path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
104114403/cell_53 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_curve, roc_auc_score, auc
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
pima = pd.read_csv('../input/pimacsv/pima.csv')
pima.shape
#Your code (first create a correlation matrix and store it in 'corm' variable, and uncomment lines below)
corm = pima.iloc[:,:-1].corr()
masko = np.zeros_like(corm, dtype = np.bool)
masko[np.triu_indices_from(masko)] = True
fig, ax = plt.subplots(figsize = (10,5))
sns.heatmap(corm, mask = masko, cmap = 'coolwarm', annot=True)
X = pima.loc[:, pima.columns != 'Outcome']
y = pima.loc[:, 'Outcome']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=10)
from sklearn.linear_model import LogisticRegression
logr = LogisticRegression(random_state=0)
logr.fit(X_train, y_train)
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score
ypred_train_logr = logr.predict(X_train)
ypred_test_logr = logr.predict(X_test)
yprob_test_logr = logr.predict_proba(X_test)
yprob_test_logr[0:5, :].round(3)
from sklearn.metrics import roc_curve, roc_auc_score, auc
fpr_logr, tpr_logr, _ = roc_curve(y_test, yprob_test_logr[:, 1])
auc_logr = auc(fpr_logr, tpr_logr)
fig = plt.figure(figsize=(8, 5))
plt.plot(fpr_logr, tpr_logr, label='AUC score is : ' + str(auc_logr))
plt.xlabel('fpr', fontsize=10)
plt.ylabel('tpr', fontsize=10)
plt.xlim([-0.01, 1])
plt.ylim([0, 1.01])
plt.legend()
plt.plot([0, 1], [0, 1], 'r--')
plt.show()
print('AUC Score for logistic regression is', roc_auc_score(y_test, yprob_test_logr[:, 1])) | code |
104114403/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
pima = pd.read_csv('../input/pimacsv/pima.csv')
pima.shape | code |
104114403/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
pima = pd.read_csv('../input/pimacsv/pima.csv')
pima.shape
#Your code (first create a correlation matrix and store it in 'corm' variable, and uncomment lines below)
corm = pima.iloc[:,:-1].corr()
masko = np.zeros_like(corm, dtype = np.bool)
masko[np.triu_indices_from(masko)] = True
fig, ax = plt.subplots(figsize = (10,5))
sns.heatmap(corm, mask = masko, cmap = 'coolwarm', annot=True)
X = pima.loc[:, pima.columns != 'Outcome']
y = pima.loc[:, 'Outcome']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=10)
from sklearn.linear_model import LogisticRegression
logr = LogisticRegression(random_state=0)
logr.fit(X_train, y_train) | code |
104114403/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
pima = pd.read_csv('../input/pimacsv/pima.csv')
pima.shape
pima['Outcome'].value_counts() | code |
104114403/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
pima = pd.read_csv('../input/pimacsv/pima.csv')
pima.head(3) | code |
130027731/cell_42 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cont_features = []
for i in df.columns:
cont_features.append(i)
df.shape
cat_dims = [len(df[col].unique()) for col in ['MSSubClass', 'MSZoning', 'Street', 'LotShape']]
embedding_dim = [(x, min(50, (x + 1) // 2)) for x in cat_dims]
embedding_dim | code |
130027731/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
from sklearn.preprocessing import LabelEncoder
lbl_encoders = {}
lbl_encoders['MSSubClass'] = LabelEncoder()
lbl_encoders['MSSubClass'].fit_transform(df['MSSubClass'])
lbl_encoders | code |
130027731/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
for i in df.columns:
print('Column name {} and unique values are {} '.format(i, len(df[i].unique()))) | code |
130027731/cell_25 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cat_features = ['MSSubClass', 'MSZoning', 'Street', 'LotShape']
out_features = 'SalePrice'
cat_features = np.stack([df['MSSubClass'], df['MSZoning'], df['Street'], df['LotShape']], 1)
cat_features
import torch
cat_features = torch.tensor(cat_features, dtype=torch.int64)
cat_features | code |
130027731/cell_57 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch
import torch
import torch.nn as nn
import torch.nn as nn
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cat_features = ['MSSubClass', 'MSZoning', 'Street', 'LotShape']
out_features = 'SalePrice'
cat_features = np.stack([df['MSSubClass'], df['MSZoning'], df['Street'], df['LotShape']], 1)
cat_features
import torch
cat_features = torch.tensor(cat_features, dtype=torch.int64)
cat_features
cont_features = []
for i in df.columns:
cont_features.append(i)
cont_values = np.stack([df[i].values for i in cont_features], axis=1)
cont_values = torch.tensor(cont_values, dtype=torch.float)
cont_values
cont_values.dtype
y = torch.tensor(df['SalePrice'].values, dtype=torch.float).reshape(-1, 1)
y
(cat_features.shape, cont_values.shape, y.shape)
df.shape
cat_dims = [len(df[col].unique()) for col in ['MSSubClass', 'MSZoning', 'Street', 'LotShape']]
embedding_dim = [(x, min(50, (x + 1) // 2)) for x in cat_dims]
import torch
import torch.nn as nn
import torch.nn.functional as F
embed_representation = nn.ModuleList([nn.Embedding(inp, out) for inp, out in embedding_dim])
embed_representation
pd.set_option('display.max_rows', 500)
embedding_val = []
for i, e in enumerate(embed_representation):
embedding_val.append(e(cat_features[:, i]))
z = torch.cat(embedding_val, 1)
z
dropout = nn.Dropout(0.4)
import torch
import torch.nn as nn
import torch.nn.functional as F
class FeedForwardNN(nn.Module):
def __init__(self, embedding_dim, n_cont, out_sz, layers, p=0.5):
super().__init__()
self.embeds = nn.ModuleList([nn.Embedding(inp, out) for inp, out in embedding_dim])
self.emb_drop = nn.Dropout(p)
self.bn_cont = nn.BatchNorm1d(n_cont)
layerlist = []
n_emb = sum((out for inp, out in embedding_dim))
n_in = n_emb + n_cont
for i in layers:
layerlist.append(nn.Linear(n_in, i))
layerlist.append(nn.ReLU(inplace=True))
layerlist.append(nn.BatchNorm1d(i))
layerlist.append(nn.Dropout(p))
n_in = i
layerlist.append(nn.Linear(layers[-1], out_sz))
self.layers = nn.Sequential(*layerlist)
def forward(self, x_cat, x_cont):
embeddings = []
for i, e in enumerate(self.embeds):
embeddings.append(e(x_cat[:, i]))
x = torch.cat(embeddings, 1)
x = self.emb_drop(x)
x_cont = self.bn_cont(x_cont)
x = torch.cat([x, x_cont], 1)
x = self.layers(x)
return x
torch.manual_seed(100)
model = FeedForwardNN(embedding_dim, len(cont_features), 1, [100, 50], p=0.4)
model.parameters | code |
130027731/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cont_features = []
for i in df.columns:
cont_features.append(i)
df.shape | code |
130027731/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
df | code |
130027731/cell_30 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cat_features = ['MSSubClass', 'MSZoning', 'Street', 'LotShape']
out_features = 'SalePrice'
cat_features = np.stack([df['MSSubClass'], df['MSZoning'], df['Street'], df['LotShape']], 1)
cat_features
import torch
cat_features = torch.tensor(cat_features, dtype=torch.int64)
cat_features
cont_features = []
for i in df.columns:
cont_features.append(i)
cont_values = np.stack([df[i].values for i in cont_features], axis=1)
cont_values = torch.tensor(cont_values, dtype=torch.float)
cont_values
y = torch.tensor(df['SalePrice'].values, dtype=torch.float).reshape(-1, 1)
y | code |
130027731/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cont_features = []
for i in df.columns:
cont_features.append(i)
len(df['MSSubClass'].unique()) | code |
130027731/cell_44 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cat_features = ['MSSubClass', 'MSZoning', 'Street', 'LotShape']
out_features = 'SalePrice'
cat_features = np.stack([df['MSSubClass'], df['MSZoning'], df['Street'], df['LotShape']], 1)
cat_features
import torch
cat_features = torch.tensor(cat_features, dtype=torch.int64)
cat_features
cont_features = []
for i in df.columns:
cont_features.append(i)
cont_values = np.stack([df[i].values for i in cont_features], axis=1)
cont_values = torch.tensor(cont_values, dtype=torch.float)
cont_values
cont_values.dtype
y = torch.tensor(df['SalePrice'].values, dtype=torch.float).reshape(-1, 1)
y
(cat_features.shape, cont_values.shape, y.shape)
cat_features | code |
130027731/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
from sklearn.preprocessing import LabelEncoder
lbl_encoders = {}
lbl_encoders['MSSubClass'] = LabelEncoder()
lbl_encoders['MSSubClass'].fit_transform(df['MSSubClass']) | code |
130027731/cell_55 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch
import torch
import torch.nn as nn
import torch.nn as nn
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cat_features = ['MSSubClass', 'MSZoning', 'Street', 'LotShape']
out_features = 'SalePrice'
cat_features = np.stack([df['MSSubClass'], df['MSZoning'], df['Street'], df['LotShape']], 1)
cat_features
import torch
cat_features = torch.tensor(cat_features, dtype=torch.int64)
cat_features
cont_features = []
for i in df.columns:
cont_features.append(i)
cont_values = np.stack([df[i].values for i in cont_features], axis=1)
cont_values = torch.tensor(cont_values, dtype=torch.float)
cont_values
cont_values.dtype
y = torch.tensor(df['SalePrice'].values, dtype=torch.float).reshape(-1, 1)
y
(cat_features.shape, cont_values.shape, y.shape)
df.shape
cat_dims = [len(df[col].unique()) for col in ['MSSubClass', 'MSZoning', 'Street', 'LotShape']]
embedding_dim = [(x, min(50, (x + 1) // 2)) for x in cat_dims]
import torch
import torch.nn as nn
import torch.nn.functional as F
embed_representation = nn.ModuleList([nn.Embedding(inp, out) for inp, out in embedding_dim])
embed_representation
pd.set_option('display.max_rows', 500)
embedding_val = []
for i, e in enumerate(embed_representation):
embedding_val.append(e(cat_features[:, i]))
z = torch.cat(embedding_val, 1)
z
dropout = nn.Dropout(0.4)
import torch
import torch.nn as nn
import torch.nn.functional as F
class FeedForwardNN(nn.Module):
def __init__(self, embedding_dim, n_cont, out_sz, layers, p=0.5):
super().__init__()
self.embeds = nn.ModuleList([nn.Embedding(inp, out) for inp, out in embedding_dim])
self.emb_drop = nn.Dropout(p)
self.bn_cont = nn.BatchNorm1d(n_cont)
layerlist = []
n_emb = sum((out for inp, out in embedding_dim))
n_in = n_emb + n_cont
for i in layers:
layerlist.append(nn.Linear(n_in, i))
layerlist.append(nn.ReLU(inplace=True))
layerlist.append(nn.BatchNorm1d(i))
layerlist.append(nn.Dropout(p))
n_in = i
layerlist.append(nn.Linear(layers[-1], out_sz))
self.layers = nn.Sequential(*layerlist)
def forward(self, x_cat, x_cont):
embeddings = []
for i, e in enumerate(self.embeds):
embeddings.append(e(x_cat[:, i]))
x = torch.cat(embeddings, 1)
x = self.emb_drop(x)
x_cont = self.bn_cont(x_cont)
x = torch.cat([x, x_cont], 1)
x = self.layers(x)
return x
torch.manual_seed(100)
model = FeedForwardNN(embedding_dim, len(cont_features), 1, [100, 50], p=0.4)
model | code |
130027731/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.head() | code |
130027731/cell_40 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cont_features = []
for i in df.columns:
cont_features.append(i)
df.shape
cat_dims = [len(df[col].unique()) for col in ['MSSubClass', 'MSZoning', 'Street', 'LotShape']]
cat_dims | code |
130027731/cell_29 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cat_features = ['MSSubClass', 'MSZoning', 'Street', 'LotShape']
out_features = 'SalePrice'
cat_features = np.stack([df['MSSubClass'], df['MSZoning'], df['Street'], df['LotShape']], 1)
cat_features
import torch
cat_features = torch.tensor(cat_features, dtype=torch.int64)
cat_features
cont_features = []
for i in df.columns:
cont_features.append(i)
cont_values = np.stack([df[i].values for i in cont_features], axis=1)
cont_values = torch.tensor(cont_values, dtype=torch.float)
cont_values
cont_values.dtype | code |
130027731/cell_39 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cont_features = []
for i in df.columns:
cont_features.append(i)
df.shape
len(df['MSSubClass'].unique()) | code |
130027731/cell_48 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch
import torch.nn as nn
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cat_features = ['MSSubClass', 'MSZoning', 'Street', 'LotShape']
out_features = 'SalePrice'
cat_features = np.stack([df['MSSubClass'], df['MSZoning'], df['Street'], df['LotShape']], 1)
cat_features
import torch
cat_features = torch.tensor(cat_features, dtype=torch.int64)
cat_features
cont_features = []
for i in df.columns:
cont_features.append(i)
cont_values = np.stack([df[i].values for i in cont_features], axis=1)
cont_values = torch.tensor(cont_values, dtype=torch.float)
cont_values
cont_values.dtype
y = torch.tensor(df['SalePrice'].values, dtype=torch.float).reshape(-1, 1)
y
(cat_features.shape, cont_values.shape, y.shape)
df.shape
cat_dims = [len(df[col].unique()) for col in ['MSSubClass', 'MSZoning', 'Street', 'LotShape']]
embedding_dim = [(x, min(50, (x + 1) // 2)) for x in cat_dims]
import torch
import torch.nn as nn
import torch.nn.functional as F
embed_representation = nn.ModuleList([nn.Embedding(inp, out) for inp, out in embedding_dim])
embed_representation
pd.set_option('display.max_rows', 500)
embedding_val = []
for i, e in enumerate(embed_representation):
embedding_val.append(e(cat_features[:, i]))
z = torch.cat(embedding_val, 1)
z | code |
130027731/cell_61 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch
import torch
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cat_features = ['MSSubClass', 'MSZoning', 'Street', 'LotShape']
out_features = 'SalePrice'
cat_features = np.stack([df['MSSubClass'], df['MSZoning'], df['Street'], df['LotShape']], 1)
cat_features
import torch
cat_features = torch.tensor(cat_features, dtype=torch.int64)
cat_features
cont_features = []
for i in df.columns:
cont_features.append(i)
cont_values = np.stack([df[i].values for i in cont_features], axis=1)
cont_values = torch.tensor(cont_values, dtype=torch.float)
cont_values
cont_values.dtype
y = torch.tensor(df['SalePrice'].values, dtype=torch.float).reshape(-1, 1)
y
(cat_features.shape, cont_values.shape, y.shape)
cont_values.shape | code |
130027731/cell_60 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch
import torch
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cat_features = ['MSSubClass', 'MSZoning', 'Street', 'LotShape']
out_features = 'SalePrice'
cat_features = np.stack([df['MSSubClass'], df['MSZoning'], df['Street'], df['LotShape']], 1)
cat_features
import torch
cat_features = torch.tensor(cat_features, dtype=torch.int64)
cat_features
cont_features = []
for i in df.columns:
cont_features.append(i)
cont_values = np.stack([df[i].values for i in cont_features], axis=1)
cont_values = torch.tensor(cont_values, dtype=torch.float)
cont_values
cont_values.dtype
y = torch.tensor(df['SalePrice'].values, dtype=torch.float).reshape(-1, 1)
y
(cat_features.shape, cont_values.shape, y.shape)
cont_values | code |
130027731/cell_50 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch
import torch.nn as nn
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cat_features = ['MSSubClass', 'MSZoning', 'Street', 'LotShape']
out_features = 'SalePrice'
cat_features = np.stack([df['MSSubClass'], df['MSZoning'], df['Street'], df['LotShape']], 1)
cat_features
import torch
cat_features = torch.tensor(cat_features, dtype=torch.int64)
cat_features
cont_features = []
for i in df.columns:
cont_features.append(i)
cont_values = np.stack([df[i].values for i in cont_features], axis=1)
cont_values = torch.tensor(cont_values, dtype=torch.float)
cont_values
cont_values.dtype
y = torch.tensor(df['SalePrice'].values, dtype=torch.float).reshape(-1, 1)
y
(cat_features.shape, cont_values.shape, y.shape)
df.shape
cat_dims = [len(df[col].unique()) for col in ['MSSubClass', 'MSZoning', 'Street', 'LotShape']]
embedding_dim = [(x, min(50, (x + 1) // 2)) for x in cat_dims]
import torch
import torch.nn as nn
import torch.nn.functional as F
embed_representation = nn.ModuleList([nn.Embedding(inp, out) for inp, out in embedding_dim])
embed_representation
pd.set_option('display.max_rows', 500)
embedding_val = []
for i, e in enumerate(embed_representation):
embedding_val.append(e(cat_features[:, i]))
z = torch.cat(embedding_val, 1)
z
dropout = nn.Dropout(0.4)
final_embed = dropout(z)
final_embed | code |
130027731/cell_52 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cont_features = []
for i in df.columns:
cont_features.append(i)
len(cont_features) | code |
130027731/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 |
130027731/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.info() | code |
130027731/cell_45 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cat_features = ['MSSubClass', 'MSZoning', 'Street', 'LotShape']
out_features = 'SalePrice'
cat_features = np.stack([df['MSSubClass'], df['MSZoning'], df['Street'], df['LotShape']], 1)
cat_features
import torch
cat_features = torch.tensor(cat_features, dtype=torch.int64)
cat_features
cont_features = []
for i in df.columns:
cont_features.append(i)
cont_values = np.stack([df[i].values for i in cont_features], axis=1)
cont_values = torch.tensor(cont_values, dtype=torch.float)
cont_values
cont_values.dtype
y = torch.tensor(df['SalePrice'].values, dtype=torch.float).reshape(-1, 1)
y
(cat_features.shape, cont_values.shape, y.shape)
cat_featuresz = cat_features[:4]
cat_featuresz | code |
130027731/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
df['MSSubClass'].unique() | code |
130027731/cell_32 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cat_features = ['MSSubClass', 'MSZoning', 'Street', 'LotShape']
out_features = 'SalePrice'
cat_features = np.stack([df['MSSubClass'], df['MSZoning'], df['Street'], df['LotShape']], 1)
cat_features
import torch
cat_features = torch.tensor(cat_features, dtype=torch.int64)
cat_features
cont_features = []
for i in df.columns:
cont_features.append(i)
cont_values = np.stack([df[i].values for i in cont_features], axis=1)
cont_values = torch.tensor(cont_values, dtype=torch.float)
cont_values
cont_values.dtype
y = torch.tensor(df['SalePrice'].values, dtype=torch.float).reshape(-1, 1)
y
(cat_features.shape, cont_values.shape, y.shape) | code |
130027731/cell_59 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cont_features = []
for i in df.columns:
cont_features.append(i)
df.shape
df.shape | code |
130027731/cell_28 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cat_features = ['MSSubClass', 'MSZoning', 'Street', 'LotShape']
out_features = 'SalePrice'
cat_features = np.stack([df['MSSubClass'], df['MSZoning'], df['Street'], df['LotShape']], 1)
cat_features
import torch
cat_features = torch.tensor(cat_features, dtype=torch.int64)
cat_features
cont_features = []
for i in df.columns:
cont_features.append(i)
cont_values = np.stack([df[i].values for i in cont_features], axis=1)
cont_values = torch.tensor(cont_values, dtype=torch.float)
cont_values | code |
130027731/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns | code |
130027731/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
df.head() | code |
130027731/cell_47 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch
import torch.nn as nn
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cat_features = ['MSSubClass', 'MSZoning', 'Street', 'LotShape']
out_features = 'SalePrice'
cat_features = np.stack([df['MSSubClass'], df['MSZoning'], df['Street'], df['LotShape']], 1)
cat_features
import torch
cat_features = torch.tensor(cat_features, dtype=torch.int64)
cat_features
cont_features = []
for i in df.columns:
cont_features.append(i)
cont_values = np.stack([df[i].values for i in cont_features], axis=1)
cont_values = torch.tensor(cont_values, dtype=torch.float)
cont_values
cont_values.dtype
y = torch.tensor(df['SalePrice'].values, dtype=torch.float).reshape(-1, 1)
y
(cat_features.shape, cont_values.shape, y.shape)
df.shape
cat_dims = [len(df[col].unique()) for col in ['MSSubClass', 'MSZoning', 'Street', 'LotShape']]
embedding_dim = [(x, min(50, (x + 1) // 2)) for x in cat_dims]
import torch
import torch.nn as nn
import torch.nn.functional as F
embed_representation = nn.ModuleList([nn.Embedding(inp, out) for inp, out in embedding_dim])
embed_representation
pd.set_option('display.max_rows', 500)
embedding_val = []
for i, e in enumerate(embed_representation):
embedding_val.append(e(cat_features[:, i]))
embedding_val | code |
130027731/cell_43 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch.nn as nn
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cont_features = []
for i in df.columns:
cont_features.append(i)
df.shape
cat_dims = [len(df[col].unique()) for col in ['MSSubClass', 'MSZoning', 'Street', 'LotShape']]
embedding_dim = [(x, min(50, (x + 1) // 2)) for x in cat_dims]
import torch
import torch.nn as nn
import torch.nn.functional as F
embed_representation = nn.ModuleList([nn.Embedding(inp, out) for inp, out in embedding_dim])
embed_representation | code |
130027731/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cont_features = []
for i in df.columns:
cont_features.append(i)
df.info() | code |
130027731/cell_24 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cat_features = ['MSSubClass', 'MSZoning', 'Street', 'LotShape']
out_features = 'SalePrice'
cat_features = np.stack([df['MSSubClass'], df['MSZoning'], df['Street'], df['LotShape']], 1)
cat_features | code |
130027731/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cont_features = []
for i in df.columns:
cont_features.append(i)
cont_features | code |
130027731/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape
df.drop('YearBuilt', axis=1, inplace=True)
df.columns
cont_features = []
for i in df.columns:
cont_features.append(i)
df.shape
cat_dims = [len(df[col].unique()) for col in ['MSSubClass', 'MSZoning', 'Street', 'LotShape']]
cat_dims | code |
130027731/cell_12 | [
"text_plain_output_1.png"
] | import datetime
import datetime
datetime.datetime.now().year | code |
130027731/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/advancehousepriceprediction/train.csv', usecols=['SalePrice', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street', 'YearBuilt', 'LotShape', '1stFlrSF', '2ndFlrSF']).dropna()
df.shape | code |
17115578/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_levels.shape
df_levels['Index'].nunique()
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
plt.tight_layout()
df_levels['CHEMBARAMBAKKAM'].plot()
plt.xlabel('CHEMBARAMBAKKAM')
plt.tight_layout() | code |
17115578/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_levels.shape
df_levels['Index'].nunique() | code |
17115578/cell_25 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_levels.shape
df_levels['Index'].nunique()
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
plt.tight_layout()
plt.tight_layout()
plt.tight_layout()
df_rainfall = pd.read_csv('../input/chennai_reservoir_rainfall.csv')
df_rainfall.index = pd.to_datetime(df_rainfall['Date'])
df_rain['Index'] = pd.to_datetime(df_rain['Date'])
df_rain.index = df_rain['Index']
del df_rain['Date']
import matplotlib.pyplot as plt
plt.subplot(411)
plt.plot(df_rain['Index'], df_rain['POONDI'])
plt.xlabel('Poondi')
plt.tight_layout()
plt.subplot(412)
plt.plot(df_rain['CHOLAVARAM'])
plt.xlabel('CHOLAVARAM')
plt.tight_layout()
plt.subplot(413)
plt.plot(df_rain['REDHILLS'])
plt.xlabel('REDHILLS')
plt.tight_layout()
plt.subplot(414)
plt.plot(df_rain['CHEMBARAMBAKKAM'])
plt.xlabel('CHEMBARAMBAKKAM')
plt.tight_layout() | code |
17115578/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels.head(2) | code |
17115578/cell_23 | [
"text_html_output_1.png"
] | df_rain.describe() | code |
17115578/cell_20 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_rainfall = pd.read_csv('../input/chennai_reservoir_rainfall.csv')
df_rainfall.index = pd.to_datetime(df_rainfall['Date'])
df_rainfall['Year'] = df_rainfall.index.year
df_rainfall['Month'] = df_rainfall.index.month
df_rainfall['Weekday Name'] = df_rainfall.index.weekday_name
df_rainfall.sample(5, random_state=0) | code |
17115578/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_levels.head(2) | code |
17115578/cell_2 | [
"image_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17115578/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
"\nplt.subplot(411)\nplt.plot(df_rain['POONDI'])\nplt.xlabel('Poondi')\nplt.tight_layout()\nplt.subplot(412)\nplt.plot(df_levels['CHOLAVARAM'])\nplt.xlabel('CHOLAVARAM')\nplt.tight_layout()\nplt.subplot(413)\nplt.plot(df_levels['REDHILLS'])\nplt.xlabel('REDHILLS')\nplt.tight_layout()\nplt.subplot(414)\nplt.plot(df_levels['CHEMBARAMBAKKAM'])\nplt.xlabel('CHEMBARAMBAKKAM')\nplt.tight_layout()\n" | code |
17115578/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_levels.shape
df_levels['Index'].nunique()
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
plt.tight_layout()
plt.tight_layout()
plt.tight_layout()
df_rainfall = pd.read_csv('../input/chennai_reservoir_rainfall.csv')
df_rainfall.index = pd.to_datetime(df_rainfall['Date'])
plt.figure(figsize=(10, 5))
df_rainfall['POONDI'].plot() | code |
17115578/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_levels.head(2) | code |
17115578/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_rainfall = pd.read_csv('../input/chennai_reservoir_rainfall.csv')
df_rainfall.index = pd.to_datetime(df_rainfall['Date'])
df_rainfall.head(2) | code |
17115578/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_levels.tail(2) | code |
17115578/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_rainfall = pd.read_csv('../input/chennai_reservoir_rainfall.csv')
df_rainfall.head() | code |
17115578/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_rainfall = pd.read_csv('../input/chennai_reservoir_rainfall.csv')
df_rainfall.index = pd.to_datetime(df_rainfall['Date'])
df_rainfall.head(2) | code |
17115578/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_rainfall = pd.read_csv('../input/chennai_reservoir_rainfall.csv')
df_rainfall.index = pd.to_datetime(df_rainfall['Date'])
df_rain['Index'] = pd.to_datetime(df_rain['Date'])
df_rain.index = df_rain['Index']
del df_rain['Date']
df_rain.head(2) | code |
17115578/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_levels.shape
df_levels['Index'].nunique()
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
plt.tight_layout()
plt.tight_layout()
df_levels['CHOLAVARAM'].plot()
plt.xlabel('CHOLAVARAM')
plt.tight_layout() | code |
17115578/cell_22 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_rainfall = pd.read_csv('../input/chennai_reservoir_rainfall.csv')
df_rainfall.index = pd.to_datetime(df_rainfall['Date'])
df_rainfall['Year'] = df_rainfall.index.year
df_rainfall['Month'] = df_rainfall.index.month
df_rainfall['Weekday Name'] = df_rainfall.index.weekday_name
df_rainfall.sample(5, random_state=0)
col_plt = ['POONDI', 'REDHILLS', 'CHOLAVARAM', 'CHEMBARAMBAKKAM']
axes = df_rainfall[col_plt].plot(marker='.', alpha=0.5, linestyle='None', figsize=(11, 9), subplots=True)
for ax in axes:
ax.set_ylabel('Daily Rainfall') | code |
17115578/cell_10 | [
"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)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_levels.shape
df_levels['Index'].nunique()
import matplotlib.pyplot as plt
plt.plot(df_levels['POONDI']) | code |
17115578/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels['Index'] = pd.to_datetime(df_levels['Date'])
df_levels.index = df_levels['Index']
del df_levels['Date']
del df_levels['Index']
df_levels.shape
df_levels['Index'].nunique()
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
df_levels['POONDI'].plot()
plt.xlabel('Poondi')
plt.tight_layout() | code |
17115578/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_levels = pd.read_csv('../input/chennai_reservoir_levels.csv')
df_levels.describe() | code |
73078726/cell_13 | [
"image_output_1.png"
] | from kaggle_datasets import KaggleDatasets
from tensorflow.keras import layers
import tensorflow as tf
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPUStrategy(tpu)
except:
strategy = tf.distribute.get_strategy()
mri_types = ['FLAIR', 'T1w', 'T1wCE', 'T2w']
IMAGE_SIZE = 128
IMAGE_DEPTH = 32
BATCH_SIZE = 32
CHANNELS = len(mri_types)
AUTO = tf.data.AUTOTUNE
def deserialize_example(serialized_string):
image_feature_description = {'image': tf.io.FixedLenFeature([], tf.string), 'MGMT_value': tf.io.FixedLenFeature([], tf.float32)}
parsed_record = tf.io.parse_single_example(serialized_string, image_feature_description)
image = tf.io.decode_raw(parsed_record['image'], tf.float64)
image = tf.reshape(image, [IMAGE_SIZE, IMAGE_SIZE, IMAGE_DEPTH, CHANNELS])
label = parsed_record['MGMT_value']
return (image, label)
GCS_PATH = KaggleDatasets().get_gcs_path('rsna-brain-tumor-classification-tfrecords')
tf_train_path = GCS_PATH + '/tfrecords/train'
tf_valid_path = GCS_PATH + '/tfrecords/valid'
train_set = tf.data.TFRecordDataset(str(tf_train_path + os.sep + 'brain_train.tfrec'), compression_type='GZIP').map(deserialize_example).batch(BATCH_SIZE)
valid_set = tf.data.TFRecordDataset(str(tf_valid_path + os.sep + 'brain_val.tfrec'), compression_type='GZIP').map(deserialize_example).batch(BATCH_SIZE)
def get_model(width=128, height=128, depth=32):
inputs = tf.keras.Input((width, height, depth, 4))
x = layers.Conv3D(filters=32, kernel_size=2, activation='relu', padding='same')(inputs)
x = layers.MaxPool3D(2)(x)
x = layers.Conv3D(filters=64, kernel_size=2, activation='relu', padding='same')(x)
x = layers.MaxPool3D(2)(x)
x = layers.Conv3D(filters=128, kernel_size=2, activation='relu', padding='same')(x)
x = layers.MaxPool3D(2)(x)
x = layers.Conv3D(filters=256, kernel_size=2, activation='relu', padding='same')(x)
x = layers.MaxPool3D(2)(x)
x = layers.Conv3D(filters=512, kernel_size=2, activation='relu', padding='same')(x)
x = layers.MaxPool3D(2)(x)
x = layers.Flatten()(x)
x = layers.Dense(units=128, activation='relu')(x)
x = layers.Dense(units=128, activation='relu')(x)
outputs = layers.Dense(units=1, activation='sigmoid')(x)
model = tf.keras.Model(inputs, outputs)
return model
USE_TPU = True
if USE_TPU:
with strategy.scope():
model = get_model(width=IMAGE_SIZE, height=IMAGE_SIZE, depth=IMAGE_DEPTH)
model.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.Adam(), metrics=['accuracy'])
else:
model = get_model(width=IMAGE_SIZE, height=IMAGE_SIZE, depth=IMAGE_DEPTH)
model.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.Adam(), metrics=['accuracy'])
model.summary() | code |
73078726/cell_2 | [
"image_output_1.png"
] | import tensorflow as tf
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
print('Device:', tpu.master())
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPUStrategy(tpu)
except:
strategy = tf.distribute.get_strategy()
print('Number of replicas:', strategy.num_replicas_in_sync) | code |
73078726/cell_11 | [
"text_plain_output_1.png"
] | from kaggle_datasets import KaggleDatasets
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPUStrategy(tpu)
except:
strategy = tf.distribute.get_strategy()
mri_types = ['FLAIR', 'T1w', 'T1wCE', 'T2w']
IMAGE_SIZE = 128
IMAGE_DEPTH = 32
BATCH_SIZE = 32
CHANNELS = len(mri_types)
AUTO = tf.data.AUTOTUNE
def deserialize_example(serialized_string):
image_feature_description = {'image': tf.io.FixedLenFeature([], tf.string), 'MGMT_value': tf.io.FixedLenFeature([], tf.float32)}
parsed_record = tf.io.parse_single_example(serialized_string, image_feature_description)
image = tf.io.decode_raw(parsed_record['image'], tf.float64)
image = tf.reshape(image, [IMAGE_SIZE, IMAGE_SIZE, IMAGE_DEPTH, CHANNELS])
label = parsed_record['MGMT_value']
return (image, label)
GCS_PATH = KaggleDatasets().get_gcs_path('rsna-brain-tumor-classification-tfrecords')
tf_train_path = GCS_PATH + '/tfrecords/train'
tf_valid_path = GCS_PATH + '/tfrecords/valid'
train_set = tf.data.TFRecordDataset(str(tf_train_path + os.sep + 'brain_train.tfrec'), compression_type='GZIP').map(deserialize_example).batch(BATCH_SIZE)
valid_set = tf.data.TFRecordDataset(str(tf_valid_path + os.sep + 'brain_val.tfrec'), compression_type='GZIP').map(deserialize_example).batch(BATCH_SIZE)
d = train_set.take(1)
for i, j in d:
image = i
label = j
img_id = np.random.randint(0, BATCH_SIZE)
channel = np.random.randint(0, CHANNELS)
plt.figure(figsize=(20, 10), facecolor=(0, 0, 0))
cols = IMAGE_DEPTH // 4
rows = 4
plt.axis('off')
for layer_idx in range(IMAGE_DEPTH):
ax = plt.subplot(rows, cols, layer_idx + 1)
ax.imshow(np.squeeze(image[img_id, :, :, layer_idx, channel]), cmap='gray')
ax.axis('off')
ax.set_title(str(layer_idx + 1), color='r', y=-0.01)
plt.suptitle(f'Batch Image NO.: {img_id}, MRI Type: {mri_types[channel]}, Shape: {image[img_id].shape}', color='w')
plt.subplots_adjust(wspace=0, hspace=0)
plt.show() | code |
73078726/cell_15 | [
"text_plain_output_1.png"
] | from kaggle_datasets import KaggleDatasets
from tensorflow.keras import layers
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPUStrategy(tpu)
except:
strategy = tf.distribute.get_strategy()
mri_types = ['FLAIR', 'T1w', 'T1wCE', 'T2w']
IMAGE_SIZE = 128
IMAGE_DEPTH = 32
BATCH_SIZE = 32
CHANNELS = len(mri_types)
AUTO = tf.data.AUTOTUNE
def deserialize_example(serialized_string):
image_feature_description = {'image': tf.io.FixedLenFeature([], tf.string), 'MGMT_value': tf.io.FixedLenFeature([], tf.float32)}
parsed_record = tf.io.parse_single_example(serialized_string, image_feature_description)
image = tf.io.decode_raw(parsed_record['image'], tf.float64)
image = tf.reshape(image, [IMAGE_SIZE, IMAGE_SIZE, IMAGE_DEPTH, CHANNELS])
label = parsed_record['MGMT_value']
return (image, label)
GCS_PATH = KaggleDatasets().get_gcs_path('rsna-brain-tumor-classification-tfrecords')
tf_train_path = GCS_PATH + '/tfrecords/train'
tf_valid_path = GCS_PATH + '/tfrecords/valid'
train_set = tf.data.TFRecordDataset(str(tf_train_path + os.sep + 'brain_train.tfrec'), compression_type='GZIP').map(deserialize_example).batch(BATCH_SIZE)
valid_set = tf.data.TFRecordDataset(str(tf_valid_path + os.sep + 'brain_val.tfrec'), compression_type='GZIP').map(deserialize_example).batch(BATCH_SIZE)
d = train_set.take(1)
for i, j in d:
image = i
label = j
img_id = np.random.randint(0, BATCH_SIZE)
channel = np.random.randint(0,CHANNELS)
plt.figure(figsize=(20,10),facecolor=(0,0,0))
cols = IMAGE_DEPTH//4
rows = 4
plt.axis("off")
for layer_idx in range(IMAGE_DEPTH):
ax = plt.subplot(rows,cols,layer_idx+1)
ax.imshow(np.squeeze(image[img_id,:,:,layer_idx,channel]), cmap="gray")
ax.axis("off")
ax.set_title(str(layer_idx+1),color='r',y=-0.01)
plt.suptitle(f"Batch Image NO.: {img_id}, MRI Type: {mri_types[channel]}, Shape: {image[img_id].shape}", color="w")
plt.subplots_adjust(wspace=0, hspace=0)
plt.show()
def get_model(width=128, height=128, depth=32):
inputs = tf.keras.Input((width, height, depth, 4))
x = layers.Conv3D(filters=32, kernel_size=2, activation='relu', padding='same')(inputs)
x = layers.MaxPool3D(2)(x)
x = layers.Conv3D(filters=64, kernel_size=2, activation='relu', padding='same')(x)
x = layers.MaxPool3D(2)(x)
x = layers.Conv3D(filters=128, kernel_size=2, activation='relu', padding='same')(x)
x = layers.MaxPool3D(2)(x)
x = layers.Conv3D(filters=256, kernel_size=2, activation='relu', padding='same')(x)
x = layers.MaxPool3D(2)(x)
x = layers.Conv3D(filters=512, kernel_size=2, activation='relu', padding='same')(x)
x = layers.MaxPool3D(2)(x)
x = layers.Flatten()(x)
x = layers.Dense(units=128, activation='relu')(x)
x = layers.Dense(units=128, activation='relu')(x)
outputs = layers.Dense(units=1, activation='sigmoid')(x)
model = tf.keras.Model(inputs, outputs)
return model
USE_TPU = True
if USE_TPU:
with strategy.scope():
model = get_model(width=IMAGE_SIZE, height=IMAGE_SIZE, depth=IMAGE_DEPTH)
model.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.Adam(), metrics=['accuracy'])
else:
model = get_model(width=IMAGE_SIZE, height=IMAGE_SIZE, depth=IMAGE_DEPTH)
model.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.Adam(), metrics=['accuracy'])
model.summary()
early_stopping_cb = tf.keras.callbacks.EarlyStopping(monitor='val_accuracy', patience=5)
history = model.fit(train_set, validation_data=valid_set, epochs=20, callbacks=[early_stopping_cb]) | code |
73078726/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
plt.figure(figsize=(16, 7))
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(acc) + 1)
ax1 = plt.subplot(1, 2, 1)
ax1.plot(epochs, acc, 'r')
ax1.plot(epochs, val_acc, 'b')
ax1.set_xticks([i for i in epochs])
ax1.set_title('Training and validation Accuracy')
ax1.legend(['Training', 'Validation'])
ax1.set_xlabel('epochs')
ax1.set_ylabel('Accuracy')
ax2 = plt.subplot(1, 2, 2)
ax2.plot(epochs, loss, 'r')
ax2.plot(epochs, val_loss, 'b')
ax2.set_xticks([i for i in epochs])
ax2.legend(['Training', 'Validation'])
ax2.set_xlabel('Epochs')
ax2.set_ylabel('Loss')
ax2.set_title('Training and validation loss')
plt.show() | code |
17141482/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pylab
import seaborn as sns
dataset = pd.read_csv('../input/census-data-set/Census Income Dataset.csv', na_values=[' ?'])
test_dataset = pd.read_csv('../input/census-test-dataset/Census Income Testset.csv', na_values=[' ?'])
def Income_bracket_binarization(feat_val):
if feat_val == '<=50K':
return 0
else:
return 1
dataset['Income_bracket'] = dataset['Income_bracket'].apply(Income_bracket_binarization)
test_dataset['Income_bracket'] = test_dataset['Income_bracket'].apply(Income_bracket_binarization)
def Plot():
#Find Indices where Income is >50K and <=50K
fig = plt.figure(figsize=(15,15))
fig.subplots_adjust(hspace=0.7, wspace=0.7)
pylab.suptitle("Analyzing the dataset", fontsize="xx-large")
plt.subplot(3,2,1)
ax = sns.countplot(x='Age', hue='Income_bracket', data=dataset)
plt.subplot(3,2,2)
ax =sns.countplot(x='workclass', hue='Income_bracket', data=dataset)
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
plt.subplot(3,2,3)
ax =sns.countplot(x='Education', hue='Income_bracket', data=dataset)
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
plt.subplot(3,2,4)
ax = sns.countplot(x='Occupation', hue='Income_bracket', data=dataset)
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
plt.subplot(3,2,5)
ax = sns.countplot(x='Gender', hue='Income_bracket', data=dataset)
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
plt.subplot(3,2,6)
ax = sns.countplot(x='hours_per_week', hue='Income_bracket', data=dataset)
return None
dataset.hist(column=['Age', 'Education', 'hours_per_week'], figsize=(6, 5))
pylab.suptitle('Analyzing distribution for the dataset', fontsize='xx-large')
Plot()
X = dataset.drop('Income_bracket', axis=1)
y = dataset['Income_bracket'] | code |
17141482/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/census-data-set/Census Income Dataset.csv', na_values=[' ?'])
test_dataset = pd.read_csv('../input/census-test-dataset/Census Income Testset.csv', na_values=[' ?'])
def Income_bracket_binarization(feat_val):
if feat_val == '<=50K':
return 0
else:
return 1
dataset['Income_bracket'] = dataset['Income_bracket'].apply(Income_bracket_binarization)
test_dataset['Income_bracket'] = test_dataset['Income_bracket'].apply(Income_bracket_binarization)
dataset.head() | code |
17141482/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/census-data-set/Census Income Dataset.csv', na_values=[' ?'])
dataset.head() | code |
17141482/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.python.framework import ops
import sklearn
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pylab
from sklearn.preprocessing import LabelEncoder
from sklearn.base import BaseEstimator, TransformerMixin
import seaborn as sns
import math
import os
print(os.listdir('../input')) | code |
17141482/cell_7 | [
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/census-data-set/Census Income Dataset.csv', na_values=[' ?'])
test_dataset = pd.read_csv('../input/census-test-dataset/Census Income Testset.csv', na_values=[' ?'])
def Income_bracket_binarization(feat_val):
if feat_val == '<=50K':
return 0
else:
return 1
dataset['Income_bracket'] = dataset['Income_bracket'].apply(Income_bracket_binarization)
test_dataset['Income_bracket'] = test_dataset['Income_bracket'].apply(Income_bracket_binarization)
obj = CategoricalImputer(columns=['workclass', 'Occupation', 'Native_Country'])
train_result = obj.fit(dataset[['workclass', 'Occupation', 'Native_Country']])
dataset[['workclass', 'Occupation', 'Native_Country']] = train_result.transform(dataset[['workclass', 'Occupation', 'Native_Country']])
test_obj = CategoricalImputer(columns=['workclass', 'Occupation', 'Native_Country'])
test_result = test_obj.fit(test_dataset[['workclass', 'Occupation', 'Native_Country']])
test_dataset[['workclass', 'Occupation', 'Native_Country']] = test_result.transform(test_dataset[['workclass', 'Occupation', 'Native_Country']]) | code |
17141482/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/census-data-set/Census Income Dataset.csv', na_values=[' ?'])
test_dataset = pd.read_csv('../input/census-test-dataset/Census Income Testset.csv', na_values=[' ?'])
test_dataset.head() | code |
17141482/cell_12 | [
"text_html_output_1.png"
] | from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pylab
import seaborn as sns
dataset = pd.read_csv('../input/census-data-set/Census Income Dataset.csv', na_values=[' ?'])
test_dataset = pd.read_csv('../input/census-test-dataset/Census Income Testset.csv', na_values=[' ?'])
def Income_bracket_binarization(feat_val):
if feat_val == '<=50K':
return 0
else:
return 1
dataset['Income_bracket'] = dataset['Income_bracket'].apply(Income_bracket_binarization)
test_dataset['Income_bracket'] = test_dataset['Income_bracket'].apply(Income_bracket_binarization)
class CategoricalImputer:
def __init__(self, columns=None, strategy='most_frequent'):
self.columns = columns
self.strategy = strategy
def fit(self, X, y=None):
if self.columns is None:
self.columns = X.columns
if self.strategy is 'most_frequent':
self.fill = {column: X[column].value_counts().index[0] for column in self.columns}
else:
self.fill = {column: '0' for column in self.columns}
return self
def transform(self, X):
for column in self.columns:
X[column] = X[column].fillna(self.fill[column])
return X
def Plot():
#Find Indices where Income is >50K and <=50K
fig = plt.figure(figsize=(15,15))
fig.subplots_adjust(hspace=0.7, wspace=0.7)
pylab.suptitle("Analyzing the dataset", fontsize="xx-large")
plt.subplot(3,2,1)
ax = sns.countplot(x='Age', hue='Income_bracket', data=dataset)
plt.subplot(3,2,2)
ax =sns.countplot(x='workclass', hue='Income_bracket', data=dataset)
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
plt.subplot(3,2,3)
ax =sns.countplot(x='Education', hue='Income_bracket', data=dataset)
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
plt.subplot(3,2,4)
ax = sns.countplot(x='Occupation', hue='Income_bracket', data=dataset)
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
plt.subplot(3,2,5)
ax = sns.countplot(x='Gender', hue='Income_bracket', data=dataset)
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
plt.subplot(3,2,6)
ax = sns.countplot(x='hours_per_week', hue='Income_bracket', data=dataset)
return None
Plot()
X = dataset.drop('Income_bracket', axis=1)
y = dataset['Income_bracket']
class Categorical_Encoder(BaseEstimator, TransformerMixin):
def __init__(self, columns=None):
self.columns = columns
self.encoders = None
def fit(self, data, target=None):
"""
Expects a data frame with named columns to encode.
"""
if self.columns is None:
self.columns = data.columns
self.encoders = {column: LabelEncoder().fit(data[column]) for column in self.columns}
return self
def transform(self, data):
"""
Uses the encoders to transform a data frame.
"""
output = data.copy()
for column, encoder in self.encoders.items():
output[column] = encoder.transform(data[column])
return output
categorical_features = {column: list(dataset[column].unique()) for column in dataset.columns if dataset[column].dtype == 'object'}
encoder = Categorical_Encoder(categorical_features.keys())
dataset = encoder.fit_transform(dataset)
dataset.head() | code |
17141482/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/census-data-set/Census Income Dataset.csv', na_values=[' ?'])
test_dataset = pd.read_csv('../input/census-test-dataset/Census Income Testset.csv', na_values=[' ?'])
test_dataset.head() | code |
34140277/cell_21 | [
"text_html_output_1.png"
] | """
cols = ['pCut::Motor_Torque',
'pCut::CTRL_Position_controller::Lag_error',
'pCut::CTRL_Position_controller::Actual_position',
'pCut::CTRL_Position_controller::Actual_speed',
'pSvolFilm::CTRL_Position_controller::Actual_position',
'pSvolFilm::CTRL_Position_controller::Actual_speed',
'pSvolFilm::CTRL_Position_controller::Lag_error', 'pSpintor::VAX_speed',
'Mode']
pca = PCA(n_components=2)
data_pca = pca.fit_transform(data[cols].values)
#data['pca-one'] = data_pca[:,0]
#data['pca-two'] = data_pca[:,1]
print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_))
""" | code |
34140277/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.cluster import DBSCAN
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/combineddataset/Combined.csv')
for i in ['Mode', 'Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month']:
df[i] = pd.to_numeric(df[i], downcast='integer')
data = df.copy()
data = data[:10000]
data = data.drop(['Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month'], axis=1)
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
num2 = scaler.fit_transform(data.drop(['timestamp'], axis=1))
num2 = pd.DataFrame(num2, columns=data.drop(['timestamp'], axis=1).columns)
from sklearn.cluster import DBSCAN
outlier_detection = DBSCAN(eps=0.2, metric='euclidean', min_samples=5, n_jobs=-1)
clusters = outlier_detection.fit_predict(num2)
clusters.shape
data['anomaly'] = pd.Series(clusters)
data.head() | code |
34140277/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.cluster import DBSCAN
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/combineddataset/Combined.csv')
for i in ['Mode', 'Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month']:
df[i] = pd.to_numeric(df[i], downcast='integer')
data = df.copy()
data = data[:10000]
data = data.drop(['Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month'], axis=1)
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
num2 = scaler.fit_transform(data.drop(['timestamp'], axis=1))
num2 = pd.DataFrame(num2, columns=data.drop(['timestamp'], axis=1).columns)
from sklearn.cluster import DBSCAN
outlier_detection = DBSCAN(eps=0.2, metric='euclidean', min_samples=5, n_jobs=-1)
clusters = outlier_detection.fit_predict(num2)
clusters.shape
data['anomaly'] = pd.Series(clusters)
data.columns
anomaly_ind = data[data['anomaly'] == -1].index
normal_ind = data[data['anomaly'] != -1].index
data.columns
import matplotlib.pyplot as plt
import seaborn as sns
features = ['pCut::Motor_Torque', 'pCut::CTRL_Position_controller::Lag_error', 'pCut::CTRL_Position_controller::Actual_position', 'pCut::CTRL_Position_controller::Actual_speed', 'pSvolFilm::CTRL_Position_controller::Actual_position', 'pSvolFilm::CTRL_Position_controller::Actual_speed', 'pSvolFilm::CTRL_Position_controller::Lag_error', 'pSpintor::VAX_speed', 'Mode']
for feature in features:
plt.figure(figsize=(15, 7))
plt.plot(data[feature], color='blue', label='normal')
plt.scatter(x=data.iloc[anomaly_ind].index, y=data.iloc[anomaly_ind][feature], color='red', label='anomalous')
plt.title(feature)
plt.legend() | code |
34140277/cell_4 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_9.png"
] | """
if not os.path.exists('/kaggle/working/compiled_df'):
os.makedirs('/kaggle/working/compiled_df')
#Saves dataframe to a csv file, removes a index
df.to_csv('/kaggle/working/compiled_df/Combined.csv', index=False)
""" | code |
34140277/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.cluster import DBSCAN
from sklearn.ensemble import IsolationForest
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/combineddataset/Combined.csv')
for i in ['Mode', 'Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month']:
df[i] = pd.to_numeric(df[i], downcast='integer')
data = df.copy()
data = data[:10000]
data = data.drop(['Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month'], axis=1)
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
num2 = scaler.fit_transform(data.drop(['timestamp'], axis=1))
num2 = pd.DataFrame(num2, columns=data.drop(['timestamp'], axis=1).columns)
from sklearn.cluster import DBSCAN
outlier_detection = DBSCAN(eps=0.2, metric='euclidean', min_samples=5, n_jobs=-1)
clusters = outlier_detection.fit_predict(num2)
clusters.shape
data['anomaly'] = pd.Series(clusters)
data.columns
anomaly_ind = data[data['anomaly'] == -1].index
normal_ind = data[data['anomaly'] != -1].index
anomaly_pca = pd.DataFrame(data_pca[anomaly_ind])
normal_pca = pd.DataFrame(data_pca[normal_ind])
anomaly_pca
data.columns
import matplotlib.pyplot as plt
import seaborn as sns
features = ['pCut::Motor_Torque', 'pCut::CTRL_Position_controller::Lag_error', 'pCut::CTRL_Position_controller::Actual_position', 'pCut::CTRL_Position_controller::Actual_speed', 'pSvolFilm::CTRL_Position_controller::Actual_position', 'pSvolFilm::CTRL_Position_controller::Actual_speed', 'pSvolFilm::CTRL_Position_controller::Lag_error', 'pSpintor::VAX_speed', 'Mode']
data = df.copy()
data = data[:10000]
data = data.drop(['timestamp', 'Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month'], axis=1)
from sklearn.ensemble import IsolationForest
rs = np.random.RandomState(0)
clf = IsolationForest(max_samples=100, random_state=rs, contamination=0.1)
clf.fit(data)
if_scores = clf.decision_function(data)
if_anomalies = clf.predict(data)
if_anomalies = pd.Series(if_anomalies).replace([-1, 1], [1, 0])
anomaly_ind = if_anomalies[if_anomalies == 1].index
features = ['pCut::Motor_Torque', 'pCut::CTRL_Position_controller::Lag_error', 'pCut::CTRL_Position_controller::Actual_position', 'pCut::CTRL_Position_controller::Actual_speed', 'pSvolFilm::CTRL_Position_controller::Actual_position', 'pSvolFilm::CTRL_Position_controller::Actual_speed', 'pSvolFilm::CTRL_Position_controller::Lag_error', 'pSpintor::VAX_speed', 'Mode']
for feature in features:
plt.figure(figsize=(15, 7))
plt.scatter(data.index, data[feature], c='green', label='normal')
plt.scatter(anomaly_ind, data.iloc[anomaly_ind][feature], c='red', label='anomaly')
plt.ylabel(feature)
plt.title(feature)
plt.legend() | code |
34140277/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.cluster import DBSCAN
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/combineddataset/Combined.csv')
for i in ['Mode', 'Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month']:
df[i] = pd.to_numeric(df[i], downcast='integer')
data = df.copy()
data = data[:10000]
data = data.drop(['Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month'], axis=1)
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
num2 = scaler.fit_transform(data.drop(['timestamp'], axis=1))
num2 = pd.DataFrame(num2, columns=data.drop(['timestamp'], axis=1).columns)
from sklearn.cluster import DBSCAN
outlier_detection = DBSCAN(eps=0.2, metric='euclidean', min_samples=5, n_jobs=-1)
clusters = outlier_detection.fit_predict(num2)
clusters.shape
data['anomaly'] = pd.Series(clusters)
data.columns
anomaly_ind = data[data['anomaly'] == -1].index
normal_ind = data[data['anomaly'] != -1].index
anomaly_pca = pd.DataFrame(data_pca[anomaly_ind])
normal_pca = pd.DataFrame(data_pca[normal_ind])
anomaly_pca | code |
34140277/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.cluster import DBSCAN
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/combineddataset/Combined.csv')
for i in ['Mode', 'Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month']:
df[i] = pd.to_numeric(df[i], downcast='integer')
data = df.copy()
data = data[:10000]
data = data.drop(['Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month'], axis=1)
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
num2 = scaler.fit_transform(data.drop(['timestamp'], axis=1))
num2 = pd.DataFrame(num2, columns=data.drop(['timestamp'], axis=1).columns)
from sklearn.cluster import DBSCAN
outlier_detection = DBSCAN(eps=0.2, metric='euclidean', min_samples=5, n_jobs=-1)
clusters = outlier_detection.fit_predict(num2)
clusters.shape
data['anomaly'] = pd.Series(clusters)
data.columns | code |
34140277/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/combineddataset/Combined.csv')
for i in ['Mode', 'Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month']:
df[i] = pd.to_numeric(df[i], downcast='integer')
df.head(10) | code |
34140277/cell_2 | [
"text_html_output_1.png"
] | import os
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 |
34140277/cell_1 | [
"text_plain_output_1.png"
] | """
import os
import glob
import pandas as pd
#os.chdir("/mydir")
files = [i for i in glob.glob('/kaggle/input/one-year-industrial-component-degradation/*.{}'.format('csv'))]
files
extension = 'csv'
all_filenames = [i for i in glob.glob('/kaggle/input/one-year-industrial-component-degradation/*[mode1].{}'.format(extension))] + [i for i in glob.glob('/kaggle/input/one-year-industrial-component-degradation/oneyeardata/*[mode1].{}'.format(extension))]
#print(all_filenames)
#combine all files in the list
df = pd.concat([pd.read_csv(f) for f in all_filenames ])
#export to csv
df.to_csv( "combined_csv.csv", index=False, encoding='utf-8-sig')
""" | code |
34140277/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.cluster import DBSCAN
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/combineddataset/Combined.csv')
for i in ['Mode', 'Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month']:
df[i] = pd.to_numeric(df[i], downcast='integer')
data = df.copy()
data = data[:10000]
data = data.drop(['Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month'], axis=1)
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
num2 = scaler.fit_transform(data.drop(['timestamp'], axis=1))
num2 = pd.DataFrame(num2, columns=data.drop(['timestamp'], axis=1).columns)
from sklearn.cluster import DBSCAN
outlier_detection = DBSCAN(eps=0.2, metric='euclidean', min_samples=5, n_jobs=-1)
clusters = outlier_detection.fit_predict(num2)
clusters.shape
data['anomaly'] = pd.Series(clusters)
X_anomaly = data[data['anomaly'] == -1]
X_normal = data[data['anomaly'] != -1]
print(X_anomaly.shape, X_normal.shape) | code |
34140277/cell_3 | [
"text_plain_output_1.png"
] | """
filenames = os.listdir('/kaggle/input/one-year-industrial-component-degradation/')
filenames = [i.strip(".csv") for i in filenames]
filenames.sort()
filenames.remove('oneyeardata')
parsed_filenames = []
for name in filenames:
temp = name.split("T")
month, date = temp[0].split("-")
rhs = temp[1].split("_")
hours, minutes, seconds = rhs[0][:2], rhs[0][2:4], rhs[0][4:]
sample_no = rhs[1]
mode = rhs[2][-1]
# Now we have Month, Date, Hours, Minutes, Seconds, Sample Number, Mode
parsed_filenames.append([month, date, hours, minutes, seconds, sample_no, mode])
parsed_filenames = pd.DataFrame(parsed_filenames, columns=["Month", "Date", "Hours", "Minutes", "Seconds", "Sample Number", "Mode"])
for i in parsed_filenames.columns:
parsed_filenames[i] = pd.to_numeric(parsed_filenames[i], errors='coerce')
path = '/kaggle/input/one-year-industrial-component-degradation/'
df = pd.DataFrame()
#f = pd.read_csv(path+filenames[0]+".csv")
#f = f.join(parsed_filenames[0:1], how='left')
#f = f.fillna(method='ffill')
#f
for ind, file in enumerate(filenames):
file_content = pd.read_csv(path+file+".csv")
file_content = file_content.join(parsed_filenames[ind:ind+1], how='left')
file_content.fillna(method='ffill', inplace=True)
if df.empty:
df = file_content
df.fillna(method='ffill', inplace=True)
else:
df = df.append(file_content, ignore_index=True)
df.fillna(method='ffill', inplace=True)
for i in ['Mode', 'Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month']:
df[i] = pd.to_numeric(df[i], downcast='integer')
df.info()
""" | code |
34140277/cell_31 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.cluster import DBSCAN
from sklearn.ensemble import IsolationForest
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/combineddataset/Combined.csv')
for i in ['Mode', 'Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month']:
df[i] = pd.to_numeric(df[i], downcast='integer')
data = df.copy()
data = data[:10000]
data = data.drop(['Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month'], axis=1)
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
num2 = scaler.fit_transform(data.drop(['timestamp'], axis=1))
num2 = pd.DataFrame(num2, columns=data.drop(['timestamp'], axis=1).columns)
from sklearn.cluster import DBSCAN
outlier_detection = DBSCAN(eps=0.2, metric='euclidean', min_samples=5, n_jobs=-1)
clusters = outlier_detection.fit_predict(num2)
clusters.shape
data['anomaly'] = pd.Series(clusters)
data.columns
anomaly_ind = data[data['anomaly'] == -1].index
normal_ind = data[data['anomaly'] != -1].index
anomaly_pca = pd.DataFrame(data_pca[anomaly_ind])
normal_pca = pd.DataFrame(data_pca[normal_ind])
anomaly_pca
data.columns
import matplotlib.pyplot as plt
import seaborn as sns
features = ['pCut::Motor_Torque', 'pCut::CTRL_Position_controller::Lag_error', 'pCut::CTRL_Position_controller::Actual_position', 'pCut::CTRL_Position_controller::Actual_speed', 'pSvolFilm::CTRL_Position_controller::Actual_position', 'pSvolFilm::CTRL_Position_controller::Actual_speed', 'pSvolFilm::CTRL_Position_controller::Lag_error', 'pSpintor::VAX_speed', 'Mode']
data = df.copy()
data = data[:10000]
data = data.drop(['timestamp', 'Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month'], axis=1)
from sklearn.ensemble import IsolationForest
rs = np.random.RandomState(0)
clf = IsolationForest(max_samples=100, random_state=rs, contamination=0.1)
clf.fit(data)
if_scores = clf.decision_function(data)
if_anomalies = clf.predict(data)
if_anomalies = pd.Series(if_anomalies).replace([-1, 1], [1, 0])
plt.figure(figsize=(12, 8))
plt.hist(if_scores)
plt.title('Histogram of Avg Anomaly Scores: Lower => More Anomalous') | code |
34140277/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.cluster import DBSCAN
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/combineddataset/Combined.csv')
for i in ['Mode', 'Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month']:
df[i] = pd.to_numeric(df[i], downcast='integer')
data = df.copy()
data = data[:10000]
data = data.drop(['Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month'], axis=1)
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
num2 = scaler.fit_transform(data.drop(['timestamp'], axis=1))
num2 = pd.DataFrame(num2, columns=data.drop(['timestamp'], axis=1).columns)
from sklearn.cluster import DBSCAN
outlier_detection = DBSCAN(eps=0.2, metric='euclidean', min_samples=5, n_jobs=-1)
clusters = outlier_detection.fit_predict(num2)
clusters.shape
data['anomaly'] = pd.Series(clusters)
data.columns
data.columns | code |
34140277/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.cluster import DBSCAN
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/combineddataset/Combined.csv')
for i in ['Mode', 'Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month']:
df[i] = pd.to_numeric(df[i], downcast='integer')
data = df.copy()
data = data[:10000]
data = data.drop(['Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month'], axis=1)
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
num2 = scaler.fit_transform(data.drop(['timestamp'], axis=1))
num2 = pd.DataFrame(num2, columns=data.drop(['timestamp'], axis=1).columns)
from sklearn.cluster import DBSCAN
outlier_detection = DBSCAN(eps=0.2, metric='euclidean', min_samples=5, n_jobs=-1)
clusters = outlier_detection.fit_predict(num2)
clusters.shape
data['anomaly'] = pd.Series(clusters)
data['anomaly'].unique() | code |
34140277/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.cluster import DBSCAN
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/combineddataset/Combined.csv')
for i in ['Mode', 'Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month']:
df[i] = pd.to_numeric(df[i], downcast='integer')
data = df.copy()
data = data[:10000]
data = data.drop(['Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month'], axis=1)
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
num2 = scaler.fit_transform(data.drop(['timestamp'], axis=1))
num2 = pd.DataFrame(num2, columns=data.drop(['timestamp'], axis=1).columns)
from sklearn.cluster import DBSCAN
outlier_detection = DBSCAN(eps=0.2, metric='euclidean', min_samples=5, n_jobs=-1)
clusters = outlier_detection.fit_predict(num2)
clusters.shape | code |
34140277/cell_5 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_9.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/combineddataset/Combined.csv')
for i in ['Mode', 'Sample Number', 'Seconds', 'Minutes', 'Hours', 'Date', 'Month']:
df[i] = pd.to_numeric(df[i], downcast='integer')
df.info() | code |
89129165/cell_34 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud,ImageColorGenerator,STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
pd.set_option('display.max_colwidth', None)
import warnings
warnings.filterwarnings('ignore')
custom_colors = ['#74a09e', '#86c1b2', '#98e2c6', '#f3c969', '#f2a553', '#d96548', '#c14953']
sns.set_style('whitegrid')
sns.despine(left=True, bottom=True)
books = pd.read_csv('/kaggle/input/goodreadsbooks/books.csv', error_bad_lines=False)
books[:10]
books.set_index('bookID', inplace=True)
books.index.rename('BookID')
books = books.drop(columns=['isbn', 'isbn13'])
books.columns
books.columns = ['Title', 'Authors', 'Avg_Rating', 'Lang_Code', '#Pages', '#Ratings', '#Text_Reviews', 'Publication_Date', 'Publisher']
books.shape
all_books = ' '.join((token for token in books['Title']))
stopwords = set(STOPWORDS)
font_path = '../input/newghanesfont/NewGhanesFont.otf'
wordcloud = WordCloud(stopwords=stopwords, font_path=font_path, max_words=500, max_font_size=350, random_state=42, width=2500, height=1000, colormap='twilight_shifted_r')
wordcloud.generate(all_books)
plt.axis('off')
books.Lang_Code.unique()
books.Lang_Code.nunique()
sns.set_style('whitegrid')
sns.set_style('whitegrid')
plt.figure(figsize=(12, 4))
sns.distplot(books.Avg_Rating, bins=30, norm_hist=False, color='Purple') | code |
89129165/cell_23 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud,ImageColorGenerator,STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
pd.set_option('display.max_colwidth', None)
import warnings
warnings.filterwarnings('ignore')
custom_colors = ['#74a09e', '#86c1b2', '#98e2c6', '#f3c969', '#f2a553', '#d96548', '#c14953']
sns.set_style('whitegrid')
sns.despine(left=True, bottom=True)
books = pd.read_csv('/kaggle/input/goodreadsbooks/books.csv', error_bad_lines=False)
books[:10]
books.set_index('bookID', inplace=True)
books.index.rename('BookID')
books = books.drop(columns=['isbn', 'isbn13'])
books.columns
books.columns = ['Title', 'Authors', 'Avg_Rating', 'Lang_Code', '#Pages', '#Ratings', '#Text_Reviews', 'Publication_Date', 'Publisher']
books.shape
all_books = ' '.join((token for token in books['Title']))
stopwords = set(STOPWORDS)
font_path = '../input/newghanesfont/NewGhanesFont.otf'
wordcloud = WordCloud(stopwords=stopwords, font_path=font_path, max_words=500, max_font_size=350, random_state=42, width=2500, height=1000, colormap='twilight_shifted_r')
wordcloud.generate(all_books)
plt.figure(figsize=(16, 8))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show() | code |
89129165/cell_30 | [
"image_output_1.png"
] | from wordcloud import WordCloud,ImageColorGenerator,STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
pd.set_option('display.max_colwidth', None)
import warnings
warnings.filterwarnings('ignore')
custom_colors = ['#74a09e', '#86c1b2', '#98e2c6', '#f3c969', '#f2a553', '#d96548', '#c14953']
sns.set_style('whitegrid')
sns.despine(left=True, bottom=True)
books = pd.read_csv('/kaggle/input/goodreadsbooks/books.csv', error_bad_lines=False)
books[:10]
books.set_index('bookID', inplace=True)
books.index.rename('BookID')
books = books.drop(columns=['isbn', 'isbn13'])
books.columns
books.columns = ['Title', 'Authors', 'Avg_Rating', 'Lang_Code', '#Pages', '#Ratings', '#Text_Reviews', 'Publication_Date', 'Publisher']
books.shape
all_books = ' '.join((token for token in books['Title']))
stopwords = set(STOPWORDS)
font_path = '../input/newghanesfont/NewGhanesFont.otf'
wordcloud = WordCloud(stopwords=stopwords, font_path=font_path, max_words=500, max_font_size=350, random_state=42, width=2500, height=1000, colormap='twilight_shifted_r')
wordcloud.generate(all_books)
plt.axis('off')
books.Lang_Code.unique()
books.Lang_Code.nunique()
plt.hist(books.Lang_Code) | code |
89129165/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
pd.set_option('display.max_colwidth', None)
import warnings
warnings.filterwarnings('ignore')
custom_colors = ['#74a09e', '#86c1b2', '#98e2c6', '#f3c969', '#f2a553', '#d96548', '#c14953']
sns.set_style('whitegrid')
sns.despine(left=True, bottom=True)
books = pd.read_csv('/kaggle/input/goodreadsbooks/books.csv', error_bad_lines=False)
books[:10]
books.set_index('bookID', inplace=True)
books.index.rename('BookID')
books = books.drop(columns=['isbn', 'isbn13'])
books.columns
books.columns = ['Title', 'Authors', 'Avg_Rating', 'Lang_Code', '#Pages', '#Ratings', '#Text_Reviews', 'Publication_Date', 'Publisher']
books.shape
books[['Title', 'Lang_Code']][books['Title'].str.find('Harry Potter') != -1] | code |
89129165/cell_6 | [
"image_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
89129165/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
pd.set_option('display.max_colwidth', None)
import warnings
warnings.filterwarnings('ignore')
custom_colors = ['#74a09e', '#86c1b2', '#98e2c6', '#f3c969', '#f2a553', '#d96548', '#c14953']
sns.set_style('whitegrid')
sns.despine(left=True, bottom=True)
books = pd.read_csv('/kaggle/input/goodreadsbooks/books.csv', error_bad_lines=False)
books[:10]
books.set_index('bookID', inplace=True)
books.index.rename('BookID')
books = books.drop(columns=['isbn', 'isbn13'])
books.columns
books.columns = ['Title', 'Authors', 'Avg_Rating', 'Lang_Code', '#Pages', '#Ratings', '#Text_Reviews', 'Publication_Date', 'Publisher']
books.shape
books.Lang_Code.unique()
books.Lang_Code.nunique() | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.