path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
128049433/cell_25 | [
"image_output_1.png"
] | from tensorflow.keras import models,layers
import tensorflow as tf
IMAGE_SIZE = (256, 256)
BATCH_SIZE = 32
CHANNELS = 3
EPOCHES = 100
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/corn-or-maize-leaf-disease-dataset/data', shuffle=True, image_size=IMAGE_SIZE, batch_size=BATCH_SIZE)
def... | code |
128049433/cell_30 | [
"text_plain_output_1.png"
] | from tensorflow.keras import models,layers
import glob as gb
import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import numpy as np
import pandas as pd
import os
IMAGE_SIZE = (256, 256)
BATCH_SIZE... | code |
128049433/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import tensorflow as tf
IMAGE_SIZE = (256, 256)
BATCH_SIZE = 32
CHANNELS = 3
EPOCHES = 100
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/corn-or-maize-leaf-disease-dataset/data', shuffle=True, image_size=IMAGE_SIZE, batch_size=BATCH_SIZE) | code |
128049433/cell_29 | [
"text_plain_output_1.png"
] | from tensorflow.keras import models,layers
import tensorflow as tf
IMAGE_SIZE = (256, 256)
BATCH_SIZE = 32
CHANNELS = 3
EPOCHES = 100
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/corn-or-maize-leaf-disease-dataset/data', shuffle=True, image_size=IMAGE_SIZE, batch_size=BATCH_SIZE)
def... | code |
128049433/cell_26 | [
"text_plain_output_1.png"
] | from tensorflow.keras import models,layers
import tensorflow as tf
IMAGE_SIZE = (256, 256)
BATCH_SIZE = 32
CHANNELS = 3
EPOCHES = 100
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/corn-or-maize-leaf-disease-dataset/data', shuffle=True, image_size=IMAGE_SIZE, batch_size=BATCH_SIZE)
def... | code |
128049433/cell_11 | [
"text_plain_output_1.png"
] | import glob as gb
import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import numpy as np
import pandas as pd
import os
IMAGE_SIZE = (256, 256)
BATCH_SIZE = 32
CHANNELS = 3
EPOCHES = 100
dataset = ... | code |
128049433/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 |
128049433/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import glob as gb
import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
import glob as gb
path = '//kaggle//input//corn-or-maize-leaf-disease-dataset//data'
size = [... | code |
128049433/cell_32 | [
"text_plain_output_1.png"
] | from tensorflow.keras import models,layers
import glob as gb
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import numpy as np
import p... | code |
128049433/cell_28 | [
"text_plain_output_1.png"
] | from tensorflow.keras import models,layers
import tensorflow as tf
IMAGE_SIZE = (256, 256)
BATCH_SIZE = 32
CHANNELS = 3
EPOCHES = 100
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/corn-or-maize-leaf-disease-dataset/data', shuffle=True, image_size=IMAGE_SIZE, batch_size=BATCH_SIZE)
def... | code |
128049433/cell_15 | [
"text_plain_output_1.png"
] | def get_dataset(ds, train_split=0.8, val_split=0.1, test_split=0.1, shuffle=True, shuffle_size=10000):
ds_size = len(ds)
if shuffle:
ds = ds.shuffle(shuffle_size, seed=8)
train_size = int(train_split * ds_size)
val_size = int(val_split * ds_size)
train_ds = ds.take(train_size)
val_ds = d... | code |
128049433/cell_10 | [
"text_plain_output_1.png"
] | import tensorflow as tf
IMAGE_SIZE = (256, 256)
BATCH_SIZE = 32
CHANNELS = 3
EPOCHES = 100
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/corn-or-maize-leaf-disease-dataset/data', shuffle=True, image_size=IMAGE_SIZE, batch_size=BATCH_SIZE)
class_names = dataset.class_names
class_names
... | code |
128049433/cell_27 | [
"text_plain_output_1.png"
] | from tensorflow.keras import models,layers
import tensorflow as tf
IMAGE_SIZE = (256, 256)
BATCH_SIZE = 32
CHANNELS = 3
EPOCHES = 100
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/corn-or-maize-leaf-disease-dataset/data', shuffle=True, image_size=IMAGE_SIZE, batch_size=BATCH_SIZE)
def... | code |
128028409/cell_13 | [
"text_plain_output_1.png"
] | import pygad
import random
target_url = 'https://filebox.ece.vt.edu/~mhsiao/ISCAS89/s27.bench'
import urllib.request
outputs = []
inputs = []
for line in urllib.request.urlopen(target_url):
l = line.decode('utf-8')
if '=' in l:
parse = l.split(' = ')
outputs.append(parse[0])
inputs.app... | code |
128028409/cell_4 | [
"text_plain_output_1.png"
] | target_url = 'https://filebox.ece.vt.edu/~mhsiao/ISCAS89/s27.bench'
import urllib.request
outputs = []
inputs = []
for line in urllib.request.urlopen(target_url):
l = line.decode('utf-8')
if '=' in l:
parse = l.split(' = ')
outputs.append(parse[0])
inputs.append(parse[1].split('(')[1].sp... | code |
128028409/cell_6 | [
"text_plain_output_1.png"
] | target_url = 'https://filebox.ece.vt.edu/~mhsiao/ISCAS89/s27.bench'
import urllib.request
outputs = []
inputs = []
for line in urllib.request.urlopen(target_url):
l = line.decode('utf-8')
if '=' in l:
parse = l.split(' = ')
outputs.append(parse[0])
inputs.append(parse[1].split('(')[1].sp... | code |
128028409/cell_2 | [
"text_plain_output_1.png"
] | target_url = 'https://filebox.ece.vt.edu/~mhsiao/ISCAS89/s27.bench'
import urllib.request
outputs = []
inputs = []
for line in urllib.request.urlopen(target_url):
l = line.decode('utf-8')
if '=' in l:
parse = l.split(' = ')
outputs.append(parse[0])
inputs.append(parse[1].split('(')[1].sp... | code |
128028409/cell_8 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | !pip install pygad
import pygad | code |
128028409/cell_15 | [
"text_plain_output_1.png"
] | import pygad
import random
target_url = 'https://filebox.ece.vt.edu/~mhsiao/ISCAS89/s27.bench'
import urllib.request
outputs = []
inputs = []
for line in urllib.request.urlopen(target_url):
l = line.decode('utf-8')
if '=' in l:
parse = l.split(' = ')
outputs.append(parse[0])
inputs.app... | code |
128028409/cell_16 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
target_url = 'https://filebox.ece.vt.edu/~mhsiao/ISCAS89/s27.bench'
import urllib.request
outputs = []
inputs = []
for line in urllib.request.urlopen(target_url):
l = line.decode('utf-8')
if '=' in l:
parse = l.split(' = ')
outputs.append(parse[0])
inputs.append(pa... | code |
128028409/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | target_url = 'https://filebox.ece.vt.edu/~mhsiao/ISCAS89/s27.bench'
import urllib.request
outputs = []
inputs = []
for line in urllib.request.urlopen(target_url):
l = line.decode('utf-8')
if '=' in l:
parse = l.split(' = ')
outputs.append(parse[0])
inputs.append(parse[1].split('(')[1].sp... | code |
128028409/cell_14 | [
"text_plain_output_1.png"
] | import pygad
import random
target_url = 'https://filebox.ece.vt.edu/~mhsiao/ISCAS89/s27.bench'
import urllib.request
outputs = []
inputs = []
for line in urllib.request.urlopen(target_url):
l = line.decode('utf-8')
if '=' in l:
parse = l.split(' = ')
outputs.append(parse[0])
inputs.app... | code |
128028409/cell_12 | [
"text_plain_output_1.png"
] | import pygad
import random
target_url = 'https://filebox.ece.vt.edu/~mhsiao/ISCAS89/s27.bench'
import urllib.request
outputs = []
inputs = []
for line in urllib.request.urlopen(target_url):
l = line.decode('utf-8')
if '=' in l:
parse = l.split(' = ')
outputs.append(parse[0])
inputs.app... | code |
128028409/cell_5 | [
"image_output_2.png",
"image_output_1.png"
] | def IC(chromosome):
zeros = 0
ones = 0
for i in chromosome:
if i == '0':
zeros += 1
else:
ones += 1
return abs(zeros - ones)
IC('0000000111111111111') | code |
17102411/cell_13 | [
"text_plain_output_1.png"
] | data.shape
data.isnull().sum()
data.address[1]
data = data.rename({'approx_cost(for two people)': 'cost'}, axis=1)
data['cost'] = data['cost'].replace(',', '', regex=True)
data['rest_type'].value_counts() | code |
17102411/cell_9 | [
"text_plain_output_1.png"
] | data.shape
data.isnull().sum()
data.address[1] | code |
17102411/cell_4 | [
"text_html_output_1.png"
] | data.shape
data.info() | code |
17102411/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
data.shape
data.isnull().sum()
data.address[1]
data = data.rename({'approx_cost(for two people)': 'cost'}, axis=1)
data['cost'] = data['cost'].replace(',', '', regex=True)
data[['votes', 'cost']] = data[['votes', 'cost']].apply(pd.to_numeric)
grouped = data.groupby(['name', 'address']).agg({'l... | code |
17102411/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
data.shape
data.isnull().sum()
data.address[1]
data = data.rename({'approx_cost(for two people)': 'cost'}, axis=1)
data['cost'] = data['cost'].replace(',', '', regex=True)
data[['votes', 'cost']] = data[['votes', 'cost']].apply(pd.to_numeric)
grouped = data.groupby(['name', 'address']).agg({'l... | code |
17102411/cell_6 | [
"text_html_output_1.png"
] | data.shape
data.tail(3) | code |
17102411/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
data.shape
data.isnull().sum()
data.address[1]
data = data.rename({'approx_cost(for two people)': 'cost'}, axis=1)
data['cost'] = data['cost'].replace(',', '', regex=True)
data[['votes', 'cost']] = data[['votes', 'cost']].apply(pd.to_numeric)
grouped = data.groupby(['name', 'address']).agg({'l... | code |
17102411/cell_7 | [
"text_html_output_1.png"
] | data.shape
data['menu_item'].value_counts() | code |
17102411/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
data.shape
data.isnull().sum()
data.address[1]
data = data.rename({'approx_cost(for two people)': 'cost'}, axis=1)
data['cost'] = data['cost'].replace(',', '', regex=True)
data[['votes', 'cost']] = data[['votes', 'cost']].apply(pd.to_numeric)
grouped = data.groupby(['name', 'address']).agg({'l... | code |
17102411/cell_8 | [
"text_html_output_1.png"
] | data.shape
data.isnull().sum() | code |
17102411/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
data.shape
data.isnull().sum()
data.address[1]
data = data.rename({'approx_cost(for two people)': 'cost'}, axis=1)
data['cost'] = data['cost'].replace(',', '', regex=True)
data[['votes', 'cost']] = data[['votes', 'cost']].apply(pd.to_numeric)
grouped = data.groupby(['name', 'address']).agg({'l... | code |
17102411/cell_3 | [
"text_plain_output_1.png"
] | data.shape | code |
17102411/cell_12 | [
"text_plain_output_1.png"
] | data.shape
data.isnull().sum()
data.address[1]
data = data.rename({'approx_cost(for two people)': 'cost'}, axis=1)
data['cost'] = data['cost'].replace(',', '', regex=True)
data['listed_in(type)'].value_counts() | code |
17102411/cell_5 | [
"text_plain_output_1.png"
] | data.shape
data.head(3) | code |
16133275/cell_9 | [
"text_plain_output_1.png"
] | from IPython.display import Image
from sklearn.model_selection import train_test_split
from tensorflow.keras import layers
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import random
import tensorflow as tf
import numpy as np
import pandas as pd
import cv2
from IPython.display import ... | code |
16133275/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
import numpy as np
import os
import numpy as np
import pandas as pd
import cv2
from IPython.display import Image
import matplotlib.pyplot as plt
import os
import tensorflow as tf
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split
onlyfiles = os.listdir('../input/utkf... | code |
16133275/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import cv2
from IPython.display import Image
import matplotlib.pyplot as plt
import os
import tensorflow as tf
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split
print(os.listdir('../input/utkface_aligned_cropped/')) | code |
16133275/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from IPython.display import Image
from tensorflow.keras import layers
import tensorflow as tf
def gen_model():
inputs = tf.keras.layers.Input(shape=(32, 32, 3))
x = inputs
x = layers.Conv2D(32, 3, activation='relu')(x)
x = layers.Conv2D(32, 3, activation='relu')(x)
x = layers.MaxPool2D(2)(x)
... | code |
16133275/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from IPython.display import Image
from sklearn.model_selection import train_test_split
from tensorflow.keras import layers
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import random
import tensorflow as tf
import numpy as np
import pandas as pd
import cv2
from IPython.display import ... | code |
16133275/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import numpy as np
import pandas as pd
import cv2
from IPython.display import Image
import matplotlib.pyplot as plt
import os
import tensorflow as tf
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split
onlyfiles = os.listdir('../input/utkface_aligned_... | code |
16133275/cell_10 | [
"text_plain_output_1.png"
] | from IPython.display import Image
from sklearn.model_selection import train_test_split
from tensorflow.keras import layers
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import random
import tensorflow as tf
import numpy as np
import pandas as pd
import cv2
from IPython.display import ... | code |
16133275/cell_5 | [
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import numpy as np
import pandas as pd
import cv2
from IPython.display import Image
import matplotlib.pyplot as plt
import os
import tensorflow as tf
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split
only... | code |
50230470/cell_4 | [
"text_plain_output_1.png"
] | import random
def random_agent(observation, configuration):
return random.randrange(configuration.banditCount) | code |
50230470/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from kaggle_environments import make
from kaggle_environments import make
env = make('n-arm-bandit', debug=True)
env.reset()
env.run(['random_agent.py', 'my-sub-file.py'])
env.reset()
env.run(['my-sub-file.py', 'my-sub-file.py'])
env.render(mode='ipython', width=800, height=700) | code |
50230470/cell_2 | [
"text_plain_output_1.png"
] | !pip install kaggle-environments --upgrade | code |
50230470/cell_3 | [
"text_plain_output_1.png"
] | import json
import numpy as np
import pandas as pd
basic_state = None
reward_full = 0
step_ending = None
def basic_mab(measurement, structure):
no_reward_step = 0.01
decay_rate = 0.99
global basic_state, reward_full, step_ending
if measurement.step == 0:
basic_state = [[1, 1] for i in range(stru... | code |
50230470/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from kaggle_environments import make
from kaggle_environments import make
env = make('n-arm-bandit', debug=True)
env.reset()
env.run(['random_agent.py', 'my-sub-file.py'])
env.render(mode='ipython', width=800, height=700) | code |
17124444/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)
data = pd.read_csv('../input/diabetes.csv')
data.head() | code |
17124444/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/diabetes.csv')
y = data['Outcome']
feature_data = data.drop('Outcome', axis=1)
scaled_features = StandardScaler().fit_transform(feature_data.values)
scaled_data = pd.D... | code |
17124444/cell_11 | [
"text_html_output_1.png"
] | import operator
dic = {}
dic = {1: 1.2, 2: 1.56, 3: 5.2, 6: 7.1, 4: 2.7}
sorted(dic.items(), key=lambda item: item[1], reverse=True) | code |
17124444/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17124444/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/diabetes.csv')
y = data['Outcome']
feature_data = data.drop('Outcome', axis=1)
scaled_features = StandardScaler().fit_transform(feature_data.values)
scaled_data = pd.D... | code |
17124444/cell_8 | [
"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
data = pd.read_csv('../input/diabetes.csv')
y = data['Outcome']
feature_data = data.drop('Outcome', axis=1)
correlation = data.corr()
plt.figure(figsize=(10, 10))
sns.heatmap(correlation, an... | code |
17124444/cell_14 | [
"text_html_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/diabetes.csv')
y = data['Outcome']
feature_data = data.drop('Outcome', axis=1)
... | code |
17124444/cell_12 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/diabetes.csv')
y = data['Outcome']
feature_data = data.drop('Outcome', axis=1)
scaled_features = StandardScaler().fit_transform(fe... | code |
74054331/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/top-indian-colleges/College_data.csv')
df.dtypes
df.isnull().sum()
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 5))
sns.barplot(x... | code |
74054331/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/top-indian-colleges/College_data.csv')
df.head() | code |
74054331/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/top-indian-colleges/College_data.csv')
df.dtypes
df.isnull().sum()
import seaborn as sns
import matplotlib.pyplot as plt
plt.xticks(rotation=90)
df_plot = df.gro... | code |
74054331/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 |
74054331/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/top-indian-colleges/College_data.csv')
df.dtypes
df.isnull().sum() | code |
74054331/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
df = pd.read_csv('../input/top-indian-colleges/College_data.csv')
df.dtypes
df.isnull().sum()
import seaborn as sns
import matplotlib.pyplot as plt
plt.xticks(rotation=90)
df_plot = df.gro... | code |
74054331/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/top-indian-colleges/College_data.csv')
df.dtypes | code |
73084093/cell_21 | [
"text_html_output_2.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text_html_output_3.png"
] | from keras.layers import Dense
from keras.layers import Dense, LSTM
from keras.layers import Dropout
from keras.layers import LSTM
from keras.models import Sequential
from keras.models import Sequential
from pmdarima.arima import ADFTest
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing ... | code |
73084093/cell_6 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_1.png"
] | !pip install yfinance --quiet
!pip install pmdarima --quiet | code |
73084093/cell_8 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | !pip install statsmodels==0.11.0rc1 --quiet
!pip install -Iv pulp==1.6.8 --quiet | code |
73084093/cell_16 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dense, LSTM
from keras.layers import Dropout
from keras.layers import LSTM
from keras.models import Sequential
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MinMaxScaler
import keras
i... | code |
73084093/cell_17 | [
"text_plain_output_1.png"
] | print(lstm_pred.shape) | code |
73084093/cell_24 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dense, LSTM
from keras.layers import Dropout
from keras.layers import LSTM
from keras.models import Sequential
from keras.models import Sequential
from pmdarima.arima import ADFTest
from pmdarima.arima import ADFTest
from pylab import rcParams
from sklear... | code |
73084093/cell_10 | [
"text_plain_output_1.png"
] | import yfinance as yf
import yfinance as yf
stock_name = 'AMD'
data = yf.download(stock_name, start='2020-03-26', end='2021-03-29') | code |
73084093/cell_27 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dense, LSTM
from keras.layers import Dropout
from keras.layers import LSTM
from keras.models import Sequential
from keras.models import Sequential
from pmdarima.arima import ADFTest
from pmdarima.arima import ADFTest
from pylab import rcParams
from sklear... | code |
130019203/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/covid19-india-news-headlines-for-nlp/raw_data.csv', usecols=['Headline', 'Sentiment', 'Description'])
df.shape
X = df.drop('Sentiment', axis=1)
Y = df['Sentiment']
X | code |
130019203/cell_23 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from tensorflow.keras.preprocessing.text import one_hot
import pandas as pd
import re
df = pd.read_csv('/kaggle/input/covid19-india-news-headlines-for-nlp/raw_data.csv', usecols=['Headline', 'Sentiment', 'Description'])
df.shape
X = df.drop('S... | code |
130019203/cell_33 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from sklearn.metrics import accuracy_score
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Embedding
from tensorflow.keras.layers import LSTM
from tensorflow.keras.model... | code |
130019203/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/covid19-india-news-headlines-for-nlp/raw_data.csv', usecols=['Headline', 'Sentiment', 'Description'])
df.head() | code |
130019203/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/covid19-india-news-headlines-for-nlp/raw_data.csv', usecols=['Headline', 'Sentiment', 'Description'])
df.shape | code |
130019203/cell_18 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
import pandas as pd
import re
df = pd.read_csv('/kaggle/input/covid19-india-news-headlines-for-nlp/raw_data.csv', usecols=['Headline', 'Sentiment', 'Description'])
df.shape
X = df.drop('Sentiment', axis=1)
Y = df['Sentiment']
messages = X.copy... | code |
130019203/cell_32 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Embedding
from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential
from tensorflow.keras.p... | code |
130019203/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/covid19-india-news-headlines-for-nlp/raw_data.csv', usecols=['Headline', 'Sentiment', 'Description'])
df.shape
df.info() | code |
130019203/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/covid19-india-news-headlines-for-nlp/raw_data.csv', usecols=['Headline', 'Sentiment', 'Description'])
df.shape
X = df.drop('Sentiment', axis=1)
Y = df['Sentiment']
messages = X.copy()
messages | code |
130019203/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/covid19-india-news-headlines-for-nlp/raw_data.csv', usecols=['Headline', 'Sentiment', 'Description'])
df.shape
X = df.drop('Sentiment', axis=1)
Y = df['Sentiment']
Y | code |
130019203/cell_27 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import one_hot
import pandas as pd
import re
df = pd.read_csv('/kaggle/input/covid19-india-news-headlines-for-nlp/raw_data.csv', usecols=... | code |
74068297/cell_21 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv', na_values='?')
data.shape
data.columns = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', ... | code |
74068297/cell_25 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv', na_values='?')
data.shape
data.columns = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status', 'occupation', 'relationship', 'race', '... | code |
74068297/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv', na_values='?')
data.shape
data.columns = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_st... | code |
74068297/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv', na_values='?')
data.shape
data.columns = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', ... | code |
74068297/cell_6 | [
"text_html_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv', na_values='?')
data.shape | code |
74068297/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv', na_values='?')
data.shape
data.columns = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_st... | code |
74068297/cell_19 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv', na_values='?')
data.shape
data.columns = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', ... | code |
74068297/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 |
74068297/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv', na_values='?')
data.shape
data.head() | code |
74068297/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv', na_values='?')
data.shape
data.columns = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_st... | code |
74068297/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv', na_values='?')
data.shape
data.columns = ['age'... | code |
74068297/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv', na_values='?')
data.shape
data.columns = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status', 'occupation', 'relationship', 'race', '... | code |
90153288/cell_21 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/prediction-of-music-genre/music_genre.csv')
import seaborn as sns
import matplotlib.pyplot as plt
df_mr = df.drop(columns=[i for ... | code |
90153288/cell_23 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/prediction-of-music-genre/music_genre.csv')
import seaborn as sns
import matplotlib.pyplot as plt
df_mr = df.drop(columns=[i for ... | code |
90153288/cell_30 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | 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
df = pd.read_csv('../input/prediction-of-music-genre/music_genre.csv')
import seaborn as sns
import matplotlib.pyplot as plt
df_mr = df.drop(columns=[i f... | code |
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