path
stringlengths 13
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sequencelengths 1
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stringlengths 0
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|---|---|---|---|
72089413/cell_30 | [
"text_plain_output_1.png"
] | from pandas.io.json import json_normalize
from tqdm import tqdm
from tqdm.notebook import tqdm
import json_lines
import pandas as pd
import random
data0 = []
with open('../input/youtubes-channels-dataset/YouTubeDataset_withChannelElapsed.json', 'rb') as f:
for i, item in enumerate(json_lines.reader(f)):
if i < 10000:
data0 += [item]
users0 = json_normalize(data0[0][0])
users0
for i, item in tqdm(enumerate(data0[0])):
if 0 < i and i < 10000:
usersi = json_normalize(item)
users0 = pd.concat([users0, usersi])
N = list(range(10000))
data1 = users0.copy()
data1['index0'] = N
data1 = data1.set_index('index0', drop=True)
data1
data2 = data1.drop(['channelId', 'videoId', 'videoPublished'], axis=1)
data2
data2 = data2.astype(float)
target = ['subscriberCount']
dataY = data2[target[0]]
dataX = data2.drop(target, axis=1)
n = len(dataX)
random.seed(2021)
random.shuffle(N)
trainX = dataX.loc[N[0:n // 4 * 3]]
trainY = dataY.loc[N[0:n // 4 * 3]]
testX = dataX.loc[N[n // 4 * 3:]]
testY = dataY.loc[N[n // 4 * 3:]]
df_columns = list(dataX.columns)
def create_numeric_feature(input_df):
use_columns = df_columns
return input_df[use_columns].copy()
from tqdm import tqdm
def to_feature(input_df):
processors = [create_numeric_feature]
out_df = pd.DataFrame()
for func in tqdm(processors, total=len(processors)):
with Timer(prefix='create' + func.__name__ + ' '):
_df = func(input_df)
assert len(_df) == len(input_df), func.__name__
out_df = pd.concat([out_df, _df], axis=1)
return out_df
train_feat_df = to_feature(trainX)
test_feat_df = to_feature(testX) | code |
72089413/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import json_lines
data0 = []
with open('../input/youtubes-channels-dataset/YouTubeDataset_withChannelElapsed.json', 'rb') as f:
for i, item in enumerate(json_lines.reader(f)):
if i < 10000:
data0 += [item]
data0[0][0] | code |
72089413/cell_26 | [
"text_plain_output_1.png"
] | from pandas.io.json import json_normalize
from tqdm.notebook import tqdm
import json_lines
import pandas as pd
import random
data0 = []
with open('../input/youtubes-channels-dataset/YouTubeDataset_withChannelElapsed.json', 'rb') as f:
for i, item in enumerate(json_lines.reader(f)):
if i < 10000:
data0 += [item]
users0 = json_normalize(data0[0][0])
users0
for i, item in tqdm(enumerate(data0[0])):
if 0 < i and i < 10000:
usersi = json_normalize(item)
users0 = pd.concat([users0, usersi])
N = list(range(10000))
data1 = users0.copy()
data1['index0'] = N
data1 = data1.set_index('index0', drop=True)
data1
data2 = data1.drop(['channelId', 'videoId', 'videoPublished'], axis=1)
data2
data2 = data2.astype(float)
target = ['subscriberCount']
dataY = data2[target[0]]
dataX = data2.drop(target, axis=1)
n = len(dataX)
random.seed(2021)
random.shuffle(N)
trainX = dataX.loc[N[0:n // 4 * 3]]
trainY = dataY.loc[N[0:n // 4 * 3]]
testX = dataX.loc[N[n // 4 * 3:]]
testY = dataY.loc[N[n // 4 * 3:]]
df_columns = list(dataX.columns)
print(df_columns) | code |
72089413/cell_11 | [
"text_plain_output_1.png"
] | from pandas.io.json import json_normalize
from tqdm.notebook import tqdm
import json_lines
import pandas as pd
data0 = []
with open('../input/youtubes-channels-dataset/YouTubeDataset_withChannelElapsed.json', 'rb') as f:
for i, item in enumerate(json_lines.reader(f)):
if i < 10000:
data0 += [item]
users0 = json_normalize(data0[0][0])
users0
for i, item in tqdm(enumerate(data0[0])):
if 0 < i and i < 10000:
usersi = json_normalize(item)
users0 = pd.concat([users0, usersi]) | code |
72089413/cell_19 | [
"text_html_output_1.png"
] | from pandas.io.json import json_normalize
from tqdm.notebook import tqdm
import json_lines
import pandas as pd
data0 = []
with open('../input/youtubes-channels-dataset/YouTubeDataset_withChannelElapsed.json', 'rb') as f:
for i, item in enumerate(json_lines.reader(f)):
if i < 10000:
data0 += [item]
users0 = json_normalize(data0[0][0])
users0
for i, item in tqdm(enumerate(data0[0])):
if 0 < i and i < 10000:
usersi = json_normalize(item)
users0 = pd.concat([users0, usersi])
N = list(range(10000))
data1 = users0.copy()
data1['index0'] = N
data1 = data1.set_index('index0', drop=True)
data1
data2 = data1.drop(['channelId', 'videoId', 'videoPublished'], axis=1)
data2
data2 = data2.astype(float)
data2 | code |
72089413/cell_1 | [
"text_plain_output_1.png"
] | !pip install json_lines | code |
72089413/cell_7 | [
"text_plain_output_1.png"
] | import json_lines
data0 = []
with open('../input/youtubes-channels-dataset/YouTubeDataset_withChannelElapsed.json', 'rb') as f:
for i, item in enumerate(json_lines.reader(f)):
if i < 10000:
data0 += [item]
data0[0][0].keys() | code |
72089413/cell_18 | [
"text_plain_output_1.png"
] | from pandas.io.json import json_normalize
from tqdm.notebook import tqdm
import json_lines
import pandas as pd
data0 = []
with open('../input/youtubes-channels-dataset/YouTubeDataset_withChannelElapsed.json', 'rb') as f:
for i, item in enumerate(json_lines.reader(f)):
if i < 10000:
data0 += [item]
users0 = json_normalize(data0[0][0])
users0
for i, item in tqdm(enumerate(data0[0])):
if 0 < i and i < 10000:
usersi = json_normalize(item)
users0 = pd.concat([users0, usersi])
N = list(range(10000))
data1 = users0.copy()
data1['index0'] = N
data1 = data1.set_index('index0', drop=True)
data1
data2 = data1.drop(['channelId', 'videoId', 'videoPublished'], axis=1)
data2
data2 = data2.astype(float)
data2.info() | code |
72089413/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
import lightgbm as lgbm
import numpy as np
import lightgbm as lgbm
from sklearn.metrics import mean_squared_error
def fit_lgbm(X, y, cv, params: dict=None, verbose: int=50):
if params is None:
params = {}
models = []
oof_pred = np.zeros_like(y, dtype=np.float)
for i, (idx_train, idx_valid) in enumerate(cv):
x_train, y_train = (X[idx_train], y[idx_train])
x_valid, y_valid = (X[idx_valid], y[idx_valid])
clf = lgbm.LGBMRegressor(**params)
with Timer(prefix='fit fold={} '.format(i)):
clf.fit(x_train, y_train, eval_set=[(x_valid, y_valid)], early_stopping_rounds=100, verbose=verbose)
pred_i = clf.predict(x_valid)
oof_pred[idx_valid] = pred_i
models.append(clf)
print(f'Fold {i} RMSLE: {mean_squared_error(y_valid, pred_i) ** 0.5:.4f}')
print()
score = mean_squared_error(y, oof_pred) ** 0.5
print('-' * 50)
print('FINISHED | Whole RMSLE: {:.4f}'.format(score))
return (oof_pred, models) | code |
72089413/cell_15 | [
"text_html_output_1.png"
] | from pandas.io.json import json_normalize
from tqdm.notebook import tqdm
import json_lines
import pandas as pd
data0 = []
with open('../input/youtubes-channels-dataset/YouTubeDataset_withChannelElapsed.json', 'rb') as f:
for i, item in enumerate(json_lines.reader(f)):
if i < 10000:
data0 += [item]
users0 = json_normalize(data0[0][0])
users0
for i, item in tqdm(enumerate(data0[0])):
if 0 < i and i < 10000:
usersi = json_normalize(item)
users0 = pd.concat([users0, usersi])
N = list(range(10000))
data1 = users0.copy()
data1['index0'] = N
data1 = data1.set_index('index0', drop=True)
data1
data2 = data1.drop(['channelId', 'videoId', 'videoPublished'], axis=1)
data2 | code |
72089413/cell_3 | [
"text_html_output_1.png"
] | import tensorflow as tf
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 |
72089413/cell_35 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from pandas.io.json import json_normalize
from tqdm import tqdm
from tqdm.notebook import tqdm
import json_lines
import pandas as pd
import random
data0 = []
with open('../input/youtubes-channels-dataset/YouTubeDataset_withChannelElapsed.json', 'rb') as f:
for i, item in enumerate(json_lines.reader(f)):
if i < 10000:
data0 += [item]
users0 = json_normalize(data0[0][0])
users0
for i, item in tqdm(enumerate(data0[0])):
if 0 < i and i < 10000:
usersi = json_normalize(item)
users0 = pd.concat([users0, usersi])
N = list(range(10000))
data1 = users0.copy()
data1['index0'] = N
data1 = data1.set_index('index0', drop=True)
data1
data2 = data1.drop(['channelId', 'videoId', 'videoPublished'], axis=1)
data2
data2 = data2.astype(float)
target = ['subscriberCount']
dataY = data2[target[0]]
dataX = data2.drop(target, axis=1)
n = len(dataX)
random.seed(2021)
random.shuffle(N)
trainX = dataX.loc[N[0:n // 4 * 3]]
trainY = dataY.loc[N[0:n // 4 * 3]]
testX = dataX.loc[N[n // 4 * 3:]]
testY = dataY.loc[N[n // 4 * 3:]]
df_columns = list(dataX.columns)
def create_numeric_feature(input_df):
use_columns = df_columns
return input_df[use_columns].copy()
from tqdm import tqdm
def to_feature(input_df):
processors = [create_numeric_feature]
out_df = pd.DataFrame()
for func in tqdm(processors, total=len(processors)):
with Timer(prefix='create' + func.__name__ + ' '):
_df = func(input_df)
assert len(_df) == len(input_df), func.__name__
out_df = pd.concat([out_df, _df], axis=1)
return out_df
y = trainY
ydf = pd.DataFrame(y)
ydf | code |
72089413/cell_14 | [
"text_plain_output_1.png"
] | from pandas.io.json import json_normalize
from tqdm.notebook import tqdm
import json_lines
import pandas as pd
data0 = []
with open('../input/youtubes-channels-dataset/YouTubeDataset_withChannelElapsed.json', 'rb') as f:
for i, item in enumerate(json_lines.reader(f)):
if i < 10000:
data0 += [item]
users0 = json_normalize(data0[0][0])
users0
for i, item in tqdm(enumerate(data0[0])):
if 0 < i and i < 10000:
usersi = json_normalize(item)
users0 = pd.concat([users0, usersi])
N = list(range(10000))
data1 = users0.copy()
data1['index0'] = N
data1 = data1.set_index('index0', drop=True)
data1 | code |
72089413/cell_22 | [
"text_html_output_1.png"
] | from pandas.io.json import json_normalize
from tqdm.notebook import tqdm
import json_lines
import pandas as pd
data0 = []
with open('../input/youtubes-channels-dataset/YouTubeDataset_withChannelElapsed.json', 'rb') as f:
for i, item in enumerate(json_lines.reader(f)):
if i < 10000:
data0 += [item]
users0 = json_normalize(data0[0][0])
users0
for i, item in tqdm(enumerate(data0[0])):
if 0 < i and i < 10000:
usersi = json_normalize(item)
users0 = pd.concat([users0, usersi])
N = list(range(10000))
data1 = users0.copy()
data1['index0'] = N
data1 = data1.set_index('index0', drop=True)
data1
data2 = data1.drop(['channelId', 'videoId', 'videoPublished'], axis=1)
data2
data2 = data2.astype(float)
target = ['subscriberCount']
dataY = data2[target[0]]
dataX = data2.drop(target, axis=1)
print(dataY[0:5].T)
print()
print(dataX[0:5].T) | code |
72089413/cell_10 | [
"text_plain_output_1.png"
] | from pandas.io.json import json_normalize
import json_lines
data0 = []
with open('../input/youtubes-channels-dataset/YouTubeDataset_withChannelElapsed.json', 'rb') as f:
for i, item in enumerate(json_lines.reader(f)):
if i < 10000:
data0 += [item]
users0 = json_normalize(data0[0][0])
users0 | code |
72089413/cell_37 | [
"text_plain_output_1.png"
] | from pandas.io.json import json_normalize
from tqdm import tqdm
from tqdm.notebook import tqdm
import json_lines
import pandas as pd
data0 = []
with open('../input/youtubes-channels-dataset/YouTubeDataset_withChannelElapsed.json', 'rb') as f:
for i, item in enumerate(json_lines.reader(f)):
if i < 10000:
data0 += [item]
users0 = json_normalize(data0[0][0])
users0
for i, item in tqdm(enumerate(data0[0])):
if 0 < i and i < 10000:
usersi = json_normalize(item)
users0 = pd.concat([users0, usersi])
N = list(range(10000))
data1 = users0.copy()
data1['index0'] = N
data1 = data1.set_index('index0', drop=True)
data1
data2 = data1.drop(['channelId', 'videoId', 'videoPublished'], axis=1)
data2
data2 = data2.astype(float)
target = ['subscriberCount']
dataY = data2[target[0]]
dataX = data2.drop(target, axis=1)
print(target) | code |
72089413/cell_12 | [
"text_plain_output_1.png"
] | from pandas.io.json import json_normalize
from tqdm.notebook import tqdm
import json_lines
import pandas as pd
data0 = []
with open('../input/youtubes-channels-dataset/YouTubeDataset_withChannelElapsed.json', 'rb') as f:
for i, item in enumerate(json_lines.reader(f)):
if i < 10000:
data0 += [item]
users0 = json_normalize(data0[0][0])
users0
for i, item in tqdm(enumerate(data0[0])):
if 0 < i and i < 10000:
usersi = json_normalize(item)
users0 = pd.concat([users0, usersi])
print(len(users0)) | code |
2015167/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import hamming_loss
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/seattleWeather_1948-2017.csv')
df = df.dropna()
X = df.loc[:, ['PRCP', 'TMAX', 'TMIN']].shift(-1).iloc[:-1].values
y = df.iloc[:-1, -1:].values.astype('int')
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
clf.fit(X, y)
y_hat = clf.predict(X)
from sklearn.metrics import hamming_loss
hamming_loss(y, y_hat) | code |
2015167/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/seattleWeather_1948-2017.csv')
df.head() | code |
2015167/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import fbeta_score
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/seattleWeather_1948-2017.csv')
df = df.dropna()
X = df.loc[:, ['PRCP', 'TMAX', 'TMIN']].shift(-1).iloc[:-1].values
y = df.iloc[:-1, -1:].values.astype('int')
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
clf.fit(X, y)
y_hat = clf.predict(X)
from sklearn.metrics import fbeta_score
fbeta_score(y, y_hat, beta=1) | code |
2015167/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/seattleWeather_1948-2017.csv')
df = df.dropna()
X = df.loc[:, ['PRCP', 'TMAX', 'TMIN']].shift(-1).iloc[:-1].values
y = df.iloc[:-1, -1:].values.astype('int')
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
clf.fit(X, y)
y_hat = clf.predict(X)
from sklearn.metrics import confusion_matrix
confusion_matrix(y, y_hat) | code |
2015167/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import precision_score, recall_score, precision_recall_curve
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/seattleWeather_1948-2017.csv')
df = df.dropna()
X = df.loc[:, ['PRCP', 'TMAX', 'TMIN']].shift(-1).iloc[:-1].values
y = df.iloc[:-1, -1:].values.astype('int')
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
clf.fit(X, y)
y_hat = clf.predict(X)
from sklearn.metrics import precision_score, recall_score, precision_recall_curve
print(precision_score(y, y_hat))
print(recall_score(y, y_hat)) | code |
2015167/cell_3 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/seattleWeather_1948-2017.csv')
df = df.dropna()
X = df.loc[:, ['PRCP', 'TMAX', 'TMIN']].shift(-1).iloc[:-1].values
y = df.iloc[:-1, -1:].values.astype('int')
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
clf.fit(X, y)
y_hat = clf.predict(X) | code |
2015167/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import precision_score, recall_score, precision_recall_curve
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/seattleWeather_1948-2017.csv')
df = df.dropna()
X = df.loc[:, ['PRCP', 'TMAX', 'TMIN']].shift(-1).iloc[:-1].values
y = df.iloc[:-1, -1:].values.astype('int')
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
clf.fit(X, y)
y_hat = clf.predict(X)
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
precision, recall, _ = precision_recall_curve(y, y_hat)
fig, ax = plt.subplots(1, figsize=(12, 6))
ax.step(recall, precision, color='steelblue', where='post')
ax.fill_between(recall, precision, step='post', color='lightgray')
plt.suptitle('Precision-Recall Tradeoff for Seattle Rain Prediction')
plt.xlabel('Recall')
plt.ylabel('Precision') | code |
2015167/cell_10 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
df = pd.read_csv('../input/seattleWeather_1948-2017.csv')
df = df.dropna()
X = df.loc[:, ['PRCP', 'TMAX', 'TMIN']].shift(-1).iloc[:-1].values
y = df.iloc[:-1, -1:].values.astype('int')
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
clf.fit(X, y)
y_hat = clf.predict(X)
import seaborn as sns
sns.heatmap(confusion_matrix(y, y_hat) / len(y), cmap='Blues', annot=True) | code |
2015167/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/seattleWeather_1948-2017.csv')
df = df.dropna()
X = df.loc[:, ['PRCP', 'TMAX', 'TMIN']].shift(-1).iloc[:-1].values
y = df.iloc[:-1, -1:].values.astype('int')
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
clf.fit(X, y)
y_hat = clf.predict(X)
from sklearn.metrics import accuracy_score
accuracy_score(y, y_hat) | code |
17144473/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from bokeh.io import output_file,show,output_notebook,push_notebook
from bokeh.models import ColumnDataSource,HoverTool,CategoricalColorMapper
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/scmp2k19.csv')
df.loc[:, ['district', 'mandal', 'location']].sample(7, random_state=1)
factors = list(df.mandal.unique())
colors = ['red', 'green', 'blue', 'black', 'orange', 'brown', 'grey', 'purple', 'yellow', 'white', 'pink', 'peru']
mapper = CategoricalColorMapper(factors=factors, palette=colors)
plot = figure()
plot.circle(x='odate', y='humidity_min', source=source, color={'field': 'Genre', 'transform': mapper})
show(plot) | code |
17144473/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/scmp2k19.csv')
df.info() | code |
17144473/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from bokeh.io import output_file,show,output_notebook,push_notebook
from bokeh.layouts import row,column,gridplot,widgetbox
p1 = figure()
p1.circle(x='district', y='Rangareddy', source=source, color='red')
p2 = figure()
p2.circle(x='district', y='Warangal', source=source, color='black')
p3 = figure()
p3.circle(x='district', y='Khammam', source=source, color='blue')
p4 = figure()
p4.circle(x='district', y='Nalgonda', source=source, color='orange')
layout1 = row(p1, p2)
layout2 = row(p3, p4)
layout3 = column(layout1, layout2)
show(layout3) | code |
17144473/cell_1 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from bokeh.io import output_file,show,output_notebook,push_notebook
import os
import numpy as np
import pandas as pd
import seaborn as sns
from ipywidgets import interact
from bokeh.io import output_file, show, output_notebook, push_notebook
from bokeh.plotting import *
from bokeh.models import ColumnDataSource, HoverTool, CategoricalColorMapper
from bokeh.layouts import row, column, gridplot, widgetbox
from bokeh.layouts import layout
from bokeh.embed import file_html
from bokeh.models import Text
from bokeh.models import Plot
from bokeh.models import Slider
from bokeh.models import Circle
from bokeh.models import Range1d
from bokeh.models import CustomJS
from bokeh.models import LinearAxis
from bokeh.models import SingleIntervalTicker
from bokeh.palettes import Spectral6
output_notebook()
import os
print(os.listdir('../input')) | code |
17144473/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from bokeh.io import output_file,show,output_notebook,push_notebook
from bokeh.layouts import row,column,gridplot,widgetbox
# Row and column
p1 = figure()
p1.circle(x = "district",y= "Rangareddy",source = source,color="red")
p2 = figure()
p2.circle(x = "district",y= "Warangal",source = source,color="black")
p3 = figure()
p3.circle(x = "district",y= "Khammam",source = source,color="blue")
p4 = figure()
p4.circle(x = "district",y= "Nalgonda",source = source,color="orange")
layout1 = row(p1,p2)
layout2 = row(p3,p4)
layout3= column(layout1,layout2)
show(layout3)
show(layout3) | code |
17144473/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/scmp2k19.csv')
df.loc[:, ['district', 'mandal', 'location']].sample(7, random_state=1) | code |
18124991/cell_4 | [
"text_plain_output_1.png"
] | from torch import nn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torchvision
from torch import nn
from fastai.vision import *
import torchvision
df = pd.read_csv('../input/train.csv')
path = '../input'
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model = torchvision.models.resnext101_32x8d(pretrained=True)
iB = ImageDataBunch.from_df(path=path, df=df, folder='train_images', seed=42, suffix='.png', test='test_images', size=224, bs=32, ds_tfms=get_transforms(do_flip=True, max_warp=0, max_rotate=0, max_lighting=0, p_affine=0, xtra_tfms=[crop_pad()]))
model1 = torchvision.models.resnext101_32x8d(pretrained=True)
model1.fc = nn.Sequential(nn.BatchNorm1d(2048), nn.Dropout(p=0.25), nn.Linear(2048, 512), nn.ReLU(), nn.BatchNorm1d(512), nn.Dropout(p=0.5), nn.Linear(512, 5))
model1.to(device)
learn1 = Learner(data=iB, model=model1, model_dir='/tmp/models', metrics=[accuracy])
learn2 = cnn_learner(data=iB, base_arch=models.resnet152, model_dir='/tmp/models', metrics=[accuracy])
learn3 = cnn_learner(data=iB, base_arch=models.densenet201, model_dir='/tmp/models', metrics=[accuracy])
learn4 = cnn_learner(data=iB, base_arch=models.vgg16_bn, model_dir='/tmp/models', metrics=[accuracy])
learn1.fit_one_cycle(7, slice(0.0008))
model1 = learn1.model
learn2.unfreeze()
learn2.fit_one_cycle(7, slice(0.003))
model2 = learn2.model
learn3.unfreeze()
learn3.fit_one_cycle(7, slice(0.003))
model3 = learn3.model
learn4.unfreeze()
learn4.fit_one_cycle(7, slice(0.003))
model4 = learn4.model
torch.save(model1, './model1.pth')
torch.save(model2, './model2.pth')
torch.save(model3, './model3.pth')
torch.save(model4, './model4.pth')
dff = pd.read_csv('../input/test.csv')
src = ImageList.from_df(dff, path='../input', folder='test_images', suffix='.png').split_none().label_empty()
model1.eval()
model2.eval()
model3.eval()
model4.eval()
iB = ImageDataBunch.create_from_ll(src, size=224, bs=32, ds_tfms=get_transforms(do_flip=True, max_warp=0, max_rotate=0, max_lighting=0, p_affine=0.2, xtra_tfms=[crop_pad()]))
predictor1 = Learner(data=iB, model=model1, model_dir='/')
preds1 = predictor1.get_preds(ds_type=DatasetType.Fix)
predictor2 = Learner(data=iB, model=model2, model_dir='/')
preds2 = predictor2.get_preds(ds_type=DatasetType.Fix)
predictor3 = Learner(data=iB, model=model3, model_dir='/')
preds3 = predictor3.get_preds(ds_type=DatasetType.Fix)
predictor4 = Learner(data=iB, model=model4, model_dir='/')
preds4 = predictor4.get_preds(ds_type=DatasetType.Fix)
labels1, labels2, labels3, labels4 = ([], [], [], [])
print('Predicting from model1....')
for pr in preds1[0]:
p = pr.tolist()
labels1.append(np.argmax(p))
print('Predicting from model2....')
for pr in preds2[0]:
p = pr.tolist()
labels2.append(np.argmax(p))
print('Predicting from model3....')
for pr in preds3[0]:
p = pr.tolist()
labels3.append(np.argmax(p))
print('Predicting from model4....')
for pr in preds4[0]:
p = pr.tolist()
labels4.append(np.argmax(p)) | code |
18124991/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from torch import nn
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torchvision
from torch import nn
from fastai.vision import *
import torchvision
df = pd.read_csv('../input/train.csv')
path = '../input'
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model = torchvision.models.resnext101_32x8d(pretrained=True)
iB = ImageDataBunch.from_df(path=path, df=df, folder='train_images', seed=42, suffix='.png', test='test_images', size=224, bs=32, ds_tfms=get_transforms(do_flip=True, max_warp=0, max_rotate=0, max_lighting=0, p_affine=0, xtra_tfms=[crop_pad()]))
model1 = torchvision.models.resnext101_32x8d(pretrained=True)
model1.fc = nn.Sequential(nn.BatchNorm1d(2048), nn.Dropout(p=0.25), nn.Linear(2048, 512), nn.ReLU(), nn.BatchNorm1d(512), nn.Dropout(p=0.5), nn.Linear(512, 5))
model1.to(device)
learn1 = Learner(data=iB, model=model1, model_dir='/tmp/models', metrics=[accuracy])
learn2 = cnn_learner(data=iB, base_arch=models.resnet152, model_dir='/tmp/models', metrics=[accuracy])
learn3 = cnn_learner(data=iB, base_arch=models.densenet201, model_dir='/tmp/models', metrics=[accuracy])
learn4 = cnn_learner(data=iB, base_arch=models.vgg16_bn, model_dir='/tmp/models', metrics=[accuracy]) | code |
18124991/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18124991/cell_3 | [
"text_html_output_4.png",
"text_plain_output_4.png",
"text_html_output_2.png",
"text_plain_output_3.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text_html_output_3.png"
] | from torch import nn
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torchvision
from torch import nn
from fastai.vision import *
import torchvision
df = pd.read_csv('../input/train.csv')
path = '../input'
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model = torchvision.models.resnext101_32x8d(pretrained=True)
iB = ImageDataBunch.from_df(path=path, df=df, folder='train_images', seed=42, suffix='.png', test='test_images', size=224, bs=32, ds_tfms=get_transforms(do_flip=True, max_warp=0, max_rotate=0, max_lighting=0, p_affine=0, xtra_tfms=[crop_pad()]))
model1 = torchvision.models.resnext101_32x8d(pretrained=True)
model1.fc = nn.Sequential(nn.BatchNorm1d(2048), nn.Dropout(p=0.25), nn.Linear(2048, 512), nn.ReLU(), nn.BatchNorm1d(512), nn.Dropout(p=0.5), nn.Linear(512, 5))
model1.to(device)
learn1 = Learner(data=iB, model=model1, model_dir='/tmp/models', metrics=[accuracy])
learn2 = cnn_learner(data=iB, base_arch=models.resnet152, model_dir='/tmp/models', metrics=[accuracy])
learn3 = cnn_learner(data=iB, base_arch=models.densenet201, model_dir='/tmp/models', metrics=[accuracy])
learn4 = cnn_learner(data=iB, base_arch=models.vgg16_bn, model_dir='/tmp/models', metrics=[accuracy])
print('Training ResNeXt101_32x8d....')
learn1.fit_one_cycle(7, slice(0.0008))
model1 = learn1.model
print('Training Resnet152....')
learn2.unfreeze()
learn2.fit_one_cycle(7, slice(0.003))
model2 = learn2.model
print('Traning Densenet201....')
learn3.unfreeze()
learn3.fit_one_cycle(7, slice(0.003))
model3 = learn3.model
print('Training VGG16......')
learn4.unfreeze()
learn4.fit_one_cycle(7, slice(0.003))
model4 = learn4.model
torch.save(model1, './model1.pth')
torch.save(model2, './model2.pth')
torch.save(model3, './model3.pth')
torch.save(model4, './model4.pth') | code |
333270/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
import numpy as np
import xgboost as xgb
ids = test['id']
test = test.drop(['id'], axis=1)
y = train['Demanda_uni_equil']
X = train[test.columns.values]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1729)
test_preds = np.zeros(test.shape[0])
xg_train = xgb.DMatrix(X_train, label=y_train)
xg_test = xgb.DMatrix(X_test)
watchlist = [(xg_train, 'train')] | code |
333270/cell_6 | [
"text_plain_output_84.png",
"text_plain_output_56.png",
"text_plain_output_35.png",
"text_plain_output_43.png",
"text_plain_output_78.png",
"text_plain_output_37.png",
"text_plain_output_90.png",
"text_plain_output_79.png",
"text_plain_output_5.png",
"text_plain_output_75.png",
"text_plain_output_48.png",
"text_plain_output_30.png",
"text_plain_output_73.png",
"text_plain_output_15.png",
"text_plain_output_70.png",
"text_plain_output_9.png",
"text_plain_output_44.png",
"text_plain_output_86.png",
"text_plain_output_40.png",
"text_plain_output_74.png",
"text_plain_output_31.png",
"text_plain_output_20.png",
"text_plain_output_60.png",
"text_plain_output_68.png",
"text_plain_output_4.png",
"text_plain_output_65.png",
"text_plain_output_64.png",
"text_plain_output_13.png",
"text_plain_output_52.png",
"text_plain_output_66.png",
"text_plain_output_45.png",
"text_plain_output_14.png",
"text_plain_output_32.png",
"text_plain_output_88.png",
"text_plain_output_29.png",
"text_plain_output_58.png",
"text_plain_output_49.png",
"text_plain_output_63.png",
"text_plain_output_27.png",
"text_plain_output_76.png",
"text_plain_output_54.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"text_plain_output_57.png",
"text_plain_output_24.png",
"text_plain_output_21.png",
"text_plain_output_47.png",
"text_plain_output_25.png",
"text_plain_output_77.png",
"text_plain_output_18.png",
"text_plain_output_50.png",
"text_plain_output_36.png",
"text_plain_output_87.png",
"text_plain_output_3.png",
"application_vnd.jupyter.stderr_output_19.png",
"text_plain_output_22.png",
"text_plain_output_81.png",
"text_plain_output_69.png",
"text_plain_output_38.png",
"text_plain_output_7.png",
"text_plain_output_91.png",
"text_plain_output_16.png",
"text_plain_output_59.png",
"text_plain_output_71.png",
"text_plain_output_8.png",
"text_plain_output_26.png",
"text_plain_output_41.png",
"text_plain_output_34.png",
"text_plain_output_85.png",
"text_plain_output_42.png",
"text_plain_output_67.png",
"text_plain_output_53.png",
"text_plain_output_23.png",
"text_plain_output_89.png",
"text_plain_output_51.png",
"text_plain_output_28.png",
"text_plain_output_72.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text_plain_output_33.png",
"text_plain_output_39.png",
"text_plain_output_55.png",
"text_plain_output_82.png",
"text_plain_output_80.png",
"text_plain_output_17.png",
"text_plain_output_11.png",
"text_plain_output_12.png",
"text_plain_output_62.png",
"text_plain_output_61.png",
"text_plain_output_83.png",
"application_vnd.jupyter.stderr_output_92.png",
"text_plain_output_46.png"
] | nrows = 5000000
dtype = {'Semana': np.uint8, 'Agencia_ID': np.uint16, 'Canal_ID': np.uint8, 'Ruta_SAK': np.uint16, 'Cliente_ID': np.uint32, 'Producto_ID': np.uint16, 'Demanda_uni_equil': np.uint16}
train_filename = '../input/train.csv'
print('Loading Train... nrows : {0}'.format(nrows))
train.head() | code |
333270/cell_11 | [
"text_html_output_1.png"
] | from sklearn.cross_validation import train_test_split
import math
import numpy as np
import pandas as pd
import xgboost as xgb
def evalerror(preds, dtrain):
labels = dtrain.get_label()
assert len(preds) == len(labels)
labels = labels.tolist()
preds = preds.tolist()
terms_to_sum = [(math.log(labels[i] + 1) - math.log(max(0, preds[i]) + 1)) ** 2.0 for i, pred in enumerate(labels)]
return ('error', (sum(terms_to_sum) * (1.0 / len(preds))) ** 0.5)
ids = test['id']
test = test.drop(['id'], axis=1)
y = train['Demanda_uni_equil']
X = train[test.columns.values]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1729)
params = {}
params['objective'] = 'reg:linear'
params['eta'] = 0.02
params['max_depth'] = 5
params['subsample'] = 0.8
params['colsample_bytree'] = 0.6
params['silent'] = True
params['booster'] = 'gbtree'
test_preds = np.zeros(test.shape[0])
xg_train = xgb.DMatrix(X_train, label=y_train)
xg_test = xgb.DMatrix(X_test)
watchlist = [(xg_train, 'train')]
chunksize = 2500000
num_rounds = 70
for train in pd.read_csv(train_filename, chunksize=chunksize, iterator=True, dtype=dtype, warn_bad_lines=True, engine='c'):
y = train['Demanda_uni_equil']
X = train[test.columns.values]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=1729)
test_preds = np.zeros(test.shape[0])
xg_train = xgb.DMatrix(X_train, label=y_train)
xg_test = xgb.DMatrix(X_test)
watchlist = [(xg_train, 'train')]
xgclassifier = xgb.train(params, xg_train, num_rounds, watchlist, feval=evalerror, early_stopping_rounds=30, verbose_eval=5, xgb_model=xgclassifier) | code |
333270/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.cross_validation import train_test_split
print('Training_Shape:', train.shape)
ids = test['id']
test = test.drop(['id'], axis=1)
y = train['Demanda_uni_equil']
X = train[test.columns.values]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1729)
print('Division_Set_Shapes:', X.shape, y.shape)
print('Validation_Set_Shapes:', X_train.shape, X_test.shape) | code |
333270/cell_16 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from ml_metrics import rmsle
from sklearn.cross_validation import train_test_split
import math
import numpy as np
import pandas as pd
import xgboost as xgb
def evalerror(preds, dtrain):
labels = dtrain.get_label()
assert len(preds) == len(labels)
labels = labels.tolist()
preds = preds.tolist()
terms_to_sum = [(math.log(labels[i] + 1) - math.log(max(0, preds[i]) + 1)) ** 2.0 for i, pred in enumerate(labels)]
return ('error', (sum(terms_to_sum) * (1.0 / len(preds))) ** 0.5)
ids = test['id']
test = test.drop(['id'], axis=1)
y = train['Demanda_uni_equil']
X = train[test.columns.values]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1729)
params = {}
params['objective'] = 'reg:linear'
params['eta'] = 0.02
params['max_depth'] = 5
params['subsample'] = 0.8
params['colsample_bytree'] = 0.6
params['silent'] = True
params['booster'] = 'gbtree'
test_preds = np.zeros(test.shape[0])
xg_train = xgb.DMatrix(X_train, label=y_train)
xg_test = xgb.DMatrix(X_test)
watchlist = [(xg_train, 'train')]
chunksize = 2500000
num_rounds = 70
for train in pd.read_csv(train_filename, chunksize=chunksize, iterator=True, dtype=dtype, warn_bad_lines=True, engine='c'):
y = train['Demanda_uni_equil']
X = train[test.columns.values]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=1729)
test_preds = np.zeros(test.shape[0])
xg_train = xgb.DMatrix(X_train, label=y_train)
xg_test = xgb.DMatrix(X_test)
watchlist = [(xg_train, 'train')]
xgclassifier = xgb.train(params, xg_train, num_rounds, watchlist, feval=evalerror, early_stopping_rounds=30, verbose_eval=5, xgb_model=xgclassifier)
preds = xgclassifier.predict(xg_test, ntree_limit=xgclassifier.best_iteration)
print('RMSLE Score:', rmsle(y_test, preds))
del preds
del y_test | code |
333270/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | num_rounds = 100 | code |
333270/cell_12 | [
"text_html_output_1.png"
] | from sklearn.cross_validation import train_test_split
import math
import numpy as np
import pandas as pd
import xgboost as xgb
def evalerror(preds, dtrain):
labels = dtrain.get_label()
assert len(preds) == len(labels)
labels = labels.tolist()
preds = preds.tolist()
terms_to_sum = [(math.log(labels[i] + 1) - math.log(max(0, preds[i]) + 1)) ** 2.0 for i, pred in enumerate(labels)]
return ('error', (sum(terms_to_sum) * (1.0 / len(preds))) ** 0.5)
ids = test['id']
test = test.drop(['id'], axis=1)
y = train['Demanda_uni_equil']
X = train[test.columns.values]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1729)
params = {}
params['objective'] = 'reg:linear'
params['eta'] = 0.02
params['max_depth'] = 5
params['subsample'] = 0.8
params['colsample_bytree'] = 0.6
params['silent'] = True
params['booster'] = 'gbtree'
test_preds = np.zeros(test.shape[0])
xg_train = xgb.DMatrix(X_train, label=y_train)
xg_test = xgb.DMatrix(X_test)
watchlist = [(xg_train, 'train')]
chunksize = 2500000
num_rounds = 70
for train in pd.read_csv(train_filename, chunksize=chunksize, iterator=True, dtype=dtype, warn_bad_lines=True, engine='c'):
y = train['Demanda_uni_equil']
X = train[test.columns.values]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=1729)
test_preds = np.zeros(test.shape[0])
xg_train = xgb.DMatrix(X_train, label=y_train)
xg_test = xgb.DMatrix(X_test)
watchlist = [(xg_train, 'train')]
xgclassifier = xgb.train(params, xg_train, num_rounds, watchlist, feval=evalerror, early_stopping_rounds=30, verbose_eval=5, xgb_model=xgclassifier)
xgb.plot_importance(xgclassifier) | code |
333270/cell_5 | [
"text_plain_output_5.png",
"text_plain_output_15.png",
"text_plain_output_9.png",
"text_plain_output_20.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"text_plain_output_14.png",
"text_plain_output_27.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"text_plain_output_24.png",
"text_plain_output_21.png",
"text_plain_output_25.png",
"text_plain_output_18.png",
"text_plain_output_3.png",
"text_plain_output_22.png",
"text_plain_output_7.png",
"text_plain_output_16.png",
"text_plain_output_8.png",
"text_plain_output_26.png",
"text_plain_output_23.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text_plain_output_19.png",
"text_plain_output_17.png",
"text_plain_output_11.png",
"text_plain_output_12.png"
] | print('Loading Test...')
dtype_test = {'id': np.uint16, 'Semana': np.uint8, 'Agencia_ID': np.uint16, 'Canal_ID': np.uint8, 'Ruta_SAK': np.uint16, 'Cliente_ID': np.uint32, 'Producto_ID': np.uint16}
test.head() | code |
73069993/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
from sklearn.model_selection import train_test_split
X_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
X_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
X_test.isnull().sum() | code |
73069993/cell_2 | [
"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 pandas as pd
from sklearn.model_selection import train_test_split
X_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
X_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
X_train.head() | code |
73069993/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 |
73069993/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
from sklearn.model_selection import train_test_split
X_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
X_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
X_train.isnull().sum()
y = X_train['target']
features = X_train.drop(['target'], axis=1, inplace=True)
X_num = X_train.select_dtypes(include=['float64'])
X_categorical = X_train.select_dtypes(include=['object'])
X_num.corr() | code |
73069993/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
from sklearn.model_selection import train_test_split
X_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
X_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
X_train.isnull().sum() | code |
1008301/cell_4 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = pd.read_csv(base + 'Labels.csv', usecols=['Label', 'FileName'])
labels['IsBlue'] = labels.Label.str.contains('blue')
labels['Num'] = labels.Label.str.split(' ').str[1].astype(int)
files = [i for i in sorted(os.listdir(base)) if 'Labels' not in i]
fl = pd.read_csv(base + files[0])
fl.info() | code |
1008301/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import os
import numpy as np
import pandas as pd
import seaborn as sns
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
base = '../input/MultiSpectralImages/' | code |
1008301/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = pd.read_csv(base + 'Labels.csv', usecols=['Label', 'FileName'])
labels['IsBlue'] = labels.Label.str.contains('blue')
labels['Num'] = labels.Label.str.split(' ').str[1].astype(int)
labels.head() | code |
1008301/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = pd.read_csv(base + 'Labels.csv', usecols=['Label', 'FileName'])
labels['IsBlue'] = labels.Label.str.contains('blue')
labels['Num'] = labels.Label.str.split(' ').str[1].astype(int)
files = [i for i in sorted(os.listdir(base)) if 'Labels' not in i]
fl = pd.read_csv(base + files[0])
temp = fl[['X', 'Y', 'Channel0']]
tmep.head() | code |
73093411/cell_21 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
tf.__version__
labels = tf.keras.utils.to_categorical(data['Recommended IND'])
output_shape = labels.shape[1]
(labels, output_shape)
v_size = 4000
max_len = 100
e_dim = 64
batch_size = 256
pre_processing_layer = TextVectorization(max_tokens=v_size, output_sequence_length=max_len, name='Notes_preprocessing_layer')
pre_processing_layer.adapt(X_train)
vocab = pre_processing_layer.get_vocabulary()
model = tf.keras.models.Sequential([tf.keras.layers.Input(shape=(1,), dtype=tf.string, name='text_input'), pre_processing_layer, tf.keras.layers.Embedding(input_dim=len(vocab), output_dim=e_dim), tf.keras.layers.Masking(mask_value=0), tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)), tf.keras.layers.Dense(16, activation='relu'), tf.keras.layers.Dense(output_shape, activation='softmax')])
metrics = [tf.keras.metrics.CategoricalAccuracy()]
model.summary()
model.compile(optimizer='adam', loss=tf.keras.losses.CategoricalCrossentropy(from_logits=False), metrics=metrics)
train_dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train))
valid_dataset = tf.data.Dataset.from_tensor_slices((X_val, y_val))
options = tf.data.Options()
options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.DATA
train_dataset = train_dataset.shuffle(X_train.shape[0]).batch(batch_size).with_options(options)
valid_dataset = valid_dataset.batch(batch_size).with_options(options)
model.fit(train_dataset, validation_data=valid_dataset, epochs=5, verbose=1)
val_probs = model.predict(valid_dataset)
val_preds = tf.argmax(test_probs, axis=1)
y_val
test_labels = tf.convert_to_tensor(list(test_labels), dtype=test_preds.dtype)
cm = tf.math.confusion_matrix(test_labels, val_preds) | code |
73093411/cell_13 | [
"text_plain_output_1.png"
] | print(X_train.shape, y_train.shape)
print(X_val.shape, y_val.shape) | code |
73093411/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
data['Recommended IND'] | code |
73093411/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape | code |
73093411/cell_20 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
tf.__version__
labels = tf.keras.utils.to_categorical(data['Recommended IND'])
output_shape = labels.shape[1]
(labels, output_shape)
v_size = 4000
max_len = 100
e_dim = 64
batch_size = 256
pre_processing_layer = TextVectorization(max_tokens=v_size, output_sequence_length=max_len, name='Notes_preprocessing_layer')
pre_processing_layer.adapt(X_train)
vocab = pre_processing_layer.get_vocabulary()
model = tf.keras.models.Sequential([tf.keras.layers.Input(shape=(1,), dtype=tf.string, name='text_input'), pre_processing_layer, tf.keras.layers.Embedding(input_dim=len(vocab), output_dim=e_dim), tf.keras.layers.Masking(mask_value=0), tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)), tf.keras.layers.Dense(16, activation='relu'), tf.keras.layers.Dense(output_shape, activation='softmax')])
metrics = [tf.keras.metrics.CategoricalAccuracy()]
model.summary()
model.compile(optimizer='adam', loss=tf.keras.losses.CategoricalCrossentropy(from_logits=False), metrics=metrics)
train_dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train))
valid_dataset = tf.data.Dataset.from_tensor_slices((X_val, y_val))
options = tf.data.Options()
options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.DATA
train_dataset = train_dataset.shuffle(X_train.shape[0]).batch(batch_size).with_options(options)
valid_dataset = valid_dataset.batch(batch_size).with_options(options)
model.fit(train_dataset, validation_data=valid_dataset, epochs=5, verbose=1) | code |
73093411/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
data['Review Text'].str.split().apply(lambda x: len(x)).describe() | code |
73093411/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.head() | code |
73093411/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
X = data['Review Text'].values
X | code |
73093411/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 |
73093411/cell_18 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
tf.__version__
labels = tf.keras.utils.to_categorical(data['Recommended IND'])
output_shape = labels.shape[1]
(labels, output_shape)
v_size = 4000
max_len = 100
e_dim = 64
batch_size = 256
pre_processing_layer = TextVectorization(max_tokens=v_size, output_sequence_length=max_len, name='Notes_preprocessing_layer')
pre_processing_layer.adapt(X_train)
vocab = pre_processing_layer.get_vocabulary()
model = tf.keras.models.Sequential([tf.keras.layers.Input(shape=(1,), dtype=tf.string, name='text_input'), pre_processing_layer, tf.keras.layers.Embedding(input_dim=len(vocab), output_dim=e_dim), tf.keras.layers.Masking(mask_value=0), tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)), tf.keras.layers.Dense(16, activation='relu'), tf.keras.layers.Dense(output_shape, activation='softmax')])
metrics = [tf.keras.metrics.CategoricalAccuracy()]
model.summary()
model.compile(optimizer='adam', loss=tf.keras.losses.CategoricalCrossentropy(from_logits=False), metrics=metrics)
print('Ready to Train') | code |
73093411/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import tensorflow as tf
tf.__version__ | code |
73093411/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape | code |
73093411/cell_17 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
tf.__version__
labels = tf.keras.utils.to_categorical(data['Recommended IND'])
output_shape = labels.shape[1]
(labels, output_shape)
v_size = 4000
max_len = 100
e_dim = 64
batch_size = 256
pre_processing_layer = TextVectorization(max_tokens=v_size, output_sequence_length=max_len, name='Notes_preprocessing_layer')
pre_processing_layer.adapt(X_train)
vocab = pre_processing_layer.get_vocabulary()
model = tf.keras.models.Sequential([tf.keras.layers.Input(shape=(1,), dtype=tf.string, name='text_input'), pre_processing_layer, tf.keras.layers.Embedding(input_dim=len(vocab), output_dim=e_dim), tf.keras.layers.Masking(mask_value=0), tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)), tf.keras.layers.Dense(16, activation='relu'), tf.keras.layers.Dense(output_shape, activation='softmax')])
metrics = [tf.keras.metrics.CategoricalAccuracy()]
model.summary() | code |
73093411/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
tf.__version__
labels = tf.keras.utils.to_categorical(data['Recommended IND'])
output_shape = labels.shape[1]
(labels, output_shape) | code |
73093411/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/womens-ecommerce-clothing-reviews/Womens Clothing E-Commerce Reviews.csv', index_col=0)
data.shape
data = data[~data['Review Text'].isnull()]
data.shape
data['Recommended IND'].isnull().sum() | code |
130022433/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime
from scipy import stats
from scipy.stats import skew, boxcox_normmax, norm
import matplotlib.gridspec as gridspec
from matplotlib.ticker import MaxNLocator
import warnings
pd.options.display.max_columns = 250
pd.options.display.max_rows = 250
warnings.filterwarnings('ignore')
plt.style.use('fivethirtyeight')
import os
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
test.describe().T | code |
130022433/cell_6 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime
from scipy import stats
from scipy.stats import skew, boxcox_normmax, norm
import matplotlib.gridspec as gridspec
from matplotlib.ticker import MaxNLocator
import warnings
pd.options.display.max_columns = 250
pd.options.display.max_rows = 250
warnings.filterwarnings('ignore')
plt.style.use('fivethirtyeight')
import os
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.head(3) | code |
130022433/cell_2 | [
"text_plain_output_1.png"
] | !pip install --upgrade scikit-learn
# Did this to use latest regressors from sklearn... | code |
130022433/cell_7 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime
from scipy import stats
from scipy.stats import skew, boxcox_normmax, norm
import matplotlib.gridspec as gridspec
from matplotlib.ticker import MaxNLocator
import warnings
pd.options.display.max_columns = 250
pd.options.display.max_rows = 250
warnings.filterwarnings('ignore')
plt.style.use('fivethirtyeight')
import os
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
test.head(3) | code |
130022433/cell_8 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime
from scipy import stats
from scipy.stats import skew, boxcox_normmax, norm
import matplotlib.gridspec as gridspec
from matplotlib.ticker import MaxNLocator
import warnings
pd.options.display.max_columns = 250
pd.options.display.max_rows = 250
warnings.filterwarnings('ignore')
plt.style.use('fivethirtyeight')
import os
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.describe().T | code |
130022433/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime
from scipy import stats
from scipy.stats import skew, boxcox_normmax, norm
import matplotlib.gridspec as gridspec
from matplotlib.ticker import MaxNLocator
import warnings
pd.options.display.max_columns = 250
pd.options.display.max_rows = 250
warnings.filterwarnings('ignore')
plt.style.use('fivethirtyeight')
import os
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
print(train.shape)
print(test.shape) | code |
72071082/cell_21 | [
"text_plain_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
data = pd.read_csv('/kaggle/input/automobile-dataset/Automobile_data.csv')
data.isnull().sum()
data['price'] = data['price'].replace('?', np.NaN)
data['normalized-losses'] = data['normalized-losses'].replace('?', np.NaN)
data['num-of-doors'] = data['num-of-doors'].replace('?', np.NaN)
data['stroke'] = data['stroke'].replace('?', np.NaN)
data['horsepower'] = data['horsepower'].replace('?', np.NaN)
data['peak-rpm'] = data['peak-rpm'].replace('?', np.NaN)
data['bore'] = data['bore'].replace('?', np.NaN)
data['num-of-doors'] = data['num-of-doors'].replace('?', np.NaN)
data = data[data['price'].notna()]
data.select_dtypes(['object']).columns
numerical_cols = data.select_dtypes(['int32', 'int64', 'float']).columns
numerical_cols
corr_matrix = data[numerical_cols].corr()
plt.figure(figsize=(10, 10))
sns.heatmap(corr_matrix['price'].sort_values(ascending=False).to_frame()[1:], square=True, annot=True)
plt.show() | code |
72071082/cell_23 | [
"text_plain_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
data = pd.read_csv('/kaggle/input/automobile-dataset/Automobile_data.csv')
data.isnull().sum()
data['price'] = data['price'].replace('?', np.NaN)
data['normalized-losses'] = data['normalized-losses'].replace('?', np.NaN)
data['num-of-doors'] = data['num-of-doors'].replace('?', np.NaN)
data['stroke'] = data['stroke'].replace('?', np.NaN)
data['horsepower'] = data['horsepower'].replace('?', np.NaN)
data['peak-rpm'] = data['peak-rpm'].replace('?', np.NaN)
data['bore'] = data['bore'].replace('?', np.NaN)
data['num-of-doors'] = data['num-of-doors'].replace('?', np.NaN)
data = data[data['price'].notna()]
data.select_dtypes(['object']).columns
numerical_cols = data.select_dtypes(['int32', 'int64', 'float']).columns
numerical_cols
corr_matrix = data[numerical_cols].corr()
plt.figure(figsize=(8, 5))
sns.scatterplot(data=data, x=data['engine-size'], y=data['price'])
plt.xlabel('Engine Size', fontsize=12)
plt.ylabel('Price', fontsize=12)
plt.title('Engine size vs Price', weight='bold', fontsize=12)
plt.show()
plt.figure(figsize=(8, 5))
sns.scatterplot(data=data, x='curb-weight', y='price')
plt.xlabel('Curb weight', fontsize=12)
plt.ylabel('Price', fontsize=12)
plt.title('Curb weight vs Price', weight='bold', fontsize=12)
plt.show()
plt.figure(figsize=(8, 5))
sns.scatterplot(data=data, x='horsepower', y='price')
plt.xlabel('Horsepower', fontsize=12)
plt.ylabel('Price', fontsize=12)
plt.title('HorsePower vs Price', weight='bold', fontsize=12)
plt.show() | code |
72071082/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/automobile-dataset/Automobile_data.csv')
data.isnull().sum()
data['price'] = data['price'].replace('?', np.NaN)
data['normalized-losses'] = data['normalized-losses'].replace('?', np.NaN)
data['num-of-doors'] = data['num-of-doors'].replace('?', np.NaN)
data['stroke'] = data['stroke'].replace('?', np.NaN)
data['horsepower'] = data['horsepower'].replace('?', np.NaN)
data['peak-rpm'] = data['peak-rpm'].replace('?', np.NaN)
data['bore'] = data['bore'].replace('?', np.NaN)
data['num-of-doors'] = data['num-of-doors'].replace('?', np.NaN)
data = data[data['price'].notna()]
data.select_dtypes(['object']).columns
numerical_cols = data.select_dtypes(['int32', 'int64', 'float']).columns
numerical_cols | code |
72071082/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/automobile-dataset/Automobile_data.csv')
data.head() | code |
72071082/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
72071082/cell_28 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | 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('/kaggle/input/automobile-dataset/Automobile_data.csv')
data.isnull().sum()
data['price'] = data['price'].replace('?', np.NaN)
data['normalized-losses'] = data['normalized-losses'].replace('?', np.NaN)
data['num-of-doors'] = data['num-of-doors'].replace('?', np.NaN)
data['stroke'] = data['stroke'].replace('?', np.NaN)
data['horsepower'] = data['horsepower'].replace('?', np.NaN)
data['peak-rpm'] = data['peak-rpm'].replace('?', np.NaN)
data['bore'] = data['bore'].replace('?', np.NaN)
data['num-of-doors'] = data['num-of-doors'].replace('?', np.NaN)
data = data[data['price'].notna()]
data.select_dtypes(['object']).columns
numerical_cols = data.select_dtypes(['int32', 'int64', 'float']).columns
numerical_cols
categorical_cols = data.select_dtypes(['object']).columns
categorical_cols
print(f'Unqiue value counts of fuel-type are: ', '\n', data['fuel-type'].value_counts())
print()
print(f'Unqiue value counts of aspiration are', '\n', data['aspiration'].value_counts())
print()
print(f'Unqiue value counts of num-of-doors are', '\n', data['num-of-doors'].value_counts())
print()
print(f'Unqiue value counts of engine-location are', '\n', data['engine-location'].value_counts()) | code |
72071082/cell_8 | [
"image_output_1.png"
] | 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('/kaggle/input/automobile-dataset/Automobile_data.csv')
data.isnull().sum()
data['price'] = data['price'].replace('?', np.NaN)
data['normalized-losses'] = data['normalized-losses'].replace('?', np.NaN)
data['num-of-doors'] = data['num-of-doors'].replace('?', np.NaN)
data['stroke'] = data['stroke'].replace('?', np.NaN)
data['horsepower'] = data['horsepower'].replace('?', np.NaN)
data['peak-rpm'] = data['peak-rpm'].replace('?', np.NaN)
data['bore'] = data['bore'].replace('?', np.NaN)
data['num-of-doors'] = data['num-of-doors'].replace('?', np.NaN)
data = data[data['price'].notna()]
data.info() | code |
72071082/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/automobile-dataset/Automobile_data.csv')
data.isnull().sum() | code |
72071082/cell_24 | [
"text_plain_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
data = pd.read_csv('/kaggle/input/automobile-dataset/Automobile_data.csv')
data.isnull().sum()
data['price'] = data['price'].replace('?', np.NaN)
data['normalized-losses'] = data['normalized-losses'].replace('?', np.NaN)
data['num-of-doors'] = data['num-of-doors'].replace('?', np.NaN)
data['stroke'] = data['stroke'].replace('?', np.NaN)
data['horsepower'] = data['horsepower'].replace('?', np.NaN)
data['peak-rpm'] = data['peak-rpm'].replace('?', np.NaN)
data['bore'] = data['bore'].replace('?', np.NaN)
data['num-of-doors'] = data['num-of-doors'].replace('?', np.NaN)
data = data[data['price'].notna()]
data.select_dtypes(['object']).columns
numerical_cols = data.select_dtypes(['int32', 'int64', 'float']).columns
numerical_cols
corr_matrix = data[numerical_cols].corr()
plt.scatter(data['symboling'], data['price'])
plt.xlabel('Symboling')
plt.ylabel('Price')
plt.title('Symboling vs Price', weight='bold', size=12)
plt.show() | code |
72071082/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/automobile-dataset/Automobile_data.csv')
data.isnull().sum()
data['price'] = data['price'].replace('?', np.NaN)
data['normalized-losses'] = data['normalized-losses'].replace('?', np.NaN)
data['num-of-doors'] = data['num-of-doors'].replace('?', np.NaN)
data['stroke'] = data['stroke'].replace('?', np.NaN)
data['horsepower'] = data['horsepower'].replace('?', np.NaN)
data['peak-rpm'] = data['peak-rpm'].replace('?', np.NaN)
data['bore'] = data['bore'].replace('?', np.NaN)
data['num-of-doors'] = data['num-of-doors'].replace('?', np.NaN)
data = data[data['price'].notna()]
data.select_dtypes(['object']).columns
data['num-of-doors'].value_counts() | code |
72071082/cell_10 | [
"text_html_output_1.png"
] | 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('/kaggle/input/automobile-dataset/Automobile_data.csv')
data.isnull().sum()
data['price'] = data['price'].replace('?', np.NaN)
data['normalized-losses'] = data['normalized-losses'].replace('?', np.NaN)
data['num-of-doors'] = data['num-of-doors'].replace('?', np.NaN)
data['stroke'] = data['stroke'].replace('?', np.NaN)
data['horsepower'] = data['horsepower'].replace('?', np.NaN)
data['peak-rpm'] = data['peak-rpm'].replace('?', np.NaN)
data['bore'] = data['bore'].replace('?', np.NaN)
data['num-of-doors'] = data['num-of-doors'].replace('?', np.NaN)
data = data[data['price'].notna()]
data.select_dtypes(['object']).columns | code |
72071082/cell_27 | [
"image_output_1.png"
] | 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('/kaggle/input/automobile-dataset/Automobile_data.csv')
data.isnull().sum()
data['price'] = data['price'].replace('?', np.NaN)
data['normalized-losses'] = data['normalized-losses'].replace('?', np.NaN)
data['num-of-doors'] = data['num-of-doors'].replace('?', np.NaN)
data['stroke'] = data['stroke'].replace('?', np.NaN)
data['horsepower'] = data['horsepower'].replace('?', np.NaN)
data['peak-rpm'] = data['peak-rpm'].replace('?', np.NaN)
data['bore'] = data['bore'].replace('?', np.NaN)
data['num-of-doors'] = data['num-of-doors'].replace('?', np.NaN)
data = data[data['price'].notna()]
data.select_dtypes(['object']).columns
numerical_cols = data.select_dtypes(['int32', 'int64', 'float']).columns
numerical_cols
categorical_cols = data.select_dtypes(['object']).columns
categorical_cols | code |
72071082/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/automobile-dataset/Automobile_data.csv')
data.isnull().sum()
print('Number of ? in columns are')
for col in data.columns:
if len(data[data[col] == '?']) > 0:
print(col, 'has ->', len(data[data[col] == '?'])) | code |
32068084/cell_42 | [
"text_plain_output_1.png"
] | from lightgbm import LGBMClassifier
from sklearn import decomposition
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import PassiveAggressiveClassifier
from sklearn.linear_model import Perceptron
from sklearn.linear_model import RidgeClassifierCV
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
import pandas as pd
train_df_final = pd.read_csv('../input/pumpitup-challenge-dataset/train_df_final.csv')
X_test_final = pd.read_csv('../input/pumpitup-challenge-dataset/X_test_final.csv')
X_test_final.shape
sc = ss()
X_train = sc.fit_transform(X_train)
X_valid = sc.transform(X_valid)
X_test = sc.transform(X_test_final)
pca = decomposition.PCA(0.95)
pca.fit(X_train)
pca.n_components_
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
X_valid_pca = pca.transform(X_valid)
decision_tree = DecisionTreeClassifier()
decision_tree.fit(X_train, y_train)
y_pred = decision_tree.predict(X_valid)
acc_decision_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_decision_tree
extra_tree = DecisionTreeClassifier()
extra_tree.fit(X_train, y_train)
y_pred = extra_tree.predict(X_valid)
acc_extra_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_extra_tree
rfc = RandomForestClassifier(criterion='entropy', n_estimators=1000, min_samples_split=8, random_state=42, verbose=5)
rfc.fit(X_train, y_train)
y_pred = rfc.predict(X_valid)
acc_rfc = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_rfc
GB = GradientBoostingClassifier(n_estimators=100, learning_rate=0.075, max_depth=13, max_features=0.5, min_samples_leaf=14, verbose=5)
GB.fit(X_train, y_train)
y_pred = GB.predict(X_valid)
acc_GB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_GB
HGB = HistGradientBoostingClassifier(learning_rate=0.075, loss='categorical_crossentropy', max_depth=8, min_samples_leaf=15)
HGB = HGB.fit(X_train_pca, y_train)
y_pred = HGB.predict(X_valid_pca)
acc_HGB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_HGB
LGB = LGBMClassifier(objective='multiclass', learning_rate=0.75, num_iterations=100, num_leaves=50, random_state=123, max_depth=8)
LGB.fit(X_train, y_train)
y_pred = LGB.predict(X_valid)
acc_LGB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_LGB
AB = AdaBoostClassifier(n_estimators=100, learning_rate=0.075)
AB.fit(X_train, y_train)
y_pred = AB.predict(X_valid)
acc_AB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_AB
BC = BaggingClassifier(n_estimators=100)
BC.fit(X_train_pca, y_train)
y_pred = BC.predict(X_valid_pca)
acc_BC = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_BC
xgb = XGBClassifier(n_estimators=1000, learning_rate=0.05, n_jobs=5)
xgb.fit(X_train, y_train, early_stopping_rounds=5, eval_set=[(X_valid, y_valid)], verbose=False)
y_pred = xgb.predict(X_valid)
acc_xgb = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_xgb
ETC = ExtraTreesClassifier(n_estimators=100)
ETC.fit(X_train, y_train)
y_pred = ETC.predict(X_valid)
acc_ETC = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_ETC
LG = LogisticRegression(solver='lbfgs', multi_class='multinomial')
LG.fit(X_train, y_train)
y_pred = LG.predict(X_valid)
acc_LG = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_LG
PAC = PassiveAggressiveClassifier()
PAC.fit(X_train, y_train)
y_pred = PAC.predict(X_valid)
acc_PAC = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_PAC
RC = RidgeClassifierCV()
RC.fit(X_train, y_train)
y_pred = RC.predict(X_valid)
acc_RC = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_RC
P = Perceptron()
P.fit(X_train, y_train)
y_pred = P.predict(X_valid)
acc_P = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_P | code |
32068084/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
train_df_final = pd.read_csv('../input/pumpitup-challenge-dataset/train_df_final.csv')
X_test_final = pd.read_csv('../input/pumpitup-challenge-dataset/X_test_final.csv')
X_test_final.shape
sc = ss()
X_train = sc.fit_transform(X_train)
X_valid = sc.transform(X_valid)
X_test = sc.transform(X_test_final)
decision_tree = DecisionTreeClassifier()
decision_tree.fit(X_train, y_train)
y_pred = decision_tree.predict(X_valid)
acc_decision_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_decision_tree
extra_tree = DecisionTreeClassifier()
extra_tree.fit(X_train, y_train)
y_pred = extra_tree.predict(X_valid)
acc_extra_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_extra_tree | code |
32068084/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df_final = pd.read_csv('../input/pumpitup-challenge-dataset/train_df_final.csv')
X_test_final = pd.read_csv('../input/pumpitup-challenge-dataset/X_test_final.csv')
train_df_final.shape
X = train_df_final.drop('label', axis=1)
y = train_df_final['label']
X.isnull().values.any() | code |
32068084/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
train_df_final = pd.read_csv('../input/pumpitup-challenge-dataset/train_df_final.csv')
X_test_final = pd.read_csv('../input/pumpitup-challenge-dataset/X_test_final.csv')
X_test_final.shape
sc = ss()
X_train = sc.fit_transform(X_train)
X_valid = sc.transform(X_valid)
X_test = sc.transform(X_test_final)
decision_tree = DecisionTreeClassifier()
decision_tree.fit(X_train, y_train)
y_pred = decision_tree.predict(X_valid)
acc_decision_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_decision_tree
extra_tree = DecisionTreeClassifier()
extra_tree.fit(X_train, y_train)
y_pred = extra_tree.predict(X_valid)
acc_extra_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_extra_tree
rfc = RandomForestClassifier(criterion='entropy', n_estimators=1000, min_samples_split=8, random_state=42, verbose=5)
rfc.fit(X_train, y_train)
y_pred = rfc.predict(X_valid)
acc_rfc = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_rfc
GB = GradientBoostingClassifier(n_estimators=100, learning_rate=0.075, max_depth=13, max_features=0.5, min_samples_leaf=14, verbose=5)
GB.fit(X_train, y_train)
y_pred = GB.predict(X_valid)
acc_GB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_GB | code |
32068084/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df_final = pd.read_csv('../input/pumpitup-challenge-dataset/train_df_final.csv')
X_test_final = pd.read_csv('../input/pumpitup-challenge-dataset/X_test_final.csv')
X_test_final.shape | code |
32068084/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
train_df_final = pd.read_csv('../input/pumpitup-challenge-dataset/train_df_final.csv')
X_test_final = pd.read_csv('../input/pumpitup-challenge-dataset/X_test_final.csv')
X_test_final.shape
sc = ss()
X_train = sc.fit_transform(X_train)
X_valid = sc.transform(X_valid)
X_test = sc.transform(X_test_final)
decision_tree = DecisionTreeClassifier()
decision_tree.fit(X_train, y_train)
y_pred = decision_tree.predict(X_valid)
acc_decision_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_decision_tree
extra_tree = DecisionTreeClassifier()
extra_tree.fit(X_train, y_train)
y_pred = extra_tree.predict(X_valid)
acc_extra_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_extra_tree
rfc = RandomForestClassifier(criterion='entropy', n_estimators=1000, min_samples_split=8, random_state=42, verbose=5)
rfc.fit(X_train, y_train)
y_pred = rfc.predict(X_valid)
acc_rfc = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_rfc | code |
32068084/cell_30 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from lightgbm import LGBMClassifier
from sklearn import decomposition
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
train_df_final = pd.read_csv('../input/pumpitup-challenge-dataset/train_df_final.csv')
X_test_final = pd.read_csv('../input/pumpitup-challenge-dataset/X_test_final.csv')
X_test_final.shape
sc = ss()
X_train = sc.fit_transform(X_train)
X_valid = sc.transform(X_valid)
X_test = sc.transform(X_test_final)
pca = decomposition.PCA(0.95)
pca.fit(X_train)
pca.n_components_
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
X_valid_pca = pca.transform(X_valid)
decision_tree = DecisionTreeClassifier()
decision_tree.fit(X_train, y_train)
y_pred = decision_tree.predict(X_valid)
acc_decision_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_decision_tree
extra_tree = DecisionTreeClassifier()
extra_tree.fit(X_train, y_train)
y_pred = extra_tree.predict(X_valid)
acc_extra_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_extra_tree
rfc = RandomForestClassifier(criterion='entropy', n_estimators=1000, min_samples_split=8, random_state=42, verbose=5)
rfc.fit(X_train, y_train)
y_pred = rfc.predict(X_valid)
acc_rfc = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_rfc
GB = GradientBoostingClassifier(n_estimators=100, learning_rate=0.075, max_depth=13, max_features=0.5, min_samples_leaf=14, verbose=5)
GB.fit(X_train, y_train)
y_pred = GB.predict(X_valid)
acc_GB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_GB
HGB = HistGradientBoostingClassifier(learning_rate=0.075, loss='categorical_crossentropy', max_depth=8, min_samples_leaf=15)
HGB = HGB.fit(X_train_pca, y_train)
y_pred = HGB.predict(X_valid_pca)
acc_HGB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_HGB
LGB = LGBMClassifier(objective='multiclass', learning_rate=0.75, num_iterations=100, num_leaves=50, random_state=123, max_depth=8)
LGB.fit(X_train, y_train)
y_pred = LGB.predict(X_valid)
acc_LGB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_LGB
AB = AdaBoostClassifier(n_estimators=100, learning_rate=0.075)
AB.fit(X_train, y_train)
y_pred = AB.predict(X_valid)
acc_AB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_AB | code |
32068084/cell_33 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from lightgbm import LGBMClassifier
from sklearn import decomposition
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
import pandas as pd
train_df_final = pd.read_csv('../input/pumpitup-challenge-dataset/train_df_final.csv')
X_test_final = pd.read_csv('../input/pumpitup-challenge-dataset/X_test_final.csv')
X_test_final.shape
sc = ss()
X_train = sc.fit_transform(X_train)
X_valid = sc.transform(X_valid)
X_test = sc.transform(X_test_final)
pca = decomposition.PCA(0.95)
pca.fit(X_train)
pca.n_components_
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
X_valid_pca = pca.transform(X_valid)
decision_tree = DecisionTreeClassifier()
decision_tree.fit(X_train, y_train)
y_pred = decision_tree.predict(X_valid)
acc_decision_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_decision_tree
extra_tree = DecisionTreeClassifier()
extra_tree.fit(X_train, y_train)
y_pred = extra_tree.predict(X_valid)
acc_extra_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_extra_tree
rfc = RandomForestClassifier(criterion='entropy', n_estimators=1000, min_samples_split=8, random_state=42, verbose=5)
rfc.fit(X_train, y_train)
y_pred = rfc.predict(X_valid)
acc_rfc = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_rfc
GB = GradientBoostingClassifier(n_estimators=100, learning_rate=0.075, max_depth=13, max_features=0.5, min_samples_leaf=14, verbose=5)
GB.fit(X_train, y_train)
y_pred = GB.predict(X_valid)
acc_GB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_GB
HGB = HistGradientBoostingClassifier(learning_rate=0.075, loss='categorical_crossentropy', max_depth=8, min_samples_leaf=15)
HGB = HGB.fit(X_train_pca, y_train)
y_pred = HGB.predict(X_valid_pca)
acc_HGB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_HGB
LGB = LGBMClassifier(objective='multiclass', learning_rate=0.75, num_iterations=100, num_leaves=50, random_state=123, max_depth=8)
LGB.fit(X_train, y_train)
y_pred = LGB.predict(X_valid)
acc_LGB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_LGB
AB = AdaBoostClassifier(n_estimators=100, learning_rate=0.075)
AB.fit(X_train, y_train)
y_pred = AB.predict(X_valid)
acc_AB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_AB
BC = BaggingClassifier(n_estimators=100)
BC.fit(X_train_pca, y_train)
y_pred = BC.predict(X_valid_pca)
acc_BC = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_BC
xgb = XGBClassifier(n_estimators=1000, learning_rate=0.05, n_jobs=5)
xgb.fit(X_train, y_train, early_stopping_rounds=5, eval_set=[(X_valid, y_valid)], verbose=False)
y_pred = xgb.predict(X_valid)
acc_xgb = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_xgb | code |
32068084/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
train_df_final = pd.read_csv('../input/pumpitup-challenge-dataset/train_df_final.csv')
X_test_final = pd.read_csv('../input/pumpitup-challenge-dataset/X_test_final.csv')
X_test_final.shape
sc = ss()
X_train = sc.fit_transform(X_train)
X_valid = sc.transform(X_valid)
X_test = sc.transform(X_test_final)
decision_tree = DecisionTreeClassifier()
decision_tree.fit(X_train, y_train)
y_pred = decision_tree.predict(X_valid)
acc_decision_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_decision_tree | code |
32068084/cell_40 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from lightgbm import LGBMClassifier
from sklearn import decomposition
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import PassiveAggressiveClassifier
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
import pandas as pd
train_df_final = pd.read_csv('../input/pumpitup-challenge-dataset/train_df_final.csv')
X_test_final = pd.read_csv('../input/pumpitup-challenge-dataset/X_test_final.csv')
X_test_final.shape
sc = ss()
X_train = sc.fit_transform(X_train)
X_valid = sc.transform(X_valid)
X_test = sc.transform(X_test_final)
pca = decomposition.PCA(0.95)
pca.fit(X_train)
pca.n_components_
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
X_valid_pca = pca.transform(X_valid)
decision_tree = DecisionTreeClassifier()
decision_tree.fit(X_train, y_train)
y_pred = decision_tree.predict(X_valid)
acc_decision_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_decision_tree
extra_tree = DecisionTreeClassifier()
extra_tree.fit(X_train, y_train)
y_pred = extra_tree.predict(X_valid)
acc_extra_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_extra_tree
rfc = RandomForestClassifier(criterion='entropy', n_estimators=1000, min_samples_split=8, random_state=42, verbose=5)
rfc.fit(X_train, y_train)
y_pred = rfc.predict(X_valid)
acc_rfc = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_rfc
GB = GradientBoostingClassifier(n_estimators=100, learning_rate=0.075, max_depth=13, max_features=0.5, min_samples_leaf=14, verbose=5)
GB.fit(X_train, y_train)
y_pred = GB.predict(X_valid)
acc_GB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_GB
HGB = HistGradientBoostingClassifier(learning_rate=0.075, loss='categorical_crossentropy', max_depth=8, min_samples_leaf=15)
HGB = HGB.fit(X_train_pca, y_train)
y_pred = HGB.predict(X_valid_pca)
acc_HGB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_HGB
LGB = LGBMClassifier(objective='multiclass', learning_rate=0.75, num_iterations=100, num_leaves=50, random_state=123, max_depth=8)
LGB.fit(X_train, y_train)
y_pred = LGB.predict(X_valid)
acc_LGB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_LGB
AB = AdaBoostClassifier(n_estimators=100, learning_rate=0.075)
AB.fit(X_train, y_train)
y_pred = AB.predict(X_valid)
acc_AB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_AB
BC = BaggingClassifier(n_estimators=100)
BC.fit(X_train_pca, y_train)
y_pred = BC.predict(X_valid_pca)
acc_BC = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_BC
xgb = XGBClassifier(n_estimators=1000, learning_rate=0.05, n_jobs=5)
xgb.fit(X_train, y_train, early_stopping_rounds=5, eval_set=[(X_valid, y_valid)], verbose=False)
y_pred = xgb.predict(X_valid)
acc_xgb = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_xgb
ETC = ExtraTreesClassifier(n_estimators=100)
ETC.fit(X_train, y_train)
y_pred = ETC.predict(X_valid)
acc_ETC = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_ETC
LG = LogisticRegression(solver='lbfgs', multi_class='multinomial')
LG.fit(X_train, y_train)
y_pred = LG.predict(X_valid)
acc_LG = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_LG
PAC = PassiveAggressiveClassifier()
PAC.fit(X_train, y_train)
y_pred = PAC.predict(X_valid)
acc_PAC = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_PAC | code |
32068084/cell_39 | [
"text_plain_output_1.png"
] | from lightgbm import LGBMClassifier
from sklearn import decomposition
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
import pandas as pd
train_df_final = pd.read_csv('../input/pumpitup-challenge-dataset/train_df_final.csv')
X_test_final = pd.read_csv('../input/pumpitup-challenge-dataset/X_test_final.csv')
X_test_final.shape
train_df_final.shape
X = train_df_final.drop('label', axis=1)
y = train_df_final['label']
sc = ss()
X_train = sc.fit_transform(X_train)
X_valid = sc.transform(X_valid)
X_test = sc.transform(X_test_final)
pca = decomposition.PCA(0.95)
pca.fit(X_train)
pca.n_components_
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
X_valid_pca = pca.transform(X_valid)
decision_tree = DecisionTreeClassifier()
decision_tree.fit(X_train, y_train)
y_pred = decision_tree.predict(X_valid)
acc_decision_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_decision_tree
extra_tree = DecisionTreeClassifier()
extra_tree.fit(X_train, y_train)
y_pred = extra_tree.predict(X_valid)
acc_extra_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_extra_tree
rfc = RandomForestClassifier(criterion='entropy', n_estimators=1000, min_samples_split=8, random_state=42, verbose=5)
rfc.fit(X_train, y_train)
y_pred = rfc.predict(X_valid)
acc_rfc = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_rfc
GB = GradientBoostingClassifier(n_estimators=100, learning_rate=0.075, max_depth=13, max_features=0.5, min_samples_leaf=14, verbose=5)
GB.fit(X_train, y_train)
y_pred = GB.predict(X_valid)
acc_GB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_GB
HGB = HistGradientBoostingClassifier(learning_rate=0.075, loss='categorical_crossentropy', max_depth=8, min_samples_leaf=15)
HGB = HGB.fit(X_train_pca, y_train)
y_pred = HGB.predict(X_valid_pca)
acc_HGB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_HGB
LGB = LGBMClassifier(objective='multiclass', learning_rate=0.75, num_iterations=100, num_leaves=50, random_state=123, max_depth=8)
LGB.fit(X_train, y_train)
y_pred = LGB.predict(X_valid)
acc_LGB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_LGB
AB = AdaBoostClassifier(n_estimators=100, learning_rate=0.075)
AB.fit(X_train, y_train)
y_pred = AB.predict(X_valid)
acc_AB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_AB
BC = BaggingClassifier(n_estimators=100)
BC.fit(X_train_pca, y_train)
y_pred = BC.predict(X_valid_pca)
acc_BC = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_BC
xgb = XGBClassifier(n_estimators=1000, learning_rate=0.05, n_jobs=5)
xgb.fit(X_train, y_train, early_stopping_rounds=5, eval_set=[(X_valid, y_valid)], verbose=False)
y_pred = xgb.predict(X_valid)
acc_xgb = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_xgb
ETC = ExtraTreesClassifier(n_estimators=100)
ETC.fit(X_train, y_train)
y_pred = ETC.predict(X_valid)
acc_ETC = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_ETC
LG = LogisticRegression(solver='lbfgs', multi_class='multinomial')
LG.fit(X_train, y_train)
y_pred = LG.predict(X_valid)
acc_LG = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_LG
coeff_df = pd.DataFrame(train_df_final.columns.delete(0))
coeff_df.columns = ['Feature']
coeff_df['Correlation'] = pd.Series(LG.coef_[0])
coeff_df.sort_values(by='Correlation', ascending=False) | code |
32068084/cell_41 | [
"text_html_output_1.png"
] | from lightgbm import LGBMClassifier
from sklearn import decomposition
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import PassiveAggressiveClassifier
from sklearn.linear_model import RidgeClassifierCV
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
import pandas as pd
train_df_final = pd.read_csv('../input/pumpitup-challenge-dataset/train_df_final.csv')
X_test_final = pd.read_csv('../input/pumpitup-challenge-dataset/X_test_final.csv')
X_test_final.shape
sc = ss()
X_train = sc.fit_transform(X_train)
X_valid = sc.transform(X_valid)
X_test = sc.transform(X_test_final)
pca = decomposition.PCA(0.95)
pca.fit(X_train)
pca.n_components_
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
X_valid_pca = pca.transform(X_valid)
decision_tree = DecisionTreeClassifier()
decision_tree.fit(X_train, y_train)
y_pred = decision_tree.predict(X_valid)
acc_decision_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_decision_tree
extra_tree = DecisionTreeClassifier()
extra_tree.fit(X_train, y_train)
y_pred = extra_tree.predict(X_valid)
acc_extra_tree = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_extra_tree
rfc = RandomForestClassifier(criterion='entropy', n_estimators=1000, min_samples_split=8, random_state=42, verbose=5)
rfc.fit(X_train, y_train)
y_pred = rfc.predict(X_valid)
acc_rfc = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_rfc
GB = GradientBoostingClassifier(n_estimators=100, learning_rate=0.075, max_depth=13, max_features=0.5, min_samples_leaf=14, verbose=5)
GB.fit(X_train, y_train)
y_pred = GB.predict(X_valid)
acc_GB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_GB
HGB = HistGradientBoostingClassifier(learning_rate=0.075, loss='categorical_crossentropy', max_depth=8, min_samples_leaf=15)
HGB = HGB.fit(X_train_pca, y_train)
y_pred = HGB.predict(X_valid_pca)
acc_HGB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_HGB
LGB = LGBMClassifier(objective='multiclass', learning_rate=0.75, num_iterations=100, num_leaves=50, random_state=123, max_depth=8)
LGB.fit(X_train, y_train)
y_pred = LGB.predict(X_valid)
acc_LGB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_LGB
AB = AdaBoostClassifier(n_estimators=100, learning_rate=0.075)
AB.fit(X_train, y_train)
y_pred = AB.predict(X_valid)
acc_AB = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_AB
BC = BaggingClassifier(n_estimators=100)
BC.fit(X_train_pca, y_train)
y_pred = BC.predict(X_valid_pca)
acc_BC = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_BC
xgb = XGBClassifier(n_estimators=1000, learning_rate=0.05, n_jobs=5)
xgb.fit(X_train, y_train, early_stopping_rounds=5, eval_set=[(X_valid, y_valid)], verbose=False)
y_pred = xgb.predict(X_valid)
acc_xgb = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_xgb
ETC = ExtraTreesClassifier(n_estimators=100)
ETC.fit(X_train, y_train)
y_pred = ETC.predict(X_valid)
acc_ETC = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_ETC
LG = LogisticRegression(solver='lbfgs', multi_class='multinomial')
LG.fit(X_train, y_train)
y_pred = LG.predict(X_valid)
acc_LG = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_LG
PAC = PassiveAggressiveClassifier()
PAC.fit(X_train, y_train)
y_pred = PAC.predict(X_valid)
acc_PAC = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_PAC
RC = RidgeClassifierCV()
RC.fit(X_train, y_train)
y_pred = RC.predict(X_valid)
acc_RC = round(accuracy_score(y_valid, y_pred) * 100, 2)
acc_RC | code |
32068084/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler as ss
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
pd.set_option('display.max_columns', None)
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import ExtraTreeClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.experimental import enable_hist_gradient_boosting
from sklearn.ensemble import HistGradientBoostingClassifier
from lightgbm import LGBMClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import RandomForestClassifier
import xgboost
from xgboost import XGBClassifier
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import SGDClassifier
from sklearn.linear_model import PassiveAggressiveClassifier
from sklearn.linear_model import RidgeClassifierCV
from sklearn.linear_model import Perceptron
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.svm import LinearSVC
from sklearn.svm import NuSVC
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.naive_bayes import BernoulliNB
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn import decomposition
print('Setup Complete') | code |
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