_plain_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv', index_col='row_id')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv', index_col='row_id')
train.dtypes
train.nunique()
plt.figure(figsize=(16, 5))
ax = sns.barplot(data=train, x='product', y='num_sold', hue='country')
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.title('Product distribution grouped by country')
plt.show()
|
code
|
105185383/cell_9
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv', index_col='row_id')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv', index_col='row_id')
train.dtypes
|
code
|
105185383/cell_20
|
[
"image_output_1.png"
] |
from scipy.stats import pearsonr
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv', index_col='row_id')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv', index_col='row_id')
train['date'] = pd.to_datetime(train['date'])
test['date'] = pd.to_datetime(test['date'])
train.dtypes
train.nunique()
plt.figure(figsize=(16,5))
ax = sns.barplot(data=train, x='product', y='num_sold', hue='country')
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.title('Product distribution grouped by country')
plt.show()
store_corr = pearsonr(train.loc[train['store'] == 'KaggleMart', 'num_sold'], train.loc[train['store'] == 'KaggleRama', 'num_sold'])[0]
mult_factor = train.loc[train['store'] == 'KaggleMart', 'num_sold'].sum() / train.loc[train['store'] == 'KaggleRama', 'num_sold'].sum()
plt.figure(figsize=(12,5))
ax = sns.lineplot(data=train.groupby(['product','date']).sum()/train.groupby(['date']).sum(), x='date', y='num_sold', hue='product')
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.title('Ratio of sales by product')
plt.ylabel('Ratio')
plt.show()
def get_fourier_features(df):
dayofbiyear = df['date'].dt.dayofyear + 365 * (1 - df['date'].dt.year % 2)
for k in [1, 2, 4]:
df[f'sin{k}'] = np.sin(2 * np.pi * k * dayofbiyear / (2 * 365))
df[f'cos{k}'] = np.cos(2 * np.pi * k * dayofbiyear / (2 * 365))
for product in df['product'].unique():
df[f'sin_{k}_{product}'] = df[f'sin{k}'] * (df['product'] == product)
df[f'cos_{k}_{product}'] = df[f'cos{k}'] * (df['product'] == product)
df = df.drop([f'sin{k}', f'cos{k}'], axis=1)
return df
def get_GDP_corr(df):
feat_corr = []
df['year'] = df['date'].dt.year
GDP = pd.read_csv('../input/gdp-of-european-countries/GDP_table.csv', index_col='year')
GDP_PC = pd.read_csv('../input/gdp-of-european-countries/GDP_per_capita_table.csv', index_col='year')
GDP_dict = GDP.unstack().to_dict()
GDP_PC_dict = GDP_PC.unstack().to_dict()
df['GDP'] = df.set_index(['country', 'year']).index.map(GDP_dict.get)
df['GDP_PC'] = df.set_index(['country', 'year']).index.map(GDP_PC_dict.get)
for country in df['country'].unique():
subset = df[(df['country'] == country) & (df['year'] <= 2019)].groupby(['year']).agg(S=('S', 'sum'), GDP=('GDP', 'mean'), GDP_PC=('GDP_PC', 'mean'))
r1 = pearsonr(subset['S'], subset['GDP'])[0]
r2 = pearsonr(subset['S'], subset['GDP_PC'])[0]
feat_corr.append([f'{country}', r1, r2])
return pd.DataFrame(feat_corr, columns=['Country', 'GDP_corr', 'GDP_PC_corr'])
corr_df = get_GDP_corr(train)
corr_df
|
code
|
105185383/cell_11
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv', index_col='row_id')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv', index_col='row_id')
train.dtypes
train.nunique()
print('Countries:', list(train['country'].unique()), '\n')
print('Stores:', list(train['store'].unique()), '\n')
print('Products:', list(train['product'].unique()))
|
code
|
105185383/cell_19
|
[
"text_plain_output_1.png"
] |
"""
def get_holidays(df):
years_list = [2017, 2018, 2019, 2020, 2021]
holiday_BE = holidays.CountryHoliday('BE', years = years_list)
holiday_FR = holidays.CountryHoliday('FR', years = years_list)
holiday_DE = holidays.CountryHoliday('DE', years = years_list)
holiday_IT = holidays.CountryHoliday('IT', years = years_list)
holiday_PL = holidays.CountryHoliday('PL', years = years_list)
holiday_ES = holidays.CountryHoliday('ES', years = years_list)
holiday_dict = holiday_BE.copy()
holiday_dict.update(holiday_FR)
holiday_dict.update(holiday_DE)
holiday_dict.update(holiday_IT)
holiday_dict.update(holiday_PL)
holiday_dict.update(holiday_ES)
df['holiday_name'] = df['date'].map(holiday_dict)
df['is_holiday'] = np.where(df['holiday_name'].notnull(), 1, 0)
df['holiday_name'] = df['holiday_name'].fillna('Not Holiday')
return df
"""
|
code
|
105185383/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
|
105185383/cell_8
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv', index_col='row_id')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv', index_col='row_id')
print('Train set shape:', train.shape)
print('Test set shape:', test.shape)
train.head(3)
|
code
|
105185383/cell_15
|
[
"image_output_1.png"
] |
from scipy.stats import pearsonr
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv', index_col='row_id')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv', index_col='row_id')
train.dtypes
train.nunique()
plt.figure(figsize=(16,5))
ax = sns.barplot(data=train, x='product', y='num_sold', hue='country')
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.title('Product distribution grouped by country')
plt.show()
store_corr = pearsonr(train.loc[train['store'] == 'KaggleMart', 'num_sold'], train.loc[train['store'] == 'KaggleRama', 'num_sold'])[0]
print(f'Store correlation: {store_corr:.4f}')
mult_factor = train.loc[train['store'] == 'KaggleMart', 'num_sold'].sum() / train.loc[train['store'] == 'KaggleRama', 'num_sold'].sum()
print(f'Multiplicative factor: {mult_factor:.4f}')
|
code
|
105185383/cell_16
|
[
"image_output_1.png"
] |
from scipy.stats import pearsonr
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv', index_col='row_id')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv', index_col='row_id')
train.dtypes
train.nunique()
plt.figure(figsize=(16,5))
ax = sns.barplot(data=train, x='product', y='num_sold', hue='country')
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.title('Product distribution grouped by country')
plt.show()
store_corr = pearsonr(train.loc[train['store'] == 'KaggleMart', 'num_sold'], train.loc[train['store'] == 'KaggleRama', 'num_sold'])[0]
mult_factor = train.loc[train['store'] == 'KaggleMart', 'num_sold'].sum() / train.loc[train['store'] == 'KaggleRama', 'num_sold'].sum()
plt.figure(figsize=(12, 5))
ax = sns.lineplot(data=train.groupby(['product', 'date']).sum() / train.groupby(['date']).sum(), x='date', y='num_sold', hue='product')
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.title('Ratio of sales by product')
plt.ylabel('Ratio')
plt.show()
|
code
|
105185383/cell_14
|
[
"text_plain_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv', index_col='row_id')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv', index_col='row_id')
train.dtypes
train.nunique()
plt.figure(figsize=(16,5))
ax = sns.barplot(data=train, x='product', y='num_sold', hue='country')
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.title('Product distribution grouped by country')
plt.show()
plt.figure(figsize=(12, 5))
sns.lineplot(data=train.groupby(['date', 'store']).sum(), x='date', y='num_sold', hue='store')
plt.title('Sales by store')
plt.show()
|
code
|
105185383/cell_10
|
[
"text_html_output_1.png",
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv', index_col='row_id')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv', index_col='row_id')
train.dtypes
train.nunique()
|
code
|
105185383/cell_12
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv', index_col='row_id')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv', index_col='row_id')
train.dtypes
train.nunique()
print('TRAIN:')
print('Min date', train['date'].min())
print('Max date', train['date'].max())
print('')
print('TEST:')
print('Min date', test['date'].min())
print('Max date', test['date'].max())
|
code
|
48165933/cell_33
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize, sent_tokenize
from sklearn import metrics
from sklearn import metrics
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.pipeline import Pipeline
from sklearn.svm import LinearSVC
from time import time
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import seaborn as sns
twenty_train = fetch_20newsgroups(subset='train', shuffle=True, random_state=42)
twenty_test = fetch_20newsgroups(subset='test')
def clean_tag(text):
return re.sub('<.*?>', '', text)
def clean_url(text):
return re.sub('http\\S+', '', text)
def clean_special_character(text):
return re.sub('[^a-zA-Z]', ' ', text)
def clean_uppercase(text):
return str(text).lower()
def sent_tokenization(text):
return sent_tokenize(text)
def tokenization(text):
return word_tokenize(text)
def clean_stop_word(tokens):
stop_words = set(stopwords.words('english'))
return [token for token in tokens if token not in stop_words]
def steam(tokens):
return [PorterStemmer().stem(token) for token in tokens]
def lenmatization(tokens):
return [WordNetLemmatizer().lemmatize(word=token, pos='v') for token in tokens]
def clean_length(tokens):
return [token for token in tokens if len(token) > 2]
def convert_2_string(text):
return ' '.join(text)
def clean(text):
res = clean_url(text)
res = clean_special_character(res)
res = clean_uppercase(res)
res = tokenization(res)
res = clean_stop_word(res)
res = lenmatization(res)
res = clean_length(res)
return convert_2_string(res)
example = twenty_train.data[0]
after_clean = clean(example)
processed_train_data = [clean(letter) for letter in twenty_train.data]
processed_test_data = [clean(letter) for letter in twenty_test.data]
reports = []
def draw_confusion_matrix(target, predicted, target_names=twenty_test.target_names, normalize=None):
cm = metrics.confusion_matrix(target, predicted, normalize=normalize)
df_cm = pd.DataFrame(cm, index=[i for i in target_names], columns=target_names)
def benchmark(pipeline, clf_name, X_train=processed_train_data, y_train=twenty_train.target, X_test=processed_test_data, y_test=twenty_test.target):
report = []
report.append(clf_name)
t0 = time()
pipeline.fit(X_train, y_train)
train_time = time() - t0
report.append(train_time)
t0 = time()
pred = pipeline.predict(X_test)
test_time = time() - t0
report.append(test_time)
accuracy = metrics.accuracy_score(y_test, pred)
report.append(accuracy)
precision = metrics.precision_score(y_test, pred, average='micro')
report.append(precision)
recall = metrics.precision_score(y_test, pred, average='micro')
report.append(recall)
f1_score = metrics.f1_score(y_test, pred, average='micro')
report.append(f1_score)
mathew = metrics.matthews_corrcoef(y_test, pred)
report.append(mathew)
reports.append(report)
clf = pipeline.named_steps.clf
vectorizer = pipeline.named_steps.vect
feature_names = vectorizer.get_feature_names()
if hasattr(clf, 'coef_'):
for i, label in enumerate(twenty_train.target_names):
top10 = np.argsort(clf.coef_[i])[-10:]
linearSVC_pip = Pipeline([('vect', CountVectorizer(max_df=0.75, ngram_range=(1, 2))), ('tf', TfidfTransformer()), ('clf', LinearSVC(C=10))])
benchmark(linearSVC_pip, 'LinearSVC')
|
code
|
48165933/cell_29
|
[
"text_plain_output_1.png"
] |
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize, sent_tokenize
from sklearn import metrics
from sklearn import metrics
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import Pipeline
from time import time
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import seaborn as sns
twenty_train = fetch_20newsgroups(subset='train', shuffle=True, random_state=42)
twenty_test = fetch_20newsgroups(subset='test')
def clean_tag(text):
return re.sub('<.*?>', '', text)
def clean_url(text):
return re.sub('http\\S+', '', text)
def clean_special_character(text):
return re.sub('[^a-zA-Z]', ' ', text)
def clean_uppercase(text):
return str(text).lower()
def sent_tokenization(text):
return sent_tokenize(text)
def tokenization(text):
return word_tokenize(text)
def clean_stop_word(tokens):
stop_words = set(stopwords.words('english'))
return [token for token in tokens if token not in stop_words]
def steam(tokens):
return [PorterStemmer().stem(token) for token in tokens]
def lenmatization(tokens):
return [WordNetLemmatizer().lemmatize(word=token, pos='v') for token in tokens]
def clean_length(tokens):
return [token for token in tokens if len(token) > 2]
def convert_2_string(text):
return ' '.join(text)
def clean(text):
res = clean_url(text)
res = clean_special_character(res)
res = clean_uppercase(res)
res = tokenization(res)
res = clean_stop_word(res)
res = lenmatization(res)
res = clean_length(res)
return convert_2_string(res)
example = twenty_train.data[0]
after_clean = clean(example)
processed_train_data = [clean(letter) for letter in twenty_train.data]
processed_test_data = [clean(letter) for letter in twenty_test.data]
reports = []
def draw_confusion_matrix(target, predicted, target_names=twenty_test.target_names, normalize=None):
cm = metrics.confusion_matrix(target, predicted, normalize=normalize)
df_cm = pd.DataFrame(cm, index=[i for i in target_names], columns=target_names)
def benchmark(pipeline, clf_name, X_train=processed_train_data, y_train=twenty_train.target, X_test=processed_test_data, y_test=twenty_test.target):
report = []
report.append(clf_name)
t0 = time()
pipeline.fit(X_train, y_train)
train_time = time() - t0
report.append(train_time)
t0 = time()
pred = pipeline.predict(X_test)
test_time = time() - t0
report.append(test_time)
accuracy = metrics.accuracy_score(y_test, pred)
report.append(accuracy)
precision = metrics.precision_score(y_test, pred, average='micro')
report.append(precision)
recall = metrics.precision_score(y_test, pred, average='micro')
report.append(recall)
f1_score = metrics.f1_score(y_test, pred, average='micro')
report.append(f1_score)
mathew = metrics.matthews_corrcoef(y_test, pred)
report.append(mathew)
reports.append(report)
clf = pipeline.named_steps.clf
vectorizer = pipeline.named_steps.vect
feature_names = vectorizer.get_feature_names()
if hasattr(clf, 'coef_'):
for i, label in enumerate(twenty_train.target_names):
top10 = np.argsort(clf.coef_[i])[-10:]
KNN_pip = Pipeline([('vect', CountVectorizer(max_df=0.75, ngram_range=(1, 2))), ('tf', TfidfTransformer()), ('clf', KNeighborsClassifier(n_neighbors=10))])
benchmark(KNN_pip, 'K Neighbors Classifier')
|
code
|
48165933/cell_7
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import nltk
import re
import nltk
from nltk.tokenize import word_tokenize, sent_tokenize
from nltk.stem import WordNetLemmatizer
from nltk.stem import PorterStemmer
from nltk.corpus import stopwords
nltk.download('stopwords')
nltk.download('punkt')
nltk.download('wordnet')
|
code
|
48165933/cell_10
|
[
"text_plain_output_2.png",
"text_plain_output_1.png"
] |
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize, sent_tokenize
from sklearn.datasets import fetch_20newsgroups
import re
twenty_train = fetch_20newsgroups(subset='train', shuffle=True, random_state=42)
def clean_tag(text):
return re.sub('<.*?>', '', text)
def clean_url(text):
return re.sub('http\\S+', '', text)
def clean_special_character(text):
return re.sub('[^a-zA-Z]', ' ', text)
def clean_uppercase(text):
return str(text).lower()
def sent_tokenization(text):
return sent_tokenize(text)
def tokenization(text):
return word_tokenize(text)
def clean_stop_word(tokens):
stop_words = set(stopwords.words('english'))
return [token for token in tokens if token not in stop_words]
def steam(tokens):
return [PorterStemmer().stem(token) for token in tokens]
def lenmatization(tokens):
return [WordNetLemmatizer().lemmatize(word=token, pos='v') for token in tokens]
def clean_length(tokens):
return [token for token in tokens if len(token) > 2]
def convert_2_string(text):
return ' '.join(text)
def clean(text):
res = clean_url(text)
res = clean_special_character(res)
res = clean_uppercase(res)
res = tokenization(res)
res = clean_stop_word(res)
res = lenmatization(res)
res = clean_length(res)
return convert_2_string(res)
example = twenty_train.data[0]
after_clean = clean(example)
print(example, after_clean)
|
code
|
90153165/cell_13
|
[
"text_plain_output_1.png"
] |
from scipy.io import arff
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from scipy.io import arff
data = arff.loadarff('../input/dry-bean-dataset/Dry_Bean_Dataset.arff')
df = pd.DataFrame(data[0])
df.isnull().sum()
df.corr()
fig, ax = plt.subplots(4,2, figsize=(20,20))
sns.distplot(df.Area, bins = 40, ax=ax[0,0])
sns.distplot(df.Perimeter, bins = 40, ax=ax[0,1])
sns.distplot(df.Eccentricity, bins = 40, ax=ax[1,0])
sns.distplot(df.roundness, bins = 40, ax=ax[1,1])
sns.distplot(df.ConvexArea, bins = 40, ax=ax[2,0])
sns.distplot(df.Extent, bins = 40, ax=ax[2,1])
sns.distplot(df.Solidity, bins = 40, ax=ax[3,0])
sns.distplot(df.roundness, bins = 40, ax=ax[3,1])
# Check for outliers
fig, ax = plt.subplots(4,2, figsize=(20,20))
sns.boxplot(df.Area, ax=ax[0,0])
sns.boxplot(df.Perimeter, ax=ax[0,1])
sns.boxplot(df.Eccentricity, ax=ax[1,0])
sns.boxplot(df.roundness, ax=ax[1,1])
sns.boxplot(df.ConvexArea, ax=ax[2,0])
sns.boxplot(df.Extent, ax=ax[2,1])
sns.boxplot(df.Solidity, ax=ax[3,0])
sns.boxplot(df.roundness, ax=ax[3,1])
df_without_outliers = df.copy()
Q1 = df_without_outliers.quantile(q=0.25)
Q3 = df_without_outliers.quantile(q=0.75)
IQR = Q3 - Q1
df_without_outliers = df_without_outliers[~((df_without_outliers < Q1 - 1.5 * IQR) | (df_without_outliers > Q3 + 1.5 * IQR)).any(axis=1)]
round((df.shape[0] - df_without_outliers.shape[0]) * 100 / df.shape[0])
|
code
|
90153165/cell_9
|
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] |
from scipy.io import arff
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from scipy.io import arff
data = arff.loadarff('../input/dry-bean-dataset/Dry_Bean_Dataset.arff')
df = pd.DataFrame(data[0])
df.isnull().sum()
df.corr()
fig, ax = plt.subplots(4, 2, figsize=(20, 20))
sns.distplot(df.Area, bins=40, ax=ax[0, 0])
sns.distplot(df.Perimeter, bins=40, ax=ax[0, 1])
sns.distplot(df.Eccentricity, bins=40, ax=ax[1, 0])
sns.distplot(df.roundness, bins=40, ax=ax[1, 1])
sns.distplot(df.ConvexArea, bins=40, ax=ax[2, 0])
sns.distplot(df.Extent, bins=40, ax=ax[2, 1])
sns.distplot(df.Solidity, bins=40, ax=ax[3, 0])
sns.distplot(df.roundness, bins=40, ax=ax[3, 1])
|
code
|
90153165/cell_4
|
[
"text_plain_output_1.png"
] |
from scipy.io import arff
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from scipy.io import arff
data = arff.loadarff('../input/dry-bean-dataset/Dry_Bean_Dataset.arff')
df = pd.DataFrame(data[0])
df.head()
|
code
|
90153165/cell_6
|
[
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] |
from scipy.io import arff
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from scipy.io import arff
data = arff.loadarff('../input/dry-bean-dataset/Dry_Bean_Dataset.arff')
df = pd.DataFrame(data[0])
df.describe().transpose()
|
code
|
90153165/cell_11
|
[
"text_html_output_1.png"
] |
from scipy.io import arff
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from scipy.io import arff
data = arff.loadarff('../input/dry-bean-dataset/Dry_Bean_Dataset.arff')
df = pd.DataFrame(data[0])
df.isnull().sum()
df.corr()
fig, ax = plt.subplots(4,2, figsize=(20,20))
sns.distplot(df.Area, bins = 40, ax=ax[0,0])
sns.distplot(df.Perimeter, bins = 40, ax=ax[0,1])
sns.distplot(df.Eccentricity, bins = 40, ax=ax[1,0])
sns.distplot(df.roundness, bins = 40, ax=ax[1,1])
sns.distplot(df.ConvexArea, bins = 40, ax=ax[2,0])
sns.distplot(df.Extent, bins = 40, ax=ax[2,1])
sns.distplot(df.Solidity, bins = 40, ax=ax[3,0])
sns.distplot(df.roundness, bins = 40, ax=ax[3,1])
fig, ax = plt.subplots(4, 2, figsize=(20, 20))
sns.boxplot(df.Area, ax=ax[0, 0])
sns.boxplot(df.Perimeter, ax=ax[0, 1])
sns.boxplot(df.Eccentricity, ax=ax[1, 0])
sns.boxplot(df.roundness, ax=ax[1, 1])
sns.boxplot(df.ConvexArea, ax=ax[2, 0])
sns.boxplot(df.Extent, ax=ax[2, 1])
sns.boxplot(df.Solidity, ax=ax[3, 0])
sns.boxplot(df.roundness, ax=ax[3, 1])
|
code
|
90153165/cell_7
|
[
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] |
from scipy.io import arff
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from scipy.io import arff
data = arff.loadarff('../input/dry-bean-dataset/Dry_Bean_Dataset.arff')
df = pd.DataFrame(data[0])
df.isnull().sum()
|
code
|
90153165/cell_8
|
[
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] |
from scipy.io import arff
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from scipy.io import arff
data = arff.loadarff('../input/dry-bean-dataset/Dry_Bean_Dataset.arff')
df = pd.DataFrame(data[0])
df.isnull().sum()
df.corr()
|
code
|
90153165/cell_17
|
[
"text_html_output_1.png"
] |
from sklearn.preprocessing import RobustScaler
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import Sequential
scaler = RobustScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
early_stop = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=25)
model = Sequential()
model.add(Dense(17, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(9, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(7, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')
model.fit(x=X_train, y=y_train, epochs=600, batch_size=32, validation_data=(X_test, y_test), callbacks=[early_stop])
|
code
|
90153165/cell_5
|
[
"text_html_output_1.png"
] |
from scipy.io import arff
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from scipy.io import arff
data = arff.loadarff('../input/dry-bean-dataset/Dry_Bean_Dataset.arff')
df = pd.DataFrame(data[0])
df.info()
|
code
|
32070358/cell_4
|
[
"text_plain_output_1.png"
] |
import matplotlib.pyplot as plt
plt.imread('../input/ckplus/CK+48/fear/S091_001_00000013.png').shape
|
code
|
32070358/cell_19
|
[
"text_plain_output_1.png"
] |
from keras import callbacks
from keras.layers import Dense , Activation , Dropout ,Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator
import cv2
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import os
data_path = '../input/ckplus/CK+48/'
for dir1 in os.listdir(data_path):
count = 0
for f in os.listdir(data_path + dir1):
count += 1
plt.imread('../input/ckplus/CK+48/fear/S091_001_00000013.png').shape
data_path = '../input/ckplus/CK+48'
data_dir_list = os.listdir(data_path)
img_rows = 256
img_cols = 256
num_channel = 1
num_epoch = 10
img_data_list = []
for dataset in data_dir_list:
img_list = os.listdir(data_path + '/' + dataset)
for img in img_list:
input_img = cv2.imread(data_path + '/' + dataset + '/' + img)
input_img_resize = cv2.resize(input_img, (48, 48))
img_data_list.append(input_img_resize)
img_data = np.array(img_data_list)
img_data = img_data.astype('float32')
img_data = img_data / 255
img_data.shape
num_classes = 7
num_of_samples = img_data.shape[0]
labels = np.ones((num_of_samples,), dtype='int64')
labels[0:134] = 0
labels[135:188] = 1
labels[189:365] = 2
labels[366:440] = 3
labels[441:647] = 4
labels[648:731] = 5
labels[732:980] = 6
names = ['anger', 'contempt', 'disgust', 'fear', 'happy', 'sadness', 'surprise']
def getLabel(id):
return ['anger', 'contempt', 'disgust', 'fear', 'happy', 'sadness', 'surprise'][id]
trainAug = ImageDataGenerator(rotation_range=30, zoom_range=0.15, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.15, horizontal_flip=True, fill_mode='nearest')
valAug = ImageDataGenerator()
input_shape = (48, 48, 3)
"\nmodel = Sequential()\nmodel.add(Conv2D(6, (5, 5), input_shape=input_shape, padding='same', activation = 'relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Conv2D(16, (5, 5), padding='same', activation = 'relu'))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Conv2D(64, (3, 3), activation = 'relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Flatten())\n\nmodel.add(Dense(128, activation = 'relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(7, activation = 'softmax'))\nmodel.compile(loss='categorical_crossentropy', metrics=['accuracy'],optimizer='adam')\n"
def build_cnn(input_shape, show_arch=True):
net = Sequential(name='DCNN')
net.add(Conv2D(filters=64, kernel_size=(3, 3), input_shape=input_shape, activation='elu', padding='same', kernel_initializer='he_normal', name='conv2d_1'))
net.add(BatchNormalization(name='batchnorm_1'))
net.add(Conv2D(filters=64, kernel_size=(3, 3), activation='elu', padding='same', kernel_initializer='he_normal', name='conv2d_2'))
net.add(BatchNormalization(name='batchnorm_2'))
net.add(MaxPooling2D(pool_size=(2, 2), name='maxpool2d_1'))
net.add(Dropout(0.45, name='dropout_1'))
net.add(Conv2D(filters=128, kernel_size=(3, 3), activation='elu', padding='same', kernel_initializer='he_normal', name='conv2d_3'))
net.add(BatchNormalization(name='batchnorm_3'))
net.add(Conv2D(filters=128, kernel_size=(3, 3), activation='elu', padding='same', kernel_initializer='he_normal', name='conv2d_4'))
net.add(BatchNormalization(name='batchnorm_4'))
net.add(MaxPooling2D(pool_size=(2, 2), name='maxpool2d_2'))
net.add(Dropout(0.45, name='dropout_2'))
net.add(Conv2D(filters=256, kernel_size=(3, 3), activation='elu', padding='same', kernel_initializer='he_normal', name='conv2d_5'))
net.add(BatchNormalization(name='batchnorm_5'))
net.add(Conv2D(filters=256, kernel_size=(3, 3), activation='elu', padding='same', kernel_initializer='he_normal', name='conv2d_6'))
net.add(BatchNormalization(name='batchnorm_6'))
net.add(MaxPooling2D(pool_size=(2, 2), name='maxpool2d_3'))
net.add(Dropout(0.5, name='dropout_3'))
net.add(Flatten(name='flatten'))
net.add(Dense(128, activation='elu', kernel_initializer='he_normal', name='dense_1'))
net.add(BatchNormalization(name='batchnorm_7'))
net.add(Dropout(0.6, name='dropout_4'))
net.add(Dense(num_classes, activation='softmax', name='out_layer'))
net.summary()
return net
model = build_cnn(input_shape)
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')
model.summary()
from keras import callbacks
filename = 'model_train_new.csv'
filepath = 'Best-weights-my_model-{epoch:03d}-{loss:.4f}-{acc:.4f}.hdf5'
csv_log = callbacks.CSVLogger(filename, separator=',', append=False)
checkpoint = callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [csv_log, checkpoint]
callbacks_list = [csv_log]
hist = model.fit_generator(trainAug.flow(X_train, y_train, batch_size=7), steps_per_epoch=len(X_train) // 7, validation_data=valAug.flow(X_test, y_test), validation_steps=len(X_test) // 7, epochs=50, callbacks=callbacks_list)
score = model.evaluate(X_test, y_test, verbose=0)
np.argmax(y_test[9])
test_image = X_test[0:1]
print(test_image.shape)
print(model.predict(test_image))
print(model.predict_classes(test_image))
print(y_test[0:1])
res = model.predict_classes(X_test[9:18])
plt.figure(figsize=(10, 10))
for i in range(0, 9):
plt.subplot(330 + 1 + i)
plt.imshow(x_test[i], cmap=plt.get_cmap('gray'))
plt.gca().get_xaxis().set_ticks([])
plt.gca().get_yaxis().set_ticks([])
plt.xlabel('true = %s' % getLabel(np.argmax(y_test[i + 9])))
plt.ylabel('prediction = %s' % getLabel(res[i]), fontsize=14)
plt.show()
|
code
|
32070358/cell_1
|
[
"text_plain_output_1.png"
] |
import numpy as np
import pandas as pd
"\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n"
|
code
|
32070358/cell_7
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import cv2
import numpy as np
import numpy as np # linear algebra
import os
data_path = '../input/ckplus/CK+48/'
for dir1 in os.listdir(data_path):
count = 0
for f in os.listdir(data_path + dir1):
count += 1
data_path = '../input/ckplus/CK+48'
data_dir_list = os.listdir(data_path)
img_rows = 256
img_cols = 256
num_channel = 1
num_epoch = 10
img_data_list = []
for dataset in data_dir_list:
img_list = os.listdir(data_path + '/' + dataset)
for img in img_list:
input_img = cv2.imread(data_path + '/' + dataset + '/' + img)
input_img_resize = cv2.resize(input_img, (48, 48))
img_data_list.append(input_img_resize)
img_data = np.array(img_data_list)
img_data = img_data.astype('float32')
img_data = img_data / 255
img_data.shape
num_classes = 7
num_of_samples = img_data.shape[0]
labels = np.ones((num_of_samples,), dtype='int64')
labels[0:134] = 0
labels[135:188] = 1
labels[189:365] = 2
labels[366:440] = 3
labels[441:647] = 4
labels[648:731] = 5
labels[732:980] = 6
names = ['anger', 'contempt', 'disgust', 'fear', 'happy', 'sadness', 'surprise']
def getLabel(id):
return ['anger', 'contempt', 'disgust', 'fear', 'happy', 'sadness', 'surprise'][id]
img_data.shape[0]
|
code
|
32070358/cell_18
|
[
"text_plain_output_1.png"
] |
import cv2
import numpy as np
import numpy as np # linear algebra
import os
data_path = '../input/ckplus/CK+48/'
for dir1 in os.listdir(data_path):
count = 0
for f in os.listdir(data_path + dir1):
count += 1
data_path = '../input/ckplus/CK+48'
data_dir_list = os.listdir(data_path)
img_rows = 256
img_cols = 256
num_channel = 1
num_epoch = 10
img_data_list = []
for dataset in data_dir_list:
img_list = os.listdir(data_path + '/' + dataset)
for img in img_list:
input_img = cv2.imread(data_path + '/' + dataset + '/' + img)
input_img_resize = cv2.resize(input_img, (48, 48))
img_data_list.append(input_img_resize)
img_data = np.array(img_data_list)
img_data = img_data.astype('float32')
img_data = img_data / 255
img_data.shape
num_classes = 7
num_of_samples = img_data.shape[0]
labels = np.ones((num_of_samples,), dtype='int64')
labels[0:134] = 0
labels[135:188] = 1
labels[189:365] = 2
labels[366:440] = 3
labels[441:647] = 4
labels[648:731] = 5
labels[732:980] = 6
names = ['anger', 'contempt', 'disgust', 'fear', 'happy', 'sadness', 'surprise']
def getLabel(id):
return ['anger', 'contempt', 'disgust', 'fear', 'happy', 'sadness', 'surprise'][id]
np.argmax(y_test[9])
|
code
|
32070358/cell_15
|
[
"text_plain_output_1.png"
] |
from keras import callbacks
from keras.layers import Dense , Activation , Dropout ,Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator
import cv2
import numpy as np
import numpy as np # linear algebra
import os
data_path = '../input/ckplus/CK+48/'
for dir1 in os.listdir(data_path):
count = 0
for f in os.listdir(data_path + dir1):
count += 1
data_path = '../input/ckplus/CK+48'
data_dir_list = os.listdir(data_path)
img_rows = 256
img_cols = 256
num_channel = 1
num_epoch = 10
img_data_list = []
for dataset in data_dir_list:
img_list = os.listdir(data_path + '/' + dataset)
for img in img_list:
input_img = cv2.imread(data_path + '/' + dataset + '/' + img)
input_img_resize = cv2.resize(input_img, (48, 48))
img_data_list.append(input_img_resize)
img_data = np.array(img_data_list)
img_data = img_data.astype('float32')
img_data = img_data / 255
img_data.shape
num_classes = 7
num_of_samples = img_data.shape[0]
labels = np.ones((num_of_samples,), dtype='int64')
labels[0:134] = 0
labels[135:188] = 1
labels[189:365] = 2
labels[366:440] = 3
labels[441:647] = 4
labels[648:731] = 5
labels[732:980] = 6
names = ['anger', 'contempt', 'disgust', 'fear', 'happy', 'sadness', 'surprise']
def getLabel(id):
return ['anger', 'contempt', 'disgust', 'fear', 'happy', 'sadness', 'surprise'][id]
trainAug = ImageDataGenerator(rotation_range=30, zoom_range=0.15, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.15, horizontal_flip=True, fill_mode='nearest')
valAug = ImageDataGenerator()
input_shape = (48, 48, 3)
"\nmodel = Sequential()\nmodel.add(Conv2D(6, (5, 5), input_shape=input_shape, padding='same', activation = 'relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Conv2D(16, (5, 5), padding='same', activation = 'relu'))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Conv2D(64, (3, 3), activation = 'relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Flatten())\n\nmodel.add(Dense(128, activation = 'relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(7, activation = 'softmax'))\nmodel.compile(loss='categorical_crossentropy', metrics=['accuracy'],optimizer='adam')\n"
def build_cnn(input_shape, show_arch=True):
net = Sequential(name='DCNN')
net.add(Conv2D(filters=64, kernel_size=(3, 3), input_shape=input_shape, activation='elu', padding='same', kernel_initializer='he_normal', name='conv2d_1'))
net.add(BatchNormalization(name='batchnorm_1'))
net.add(Conv2D(filters=64, kernel_size=(3, 3), activation='elu', padding='same', kernel_initializer='he_normal', name='conv2d_2'))
net.add(BatchNormalization(name='batchnorm_2'))
net.add(MaxPooling2D(pool_size=(2, 2), name='maxpool2d_1'))
net.add(Dropout(0.45, name='dropout_1'))
net.add(Conv2D(filters=128, kernel_size=(3, 3), activation='elu', padding='same', kernel_initializer='he_normal', name='conv2d_3'))
net.add(BatchNormalization(name='batchnorm_3'))
net.add(Conv2D(filters=128, kernel_size=(3, 3), activation='elu', padding='same', kernel_initializer='he_normal', name='conv2d_4'))
net.add(BatchNormalization(name='batchnorm_4'))
net.add(MaxPooling2D(pool_size=(2, 2), name='maxpool2d_2'))
net.add(Dropout(0.45, name='dropout_2'))
net.add(Conv2D(filters=256, kernel_size=(3, 3), activation='elu', padding='same', kernel_initializer='he_normal', name='conv2d_5'))
net.add(BatchNormalization(name='batchnorm_5'))
net.add(Conv2D(filters=256, kernel_size=(3, 3), activation='elu', padding='same', kernel_initializer='he_normal', name='conv2d_6'))
net.add(BatchNormalization(name='batchnorm_6'))
net.add(MaxPooling2D(pool_size=(2, 2), name='maxpool2d_3'))
net.add(Dropout(0.5, name='dropout_3'))
net.add(Flatten(name='flatten'))
net.add(Dense(128, activation='elu', kernel_initializer='he_normal', name='dense_1'))
net.add(BatchNormalization(name='batchnorm_7'))
net.add(Dropout(0.6, name='dropout_4'))
net.add(Dense(num_classes, activation='softmax', name='out_layer'))
net.summary()
return net
model = build_cnn(input_shape)
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')
model.summary()
from keras import callbacks
filename = 'model_train_new.csv'
filepath = 'Best-weights-my_model-{epoch:03d}-{loss:.4f}-{acc:.4f}.hdf5'
csv_log = callbacks.CSVLogger(filename, separator=',', append=False)
checkpoint = callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [csv_log, checkpoint]
callbacks_list = [csv_log]
hist = model.fit_generator(trainAug.flow(X_train, y_train, batch_size=7), steps_per_epoch=len(X_train) // 7, validation_data=valAug.flow(X_test, y_test), validation_steps=len(X_test) // 7, epochs=50, callbacks=callbacks_list)
|
code
|
32070358/cell_16
|
[
"text_plain_output_1.png"
] |
from keras import callbacks
from keras.layers import Dense , Activation , Dropout ,Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator
import cv2
import numpy as np
import numpy as np # linear algebra
import os
data_path = '../input/ckplus/CK+48/'
for dir1 in os.listdir(data_path):
count = 0
for f in os.listdir(data_path + dir1):
count += 1
data_path = '../input/ckplus/CK+48'
data_dir_list = os.listdir(data_path)
img_rows = 256
img_cols = 256
num_channel = 1
num_epoch = 10
img_data_list = []
for dataset in data_dir_list:
img_list = os.listdir(data_path + '/' + dataset)
for img in img_list:
input_img = cv2.imread(data_path + '/' + dataset + '/' + img)
input_img_resize = cv2.resize(input_img, (48, 48))
img_data_list.append(input_img_resize)
img_data = np.array(img_data_list)
img_data = img_data.astype('float32')
img_data = img_data / 255
img_data.shape
num_classes = 7
num_of_samples = img_data.shape[0]
labels = np.ones((num_of_samples,), dtype='int64')
labels[0:134] = 0
labels[135:188] = 1
labels[189:365] = 2
labels[366:440] = 3
labels[441:647] = 4
labels[648:731] = 5
labels[732:980] = 6
names = ['anger', 'contempt', 'disgust', 'fear', 'happy', 'sadness', 'surprise']
def getLabel(id):
return ['anger', 'contempt', 'disgust', 'fear', 'happy', 'sadness', 'surprise'][id]
trainAug = ImageDataGenerator(rotation_range=30, zoom_range=0.15, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.15, horizontal_flip=True, fill_mode='nearest')
valAug = ImageDataGenerator()
input_shape = (48, 48, 3)
"\nmodel = Sequential()\nmodel.add(Conv2D(6, (5, 5), input_shape=input_shape, padding='same', activation = 'relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Conv2D(16, (5, 5), padding='same', activation = 'relu'))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Conv2D(64, (3, 3), activation = 'relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Flatten())\n\nmodel.add(Dense(128, activation = 'relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(7, activation = 'softmax'))\nmodel.compile(loss='categorical_crossentropy', metrics=['accuracy'],optimizer='adam')\n"
def build_cnn(input_shape, show_arch=True):
net = Sequential(name='DCNN')
net.add(Conv2D(filters=64, kernel_size=(3, 3), input_shape=input_shape, activation='elu', padding='same', kernel_initializer='he_normal', name='conv2d_1'))
net.add(BatchNormalization(name='batchnorm_1'))
net.add(Conv2D(filters=64, kernel_size=(3, 3), activation='elu', padding='same', kernel_initializer='he_normal', name='conv2d_2'))
net.add(BatchNormalization(name='batchnorm_2'))
net.add(MaxPooling2D(pool_size=(2, 2), name='maxpool2d_1'))
net.add(Dropout(0.45, name='dropout_1'))
net.add(Conv2D(filters=128, kernel_size=(3, 3), activation='elu', padding='same', kernel_initializer='he_normal', name='conv2d_3'))
net.add(BatchNormalization(name='batchnorm_3'))
net.add(Conv2D(filters=128, kernel_size=(3, 3), activation='elu', padding='same', kernel_initializer='he_normal', name='conv2d_4'))
net.add(BatchNormalization(name='batchnorm_4'))
net.add(MaxPooling2D(pool_size=(2, 2), name='maxpool2d_2'))
net.add(Dropout(0.45, name='dropout_2'))
net.add(Conv2D(filters=256, kernel_size=(3, 3), activation='elu', padding='same', kernel_initializer='he_normal', name='conv2d_5'))
net.add(BatchNormalization(name='batchnorm_5'))
net.add(Conv2D(filters=256, kernel_size=(3, 3), activation='elu', padding='same', kernel_initializer='he_normal', name='conv2d_6'))
net.add(BatchNormalization(name='batchnorm_6'))
net.add(MaxPooling2D(pool_size=(2, 2), name='maxpool2d_3'))
net.add(Dropout(0.5, name='dropout_3'))
net.add(Flatten(name='flatten'))
net.add(Dense(128, activation='elu', kernel_initializer='he_normal', name='dense_1'))
net.add(BatchNormalization(name='batchnorm_7'))
net.add(Dropout(0.6, name='dropout_4'))
net.add(Dense(num_classes, activation='softmax', name='out_layer'))
net.summary()
return net
model = build_cnn(input_shape)
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')
model.summary()
from keras import callbacks
filename = 'model_train_new.csv'
filepath = 'Best-weights-my_model-{epoch:03d}-{loss:.4f}-{acc:.4f}.hdf5'
csv_log = callbacks.CSVLogger(filename, separator=',', append=False)
checkpoint = callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [csv_log, checkpoint]
callbacks_list = [csv_log]
hist = model.fit_generator(trainAug.flow(X_train, y_train, batch_size=7), steps_per_epoch=len(X_train) // 7, validation_data=valAug.flow(X_test, y_test), validation_steps=len(X_test) // 7, epochs=50, callbacks=callbacks_list)
score = model.evaluate(X_test, y_test, verbose=0)
print('Test Loss:', score[0])
print('Test accuracy:', score[1])
|
code
|
32070358/cell_3
|
[
"text_plain_output_1.png"
] |
import os
data_path = '../input/ckplus/CK+48/'
for dir1 in os.listdir(data_path):
count = 0
for f in os.listdir(data_path + dir1):
count += 1
print(f'{dir1} has {count} images')
|
code
|
32070358/cell_17
|
[
"text_plain_output_1.png"
] |
y_test[9:18]
|
code
|
32070358/cell_14
|
[
"text_plain_output_1.png"
] |
from keras import callbacks
from keras import callbacks
filename = 'model_train_new.csv'
filepath = 'Best-weights-my_model-{epoch:03d}-{loss:.4f}-{acc:.4f}.hdf5'
csv_log = callbacks.CSVLogger(filename, separator=',', append=False)
checkpoint = callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [csv_log, checkpoint]
callbacks_list = [csv_log]
callbacks_list
|
code
|
32070358/cell_12
|
[
"text_plain_output_1.png"
] |
from keras.layers import Dense , Activation , Dropout ,Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.models import Sequential
import cv2
import numpy as np
import numpy as np # linear algebra
import os
data_path = '../input/ckplus/CK+48/'
for dir1 in os.listdir(data_path):
count = 0
for f in os.listdir(data_path + dir1):
count += 1
data_path = '../input/ckplus/CK+48'
data_dir_list = os.listdir(data_path)
img_rows = 256
img_cols = 256
num_channel = 1
num_epoch = 10
img_data_list = []
for dataset in data_dir_list:
img_list = os.listdir(data_path + '/' + dataset)
for img in img_list:
input_img = cv2.imread(data_path + '/' + dataset + '/' + img)
input_img_resize = cv2.resize(input_img, (48, 48))
img_data_list.append(input_img_resize)
img_data = np.array(img_data_list)
img_data = img_data.astype('float32')
img_data = img_data / 255
img_data.shape
num_classes = 7
num_of_samples = img_data.shape[0]
labels = np.ones((num_of_samples,), dtype='int64')
labels[0:134] = 0
labels[135:188] = 1
labels[189:365] = 2
labels[366:440] = 3
labels[441:647] = 4
labels[648:731] = 5
labels[732:980] = 6
names = ['anger', 'contempt', 'disgust', 'fear', 'happy', 'sadness', 'surprise']
def getLabel(id):
return ['anger', 'contempt', 'disgust', 'fear', 'happy', 'sadness', 'surprise'][id]
input_shape = (48, 48, 3)
"\nmodel = Sequential()\nmodel.add(Conv2D(6, (5, 5), input_shape=input_shape, padding='same', activation = 'relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Conv2D(16, (5, 5), padding='same', activation = 'relu'))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Conv2D(64, (3, 3), activation = 'relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Flatten())\n\nmodel.add(Dense(128, activation = 'relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(7, activation = 'softmax'))\nmodel.compile(loss='categorical_crossentropy', metrics=['accuracy'],optimizer='adam')\n"
def build_cnn(input_shape, show_arch=True):
net = Sequential(name='DCNN')
net.add(Conv2D(filters=64, kernel_size=(3, 3), input_shape=input_shape, activation='elu', padding='same', kernel_initializer='he_normal', name='conv2d_1'))
net.add(BatchNormalization(name='batchnorm_1'))
net.add(Conv2D(filters=64, kernel_size=(3, 3), activation='elu', padding='same', kernel_initializer='he_normal', name='conv2d_2'))
net.add(BatchNormalization(name='batchnorm_2'))
net.add(MaxPooling2D(pool_size=(2, 2), name='maxpool2d_1'))
net.add(Dropout(0.45, name='dropout_1'))
net.add(Conv2D(filters=128, kernel_size=(3, 3), activation='elu', padding='same', kernel_initializer='he_normal', name='conv2d_3'))
net.add(BatchNormalization(name='batchnorm_3'))
net.add(Conv2D(filters=128, kernel_size=(3, 3), activation='elu', padding='same', kernel_initializer='he_normal', name='conv2d_4'))
net.add(BatchNormalization(name='batchnorm_4'))
net.add(MaxPooling2D(pool_size=(2, 2), name='maxpool2d_2'))
net.add(Dropout(0.45, name='dropout_2'))
net.add(Conv2D(filters=256, kernel_size=(3, 3), activation='elu', padding='same', kernel_initializer='he_normal', name='conv2d_5'))
net.add(BatchNormalization(name='batchnorm_5'))
net.add(Conv2D(filters=256, kernel_size=(3, 3), activation='elu', padding='same', kernel_initializer='he_normal', name='conv2d_6'))
net.add(BatchNormalization(name='batchnorm_6'))
net.add(MaxPooling2D(pool_size=(2, 2), name='maxpool2d_3'))
net.add(Dropout(0.5, name='dropout_3'))
net.add(Flatten(name='flatten'))
net.add(Dense(128, activation='elu', kernel_initializer='he_normal', name='dense_1'))
net.add(BatchNormalization(name='batchnorm_7'))
net.add(Dropout(0.6, name='dropout_4'))
net.add(Dense(num_classes, activation='softmax', name='out_layer'))
net.summary()
return net
model = build_cnn(input_shape)
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')
model.summary()
|
code
|
32070358/cell_5
|
[
"text_plain_output_1.png"
] |
import cv2
import numpy as np
import numpy as np # linear algebra
import os
data_path = '../input/ckplus/CK+48/'
for dir1 in os.listdir(data_path):
count = 0
for f in os.listdir(data_path + dir1):
count += 1
data_path = '../input/ckplus/CK+48'
data_dir_list = os.listdir(data_path)
img_rows = 256
img_cols = 256
num_channel = 1
num_epoch = 10
img_data_list = []
for dataset in data_dir_list:
img_list = os.listdir(data_path + '/' + dataset)
for img in img_list:
input_img = cv2.imread(data_path + '/' + dataset + '/' + img)
input_img_resize = cv2.resize(input_img, (48, 48))
img_data_list.append(input_img_resize)
img_data = np.array(img_data_list)
img_data = img_data.astype('float32')
img_data = img_data / 255
img_data.shape
|
code
|
16118884/cell_63
|
[
"text_html_output_1.png"
] |
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app_data = app_data.drop(nan_rows, axis=0)
app_data = app_data.drop(columns=['Size', 'Current Ver', 'Android Ver'])
app_data = app_data.sort_values(by='Installs', ascending=False)
top_10_df = app_data[app_data['Installs'] >= 50000]
top_10_df[top_10_df['ReviewRatio'] > 1]
|
code
|
16118884/cell_13
|
[
"text_html_output_1.png"
] |
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.head(3)
|
code
|
16118884/cell_56
|
[
"text_html_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app_data = app_data.drop(nan_rows, axis=0)
app_data = app_data.drop(columns=['Size', 'Current Ver', 'Android Ver'])
app_data = app_data.sort_values(by='Installs', ascending=False)
top_10_df = app_data[app_data['Installs'] >= 50000]
paid_apps = top_10_df['Price']
paid_apps = top_10_df[top_10_df['Price'] > 0]
paid_apps[paid_apps['Price'] > 50]
|
code
|
16118884/cell_26
|
[
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app_data = app_data.drop(nan_rows, axis=0)
app_data = app_data.drop(columns=['Size', 'Current Ver', 'Android Ver'])
app_data = app_data.sort_values(by='Installs', ascending=False)
plt.figure(figsize=(13, 8))
app_data['Installs'].value_counts().plot(kind='bar')
plt.title('Count of Popular Apps in our Dataset')
plt.ylabel('Count')
plt.xlabel('Installs')
plt.show()
|
code
|
16118884/cell_65
|
[
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app_data = app_data.drop(nan_rows, axis=0)
app_data = app_data.drop(columns=['Size', 'Current Ver', 'Android Ver'])
app_data = app_data.sort_values(by='Installs', ascending=False)
top_10_df = app_data[app_data['Installs'] >= 50000]
paid_apps = top_10_df['Price']
paid_apps = top_10_df[top_10_df['Price'] > 0]
paid_apps[paid_apps['Price'] > 50]
top_10_df['ReviewRatio'].hist()
plt.title('Review Ratio Distribution for Apps in Top 10 Bins')
plt.xlabel('Review Ratio')
plt.ylabel('App Count')
plt.show()
|
code
|
16118884/cell_54
|
[
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app_data = app_data.drop(nan_rows, axis=0)
app_data = app_data.drop(columns=['Size', 'Current Ver', 'Android Ver'])
app_data = app_data.sort_values(by='Installs', ascending=False)
top_10_df = app_data[app_data['Installs'] >= 50000]
paid_apps = top_10_df['Price']
paid_apps.hist()
plt.title('Pricing Distribution For Apps In Top 10 Bins')
plt.ylabel('App Count')
plt.xlabel('Price (U.S $)')
plt.show()
|
code
|
16118884/cell_67
|
[
"text_plain_output_1.png"
] |
import numpy as np
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app_data = app_data.drop(nan_rows, axis=0)
app_data = app_data.drop(columns=['Size', 'Current Ver', 'Android Ver'])
app_data = app_data.sort_values(by='Installs', ascending=False)
top_10 = np.unique(app_data['Installs'])
top_10 = sorted(top_10, key=len, reverse=True)[:10]
del top_10
installs = [np.int(i.replace('+', '').replace(',', '')) for i in app_data['Installs']]
app_data['Installs'] = [i for i in installs]
del installs
top_10_df = app_data[app_data['Installs'] >= 50000]
# A dataframe to contain the most reviewed app from each bin:
most_reviewed = pd.DataFrame()
# Get the most reviewed app from each bin and add it to the most_reviewed dataframe
for bins in np.unique(top_10_df["Installs"]):
top_row = top_10_df[top_10_df["Installs"] == bins]
top_row = top_row.sort_values(by="ReviewRatio", ascending=False)
top_row = top_row.head(1)
most_reviewed = most_reviewed.append(top_row)
# Clear this dataframe of irrelevant columns for enhanced visibility
most_reviewed = most_reviewed.drop(columns=["Category","Type","Price","Content Rating","Genres","Last Updated"])
most_reviewed
print('68th: {}'.format(round(np.percentile(top_10_df['ReviewRatio'], 68), 4)))
print('95th: {}'.format(round(np.percentile(top_10_df['ReviewRatio'], 95), 4)))
print('99th: {}'.format(round(np.percentile(top_10_df['ReviewRatio'], 99), 4)))
print('Max: {}'.format(max(top_10_df['ReviewRatio'])))
|
code
|
16118884/cell_60
|
[
"text_plain_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app_data = app_data.drop(nan_rows, axis=0)
app_data = app_data.drop(columns=['Size', 'Current Ver', 'Android Ver'])
app_data = app_data.sort_values(by='Installs', ascending=False)
top_10_df = app_data[app_data['Installs'] >= 50000]
paid_apps = top_10_df['Price']
paid_apps = top_10_df[top_10_df['Price'] > 0]
paid_apps[paid_apps['Price'] > 50]
print('$ {}'.format(paid_apps['Price'].min()))
|
code
|
16118884/cell_19
|
[
"text_html_output_1.png"
] |
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
app_data.head()
|
code
|
16118884/cell_45
|
[
"text_plain_output_1.png"
] |
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app_data = app_data.drop(nan_rows, axis=0)
app_data = app_data.drop(columns=['Size', 'Current Ver', 'Android Ver'])
app_data = app_data.sort_values(by='Installs', ascending=False)
top_10_df = app_data[app_data['Installs'] >= 50000]
top_10_df['Rating'].describe()
|
code
|
16118884/cell_49
|
[
"text_html_output_1.png"
] |
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app_data = app_data.drop(nan_rows, axis=0)
app_data = app_data.drop(columns=['Size', 'Current Ver', 'Android Ver'])
app_data = app_data.sort_values(by='Installs', ascending=False)
top_10_df = app_data[app_data['Installs'] >= 50000]
top_10_df[top_10_df['Rating'] > 4.7].sort_values(by=['Rating', 'ReviewRatio'], ascending=False).head()
|
code
|
16118884/cell_32
|
[
"text_plain_output_1.png"
] |
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app_data = app_data.drop(nan_rows, axis=0)
app_data = app_data.drop(columns=['Size', 'Current Ver', 'Android Ver'])
app_data = app_data.sort_values(by='Installs', ascending=False)
top_10_df = app_data[app_data['Installs'] >= 50000]
print(str(round(len(top_10_df) / len(app_data) * 100, 0)) + '%')
|
code
|
16118884/cell_58
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app_data = app_data.drop(nan_rows, axis=0)
app_data = app_data.drop(columns=['Size', 'Current Ver', 'Android Ver'])
app_data = app_data.sort_values(by='Installs', ascending=False)
top_10_df = app_data[app_data['Installs'] >= 50000]
paid_apps = top_10_df['Price']
paid_apps = top_10_df[top_10_df['Price'] > 0]
paid_apps[paid_apps['Price'] > 50]
paid_apps[paid_apps['Price'] < 40]['Price'].hist()
plt.title('Pricing Distribution For Apps Costing Less Than $40')
plt.ylabel('App Count')
plt.xlabel('Price (U.S $)')
|
code
|
16118884/cell_28
|
[
"text_plain_output_1.png"
] |
import numpy as np
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app_data = app_data.drop(nan_rows, axis=0)
app_data = app_data.drop(columns=['Size', 'Current Ver', 'Android Ver'])
app_data = app_data.sort_values(by='Installs', ascending=False)
top_10 = np.unique(app_data['Installs'])
top_10 = sorted(top_10, key=len, reverse=True)[:10]
print(top_10)
del top_10
|
code
|
16118884/cell_15
|
[
"text_plain_output_2.png",
"text_plain_output_1.png"
] |
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
print('Missing Values' + '\n' + '-' * 15)
app_data.isnull().sum()
|
code
|
16118884/cell_38
|
[
"text_html_output_1.png"
] |
import numpy as np
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app_data = app_data.drop(nan_rows, axis=0)
app_data = app_data.drop(columns=['Size', 'Current Ver', 'Android Ver'])
app_data = app_data.sort_values(by='Installs', ascending=False)
top_10 = np.unique(app_data['Installs'])
top_10 = sorted(top_10, key=len, reverse=True)[:10]
del top_10
installs = [np.int(i.replace('+', '').replace(',', '')) for i in app_data['Installs']]
app_data['Installs'] = [i for i in installs]
del installs
top_10_df = app_data[app_data['Installs'] >= 50000]
# A dataframe to contain the most reviewed app from each bin:
most_reviewed = pd.DataFrame()
# Get the most reviewed app from each bin and add it to the most_reviewed dataframe
for bins in np.unique(top_10_df["Installs"]):
top_row = top_10_df[top_10_df["Installs"] == bins]
top_row = top_row.sort_values(by="ReviewRatio", ascending=False)
top_row = top_row.head(1)
most_reviewed = most_reviewed.append(top_row)
# Clear this dataframe of irrelevant columns for enhanced visibility
most_reviewed = most_reviewed.drop(columns=["Category","Type","Price","Content Rating","Genres","Last Updated"])
most_reviewed
highest_rated = most_reviewed.sort_values(by='Rating', ascending=False).head(3)
highest_rated
|
code
|
16118884/cell_47
|
[
"text_plain_output_1.png"
] |
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app_data = app_data.drop(nan_rows, axis=0)
app_data = app_data.drop(columns=['Size', 'Current Ver', 'Android Ver'])
app_data = app_data.sort_values(by='Installs', ascending=False)
top_10_df = app_data[app_data['Installs'] >= 50000]
len(top_10_df[top_10_df['Rating'] > 4.7])
|
code
|
16118884/cell_17
|
[
"text_html_output_1.png"
] |
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data.head(4)
|
code
|
16118884/cell_43
|
[
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app_data = app_data.drop(nan_rows, axis=0)
app_data = app_data.drop(columns=['Size', 'Current Ver', 'Android Ver'])
app_data = app_data.sort_values(by='Installs', ascending=False)
top_10_df = app_data[app_data['Installs'] >= 50000]
top_10_df['Rating'].hist()
plt.title(' Google Play Apps Rating Distribution')
plt.ylabel('App Count')
plt.xlabel('Rating out of 5.0')
plt.show()
|
code
|
16118884/cell_36
|
[
"text_html_output_1.png"
] |
import numpy as np
import pandas as pd
app_data = pd.read_csv('../input/googleplaystore.csv')
app_data.isnull().sum()
app_data = app_data.sort_values(by='Installs', ascending=False)
app_data = app_data.sort_values(by='Installs')
nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app_data = app_data.drop(nan_rows, axis=0)
app_data = app_data.drop(columns=['Size', 'Current Ver', 'Android Ver'])
app_data = app_data.sort_values(by='Installs', ascending=False)
top_10 = np.unique(app_data['Installs'])
top_10 = sorted(top_10, key=len, reverse=True)[:10]
del top_10
installs = [np.int(i.replace('+', '').replace(',', '')) for i in app_data['Installs']]
app_data['Installs'] = [i for i in installs]
del installs
top_10_df = app_data[app_data['Installs'] >= 50000]
most_reviewed = pd.DataFrame()
for bins in np.unique(top_10_df['Installs']):
top_row = top_10_df[top_10_df['Installs'] == bins]
top_row = top_row.sort_values(by='ReviewRatio', ascending=False)
top_row = top_row.head(1)
most_reviewed = most_reviewed.append(top_row)
most_reviewed = most_reviewed.drop(columns=['Category', 'Type', 'Price', 'Content Rating', 'Genres', 'Last Updated'])
most_reviewed
|
code
|
2036883/cell_13
|
[
"text_html_output_1.png"
] |
from matplotlib import style
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
from sklearn.preprocessing import Imputer
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import scale
from collections import Counter
import seaborn as sns
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from subprocess import check_output
train = pd.read_csv('../input/training-set/Train_A102.csv')
test = pd.read_csv('../input/test-a102csv/Test_A102 (1).csv')
test['Item_Outlet_Sales'] = np.nan
combined_set = train.append(test)
combined_set.replace('', np.NaN)
combined_meanset = combined_set.copy(deep=True)
combined_meanset['Item_Weight'].fillna(value=12.792854, inplace=True)
combined_medianset = combined_set.copy(deep=True)
combined_medianset['Item_Weight'].fillna(value=12.6, inplace=True)
combined_set['Item_Weight'].fillna(value=12.6, inplace=True)
train.plot.density()
combined_set['Outlet_Size'].fillna(value='Medium', inplace=True)
combined_set['Item_Outlet_Sales'].fillna(value=-999, inplace=True)
X = train.iloc[:, 0:].values
item_outlet_sales = X[:, 11]
Outliers = item_outlet_sales > 6501
combined_set['Item_Fat_Content'] = combined_set['Item_Fat_Content'].replace({'low fat': 'LF', 'Low Fat': 'LF', 'Regular': 'reg'})
combined_mean_modelset = combined_set.copy(deep=True)
combined_mean_modelset['Item_Outlet_Sales'] = combined_mean_modelset['Item_Outlet_Sales'].replace(to_replace=-999, value=2181.3)
X = combined_mean_modelset.iloc[:, :-1].values
Y = combined_mean_modelset.iloc[:, 11].values
X = pd.DataFrame(X)
X.dtypes
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[:, 0] = labelencoder_X.fit_transform(X[:, 0])
X = pd.DataFrame(X)
X_extra = pd.get_dummies(X.iloc[:, 0])
X_extra.head()
|
code
|
2036883/cell_9
|
[
"text_html_output_1.png"
] |
from matplotlib import style
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
from sklearn.preprocessing import Imputer
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import scale
from collections import Counter
import seaborn as sns
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from subprocess import check_output
train = pd.read_csv('../input/training-set/Train_A102.csv')
test = pd.read_csv('../input/test-a102csv/Test_A102 (1).csv')
test['Item_Outlet_Sales'] = np.nan
combined_set = train.append(test)
combined_set.replace('', np.NaN)
combined_meanset = combined_set.copy(deep=True)
combined_meanset['Item_Weight'].fillna(value=12.792854, inplace=True)
combined_medianset = combined_set.copy(deep=True)
combined_medianset['Item_Weight'].fillna(value=12.6, inplace=True)
combined_set['Item_Weight'].fillna(value=12.6, inplace=True)
train.plot.density()
combined_set['Outlet_Size'].fillna(value='Medium', inplace=True)
combined_set['Item_Outlet_Sales'].fillna(value=-999, inplace=True)
X = train.iloc[:, 0:].values
item_outlet_sales = X[:, 11]
Outliers = item_outlet_sales > 6501
combined_set['Item_Fat_Content'] = combined_set['Item_Fat_Content'].replace({'low fat': 'LF', 'Low Fat': 'LF', 'Regular': 'reg'})
combined_mean_modelset = combined_set.copy(deep=True)
combined_mean_modelset['Item_Outlet_Sales'] = combined_mean_modelset['Item_Outlet_Sales'].replace(to_replace=-999, value=2181.3)
train_new = combined_mean_modelset[0:8523]
test_new = combined_mean_modelset[8523:]
test_new.shape
|
code
|
2036883/cell_4
|
[
"text_plain_output_1.png"
] |
from matplotlib import style
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
from sklearn.preprocessing import Imputer
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import scale
from collections import Counter
import seaborn as sns
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from subprocess import check_output
train = pd.read_csv('../input/training-set/Train_A102.csv')
test = pd.read_csv('../input/test-a102csv/Test_A102 (1).csv')
test['Item_Outlet_Sales'] = np.nan
combined_set = train.append(test)
combined_set.replace('', np.NaN)
combined_meanset = combined_set.copy(deep=True)
combined_meanset['Item_Weight'].fillna(value=12.792854, inplace=True)
combined_medianset = combined_set.copy(deep=True)
combined_medianset['Item_Weight'].fillna(value=12.6, inplace=True)
combined_set['Item_Weight'].fillna(value=12.6, inplace=True)
train.plot.density()
combined_set['Outlet_Size'].fillna(value='Medium', inplace=True)
combined_set['Item_Outlet_Sales'].fillna(value=-999, inplace=True)
X = train.iloc[:, 0:].values
item_outlet_sales = X[:, 11]
Outliers = item_outlet_sales > 6501
combined_set['Item_Fat_Content'] = combined_set['Item_Fat_Content'].replace({'low fat': 'LF', 'Low Fat': 'LF', 'Regular': 'reg'})
combined_mean_modelset = combined_set.copy(deep=True)
combined_mean_modelset['Item_Outlet_Sales'] = combined_mean_modelset['Item_Outlet_Sales'].replace(to_replace=-999, value=2181.3)
combined_mean_modelset.tail()
|
code
|
2036883/cell_6
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
from matplotlib import style
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
from sklearn.preprocessing import Imputer
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import scale
from collections import Counter
import seaborn as sns
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from subprocess import check_output
train = pd.read_csv('../input/training-set/Train_A102.csv')
test = pd.read_csv('../input/test-a102csv/Test_A102 (1).csv')
test['Item_Outlet_Sales'] = np.nan
combined_set = train.append(test)
combined_set.replace('', np.NaN)
combined_meanset = combined_set.copy(deep=True)
combined_meanset['Item_Weight'].fillna(value=12.792854, inplace=True)
combined_medianset = combined_set.copy(deep=True)
combined_medianset['Item_Weight'].fillna(value=12.6, inplace=True)
combined_set['Item_Weight'].fillna(value=12.6, inplace=True)
train.plot.density()
combined_set['Outlet_Size'].fillna(value='Medium', inplace=True)
combined_set['Item_Outlet_Sales'].fillna(value=-999, inplace=True)
X = train.iloc[:, 0:].values
item_outlet_sales = X[:, 11]
Outliers = item_outlet_sales > 6501
combined_set['Item_Fat_Content'] = combined_set['Item_Fat_Content'].replace({'low fat': 'LF', 'Low Fat': 'LF', 'Regular': 'reg'})
combined_mean_modelset = combined_set.copy(deep=True)
combined_mean_modelset['Item_Outlet_Sales'] = combined_mean_modelset['Item_Outlet_Sales'].replace(to_replace=-999, value=2181.3)
combined_median_modelset = combined_set.copy(deep=True)
combined_median_modelset['Item_Outlet_Sales'] = combined_median_modelset['Item_Outlet_Sales'].replace(to_replace=-999, value=1794.3)
combined_median_modelset.tail()
|
code
|
2036883/cell_11
|
[
"text_html_output_1.png"
] |
from matplotlib import style
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
from sklearn.preprocessing import Imputer
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import scale
from collections import Counter
import seaborn as sns
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from subprocess import check_output
train = pd.read_csv('../input/training-set/Train_A102.csv')
test = pd.read_csv('../input/test-a102csv/Test_A102 (1).csv')
test['Item_Outlet_Sales'] = np.nan
combined_set = train.append(test)
combined_set.replace('', np.NaN)
combined_meanset = combined_set.copy(deep=True)
combined_meanset['Item_Weight'].fillna(value=12.792854, inplace=True)
combined_medianset = combined_set.copy(deep=True)
combined_medianset['Item_Weight'].fillna(value=12.6, inplace=True)
combined_set['Item_Weight'].fillna(value=12.6, inplace=True)
train.plot.density()
combined_set['Outlet_Size'].fillna(value='Medium', inplace=True)
combined_set['Item_Outlet_Sales'].fillna(value=-999, inplace=True)
X = train.iloc[:, 0:].values
item_outlet_sales = X[:, 11]
Outliers = item_outlet_sales > 6501
combined_set['Item_Fat_Content'] = combined_set['Item_Fat_Content'].replace({'low fat': 'LF', 'Low Fat': 'LF', 'Regular': 'reg'})
combined_mean_modelset = combined_set.copy(deep=True)
combined_mean_modelset['Item_Outlet_Sales'] = combined_mean_modelset['Item_Outlet_Sales'].replace(to_replace=-999, value=2181.3)
X = combined_mean_modelset.iloc[:, :-1].values
print(X)
Y = combined_mean_modelset.iloc[:, 11].values
print(Y)
|
code
|
2036883/cell_1
|
[
"image_output_5.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] |
from matplotlib import style
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
from sklearn.preprocessing import Imputer
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import scale
from collections import Counter
import seaborn as sns
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
train = pd.read_csv('../input/training-set/Train_A102.csv')
test = pd.read_csv('../input/test-a102csv/Test_A102 (1).csv')
print(train.head())
print(test.head())
train.info()
print(train.dtypes)
print(train.describe(include='all'))
print(train.shape)
test.info()
print(test.dtypes)
print(test.describe(include='all'))
print(test.shape)
test['Item_Outlet_Sales'] = np.nan
print(test.head())
combined_set = train.append(test)
print(combined_set.head())
print(combined_set.describe(include='all'))
print(combined_set.shape)
print(combined_set.max())
print(combined_set.min())
combined_set.replace('', np.NaN)
combined_meanset = combined_set.copy(deep=True)
combined_meanset['Item_Weight'].fillna(value=12.792854, inplace=True)
print(combined_meanset.head(10))
combined_medianset = combined_set.copy(deep=True)
combined_medianset['Item_Weight'].fillna(value=12.6, inplace=True)
print(combined_medianset.head(10))
print(combined_set.head(10))
combined_meanset['Item_Weight'].hist(color='white', edgecolor='green')
plt.title('Mean Histogram')
plt.xlabel('X-axis')
plt.ylabel('Item_Weight')
plt.show()
combined_medianset['Item_Weight'].hist(color='white', edgecolor='green')
plt.title('Median Histogram')
plt.xlabel('X-axis')
plt.ylabel('Item_Weight')
plt.show()
combined_set['Item_Weight'].hist(color='white', edgecolor='green')
plt.title('Actual Value Histogram')
plt.xlabel('X-axis')
plt.ylabel('Item_Weight')
plt.show()
combined_set['Item_Weight'].fillna(value=12.6, inplace=True)
train.plot.density()
plt.show()
combined_set.boxplot()
plt.plot()
combined_set.boxplot('Item_Visibility', figsize=(20, 8))
plt.plot()
combined_set.boxplot('Outlet_Establishment_Year', figsize=(12, 8))
plt.plot()
print(combined_set.groupby('Item_Fat_Content').Outlet_Size.value_counts(dropna=False))
print(combined_set.groupby('Item_Type').Outlet_Size.value_counts(dropna=False))
print(combined_set.groupby('Outlet_Identifier').Outlet_Size.value_counts(dropna=False))
print(combined_set.groupby('Outlet_Type').Outlet_Size.value_counts(dropna=False))
print(combined_set.groupby('Outlet_Location_Type').Outlet_Size.value_counts(dropna=False))
combined_set['Outlet_Size'].fillna(value='Medium', inplace=True)
print(combined_set.head())
combined_set['Outlet_Size'].hist(color='white', edgecolor='blue')
plt.title('Histogram of Outlet_Size')
plt.xlabel('X-axis')
plt.ylabel('Outlet_Size')
plt.show()
combined_set['Item_Outlet_Sales'].fillna(value=-999, inplace=True)
print(combined_set.tail())
print(combined_set.shape[0])
X = train.iloc[:, 0:].values
item_outlet_sales = X[:, 11]
Outliers = item_outlet_sales > 6501
print(train[Outliers])
print(combined_set['Item_Fat_Content'].value_counts())
combined_set['Item_Fat_Content'] = combined_set['Item_Fat_Content'].replace({'low fat': 'LF', 'Low Fat': 'LF', 'Regular': 'reg'})
print(combined_set.head())
|
code
|
2036883/cell_7
|
[
"text_html_output_1.png"
] |
from matplotlib import style
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
from sklearn.preprocessing import Imputer
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import scale
from collections import Counter
import seaborn as sns
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from subprocess import check_output
train = pd.read_csv('../input/training-set/Train_A102.csv')
test = pd.read_csv('../input/test-a102csv/Test_A102 (1).csv')
test['Item_Outlet_Sales'] = np.nan
combined_set = train.append(test)
combined_set.replace('', np.NaN)
combined_meanset = combined_set.copy(deep=True)
combined_meanset['Item_Weight'].fillna(value=12.792854, inplace=True)
combined_medianset = combined_set.copy(deep=True)
combined_medianset['Item_Weight'].fillna(value=12.6, inplace=True)
combined_set['Item_Weight'].fillna(value=12.6, inplace=True)
train.plot.density()
combined_set['Outlet_Size'].fillna(value='Medium', inplace=True)
combined_set['Item_Outlet_Sales'].fillna(value=-999, inplace=True)
X = train.iloc[:, 0:].values
item_outlet_sales = X[:, 11]
Outliers = item_outlet_sales > 6501
combined_set['Item_Fat_Content'] = combined_set['Item_Fat_Content'].replace({'low fat': 'LF', 'Low Fat': 'LF', 'Regular': 'reg'})
combined_mean_modelset = combined_set.copy(deep=True)
combined_mean_modelset['Item_Outlet_Sales'] = combined_mean_modelset['Item_Outlet_Sales'].replace(to_replace=-999, value=2181.3)
train_new = combined_mean_modelset[0:8523]
print(train_new.tail())
test_new = combined_mean_modelset[8523:]
print(test_new.tail())
|
code
|
2036883/cell_8
|
[
"text_html_output_1.png"
] |
from matplotlib import style
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
from sklearn.preprocessing import Imputer
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import scale
from collections import Counter
import seaborn as sns
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from subprocess import check_output
train = pd.read_csv('../input/training-set/Train_A102.csv')
test = pd.read_csv('../input/test-a102csv/Test_A102 (1).csv')
test['Item_Outlet_Sales'] = np.nan
combined_set = train.append(test)
combined_set.replace('', np.NaN)
combined_meanset = combined_set.copy(deep=True)
combined_meanset['Item_Weight'].fillna(value=12.792854, inplace=True)
combined_medianset = combined_set.copy(deep=True)
combined_medianset['Item_Weight'].fillna(value=12.6, inplace=True)
combined_set['Item_Weight'].fillna(value=12.6, inplace=True)
train.plot.density()
combined_set['Outlet_Size'].fillna(value='Medium', inplace=True)
combined_set['Item_Outlet_Sales'].fillna(value=-999, inplace=True)
X = train.iloc[:, 0:].values
item_outlet_sales = X[:, 11]
Outliers = item_outlet_sales > 6501
combined_set['Item_Fat_Content'] = combined_set['Item_Fat_Content'].replace({'low fat': 'LF', 'Low Fat': 'LF', 'Regular': 'reg'})
combined_mean_modelset = combined_set.copy(deep=True)
combined_mean_modelset['Item_Outlet_Sales'] = combined_mean_modelset['Item_Outlet_Sales'].replace(to_replace=-999, value=2181.3)
train_new = combined_mean_modelset[0:8523]
test_new = combined_mean_modelset[8523:]
train_new.shape
|
code
|
2036883/cell_15
|
[
"text_plain_output_1.png"
] |
from matplotlib import style
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
from sklearn.preprocessing import Imputer
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import scale
from collections import Counter
import seaborn as sns
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from subprocess import check_output
train = pd.read_csv('../input/training-set/Train_A102.csv')
test = pd.read_csv('../input/test-a102csv/Test_A102 (1).csv')
test['Item_Outlet_Sales'] = np.nan
combined_set = train.append(test)
combined_set.replace('', np.NaN)
combined_meanset = combined_set.copy(deep=True)
combined_meanset['Item_Weight'].fillna(value=12.792854, inplace=True)
combined_medianset = combined_set.copy(deep=True)
combined_medianset['Item_Weight'].fillna(value=12.6, inplace=True)
combined_set['Item_Weight'].fillna(value=12.6, inplace=True)
train.plot.density()
combined_set['Outlet_Size'].fillna(value='Medium', inplace=True)
combined_set['Item_Outlet_Sales'].fillna(value=-999, inplace=True)
X = train.iloc[:, 0:].values
item_outlet_sales = X[:, 11]
Outliers = item_outlet_sales > 6501
combined_set['Item_Fat_Content'] = combined_set['Item_Fat_Content'].replace({'low fat': 'LF', 'Low Fat': 'LF', 'Regular': 'reg'})
combined_mean_modelset = combined_set.copy(deep=True)
combined_mean_modelset['Item_Outlet_Sales'] = combined_mean_modelset['Item_Outlet_Sales'].replace(to_replace=-999, value=2181.3)
X = combined_mean_modelset.iloc[:, :-1].values
Y = combined_mean_modelset.iloc[:, 11].values
X = pd.DataFrame(X)
X.dtypes
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[:, 0] = labelencoder_X.fit_transform(X[:, 0])
X = pd.DataFrame(X)
X_extra = pd.get_dummies(X.iloc[:, 0])
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
X.iloc[:, 2] = labelencoder_X.fit_transform(X.iloc[:, 2])
X_extra = pd.get_dummies(X.iloc[:, 2])
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
X.iloc[:, 4] = labelencoder_X.fit_transform(X.iloc[:, 4])
X_extra = pd.get_dummies(X.iloc[:, 4])
X_extra.head()
|
code
|
2036883/cell_3
|
[
"text_html_output_1.png"
] |
from matplotlib import style
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
from sklearn.preprocessing import Imputer
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import scale
from collections import Counter
import seaborn as sns
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from subprocess import check_output
train = pd.read_csv('../input/training-set/Train_A102.csv')
test = pd.read_csv('../input/test-a102csv/Test_A102 (1).csv')
test['Item_Outlet_Sales'] = np.nan
combined_set = train.append(test)
combined_set.replace('', np.NaN)
combined_meanset = combined_set.copy(deep=True)
combined_meanset['Item_Weight'].fillna(value=12.792854, inplace=True)
combined_medianset = combined_set.copy(deep=True)
combined_medianset['Item_Weight'].fillna(value=12.6, inplace=True)
combined_set['Item_Weight'].fillna(value=12.6, inplace=True)
train.plot.density()
combined_set['Outlet_Size'].fillna(value='Medium', inplace=True)
combined_set['Item_Outlet_Sales'].fillna(value=-999, inplace=True)
X = train.iloc[:, 0:].values
item_outlet_sales = X[:, 11]
Outliers = item_outlet_sales > 6501
combined_set['Item_Fat_Content'] = combined_set['Item_Fat_Content'].replace({'low fat': 'LF', 'Low Fat': 'LF', 'Regular': 'reg'})
combined_mean_modelset = combined_set.copy(deep=True)
combined_mean_modelset['Item_Outlet_Sales'] = combined_mean_modelset['Item_Outlet_Sales'].replace(to_replace=-999, value=2181.3)
print(combined_mean_modelset.tail())
|
code
|
2036883/cell_14
|
[
"text_plain_output_1.png"
] |
from matplotlib import style
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
from sklearn.preprocessing import Imputer
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import scale
from collections import Counter
import seaborn as sns
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from subprocess import check_output
train = pd.read_csv('../input/training-set/Train_A102.csv')
test = pd.read_csv('../input/test-a102csv/Test_A102 (1).csv')
test['Item_Outlet_Sales'] = np.nan
combined_set = train.append(test)
combined_set.replace('', np.NaN)
combined_meanset = combined_set.copy(deep=True)
combined_meanset['Item_Weight'].fillna(value=12.792854, inplace=True)
combined_medianset = combined_set.copy(deep=True)
combined_medianset['Item_Weight'].fillna(value=12.6, inplace=True)
combined_set['Item_Weight'].fillna(value=12.6, inplace=True)
train.plot.density()
combined_set['Outlet_Size'].fillna(value='Medium', inplace=True)
combined_set['Item_Outlet_Sales'].fillna(value=-999, inplace=True)
X = train.iloc[:, 0:].values
item_outlet_sales = X[:, 11]
Outliers = item_outlet_sales > 6501
combined_set['Item_Fat_Content'] = combined_set['Item_Fat_Content'].replace({'low fat': 'LF', 'Low Fat': 'LF', 'Regular': 'reg'})
combined_mean_modelset = combined_set.copy(deep=True)
combined_mean_modelset['Item_Outlet_Sales'] = combined_mean_modelset['Item_Outlet_Sales'].replace(to_replace=-999, value=2181.3)
X = combined_mean_modelset.iloc[:, :-1].values
Y = combined_mean_modelset.iloc[:, 11].values
X = pd.DataFrame(X)
X.dtypes
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[:, 0] = labelencoder_X.fit_transform(X[:, 0])
X = pd.DataFrame(X)
X_extra = pd.get_dummies(X.iloc[:, 0])
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
X.iloc[:, 2] = labelencoder_X.fit_transform(X.iloc[:, 2])
X_extra = pd.get_dummies(X.iloc[:, 2])
X_extra.head()
|
code
|
2036883/cell_10
|
[
"text_plain_output_1.png"
] |
from matplotlib import style
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
from sklearn.preprocessing import Imputer
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import scale
from collections import Counter
import seaborn as sns
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from subprocess import check_output
train = pd.read_csv('../input/training-set/Train_A102.csv')
test = pd.read_csv('../input/test-a102csv/Test_A102 (1).csv')
test['Item_Outlet_Sales'] = np.nan
combined_set = train.append(test)
combined_set.replace('', np.NaN)
combined_meanset = combined_set.copy(deep=True)
combined_meanset['Item_Weight'].fillna(value=12.792854, inplace=True)
combined_medianset = combined_set.copy(deep=True)
combined_medianset['Item_Weight'].fillna(value=12.6, inplace=True)
combined_set['Item_Weight'].fillna(value=12.6, inplace=True)
train.plot.density()
combined_set['Outlet_Size'].fillna(value='Medium', inplace=True)
combined_set['Item_Outlet_Sales'].fillna(value=-999, inplace=True)
X = train.iloc[:, 0:].values
item_outlet_sales = X[:, 11]
Outliers = item_outlet_sales > 6501
combined_set['Item_Fat_Content'] = combined_set['Item_Fat_Content'].replace({'low fat': 'LF', 'Low Fat': 'LF', 'Regular': 'reg'})
combined_mean_modelset = combined_set.copy(deep=True)
combined_mean_modelset['Item_Outlet_Sales'] = combined_mean_modelset['Item_Outlet_Sales'].replace(to_replace=-999, value=2181.3)
combined_mean_modelset.tail()
|
code
|
2036883/cell_12
|
[
"text_plain_output_1.png"
] |
from matplotlib import style
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
from sklearn.preprocessing import Imputer
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import scale
from collections import Counter
import seaborn as sns
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from subprocess import check_output
train = pd.read_csv('../input/training-set/Train_A102.csv')
test = pd.read_csv('../input/test-a102csv/Test_A102 (1).csv')
test['Item_Outlet_Sales'] = np.nan
combined_set = train.append(test)
combined_set.replace('', np.NaN)
combined_meanset = combined_set.copy(deep=True)
combined_meanset['Item_Weight'].fillna(value=12.792854, inplace=True)
combined_medianset = combined_set.copy(deep=True)
combined_medianset['Item_Weight'].fillna(value=12.6, inplace=True)
combined_set['Item_Weight'].fillna(value=12.6, inplace=True)
train.plot.density()
combined_set['Outlet_Size'].fillna(value='Medium', inplace=True)
combined_set['Item_Outlet_Sales'].fillna(value=-999, inplace=True)
X = train.iloc[:, 0:].values
item_outlet_sales = X[:, 11]
Outliers = item_outlet_sales > 6501
combined_set['Item_Fat_Content'] = combined_set['Item_Fat_Content'].replace({'low fat': 'LF', 'Low Fat': 'LF', 'Regular': 'reg'})
combined_mean_modelset = combined_set.copy(deep=True)
combined_mean_modelset['Item_Outlet_Sales'] = combined_mean_modelset['Item_Outlet_Sales'].replace(to_replace=-999, value=2181.3)
X = combined_mean_modelset.iloc[:, :-1].values
Y = combined_mean_modelset.iloc[:, 11].values
X = pd.DataFrame(X)
X.dtypes
|
code
|
2036883/cell_5
|
[
"text_plain_output_1.png"
] |
from matplotlib import style
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import style
style.use('ggplot')
from sklearn.preprocessing import Imputer
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import scale
from collections import Counter
import seaborn as sns
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from subprocess import check_output
train = pd.read_csv('../input/training-set/Train_A102.csv')
test = pd.read_csv('../input/test-a102csv/Test_A102 (1).csv')
test['Item_Outlet_Sales'] = np.nan
combined_set = train.append(test)
combined_set.replace('', np.NaN)
combined_meanset = combined_set.copy(deep=True)
combined_meanset['Item_Weight'].fillna(value=12.792854, inplace=True)
combined_medianset = combined_set.copy(deep=True)
combined_medianset['Item_Weight'].fillna(value=12.6, inplace=True)
combined_set['Item_Weight'].fillna(value=12.6, inplace=True)
train.plot.density()
combined_set['Outlet_Size'].fillna(value='Medium', inplace=True)
combined_set['Item_Outlet_Sales'].fillna(value=-999, inplace=True)
X = train.iloc[:, 0:].values
item_outlet_sales = X[:, 11]
Outliers = item_outlet_sales > 6501
combined_set['Item_Fat_Content'] = combined_set['Item_Fat_Content'].replace({'low fat': 'LF', 'Low Fat': 'LF', 'Regular': 'reg'})
combined_mean_modelset = combined_set.copy(deep=True)
combined_mean_modelset['Item_Outlet_Sales'] = combined_mean_modelset['Item_Outlet_Sales'].replace(to_replace=-999, value=2181.3)
combined_median_modelset = combined_set.copy(deep=True)
combined_median_modelset['Item_Outlet_Sales'] = combined_median_modelset['Item_Outlet_Sales'].replace(to_replace=-999, value=1794.3)
print(combined_median_modelset.tail())
|
code
|
129018697/cell_9
|
[
"text_plain_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
sample = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
train.describe().T
train.isna().mean()
plt.figure(figsize=(16, 6))
heatmap = sns.heatmap(train.corr(), vmin=-1, vmax=1, annot=True)
heatmap.set_title('Correlation Heatmap', fontdict={'fontsize':12}, pad=12);
pd.plotting.scatter_matrix(train[['MaxOfUpperTRange', 'MinOfUpperTRange', 'AverageOfUpperTRange', 'MaxOfLowerTRange', 'MinOfLowerTRange', 'AverageOfLowerTRange']], figsize=(10, 10), diagonal='hist')
plt.show()
|
code
|
129018697/cell_4
|
[
"image_output_1.png"
] |
import pandas as pd
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
sample = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
train.head()
|
code
|
129018697/cell_6
|
[
"image_output_1.png"
] |
import pandas as pd
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
sample = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
train.describe().T
train.isna().mean()
|
code
|
129018697/cell_19
|
[
"text_plain_output_1.png"
] |
from sklearn.decomposition import PCA
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import KFold
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
import lightgbm as lgb
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
sample = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
train.describe().T
train.isna().mean()
plt.figure(figsize=(16, 6))
heatmap = sns.heatmap(train.corr(), vmin=-1, vmax=1, annot=True)
heatmap.set_title('Correlation Heatmap', fontdict={'fontsize':12}, pad=12);
from sklearn.decomposition import PCA
def perform_feature_engineering(df):
df['total_bees'] = df['honeybee'] + df['bumbles'] + df['andrena'] + df['osmia']
df['temperature_range'] = df['MaxOfUpperTRange'] - df['MinOfUpperTRange'] + df['MaxOfLowerTRange'] - df['MinOfLowerTRange']
df['rainfall_intensity'] = df['RainingDays'] * df['AverageRainingDays']
df['fruit_quality'] = df['fruitset'] * df['fruitmass']
df['seed_to_fruit_ratio'] = df['seeds'] / df['fruitmass']
temp_range_features = ['MaxOfUpperTRange', 'MinOfUpperTRange', 'AverageOfUpperTRange', 'MaxOfLowerTRange', 'MinOfLowerTRange', 'AverageOfLowerTRange']
pca = PCA(n_components=1)
df[['temperature_pca']] = pca.fit_transform(df[temp_range_features])
df = df.drop(temp_range_features, axis=1)
return df
train = perform_feature_engineering(train)
test = perform_feature_engineering(test)
train.columns
features = ['clonesize', 'honeybee', 'bumbles', 'andrena', 'osmia', 'RainingDays', 'AverageRainingDays', 'fruitset', 'fruitmass', 'seeds', 'total_bees', 'temperature_range', 'rainfall_intensity', 'fruit_quality', 'seed_to_fruit_ratio', 'temperature_pca']
target = 'yield'
from sklearn.model_selection import train_test_split
X = train[features]
y = train[target]
import lightgbm as lgb
from sklearn.model_selection import KFold
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_absolute_error
params_lgb = {'n_estimators': 9999, 'learning_rate': 0.03, 'max_depth': 5, 'num_leaves': 16, 'subsample': 0.7, 'colsample_bytree': 0.8, 'reg_lambda': 5e-07, 'objective': 'regression_l1', 'metric': 'mean_absolute_error', 'boosting_type': 'gbdt', 'device': 'GPU', 'random_state': 42}
n_splits = 5
kf = KFold(n_splits=n_splits, shuffle=True, random_state=42)
lgb_model = Pipeline([('scaler', StandardScaler()), ('lgbm', lgb.LGBMRegressor(**params_lgb))])
mae_scores = []
for train_index, test_index in kf.split(X):
X_train, y_train = (X.iloc[train_index], y.iloc[train_index])
X_test, y_test = (X.iloc[test_index], y.iloc[test_index])
lgb_model.fit(X_train, y_train)
y_pred_lgb = lgb_model.predict(X_test)
mae_lgb = mean_absolute_error(y_test, y_pred_lgb)
mae_scores.append(mae_lgb)
print('Mean absolute error:', np.mean(mae_scores))
print('Scores:', mae_scores)
|
code
|
129018697/cell_1
|
[
"text_plain_output_1.png"
] |
import os
import warnings
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
|
code
|
129018697/cell_8
|
[
"text_plain_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
sample = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
train.describe().T
train.isna().mean()
plt.figure(figsize=(16, 6))
heatmap = sns.heatmap(train.corr(), vmin=-1, vmax=1, annot=True)
heatmap.set_title('Correlation Heatmap', fontdict={'fontsize': 12}, pad=12)
|
code
|
129018697/cell_16
|
[
"text_html_output_1.png"
] |
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
sample = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
train.describe().T
train.isna().mean()
plt.figure(figsize=(16, 6))
heatmap = sns.heatmap(train.corr(), vmin=-1, vmax=1, annot=True)
heatmap.set_title('Correlation Heatmap', fontdict={'fontsize':12}, pad=12);
from sklearn.decomposition import PCA
def perform_feature_engineering(df):
df['total_bees'] = df['honeybee'] + df['bumbles'] + df['andrena'] + df['osmia']
df['temperature_range'] = df['MaxOfUpperTRange'] - df['MinOfUpperTRange'] + df['MaxOfLowerTRange'] - df['MinOfLowerTRange']
df['rainfall_intensity'] = df['RainingDays'] * df['AverageRainingDays']
df['fruit_quality'] = df['fruitset'] * df['fruitmass']
df['seed_to_fruit_ratio'] = df['seeds'] / df['fruitmass']
temp_range_features = ['MaxOfUpperTRange', 'MinOfUpperTRange', 'AverageOfUpperTRange', 'MaxOfLowerTRange', 'MinOfLowerTRange', 'AverageOfLowerTRange']
pca = PCA(n_components=1)
df[['temperature_pca']] = pca.fit_transform(df[temp_range_features])
df = df.drop(temp_range_features, axis=1)
return df
train = perform_feature_engineering(train)
test = perform_feature_engineering(test)
train.columns
|
code
|
129018697/cell_10
|
[
"text_html_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
sample = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
train.describe().T
train.isna().mean()
plt.figure(figsize=(16, 6))
heatmap = sns.heatmap(train.corr(), vmin=-1, vmax=1, annot=True)
heatmap.set_title('Correlation Heatmap', fontdict={'fontsize':12}, pad=12);
sns.boxplot(x='RainingDays', y='fruitset', data=train)
plt.xlabel('Raining Days')
plt.ylabel('Fruitset (%)')
plt.title('Raining Days vs Fruitset')
plt.show()
|
code
|
129018697/cell_5
|
[
"image_output_1.png"
] |
import pandas as pd
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
sample = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
train.describe().T
|
code
|
72081303/cell_9
|
[
"text_plain_output_1.png"
] |
from sklearn.model_selection import GridSearchCV
from xgboost import XGBClassifier
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
train_labels = train_df['Survived']
train_df = train_df.drop(['Survived'], axis=1)
df = pd.concat([train_df, test_df])
def substrings_in_string(big_string, substrings):
for substring in substrings:
if str(big_string).find(substring) != -1:
return substring
return np.nan
title_list = ['Mrs', 'Mr', 'Master', 'Miss', 'Major', 'Rev', 'Dr', 'Ms', 'Mlle', 'Col', 'Capt', 'Mme', 'Countess', 'Don', 'Jonkheer']
df['Title'] = df['Name'].map(lambda x: substrings_in_string(x, title_list))
cabin_list = ['A', 'B', 'C', 'D', 'E', 'F', 'T', 'G', 'Unknown']
df['Deck'] = df['Cabin'].map(lambda x: substrings_in_string(x, cabin_list))
df['Alone'] = (df['SibSp'] + df['Parch'] == 0).astype('object')
numeric_columns = ['Age', 'SibSp', 'Parch', 'Fare']
categorical_columns = ['Pclass', 'Sex', 'Embarked', 'Title', 'Deck', 'Alone']
df['Pclass'] = df['Pclass'].astype('object')
data = pd.get_dummies(df[numeric_columns + categorical_columns], dummy_na=True)
data[numeric_columns] -= data.mean()
data[numeric_columns] /= data.std()
data[numeric_columns] = data[numeric_columns].fillna(0)
n_train = len(train_labels)
train_data = data.iloc[:n_train, :].values
test_data = data.iloc[n_train:, :].values
estimators = {}
estimator = XGBClassifier()
cv = GridSearchCV(estimator=estimator, param_grid={'n_estimators': list(range(70, 90)), 'max_depth': list(range(2, 7)), 'eval_metric': ['logloss'], 'use_label_encoder': [False]})
cv.fit(train_data, train_labels)
print(cv.best_score_)
print(cv.best_params_)
estimators['xgboost'] = cv.best_estimator_
|
code
|
72081303/cell_6
|
[
"text_plain_output_1.png"
] |
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
train_labels = train_df['Survived']
train_df = train_df.drop(['Survived'], axis=1)
df = pd.concat([train_df, test_df])
def substrings_in_string(big_string, substrings):
for substring in substrings:
if str(big_string).find(substring) != -1:
return substring
return np.nan
title_list = ['Mrs', 'Mr', 'Master', 'Miss', 'Major', 'Rev', 'Dr', 'Ms', 'Mlle', 'Col', 'Capt', 'Mme', 'Countess', 'Don', 'Jonkheer']
df['Title'] = df['Name'].map(lambda x: substrings_in_string(x, title_list))
cabin_list = ['A', 'B', 'C', 'D', 'E', 'F', 'T', 'G', 'Unknown']
df['Deck'] = df['Cabin'].map(lambda x: substrings_in_string(x, cabin_list))
df['Alone'] = (df['SibSp'] + df['Parch'] == 0).astype('object')
numeric_columns = ['Age', 'SibSp', 'Parch', 'Fare']
categorical_columns = ['Pclass', 'Sex', 'Embarked', 'Title', 'Deck', 'Alone']
df['Pclass'] = df['Pclass'].astype('object')
df[categorical_columns].describe()
|
code
|
72081303/cell_3
|
[
"text_html_output_1.png"
] |
import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
train_labels = train_df['Survived']
train_df = train_df.drop(['Survived'], axis=1)
df = pd.concat([train_df, test_df])
df.head()
|
code
|
72081303/cell_10
|
[
"text_html_output_1.png"
] |
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from xgboost import XGBClassifier
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
train_labels = train_df['Survived']
train_df = train_df.drop(['Survived'], axis=1)
df = pd.concat([train_df, test_df])
def substrings_in_string(big_string, substrings):
for substring in substrings:
if str(big_string).find(substring) != -1:
return substring
return np.nan
title_list = ['Mrs', 'Mr', 'Master', 'Miss', 'Major', 'Rev', 'Dr', 'Ms', 'Mlle', 'Col', 'Capt', 'Mme', 'Countess', 'Don', 'Jonkheer']
df['Title'] = df['Name'].map(lambda x: substrings_in_string(x, title_list))
cabin_list = ['A', 'B', 'C', 'D', 'E', 'F', 'T', 'G', 'Unknown']
df['Deck'] = df['Cabin'].map(lambda x: substrings_in_string(x, cabin_list))
df['Alone'] = (df['SibSp'] + df['Parch'] == 0).astype('object')
numeric_columns = ['Age', 'SibSp', 'Parch', 'Fare']
categorical_columns = ['Pclass', 'Sex', 'Embarked', 'Title', 'Deck', 'Alone']
df['Pclass'] = df['Pclass'].astype('object')
data = pd.get_dummies(df[numeric_columns + categorical_columns], dummy_na=True)
data[numeric_columns] -= data.mean()
data[numeric_columns] /= data.std()
data[numeric_columns] = data[numeric_columns].fillna(0)
n_train = len(train_labels)
train_data = data.iloc[:n_train, :].values
test_data = data.iloc[n_train:, :].values
estimators = {}
estimator = XGBClassifier()
cv = GridSearchCV(estimator=estimator, param_grid={'n_estimators': list(range(70, 90)), 'max_depth': list(range(2, 7)), 'eval_metric': ['logloss'], 'use_label_encoder': [False]})
cv.fit(train_data, train_labels)
estimators['xgboost'] = cv.best_estimator_
estimator = RandomForestClassifier()
cv = GridSearchCV(estimator=estimator, param_grid={'n_estimators': list(range(200, 240))})
cv.fit(train_data, train_labels)
print(cv.best_score_)
print(cv.best_params_)
estimators['random_forest'] = cv.best_estimator_
|
code
|
72081303/cell_5
|
[
"text_html_output_1.png"
] |
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
train_labels = train_df['Survived']
train_df = train_df.drop(['Survived'], axis=1)
df = pd.concat([train_df, test_df])
def substrings_in_string(big_string, substrings):
for substring in substrings:
if str(big_string).find(substring) != -1:
return substring
return np.nan
title_list = ['Mrs', 'Mr', 'Master', 'Miss', 'Major', 'Rev', 'Dr', 'Ms', 'Mlle', 'Col', 'Capt', 'Mme', 'Countess', 'Don', 'Jonkheer']
df['Title'] = df['Name'].map(lambda x: substrings_in_string(x, title_list))
cabin_list = ['A', 'B', 'C', 'D', 'E', 'F', 'T', 'G', 'Unknown']
df['Deck'] = df['Cabin'].map(lambda x: substrings_in_string(x, cabin_list))
df['Alone'] = (df['SibSp'] + df['Parch'] == 0).astype('object')
numeric_columns = ['Age', 'SibSp', 'Parch', 'Fare']
categorical_columns = ['Pclass', 'Sex', 'Embarked', 'Title', 'Deck', 'Alone']
df['Pclass'] = df['Pclass'].astype('object')
df[numeric_columns].describe()
|
code
|
88087713/cell_42
|
[
"text_plain_output_1.png"
] |
se
|
code
|
88087713/cell_21
|
[
"text_html_output_1.png"
] |
import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
trans_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
trans_data.dtypes
|
code
|
88087713/cell_9
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
trans_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
customers_data_new = customers_data[customers_data['club_member_status'] == 'ACTIVE']
customers_data_new.drop(labels=['FN', 'Active', 'club_member_status', 'fashion_news_frequency'], axis=1, inplace=True)
customers_data_new.reset_index(drop=True, inplace=True)
customers_data_new.head(5)
|
code
|
88087713/cell_4
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
trans_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
customers_data['club_member_status'].value_counts()
|
code
|
88087713/cell_30
|
[
"text_plain_output_1.png"
] |
import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
trans_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
trans_data.dtypes
trans_data['t_dat'] = trans_data['t_dat'].astype('datetime64')
trans_data.dtypes
sample_trans_data = trans_data[trans_data['year_trans'] == 2019]
sample_trans_data.isna().sum()
sample_trans_data.drop(labels=['t_dat', 'sales_channel_id'], axis=1, inplace=True)
sample_trans_data.reset_index(drop=True, inplace=True)
sample_trans_data.isna().sum()
sample_trans_data.info()
|
code
|
88087713/cell_33
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
import pandas as pd
import seaborn as sns
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
trans_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
customers_data_new = customers_data[customers_data['club_member_status'] == 'ACTIVE']
customers_data_new.drop(labels=['FN', 'Active', 'club_member_status', 'fashion_news_frequency'], axis=1, inplace=True)
customers_data_new.reset_index(drop=True, inplace=True)
customers_data_new.drop(labels=['postal_code'], axis=1, inplace=True)
customers_data_new.reset_index(drop=True, inplace=True)
sns.set_style('whitegrid')
customers_data_new['age'].plot(kind='hist')
|
code
|
88087713/cell_6
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
trans_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
customers_data_new = customers_data[customers_data['club_member_status'] == 'ACTIVE']
customers_data_new.head(5)
|
code
|
88087713/cell_29
|
[
"text_plain_output_1.png"
] |
import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
trans_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
trans_data.dtypes
trans_data['t_dat'] = trans_data['t_dat'].astype('datetime64')
trans_data.dtypes
sample_trans_data = trans_data[trans_data['year_trans'] == 2019]
sample_trans_data.isna().sum()
sample_trans_data.drop(labels=['t_dat', 'sales_channel_id'], axis=1, inplace=True)
sample_trans_data.reset_index(drop=True, inplace=True)
sample_trans_data.isna().sum()
|
code
|
88087713/cell_39
|
[
"text_html_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
trans_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
customers_data_new = customers_data[customers_data['club_member_status'] == 'ACTIVE']
customers_data_new.drop(labels=['FN', 'Active', 'club_member_status', 'fashion_news_frequency'], axis=1, inplace=True)
customers_data_new.reset_index(drop=True, inplace=True)
customers_data_new.drop(labels=['postal_code'], axis=1, inplace=True)
customers_data_new.reset_index(drop=True, inplace=True)
trans_data.dtypes
trans_data['t_dat'] = trans_data['t_dat'].astype('datetime64')
trans_data.dtypes
sample_trans_data = trans_data[trans_data['year_trans'] == 2019]
sample_trans_data.isna().sum()
sample_trans_data.drop(labels=['t_dat', 'sales_channel_id'], axis=1, inplace=True)
sample_trans_data.reset_index(drop=True, inplace=True)
sample_trans_data.isna().sum()
sns.set_style('whitegrid')
interval_range_age = pd.interval_range(start=0, freq=10, end=100)
customers_data_new['age_group'] = pd.cut(customers_data_new['age'], bins=interval_range_age)
customers_data_new.isna().sum()
purchases_2019 = sample_trans_data.merge(customers_data_new, how='left', on='customer_id')
customers_temp = purchases_2019.groupby(['age_group'])['customer_id'].count()
data_temp_customer = pd.DataFrame({'Group Age': customers_temp.index, 'Customers': customers_temp.values})
data_temp_customer = data_temp_customer.sort_values(['Group Age'], ascending=False)
plt.figure(figsize=(7, 7))
plt.title(f'Group Age')
sns.set_color_codes('pastel')
s = sns.barplot(x='Group Age', y='Customers', data=data_temp_customer)
s.set_xticklabels(s.get_xticklabels(), rotation=45)
locs, labels = plt.xticks()
plt.show
|
code
|
88087713/cell_26
|
[
"text_plain_output_1.png"
] |
import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
trans_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
trans_data.dtypes
trans_data['t_dat'] = trans_data['t_dat'].astype('datetime64')
trans_data.dtypes
sample_trans_data = trans_data[trans_data['year_trans'] == 2019]
sample_trans_data.isna().sum()
|
code
|
88087713/cell_41
|
[
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
trans_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
customers_data_new = customers_data[customers_data['club_member_status'] == 'ACTIVE']
customers_data_new.drop(labels=['FN', 'Active', 'club_member_status', 'fashion_news_frequency'], axis=1, inplace=True)
customers_data_new.reset_index(drop=True, inplace=True)
customers_data_new.drop(labels=['postal_code'], axis=1, inplace=True)
customers_data_new.reset_index(drop=True, inplace=True)
trans_data.dtypes
trans_data['t_dat'] = trans_data['t_dat'].astype('datetime64')
trans_data.dtypes
sample_trans_data = trans_data[trans_data['year_trans'] == 2019]
sample_trans_data.isna().sum()
sample_trans_data.drop(labels=['t_dat', 'sales_channel_id'], axis=1, inplace=True)
sample_trans_data.reset_index(drop=True, inplace=True)
sample_trans_data.isna().sum()
sns.set_style('whitegrid')
interval_range_age = pd.interval_range(start=0, freq=10, end=100)
customers_data_new['age_group'] = pd.cut(customers_data_new['age'], bins=interval_range_age)
customers_data_new.isna().sum()
purchases_2019 = sample_trans_data.merge(customers_data_new, how='left', on='customer_id')
customers_temp = purchases_2019.groupby(['age_group'])['customer_id'].count()
data_temp_customer = pd.DataFrame({
'Group Age' : customers_temp.index,
'Customers' : customers_temp.values
})
data_temp_customer = data_temp_customer.sort_values(['Group Age'],ascending=False)
plt.figure(figsize=(7,7))
plt.title(f'Group Age')
sns.set_color_codes('pastel')
s = sns.barplot(x='Group Age', y='Customers', data=data_temp_customer)
s.set_xticklabels(s.get_xticklabels(),rotation=45)
locs, labels = plt.xticks()
plt.show
most_age_group_transaction = purchases_2019[purchases_2019['age_group'] == purchases_2019['age_group'].mode()[0]]
customers_temp_most = most_age_group_transaction.groupby(['day_trans'])['customer_id'].count()
data_temp_customer_most = pd.DataFrame({'Day Transaction': customers_temp_most.index, 'Customers': customers_temp_most.values})
data_temp_customer_most = data_temp_customer_most.sort_values(['Customers'], ascending=False)
plt.figure(figsize=(7, 7))
plt.title(f'Day Transaction of Most Age Group Purchases')
sns.set_color_codes('pastel')
s = sns.barplot(x='Day Transaction', y='Customers', data=data_temp_customer_most)
s.set_xticklabels(s.get_xticklabels())
locs, labels = plt.xticks()
plt.show()
|
code
|
88087713/cell_19
|
[
"text_html_output_1.png"
] |
import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
trans_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
articles_data_new = articles_data[['prod_name', 'product_type_name', 'product_group_name']].copy()
articles_data_new.isna().sum()
articles_data_new.info()
|
code
|
88087713/cell_18
|
[
"text_plain_output_1.png"
] |
import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
trans_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
articles_data_new = articles_data[['prod_name', 'product_type_name', 'product_group_name']].copy()
articles_data_new.isna().sum()
|
code
|
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