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stringlengths 13
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122251329/cell_33 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
target = ['HeartDisease']
num_attribs = ['Age', 'RestingBP', 'Cholesterol', 'MaxHR', 'Oldpeak']
cat_nom_attribs = ['ChestPainType', 'RestingECG', 'ST_Slope']
cat_bin_attribs = ['Sex', 'FastingBS', 'ExerciseAngina']
cat_attribs = cat_nom_attribs + cat_bin_attribs
attribs = num_attribs + target
ncol = 3
nrow = int(np.ceil(len(num_attribs)/ncol))
fig, axs = plt.subplots(nrow, ncol, figsize=(10, 5), facecolor=None)
i = 1
for col in num_attribs:
plt.subplot(nrow, ncol, i)
ax = sns.histplot(data=hrz, x=col, hue=target[0], multiple="stack", palette='colorblind') #kdeplot
ax.set_xlabel(col, fontsize=12)
ax.set_ylabel("count", fontsize=12)
sns.despine(right=True)
sns.despine(offset=0, trim=False)
i+=1
fig.delaxes(axs[nrow-1, ncol-1])
plt.suptitle('Distribution of Numerical Features', fontsize = 14);
plt.tight_layout()
ncol = 3
nrow = int(np.ceil(len(num_attribs)/ncol))
f, axes = plt.subplots(nrow, ncol, figsize=(8,6))
for name, ax in zip(num_attribs, axes.flatten()):
sns.boxplot(y=name, x= "HeartDisease", data=hrz, orient='v', ax=ax)
f.delaxes(axes[nrow-1, ncol-1])
plt.suptitle('Box-and-whisker plot', fontsize = 14);
plt.tight_layout()
grid = sns.FacetGrid(hrz, col='ST_Slope', height=3.0, aspect=1.2)
grid.map(sns.pointplot, 'ChestPainType', 'HeartDisease', 'Sex', hue_order=['M', 'F'], order=['ASY', 'ATA', 'NAP', 'TA'], palette='colorblind')
grid.add_legend()
grid = sns.FacetGrid(hrz, col='ExerciseAngina', height=3.0, aspect=1.2)
grid.map(sns.pointplot, 'ChestPainType', 'HeartDisease', 'Sex', hue_order=['M', 'F'], order=['ASY', 'ATA', 'NAP', 'TA'], palette='colorblind')
grid.add_legend()
grid = sns.FacetGrid(hrz, col='ExerciseAngina', height=3.0, aspect=1.2)
grid.map(sns.pointplot, 'RestingECG', 'HeartDisease', 'Sex', hue_order=['M', 'F'], order=['Normal', 'LVH', 'ST'], palette='colorblind')
grid.add_legend() | code |
122251329/cell_44 | [
"image_output_1.png"
] | from scipy import stats
from sklearn.model_selection import train_test_split, StratifiedShuffleSplit, KFold
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
target = ['HeartDisease']
num_attribs = ['Age', 'RestingBP', 'Cholesterol', 'MaxHR', 'Oldpeak']
cat_nom_attribs = ['ChestPainType', 'RestingECG', 'ST_Slope']
cat_bin_attribs = ['Sex', 'FastingBS', 'ExerciseAngina']
cat_attribs = cat_nom_attribs + cat_bin_attribs
attribs = num_attribs + target
ncol = 3
nrow = int(np.ceil(len(num_attribs)/ncol))
fig, axs = plt.subplots(nrow, ncol, figsize=(10, 5), facecolor=None)
i = 1
for col in num_attribs:
plt.subplot(nrow, ncol, i)
ax = sns.histplot(data=hrz, x=col, hue=target[0], multiple="stack", palette='colorblind') #kdeplot
ax.set_xlabel(col, fontsize=12)
ax.set_ylabel("count", fontsize=12)
sns.despine(right=True)
sns.despine(offset=0, trim=False)
i+=1
fig.delaxes(axs[nrow-1, ncol-1])
plt.suptitle('Distribution of Numerical Features', fontsize = 14);
plt.tight_layout()
ncol = 3
nrow = int(np.ceil(len(num_attribs)/ncol))
f, axes = plt.subplots(nrow, ncol, figsize=(8,6))
for name, ax in zip(num_attribs, axes.flatten()):
sns.boxplot(y=name, x= "HeartDisease", data=hrz, orient='v', ax=ax)
f.delaxes(axes[nrow-1, ncol-1])
plt.suptitle('Box-and-whisker plot', fontsize = 14);
plt.tight_layout()
grid = sns.FacetGrid(hrz, col='ST_Slope', height=3.0, aspect=1.2)
grid.map(sns.pointplot, 'ChestPainType', 'HeartDisease', 'Sex', hue_order=['M', 'F'], order=['ASY', 'ATA', 'NAP', 'TA'], palette='colorblind')
grid.add_legend()
grid = sns.FacetGrid(hrz, col='ExerciseAngina', height=3.0, aspect=1.2)
grid.map(sns.pointplot, 'ChestPainType', 'HeartDisease', 'Sex', hue_order=['M', 'F'], order=['ASY', 'ATA', 'NAP', 'TA'], palette='colorblind')
grid.add_legend()
grid = sns.FacetGrid(hrz, col='ExerciseAngina', height=3.0, aspect=1.2)
grid.map(sns.pointplot, 'RestingECG', 'HeartDisease', 'Sex', hue_order=['M', 'F'], order=['Normal', 'LVH', 'ST'], palette='colorblind')
grid.add_legend()
# the cramers_v function is taken from https://towardsdatascience.com/the-search-for-categorical-correlation-a1cf7f1888c9
def cramers_v(x, y):
confusion_matrix = pd.crosstab(x,y)
chi2 = stats.chi2_contingency(confusion_matrix)[0]
n = confusion_matrix.sum().sum()
phi2 = chi2/n
r,k = confusion_matrix.shape
phi2corr = max(0, phi2-((k-1)*(r-1))/(n-1))
rcorr = r-((r-1)**2)/(n-1)
kcorr = k-((k-1)**2)/(n-1)
return np.sqrt(phi2corr/min((kcorr-1),(rcorr-1)))
# calculate the correlation coefficients
hrz_ = hrz[cat_attribs+target]
rows= []
for x in hrz_:
col = []
for y in hrz_ :
col.append(cramers_v(hrz_[x], hrz_[y]) )
rows.append(col)
cramers_results = np.array(rows)
df = pd.DataFrame(cramers_results, columns = hrz_.columns, index = hrz_.columns)
# heatmap plot
mask = np.triu(np.ones_like(df, dtype=bool))
fig, ax = plt.subplots(figsize=(8, 6), facecolor=None)
sns.heatmap(df, cmap=sns.color_palette("husl", as_cmap=True),
vmin=0, vmax=1.0, center=0, annot=True, fmt='.2f',
square=True, linewidths=.01, cbar_kws={"shrink": 0.8})
ax.set_title("Association between categorical variables (Cramer's V)", fontsize=14);
def splitting(test_fraction, df, seed):
split_instanse = StratifiedShuffleSplit(n_splits=1, test_size=test_fraction, random_state=seed)
for train_index, test_index in split_instanse.split(df, df['HeartDisease']):
strat_train_set = df.loc[train_index]
strat_test_set = df.loc[test_index]
return (strat_train_set, strat_test_set)
seed = 123
test_fraction = 0.15
strat_train_set, strat_test_set = splitting(test_fraction, hrz, seed)
print('Fractions of heart diseases in the original, train and test dataset: %.3f, %.3f, %.3f' % (hrz.loc[hrz['HeartDisease'] == 1].shape[0] / hrz.shape[0], strat_train_set.loc[strat_train_set['HeartDisease'] == 1].shape[0] / strat_train_set.shape[0], strat_test_set.loc[strat_test_set['HeartDisease'] == 1].shape[0] / strat_test_set.shape[0])) | code |
122251329/cell_29 | [
"image_output_1.png"
] | from IPython.display import display
import pandas as pd
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
target = ['HeartDisease']
num_attribs = ['Age', 'RestingBP', 'Cholesterol', 'MaxHR', 'Oldpeak']
cat_nom_attribs = ['ChestPainType', 'RestingECG', 'ST_Slope']
cat_bin_attribs = ['Sex', 'FastingBS', 'ExerciseAngina']
cat_attribs = cat_nom_attribs + cat_bin_attribs
for attr in cat_attribs:
display(hrz[[attr, 'HeartDisease']].groupby(attr, as_index=False).mean().sort_values(by='HeartDisease', ascending=False)) | code |
122251329/cell_39 | [
"image_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
target = ['HeartDisease']
num_attribs = ['Age', 'RestingBP', 'Cholesterol', 'MaxHR', 'Oldpeak']
cat_nom_attribs = ['ChestPainType', 'RestingECG', 'ST_Slope']
cat_bin_attribs = ['Sex', 'FastingBS', 'ExerciseAngina']
cat_attribs = cat_nom_attribs + cat_bin_attribs
attribs = num_attribs + target
ncol = 3
nrow = int(np.ceil(len(num_attribs)/ncol))
fig, axs = plt.subplots(nrow, ncol, figsize=(10, 5), facecolor=None)
i = 1
for col in num_attribs:
plt.subplot(nrow, ncol, i)
ax = sns.histplot(data=hrz, x=col, hue=target[0], multiple="stack", palette='colorblind') #kdeplot
ax.set_xlabel(col, fontsize=12)
ax.set_ylabel("count", fontsize=12)
sns.despine(right=True)
sns.despine(offset=0, trim=False)
i+=1
fig.delaxes(axs[nrow-1, ncol-1])
plt.suptitle('Distribution of Numerical Features', fontsize = 14);
plt.tight_layout()
ncol = 3
nrow = int(np.ceil(len(num_attribs)/ncol))
f, axes = plt.subplots(nrow, ncol, figsize=(8,6))
for name, ax in zip(num_attribs, axes.flatten()):
sns.boxplot(y=name, x= "HeartDisease", data=hrz, orient='v', ax=ax)
f.delaxes(axes[nrow-1, ncol-1])
plt.suptitle('Box-and-whisker plot', fontsize = 14);
plt.tight_layout()
grid = sns.FacetGrid(hrz, col='ST_Slope', height=3.0, aspect=1.2)
grid.map(sns.pointplot, 'ChestPainType', 'HeartDisease', 'Sex', hue_order=['M', 'F'], order=['ASY', 'ATA', 'NAP', 'TA'], palette='colorblind')
grid.add_legend()
grid = sns.FacetGrid(hrz, col='ExerciseAngina', height=3.0, aspect=1.2)
grid.map(sns.pointplot, 'ChestPainType', 'HeartDisease', 'Sex', hue_order=['M', 'F'], order=['ASY', 'ATA', 'NAP', 'TA'], palette='colorblind')
grid.add_legend()
grid = sns.FacetGrid(hrz, col='ExerciseAngina', height=3.0, aspect=1.2)
grid.map(sns.pointplot, 'RestingECG', 'HeartDisease', 'Sex', hue_order=['M', 'F'], order=['Normal', 'LVH', 'ST'], palette='colorblind')
grid.add_legend()
# the cramers_v function is taken from https://towardsdatascience.com/the-search-for-categorical-correlation-a1cf7f1888c9
def cramers_v(x, y):
confusion_matrix = pd.crosstab(x,y)
chi2 = stats.chi2_contingency(confusion_matrix)[0]
n = confusion_matrix.sum().sum()
phi2 = chi2/n
r,k = confusion_matrix.shape
phi2corr = max(0, phi2-((k-1)*(r-1))/(n-1))
rcorr = r-((r-1)**2)/(n-1)
kcorr = k-((k-1)**2)/(n-1)
return np.sqrt(phi2corr/min((kcorr-1),(rcorr-1)))
# calculate the correlation coefficients
hrz_ = hrz[cat_attribs+target]
rows= []
for x in hrz_:
col = []
for y in hrz_ :
col.append(cramers_v(hrz_[x], hrz_[y]) )
rows.append(col)
cramers_results = np.array(rows)
df = pd.DataFrame(cramers_results, columns = hrz_.columns, index = hrz_.columns)
# heatmap plot
mask = np.triu(np.ones_like(df, dtype=bool))
fig, ax = plt.subplots(figsize=(8, 6), facecolor=None)
sns.heatmap(df, cmap=sns.color_palette("husl", as_cmap=True),
vmin=0, vmax=1.0, center=0, annot=True, fmt='.2f',
square=True, linewidths=.01, cbar_kws={"shrink": 0.8})
ax.set_title("Association between categorical variables (Cramer's V)", fontsize=14);
corr_matrix = hrz[num_attribs + target].corr()
dataplot = sns.heatmap(corr_matrix, cmap='YlGnBu', annot=True, fmt='.2f')
plt.show() | code |
122251329/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
hrz.info() | code |
122251329/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
print(hrz[hrz['Cholesterol'] == 0].shape[0])
print(hrz[hrz['RestingBP'] == 0].shape[0]) | code |
122251329/cell_32 | [
"text_html_output_4.png",
"text_html_output_6.png",
"text_html_output_2.png",
"text_html_output_5.png",
"text_html_output_1.png",
"text_html_output_3.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
target = ['HeartDisease']
num_attribs = ['Age', 'RestingBP', 'Cholesterol', 'MaxHR', 'Oldpeak']
cat_nom_attribs = ['ChestPainType', 'RestingECG', 'ST_Slope']
cat_bin_attribs = ['Sex', 'FastingBS', 'ExerciseAngina']
cat_attribs = cat_nom_attribs + cat_bin_attribs
attribs = num_attribs + target
ncol = 3
nrow = int(np.ceil(len(num_attribs)/ncol))
fig, axs = plt.subplots(nrow, ncol, figsize=(10, 5), facecolor=None)
i = 1
for col in num_attribs:
plt.subplot(nrow, ncol, i)
ax = sns.histplot(data=hrz, x=col, hue=target[0], multiple="stack", palette='colorblind') #kdeplot
ax.set_xlabel(col, fontsize=12)
ax.set_ylabel("count", fontsize=12)
sns.despine(right=True)
sns.despine(offset=0, trim=False)
i+=1
fig.delaxes(axs[nrow-1, ncol-1])
plt.suptitle('Distribution of Numerical Features', fontsize = 14);
plt.tight_layout()
ncol = 3
nrow = int(np.ceil(len(num_attribs)/ncol))
f, axes = plt.subplots(nrow, ncol, figsize=(8,6))
for name, ax in zip(num_attribs, axes.flatten()):
sns.boxplot(y=name, x= "HeartDisease", data=hrz, orient='v', ax=ax)
f.delaxes(axes[nrow-1, ncol-1])
plt.suptitle('Box-and-whisker plot', fontsize = 14);
plt.tight_layout()
grid = sns.FacetGrid(hrz, col='ST_Slope', height=3.0, aspect=1.2)
grid.map(sns.pointplot, 'ChestPainType', 'HeartDisease', 'Sex', hue_order=['M', 'F'], order=['ASY', 'ATA', 'NAP', 'TA'], palette='colorblind')
grid.add_legend()
grid = sns.FacetGrid(hrz, col='ExerciseAngina', height=3.0, aspect=1.2)
grid.map(sns.pointplot, 'ChestPainType', 'HeartDisease', 'Sex', hue_order=['M', 'F'], order=['ASY', 'ATA', 'NAP', 'TA'], palette='colorblind')
grid.add_legend() | code |
122251329/cell_15 | [
"text_plain_output_1.png"
] | from IPython.display import display
import pandas as pd
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
target = ['HeartDisease']
num_attribs = ['Age', 'RestingBP', 'Cholesterol', 'MaxHR', 'Oldpeak']
cat_nom_attribs = ['ChestPainType', 'RestingECG', 'ST_Slope']
cat_bin_attribs = ['Sex', 'FastingBS', 'ExerciseAngina']
cat_attribs = cat_nom_attribs + cat_bin_attribs
for attr in target + cat_attribs:
display(hrz[attr].value_counts(normalize=True)) | code |
122251329/cell_17 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
hrz.describe() | code |
122251329/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
print('MaxHR values for F, M:')
print('HeartDisease=0', round(hrz.loc[(hrz['Sex'] == 'F') & (hrz['HeartDisease'] == 0)]['MaxHR'].mean(), 2), round(hrz.loc[(hrz['Sex'] == 'M') & (hrz['HeartDisease'] == 0)]['MaxHR'].mean(), 2))
print('HeartDisease=1', round(hrz.loc[(hrz['Sex'] == 'F') & (hrz['HeartDisease'] == 1)]['MaxHR'].mean(), 2), round(hrz.loc[(hrz['Sex'] == 'M') & (hrz['HeartDisease'] == 1)]['MaxHR'].mean(), 2))
print('Oldpeak values for F, M:')
print('HeartDisease=0', round(hrz.loc[(hrz['Sex'] == 'F') & (hrz['HeartDisease'] == 0)]['Oldpeak'].mean(), 2), round(hrz.loc[(hrz['Sex'] == 'M') & (hrz['HeartDisease'] == 0)]['Oldpeak'].mean(), 2))
print('HeartDisease=1', round(hrz.loc[(hrz['Sex'] == 'F') & (hrz['HeartDisease'] == 1)]['Oldpeak'].mean(), 2), round(hrz.loc[(hrz['Sex'] == 'M') & (hrz['HeartDisease'] == 1)]['Oldpeak'].mean(), 2)) | code |
122251329/cell_37 | [
"text_plain_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
target = ['HeartDisease']
num_attribs = ['Age', 'RestingBP', 'Cholesterol', 'MaxHR', 'Oldpeak']
cat_nom_attribs = ['ChestPainType', 'RestingECG', 'ST_Slope']
cat_bin_attribs = ['Sex', 'FastingBS', 'ExerciseAngina']
cat_attribs = cat_nom_attribs + cat_bin_attribs
attribs = num_attribs + target
ncol = 3
nrow = int(np.ceil(len(num_attribs)/ncol))
fig, axs = plt.subplots(nrow, ncol, figsize=(10, 5), facecolor=None)
i = 1
for col in num_attribs:
plt.subplot(nrow, ncol, i)
ax = sns.histplot(data=hrz, x=col, hue=target[0], multiple="stack", palette='colorblind') #kdeplot
ax.set_xlabel(col, fontsize=12)
ax.set_ylabel("count", fontsize=12)
sns.despine(right=True)
sns.despine(offset=0, trim=False)
i+=1
fig.delaxes(axs[nrow-1, ncol-1])
plt.suptitle('Distribution of Numerical Features', fontsize = 14);
plt.tight_layout()
ncol = 3
nrow = int(np.ceil(len(num_attribs)/ncol))
f, axes = plt.subplots(nrow, ncol, figsize=(8,6))
for name, ax in zip(num_attribs, axes.flatten()):
sns.boxplot(y=name, x= "HeartDisease", data=hrz, orient='v', ax=ax)
f.delaxes(axes[nrow-1, ncol-1])
plt.suptitle('Box-and-whisker plot', fontsize = 14);
plt.tight_layout()
grid = sns.FacetGrid(hrz, col='ST_Slope', height=3.0, aspect=1.2)
grid.map(sns.pointplot, 'ChestPainType', 'HeartDisease', 'Sex', hue_order=['M', 'F'], order=['ASY', 'ATA', 'NAP', 'TA'], palette='colorblind')
grid.add_legend()
grid = sns.FacetGrid(hrz, col='ExerciseAngina', height=3.0, aspect=1.2)
grid.map(sns.pointplot, 'ChestPainType', 'HeartDisease', 'Sex', hue_order=['M', 'F'], order=['ASY', 'ATA', 'NAP', 'TA'], palette='colorblind')
grid.add_legend()
grid = sns.FacetGrid(hrz, col='ExerciseAngina', height=3.0, aspect=1.2)
grid.map(sns.pointplot, 'RestingECG', 'HeartDisease', 'Sex', hue_order=['M', 'F'], order=['Normal', 'LVH', 'ST'], palette='colorblind')
grid.add_legend()
def cramers_v(x, y):
confusion_matrix = pd.crosstab(x, y)
chi2 = stats.chi2_contingency(confusion_matrix)[0]
n = confusion_matrix.sum().sum()
phi2 = chi2 / n
r, k = confusion_matrix.shape
phi2corr = max(0, phi2 - (k - 1) * (r - 1) / (n - 1))
rcorr = r - (r - 1) ** 2 / (n - 1)
kcorr = k - (k - 1) ** 2 / (n - 1)
return np.sqrt(phi2corr / min(kcorr - 1, rcorr - 1))
hrz_ = hrz[cat_attribs + target]
rows = []
for x in hrz_:
col = []
for y in hrz_:
col.append(cramers_v(hrz_[x], hrz_[y]))
rows.append(col)
cramers_results = np.array(rows)
df = pd.DataFrame(cramers_results, columns=hrz_.columns, index=hrz_.columns)
mask = np.triu(np.ones_like(df, dtype=bool))
fig, ax = plt.subplots(figsize=(8, 6), facecolor=None)
sns.heatmap(df, cmap=sns.color_palette('husl', as_cmap=True), vmin=0, vmax=1.0, center=0, annot=True, fmt='.2f', square=True, linewidths=0.01, cbar_kws={'shrink': 0.8})
ax.set_title("Association between categorical variables (Cramer's V)", fontsize=14) | code |
1009964/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
train_df.head(10) | code |
1009964/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
NAs = pd.concat([train_df.isnull().sum(), test_df.isnull().sum()], axis=1, keys=['Train', 'Test'])
NAs[NAs.sum(axis=1) > 1] | code |
1009964/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import random as rnd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import Perceptron
from sklearn.linear_model import SGDClassifier
from sklearn.tree import DecisionTreeClassifier
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1009964/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
NAs = pd.concat([train_df.isnull().sum(), test_df.isnull().sum()], axis=1, keys=['Train', 'Test'])
NAs[NAs.sum(axis=1) > 1]
train_df['FireplaceQu'] | code |
1009964/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
train_df.info() | code |
1010160/cell_4 | [
"text_plain_output_1.png"
] | from scipy.misc import imread
import cv2 as cv
import glob
import numpy as np
import os
import random
species = ['ALB', 'BET', 'DOL', 'LAG', 'NoF', 'OTHER', 'SHARK', 'YFT']
select = 1000
ROWS = 90
COLS = 160
CHANNELS = 3
PATH = './input/'
def get_image(file):
pos1 = file.rfind('/img_')
return file[pos1 + 1:]
def get_id(file):
pos1 = file.rfind('_')
pos2 = file.rfind('.')
return file[pos1 + 1:pos2]
def load_train_data(select):
train_files = sorted(glob.glob(PATH + '/train/*/*.jpg'), key=lambda x: random.random())[:select]
train = np.array([imread(img) for img in train_files])
X_train = np.array([cv.resize(img, (ROWS, COLS)) for img in train])
y = np.array([species.index(os.path.dirname(img).replace(PATH + '/train/', '')) for img in train_files])
ids = np.array([get_id(img) for img in train_files])
X_train = np.array(X_train, dtype=np.float32) / 255
return (X_train, y, ids)
def load_test_data():
test_files = sorted(glob.glob(PATH + '/test_stg1/*.jpg'))
test = np.array([imread(img) for img in test_files])
X_test = np.array([cv.resize(img, (ROWS, COLS)) for img in test])
X_test = np.array(X_test, dtype=np.float32) / 255
ids = np.array([get_image(img) for img in test_files])
return (X_test, ids)
X, y, ids = load_train_data(select)
print(X.shape) | code |
1010160/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense,Convolution2D,MaxPooling2D,Flatten,Activation
from keras.layers import Dropout
from keras.models import Sequential
from keras.optimizers import Adam
from scipy.misc import imread
import cv2 as cv
import glob
import numpy as np
import os
import random
species = ['ALB', 'BET', 'DOL', 'LAG', 'NoF', 'OTHER', 'SHARK', 'YFT']
select = 1000
ROWS = 90
COLS = 160
CHANNELS = 3
PATH = './input/'
def get_image(file):
pos1 = file.rfind('/img_')
return file[pos1 + 1:]
def get_id(file):
pos1 = file.rfind('_')
pos2 = file.rfind('.')
return file[pos1 + 1:pos2]
def load_train_data(select):
train_files = sorted(glob.glob(PATH + '/train/*/*.jpg'), key=lambda x: random.random())[:select]
train = np.array([imread(img) for img in train_files])
X_train = np.array([cv.resize(img, (ROWS, COLS)) for img in train])
y = np.array([species.index(os.path.dirname(img).replace(PATH + '/train/', '')) for img in train_files])
ids = np.array([get_id(img) for img in train_files])
X_train = np.array(X_train, dtype=np.float32) / 255
return (X_train, y, ids)
def load_test_data():
test_files = sorted(glob.glob(PATH + '/test_stg1/*.jpg'))
test = np.array([imread(img) for img in test_files])
X_test = np.array([cv.resize(img, (ROWS, COLS)) for img in test])
X_test = np.array(X_test, dtype=np.float32) / 255
ids = np.array([get_image(img) for img in test_files])
return (X_test, ids)
from keras.models import load_model
from keras.layers import Dropout
model = Sequential()
model.add(Convolution2D(nb_filter=32, nb_row=5, nb_col=5, border_mode='same', input_shape=(3, ROWS, COLS)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), border_mode='same'))
model.add(Convolution2D(64, 5, 5, border_mode='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), border_mode='same'))
model.add(Convolution2D(128, 5, 5, border_mode='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), border_mode='same'))
model.add(Flatten())
model.add(Dense(128))
model.add(Dropout(0.5))
model.add(Activation('relu'))
model.add(Dense(8))
model.add(Dropout(0.5))
model.add(Activation('softmax'))
adam = Adam()
model.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, nb_epoch=50, batch_size=32)
loss, accuracy = model.evaluate(X_test, y_test)
print('\n test loss:', loss)
print('\n test accuracy', accuracy)
model.save('my_mode.h5') | code |
1010160/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import cv2 as cv
import glob
import random
import numpy as np
from scipy.misc import imread
import os
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Convolution2D, MaxPooling2D, Flatten, Activation
from keras.optimizers import Adam
from sklearn.cross_validation import train_test_split | code |
1010160/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense,Convolution2D,MaxPooling2D,Flatten,Activation
from keras.layers import Dropout
from keras.models import Sequential
from keras.optimizers import Adam
from scipy.misc import imread
import cv2 as cv
import glob
import numpy as np
import os
import random
species = ['ALB', 'BET', 'DOL', 'LAG', 'NoF', 'OTHER', 'SHARK', 'YFT']
select = 1000
ROWS = 90
COLS = 160
CHANNELS = 3
PATH = './input/'
def get_image(file):
pos1 = file.rfind('/img_')
return file[pos1 + 1:]
def get_id(file):
pos1 = file.rfind('_')
pos2 = file.rfind('.')
return file[pos1 + 1:pos2]
def load_train_data(select):
train_files = sorted(glob.glob(PATH + '/train/*/*.jpg'), key=lambda x: random.random())[:select]
train = np.array([imread(img) for img in train_files])
X_train = np.array([cv.resize(img, (ROWS, COLS)) for img in train])
y = np.array([species.index(os.path.dirname(img).replace(PATH + '/train/', '')) for img in train_files])
ids = np.array([get_id(img) for img in train_files])
X_train = np.array(X_train, dtype=np.float32) / 255
return (X_train, y, ids)
def load_test_data():
test_files = sorted(glob.glob(PATH + '/test_stg1/*.jpg'))
test = np.array([imread(img) for img in test_files])
X_test = np.array([cv.resize(img, (ROWS, COLS)) for img in test])
X_test = np.array(X_test, dtype=np.float32) / 255
ids = np.array([get_image(img) for img in test_files])
return (X_test, ids)
from keras.models import load_model
from keras.layers import Dropout
model = Sequential()
model.add(Convolution2D(nb_filter=32, nb_row=5, nb_col=5, border_mode='same', input_shape=(3, ROWS, COLS)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), border_mode='same'))
model.add(Convolution2D(64, 5, 5, border_mode='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), border_mode='same'))
model.add(Convolution2D(128, 5, 5, border_mode='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), border_mode='same'))
model.add(Flatten())
model.add(Dense(128))
model.add(Dropout(0.5))
model.add(Activation('relu'))
model.add(Dense(8))
model.add(Dropout(0.5))
model.add(Activation('softmax'))
adam = Adam()
model.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, nb_epoch=50, batch_size=32)
loss, accuracy = model.evaluate(X_test, y_test)
model.save('my_mode.h5')
test, ids = load_test_data()
data = test.transpose((0, 3, 2, 1))
predictions = model.predict(data, verbose=1) | code |
1010160/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense,Convolution2D,MaxPooling2D,Flatten,Activation
from keras.layers import Dropout
from keras.models import Sequential
from keras.optimizers import Adam
from scipy.misc import imread
import cv2 as cv
import datetime
import glob
import numpy as np
import os
import pandas as pd
import random
species = ['ALB', 'BET', 'DOL', 'LAG', 'NoF', 'OTHER', 'SHARK', 'YFT']
select = 1000
ROWS = 90
COLS = 160
CHANNELS = 3
PATH = './input/'
def get_image(file):
pos1 = file.rfind('/img_')
return file[pos1 + 1:]
def get_id(file):
pos1 = file.rfind('_')
pos2 = file.rfind('.')
return file[pos1 + 1:pos2]
def load_train_data(select):
train_files = sorted(glob.glob(PATH + '/train/*/*.jpg'), key=lambda x: random.random())[:select]
train = np.array([imread(img) for img in train_files])
X_train = np.array([cv.resize(img, (ROWS, COLS)) for img in train])
y = np.array([species.index(os.path.dirname(img).replace(PATH + '/train/', '')) for img in train_files])
ids = np.array([get_id(img) for img in train_files])
X_train = np.array(X_train, dtype=np.float32) / 255
return (X_train, y, ids)
def load_test_data():
test_files = sorted(glob.glob(PATH + '/test_stg1/*.jpg'))
test = np.array([imread(img) for img in test_files])
X_test = np.array([cv.resize(img, (ROWS, COLS)) for img in test])
X_test = np.array(X_test, dtype=np.float32) / 255
ids = np.array([get_image(img) for img in test_files])
return (X_test, ids)
from keras.models import load_model
from keras.layers import Dropout
model = Sequential()
model.add(Convolution2D(nb_filter=32, nb_row=5, nb_col=5, border_mode='same', input_shape=(3, ROWS, COLS)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), border_mode='same'))
model.add(Convolution2D(64, 5, 5, border_mode='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), border_mode='same'))
model.add(Convolution2D(128, 5, 5, border_mode='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), border_mode='same'))
model.add(Flatten())
model.add(Dense(128))
model.add(Dropout(0.5))
model.add(Activation('relu'))
model.add(Dense(8))
model.add(Dropout(0.5))
model.add(Activation('softmax'))
adam = Adam()
model.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, nb_epoch=50, batch_size=32)
loss, accuracy = model.evaluate(X_test, y_test)
model.save('my_mode.h5')
test, ids = load_test_data()
data = test.transpose((0, 3, 2, 1))
predictions = model.predict(data, verbose=1)
import pandas as pd
import datetime
result1 = pd.DataFrame(predictions, columns=['ALB', 'BET', 'DOL', 'LAG', 'NoF', 'OTHER', 'SHARK', 'YFT'])
result1.loc[:, 'image'] = pd.Series(ids, index=result1.index)
now = datetime.datetime.now()
sub_file = 'submission_' + str(now.strftime('%Y-%m-%d-%H-%M')) + '.csv'
result1.to_csv(sub_file, index=False) | code |
121150047/cell_13 | [
"text_plain_output_1.png"
] | from colorama import Style, Fore
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from xgboost import XGBRegressor
from sklearn.metrics import mean_squared_error
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.base import TransformerMixin
from sklearn.model_selection import KFold
from sklearn.ensemble import GradientBoostingRegressor
from lightgbm import LGBMRegressor
rc = {'axes.facecolor': '#FFF9ED', 'figure.facecolor': '#FFF9ED', 'axes.edgecolor': '#000000', 'grid.color': '#EBEBE7', 'font.family': 'serif', 'axes.labelcolor': '#000000', 'xtick.color': '#000000', 'ytick.color': '#000000', 'grid.alpha': 0.4}
sns.set(rc=rc)
from colorama import Style, Fore
red = Style.BRIGHT + Fore.RED
blue = Style.BRIGHT + Fore.BLUE
megnta = Style.BRIGHT + Fore.MAGENTA
gold = Style.BRIGHT + Fore.YELLOW
res = Style.RESET_ALL
import warnings
warnings.filterwarnings('ignore')
old_df = pd.read_csv('/kaggle/input/regression-with-neural-networking/concrete_data.csv')
original_df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv').drop(columns=['id'])
test_df = pd.read_csv('/kaggle/input/playground-series-s3e9/test.csv').drop(columns=['id'])
sample = pd.read_csv('/kaggle/input/playground-series-s3e9/sample_submission.csv')
original_num_cols_int = [col for col in original_df.columns if original_df[col].dtype == 'int']
original_num_cols_float = [col for col in original_df.columns if original_df[col].dtype == 'float']
original_num_cols_obj = [col for col in original_df.columns if original_df[col].dtype == 'o']
old_num_cols_int = [col for col in old_df.columns if old_df[col].dtype == 'int']
old_num_cols_float = [col for col in old_df.columns if old_df[col].dtype == 'float']
old_num_cols_obj = [col for col in old_df.columns if old_df[col].dtype == 'o']
test_num_cols_int = [col for col in test_df.columns if test_df[col].dtype == 'int']
test_num_cols_float = [col for col in test_df.columns if test_df[col].dtype == 'float']
test_num_cols_obj = [col for col in test_df.columns if test_df[col].dtype == 'o']
original_df_column = [col for col in original_df.columns]
old_df_columns = [col for col in old_df.columns]
name_cng_dict = {key: value for key, value in zip(old_df_columns, original_df_column)}
old_df.rename(columns=name_cng_dict, inplace=True)
fig,ax = plt.subplots(3,3,figsize=(15,10),dpi=100)
ax = ax.flatten()
for i,column in enumerate(original_df.columns[:-1]):
plot_axes = [ax[i]]
sns.kdeplot(
original_df[column], label='Original_df',
ax=ax[i], color='#9E3F00'
)
sns.kdeplot(
test_df[column], label='Test_df',
ax=ax[i], color='yellow'
)
sns.kdeplot(
old_df[column], label='Old_df',
ax=ax[i], color='#20BEFF'
)
# titles
ax[i].set_title(f'{column} Distribution');
ax[i].set_xlabel(None)
# remove axes to show only one at the end
plot_axes = [ax[i]]
handles = []
labels = []
for plot_ax in plot_axes:
handles += plot_ax.get_legend_handles_labels()[0]
labels += plot_ax.get_legend_handles_labels()[1]
plot_ax.legend().remove()
for i in range(i+1, len(ax)):
ax[i].axis('off')
fig.suptitle(f'Dataset Feature Distributions\n\n\n', ha='center', fontweight='bold', fontsize=25)
fig.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, 0.96), fontsize=25, ncol=3)
plt.tight_layout()
# correlation
corr = original_df.corr()
# Generate a mask for the upper triangle
mask = np.triu(np.ones_like(corr, dtype=np.bool))
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(11, 9))
# Generate a custom diverging colormap
cmap = sns.diverging_palette(220, 10, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
f.suptitle(f'Heatmap for Original DF\n\n\n', ha='center', fontweight='bold', fontsize=25)
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,
square=True, linewidths=.5, cbar_kws={"shrink": .5})
corr = old_df.corr()
mask = np.triu(np.ones_like(corr, dtype=np.bool))
f, ax = plt.subplots(figsize=(11, 9))
cmap = sns.diverging_palette(220, 10, as_cmap=True)
f.suptitle(f'Heatmap for Old DF\n\n\n', ha='center', fontweight='bold', fontsize=25)
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=0.3, center=0, square=True, linewidths=0.5, cbar_kws={'shrink': 0.5}) | code |
121150047/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from colorama import Style, Fore
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import KFold
from sklearn.pipeline import Pipeline
from xgboost import XGBRegressor
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from xgboost import XGBRegressor
from sklearn.metrics import mean_squared_error
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.base import TransformerMixin
from sklearn.model_selection import KFold
from sklearn.ensemble import GradientBoostingRegressor
from lightgbm import LGBMRegressor
rc = {'axes.facecolor': '#FFF9ED', 'figure.facecolor': '#FFF9ED', 'axes.edgecolor': '#000000', 'grid.color': '#EBEBE7', 'font.family': 'serif', 'axes.labelcolor': '#000000', 'xtick.color': '#000000', 'ytick.color': '#000000', 'grid.alpha': 0.4}
sns.set(rc=rc)
from colorama import Style, Fore
red = Style.BRIGHT + Fore.RED
blue = Style.BRIGHT + Fore.BLUE
megnta = Style.BRIGHT + Fore.MAGENTA
gold = Style.BRIGHT + Fore.YELLOW
res = Style.RESET_ALL
import warnings
warnings.filterwarnings('ignore')
old_df = pd.read_csv('/kaggle/input/regression-with-neural-networking/concrete_data.csv')
original_df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv').drop(columns=['id'])
test_df = pd.read_csv('/kaggle/input/playground-series-s3e9/test.csv').drop(columns=['id'])
sample = pd.read_csv('/kaggle/input/playground-series-s3e9/sample_submission.csv')
original_num_cols_int = [col for col in original_df.columns if original_df[col].dtype == 'int']
original_num_cols_float = [col for col in original_df.columns if original_df[col].dtype == 'float']
original_num_cols_obj = [col for col in original_df.columns if original_df[col].dtype == 'o']
old_num_cols_int = [col for col in old_df.columns if old_df[col].dtype == 'int']
old_num_cols_float = [col for col in old_df.columns if old_df[col].dtype == 'float']
old_num_cols_obj = [col for col in old_df.columns if old_df[col].dtype == 'o']
test_num_cols_int = [col for col in test_df.columns if test_df[col].dtype == 'int']
test_num_cols_float = [col for col in test_df.columns if test_df[col].dtype == 'float']
test_num_cols_obj = [col for col in test_df.columns if test_df[col].dtype == 'o']
original_df_column = [col for col in original_df.columns]
old_df_columns = [col for col in old_df.columns]
name_cng_dict = {key: value for key, value in zip(old_df_columns, original_df_column)}
old_df.rename(columns=name_cng_dict, inplace=True)
fig,ax = plt.subplots(3,3,figsize=(15,10),dpi=100)
ax = ax.flatten()
for i,column in enumerate(original_df.columns[:-1]):
plot_axes = [ax[i]]
sns.kdeplot(
original_df[column], label='Original_df',
ax=ax[i], color='#9E3F00'
)
sns.kdeplot(
test_df[column], label='Test_df',
ax=ax[i], color='yellow'
)
sns.kdeplot(
old_df[column], label='Old_df',
ax=ax[i], color='#20BEFF'
)
# titles
ax[i].set_title(f'{column} Distribution');
ax[i].set_xlabel(None)
# remove axes to show only one at the end
plot_axes = [ax[i]]
handles = []
labels = []
for plot_ax in plot_axes:
handles += plot_ax.get_legend_handles_labels()[0]
labels += plot_ax.get_legend_handles_labels()[1]
plot_ax.legend().remove()
for i in range(i+1, len(ax)):
ax[i].axis('off')
fig.suptitle(f'Dataset Feature Distributions\n\n\n', ha='center', fontweight='bold', fontsize=25)
fig.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, 0.96), fontsize=25, ncol=3)
plt.tight_layout()
# correlation
corr = original_df.corr()
# Generate a mask for the upper triangle
mask = np.triu(np.ones_like(corr, dtype=np.bool))
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(11, 9))
# Generate a custom diverging colormap
cmap = sns.diverging_palette(220, 10, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
f.suptitle(f'Heatmap for Original DF\n\n\n', ha='center', fontweight='bold', fontsize=25)
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,
square=True, linewidths=.5, cbar_kws={"shrink": .5})
# correlation
corr = old_df.corr()
# Generate a mask for the upper triangle
mask = np.triu(np.ones_like(corr, dtype=np.bool))
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(11, 9))
# Generate a custom diverging colormap
cmap = sns.diverging_palette(220, 10, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
f.suptitle(f'Heatmap for Old DF\n\n\n', ha='center', fontweight='bold', fontsize=25)
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,
square=True, linewidths=.5, cbar_kws={"shrink": .5})
final_df = pd.concat([original_df, old_df], ignore_index=True)
def model_training(train, test, model):
kf = KFold(n_splits=5)
X = train.copy()
test_ = test.copy()
preprocess_pipeline = preprocess()
predictions = []
scores = []
for i, (train, val) in enumerate(kf.split(X)):
X_train = X.iloc[train].drop(columns=['Strength'])
y_train = X['Strength'].iloc[train]
y_train = y_train.to_numpy()
X_val = X.iloc[val].drop(columns=['Strength'])
y_val = X['Strength'].iloc[val]
y_val = y_val.to_numpy()
pipeline = Pipeline([('preprocess', preprocess_pipeline), ('training', model)])
pipeline.fit(X_train, y_train)
test_pred = pipeline.predict(test_)
predictions.append(test_pred)
y_pred = pipeline.predict(X_val)
mse = mean_squared_error(y_val, y_pred)
rmse = np.sqrt(mse)
scores.append(rmse)
return (predictions, model)
learning_rate = 0.2
n_estimators = 250
n_jobs = -1
max_depth = 5
min_child_weight = 2
gamma = 0.01
reg_alpha = 0.01
reg_lambda = 0.01
subsample = 0.8
colsample_bytree = 0.67
seed = 42
xgbr = XGBRegressor(n_estimators=n_estimators, learning_rate=learning_rate, max_depth=max_depth, min_child_weight=min_child_weight, gamma=gamma, reg_alpha=reg_alpha, reg_lambda=reg_lambda, subsample=subsample, colsample_bytree=colsample_bytree, seed=seed, n_jobs=n_jobs)
xgbr, model_xgb = model_training(final_df, test_df, xgbr) | code |
121150047/cell_6 | [
"text_plain_output_1.png"
] | from colorama import Style, Fore
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from xgboost import XGBRegressor
from sklearn.metrics import mean_squared_error
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.base import TransformerMixin
from sklearn.model_selection import KFold
from sklearn.ensemble import GradientBoostingRegressor
from lightgbm import LGBMRegressor
rc = {'axes.facecolor': '#FFF9ED', 'figure.facecolor': '#FFF9ED', 'axes.edgecolor': '#000000', 'grid.color': '#EBEBE7', 'font.family': 'serif', 'axes.labelcolor': '#000000', 'xtick.color': '#000000', 'ytick.color': '#000000', 'grid.alpha': 0.4}
sns.set(rc=rc)
from colorama import Style, Fore
red = Style.BRIGHT + Fore.RED
blue = Style.BRIGHT + Fore.BLUE
megnta = Style.BRIGHT + Fore.MAGENTA
gold = Style.BRIGHT + Fore.YELLOW
res = Style.RESET_ALL
import warnings
warnings.filterwarnings('ignore')
old_df = pd.read_csv('/kaggle/input/regression-with-neural-networking/concrete_data.csv')
original_df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv').drop(columns=['id'])
test_df = pd.read_csv('/kaggle/input/playground-series-s3e9/test.csv').drop(columns=['id'])
sample = pd.read_csv('/kaggle/input/playground-series-s3e9/sample_submission.csv')
print(f'{red} [Name] ->{blue} Shape\n')
print(f'{gold}[+] {red} [original_df] -> {blue}{original_df.shape}\n')
print(f'{gold}[+] {red} [old_df] -> {blue}{old_df.shape}\n')
print(f'{gold}[+] {red} [test_df] -> {blue}{test_df.shape}\n')
print('\n')
print(f'{red} [Name] ->{blue} Missing Values\n')
print(f'{gold}[+] {red} [original_df] -> {blue}{original_df.isna().any().any()}\n')
print(f'{gold}[+] {red} [old_df] -> {blue}{old_df.isna().any().any()}\n')
print(f'{gold}[+] {red} [test_df] -> {blue}{test_df.isna().any().any()}\n')
print('\n')
original_num_cols_int = [col for col in original_df.columns if original_df[col].dtype == 'int']
original_num_cols_float = [col for col in original_df.columns if original_df[col].dtype == 'float']
original_num_cols_obj = [col for col in original_df.columns if original_df[col].dtype == 'o']
old_num_cols_int = [col for col in old_df.columns if old_df[col].dtype == 'int']
old_num_cols_float = [col for col in old_df.columns if old_df[col].dtype == 'float']
old_num_cols_obj = [col for col in old_df.columns if old_df[col].dtype == 'o']
test_num_cols_int = [col for col in test_df.columns if test_df[col].dtype == 'int']
test_num_cols_float = [col for col in test_df.columns if test_df[col].dtype == 'float']
test_num_cols_obj = [col for col in test_df.columns if test_df[col].dtype == 'o']
print(f'{red} [Name] ->{blue} Dtype -> {megnta}Total Col.\n')
print('\n')
print(f'{gold}[+] {red} [original_df] -> {blue} int32 ->{megnta} {len(original_num_cols_int)} \n')
print(f'{gold}[+] {red} [original_df] -> {blue} flaot32 ->{megnta} {len(original_num_cols_float)}\n')
print(f'{gold}[+] {red} [original_df] -> {blue} obj ->{megnta} {len(original_num_cols_obj)}\n')
print('\n')
print(f'{gold}[+] {red} [old_df] -> {blue} int32 ->{megnta} {len(old_num_cols_int)} \n')
print(f'{gold}[+] {red} [old_df] -> {blue} flaot32 ->{megnta} {len(old_num_cols_float)}\n')
print(f'{gold}[+] {red} [old_df] -> {blue} obj ->{megnta} {len(old_num_cols_obj)}\n')
print('\n')
print(f'{gold}[+] {red} [test_df] -> {blue} int32 ->{megnta} {len(test_num_cols_int)} \n')
print(f'{gold}[+] {red} [test_df] -> {blue} flaot32 ->{megnta} {len(test_num_cols_float)}\n')
print(f'{gold}[+] {red} [test_df] -> {blue} obj ->{megnta} {len(test_num_cols_obj)}\n')
print('\n') | code |
121150047/cell_2 | [
"image_output_1.png"
] | from colorama import Style, Fore
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from xgboost import XGBRegressor
from sklearn.metrics import mean_squared_error
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.base import TransformerMixin
from sklearn.model_selection import KFold
from sklearn.ensemble import GradientBoostingRegressor
from lightgbm import LGBMRegressor
rc = {'axes.facecolor': '#FFF9ED', 'figure.facecolor': '#FFF9ED', 'axes.edgecolor': '#000000', 'grid.color': '#EBEBE7', 'font.family': 'serif', 'axes.labelcolor': '#000000', 'xtick.color': '#000000', 'ytick.color': '#000000', 'grid.alpha': 0.4}
sns.set(rc=rc)
from colorama import Style, Fore
red = Style.BRIGHT + Fore.RED
blue = Style.BRIGHT + Fore.BLUE
megnta = Style.BRIGHT + Fore.MAGENTA
gold = Style.BRIGHT + Fore.YELLOW
res = Style.RESET_ALL
import warnings
warnings.filterwarnings('ignore') | code |
121150047/cell_12 | [
"text_html_output_1.png"
] | from colorama import Style, Fore
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from xgboost import XGBRegressor
from sklearn.metrics import mean_squared_error
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.base import TransformerMixin
from sklearn.model_selection import KFold
from sklearn.ensemble import GradientBoostingRegressor
from lightgbm import LGBMRegressor
rc = {'axes.facecolor': '#FFF9ED', 'figure.facecolor': '#FFF9ED', 'axes.edgecolor': '#000000', 'grid.color': '#EBEBE7', 'font.family': 'serif', 'axes.labelcolor': '#000000', 'xtick.color': '#000000', 'ytick.color': '#000000', 'grid.alpha': 0.4}
sns.set(rc=rc)
from colorama import Style, Fore
red = Style.BRIGHT + Fore.RED
blue = Style.BRIGHT + Fore.BLUE
megnta = Style.BRIGHT + Fore.MAGENTA
gold = Style.BRIGHT + Fore.YELLOW
res = Style.RESET_ALL
import warnings
warnings.filterwarnings('ignore')
old_df = pd.read_csv('/kaggle/input/regression-with-neural-networking/concrete_data.csv')
original_df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv').drop(columns=['id'])
test_df = pd.read_csv('/kaggle/input/playground-series-s3e9/test.csv').drop(columns=['id'])
sample = pd.read_csv('/kaggle/input/playground-series-s3e9/sample_submission.csv')
original_num_cols_int = [col for col in original_df.columns if original_df[col].dtype == 'int']
original_num_cols_float = [col for col in original_df.columns if original_df[col].dtype == 'float']
original_num_cols_obj = [col for col in original_df.columns if original_df[col].dtype == 'o']
old_num_cols_int = [col for col in old_df.columns if old_df[col].dtype == 'int']
old_num_cols_float = [col for col in old_df.columns if old_df[col].dtype == 'float']
old_num_cols_obj = [col for col in old_df.columns if old_df[col].dtype == 'o']
test_num_cols_int = [col for col in test_df.columns if test_df[col].dtype == 'int']
test_num_cols_float = [col for col in test_df.columns if test_df[col].dtype == 'float']
test_num_cols_obj = [col for col in test_df.columns if test_df[col].dtype == 'o']
original_df_column = [col for col in original_df.columns]
old_df_columns = [col for col in old_df.columns]
name_cng_dict = {key: value for key, value in zip(old_df_columns, original_df_column)}
old_df.rename(columns=name_cng_dict, inplace=True)
fig,ax = plt.subplots(3,3,figsize=(15,10),dpi=100)
ax = ax.flatten()
for i,column in enumerate(original_df.columns[:-1]):
plot_axes = [ax[i]]
sns.kdeplot(
original_df[column], label='Original_df',
ax=ax[i], color='#9E3F00'
)
sns.kdeplot(
test_df[column], label='Test_df',
ax=ax[i], color='yellow'
)
sns.kdeplot(
old_df[column], label='Old_df',
ax=ax[i], color='#20BEFF'
)
# titles
ax[i].set_title(f'{column} Distribution');
ax[i].set_xlabel(None)
# remove axes to show only one at the end
plot_axes = [ax[i]]
handles = []
labels = []
for plot_ax in plot_axes:
handles += plot_ax.get_legend_handles_labels()[0]
labels += plot_ax.get_legend_handles_labels()[1]
plot_ax.legend().remove()
for i in range(i+1, len(ax)):
ax[i].axis('off')
fig.suptitle(f'Dataset Feature Distributions\n\n\n', ha='center', fontweight='bold', fontsize=25)
fig.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, 0.96), fontsize=25, ncol=3)
plt.tight_layout()
corr = original_df.corr()
mask = np.triu(np.ones_like(corr, dtype=np.bool))
f, ax = plt.subplots(figsize=(11, 9))
cmap = sns.diverging_palette(220, 10, as_cmap=True)
f.suptitle(f'Heatmap for Original DF\n\n\n', ha='center', fontweight='bold', fontsize=25)
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=0.3, center=0, square=True, linewidths=0.5, cbar_kws={'shrink': 0.5}) | code |
121150047/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
old_df = pd.read_csv('/kaggle/input/regression-with-neural-networking/concrete_data.csv')
original_df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv').drop(columns=['id'])
test_df = pd.read_csv('/kaggle/input/playground-series-s3e9/test.csv').drop(columns=['id'])
sample = pd.read_csv('/kaggle/input/playground-series-s3e9/sample_submission.csv')
original_df.head(3) | code |
90108212/cell_13 | [
"text_html_output_1.png"
] | data | code |
90108212/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
gender = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
women = train_data.loc[train_data.Sex == 'female']['Survived']
rate_women = sum(women) / len(women)
men = train_data.loc[train_data.Sex == 'male']['Survived']
rate_men = sum(men) / len(women)
train_data | code |
90108212/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
gender = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_data.tail() | code |
90108212/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
gender = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
women = train_data.loc[train_data.Sex == 'female']['Survived']
rate_women = sum(women) / len(women)
print('% of women who survived:', rate_women) | code |
90108212/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
gender = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
women = train_data.loc[train_data.Sex == 'female']['Survived']
rate_women = sum(women) / len(women)
men = train_data.loc[train_data.Sex == 'male']['Survived']
rate_men = sum(men) / len(women)
print('% of men who survived:', rate_men) | code |
90108212/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
gender = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_data.head() | code |
90108212/cell_12 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
gender = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
women = train_data.loc[train_data.Sex == 'female']['Survived']
rate_women = sum(women) / len(women)
men = train_data.loc[train_data.Sex == 'male']['Survived']
rate_men = sum(men) / len(women)
from sklearn.ensemble import RandomForestClassifier
y = train_data['Survived']
features = ['Pclass', 'Sex', 'SibSp', 'Parch']
X = pd.get_dummies(train_data[features])
X_test = pd.get_dummies(test_data[features])
model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)
model.fit(X, y)
predictions = model.predict(X_test)
output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions})
output.to_csv('submission.csv', index=False)
print('Your submission was successfully saved!') | code |
90108212/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
gender = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
gender.head() | code |
90148441/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../input/movielens/ratings.dat', sep='::', header=None, names=['UserId', 'MovieId', 'Rating', 'Timestamp'], encoding='latin 1')
data_users = pd.read_csv('../input/movielens/users.dat', sep='::', header=None, names=['UserId', 'Gender', 'Age', 'Occupation', 'Zip-code'], encoding='latin 1')
data_new = [data_users['Age'], data_movies['Genres']]
headers_new = ['Age1', 'Genres1']
df4 = pd.concat(data_new, axis=1, keys=headers_new)
sns.regplot(x=df4['Age1'], y=df4['Genres1']) | code |
90148441/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../input/movielens/ratings.dat', sep='::', header=None, names=['UserId', 'MovieId', 'Rating', 'Timestamp'], encoding='latin 1')
data_users = pd.read_csv('../input/movielens/users.dat', sep='::', header=None, names=['UserId', 'Gender', 'Age', 'Occupation', 'Zip-code'], encoding='latin 1')
sns.kdeplot(data=data_users['Age'], shade=True) | code |
90148441/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../input/movielens/ratings.dat', sep='::', header=None, names=['UserId', 'MovieId', 'Rating', 'Timestamp'], encoding='latin 1')
data_users = pd.read_csv('../input/movielens/users.dat', sep='::', header=None, names=['UserId', 'Gender', 'Age', 'Occupation', 'Zip-code'], encoding='latin 1')
data_movies.head() | code |
90148441/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../input/movielens/ratings.dat', sep='::', header=None, names=['UserId', 'MovieId', 'Rating', 'Timestamp'], encoding='latin 1')
data_users = pd.read_csv('../input/movielens/users.dat', sep='::', header=None, names=['UserId', 'Gender', 'Age', 'Occupation', 'Zip-code'], encoding='latin 1')
data_ratings.head() | code |
90148441/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../input/movielens/ratings.dat', sep='::', header=None, names=['UserId', 'MovieId', 'Rating', 'Timestamp'], encoding='latin 1')
data_users = pd.read_csv('../input/movielens/users.dat', sep='::', header=None, names=['UserId', 'Gender', 'Age', 'Occupation', 'Zip-code'], encoding='latin 1')
data_new = [data_users['Age'], data_movies['Genres']]
headers_new = ['Age1', 'Genres1']
df4 = pd.concat(data_new, axis=1, keys=headers_new)
df4.head() | code |
90148441/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 |
90148441/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../input/movielens/ratings.dat', sep='::', header=None, names=['UserId', 'MovieId', 'Rating', 'Timestamp'], encoding='latin 1')
data_users = pd.read_csv('../input/movielens/users.dat', sep='::', header=None, names=['UserId', 'Gender', 'Age', 'Occupation', 'Zip-code'], encoding='latin 1')
data_ratings.sort_values(by='Rating', ascending=False).head(25) | code |
90148441/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../input/movielens/ratings.dat', sep='::', header=None, names=['UserId', 'MovieId', 'Rating', 'Timestamp'], encoding='latin 1')
data_users = pd.read_csv('../input/movielens/users.dat', sep='::', header=None, names=['UserId', 'Gender', 'Age', 'Occupation', 'Zip-code'], encoding='latin 1')
data_users.head() | code |
90148441/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../input/movielens/ratings.dat', sep='::', header=None, names=['UserId', 'MovieId', 'Rating', 'Timestamp'], encoding='latin 1')
data_users = pd.read_csv('../input/movielens/users.dat', sep='::', header=None, names=['UserId', 'Gender', 'Age', 'Occupation', 'Zip-code'], encoding='latin 1') | code |
322568/cell_13 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0
titanic.loc[titanic['Sex'] == 'female', 'Sex'] = 1
titanic.loc[titanic['Embarked'] == 'S'] = 0
titanic.loc[titanic['Embarked'] == 'C'] = 1
titanic.loc[titanic['Embarked'] == 'Q'] = 2
print(titanic['Embarked']) | code |
322568/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_8.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0
titanic.loc[titanic['Sex'] == 'female', 'Sex'] = 1
print(titanic['Sex']) | code |
322568/cell_4 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
print(titanic.head()) | code |
322568/cell_1 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
titanic = pd.read_csv('../input/train.csv')
print(titanic.describe()) | code |
322568/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0
print(titanic['Sex']) | code |
322568/cell_15 | [
"text_plain_output_1.png"
] | from sklearn import cross_validation
from sklearn.linear_model import LogisticRegression
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0
titanic.loc[titanic['Sex'] == 'female', 'Sex'] = 1
titanic.loc[titanic['Embarked'] == 'S'] = 0
titanic.loc[titanic['Embarked'] == 'C'] = 1
titanic.loc[titanic['Embarked'] == 'Q'] = 2
from sklearn.linear_model import LinearRegression
from sklearn.cross_validation import KFold
predictors = ['Pclass', 'Sex', 'Age', 'SibSp', 'Embarked', 'Fare']
from sklearn import cross_validation
from sklearn.linear_model import LogisticRegression
alg = LogisticRegression(random_state=1)
accuracy = cross_validation.cross_val_score(alg, titanic[predictors], titanic['Survived'], cv=3)
mean = np.mean(accuracy)
titanic_test = pd.read_csv('../input/test.csv')
titanic_test['Age'] = titanic_test['Age'].fillna(titanic['Age'].median())
titanic_test['Fare'] = titanic_test['Fare'].fillna(titanic_test['Fare'].median())
titanic_test.loc[titanic_test['Sex'] == 'male', 'Sex'] = 0
titanic_test.loc[titanic_test['Sex'] == 'female', 'Sex'] = 1
titanic_test['Embarked'] = titanic_test['Embarked'].fillna('S')
titanic_test.loc[titanic_test['Embarked'] == 'S', 'Embarked'] = 0
titanic_test.loc[titanic_test['Embarked'] == 'C', 'Embarked'] = 1
titanic_test.loc[titanic_test['Embarked'] == 'Q', 'Embarked'] = 2
alg = LogisticRegression(random_state=1)
alg.fit(titanic[predictors], titanic['Survived'])
predictions = alg.predict(titanic_test[predictors])
print(len(predictions)) | code |
322568/cell_3 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
print(titanic.describe()) | code |
322568/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.cross_validation import KFold
predictors = ['Pclass', 'Sex', 'Age', 'SibSp', 'Embarked', 'Fare'] | code |
322568/cell_10 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0
titanic.loc[titanic['Sex'] == 'female', 'Sex'] = 1
print(titanic['Embarked'].count())
print(titanic['Embarked'].unique()) | code |
322568/cell_12 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0
titanic.loc[titanic['Sex'] == 'female', 'Sex'] = 1
print(titanic['Embarked'].unique()) | code |
322568/cell_5 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
print(titanic['Cabin'].count()) | code |
34147377/cell_63 | [
"text_plain_output_1.png"
] | from scipy.special import boxcox1p
import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
test_data.shape
cat_data = train_data.select_dtypes(include='object')
cat_cols = cat_data.columns
num_data = train_data.select_dtypes(exclude='object')
num_cols = num_data.columns
num_to_cat_cols = ['MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond']
num_cols = [i for i in num_cols if not i in num_to_cat_cols]
num_cols = [i for i in num_cols if not i in ['Id']]
num_data = num_data.drop(['Id', 'MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond'], axis=1)
skew_dict = {}
for cols in num_cols:
skew_dict[cols] = {'Skewness': train_data[cols].skew()}
skew_df = pd.DataFrame(skew_dict).transpose()
skew_df.columns = ['Skewness']
skew_df.sort_values(by=['Skewness'], ascending=False)
train_data = train_data.drop(train_data[train_data['Id'] == 524].index)
train_data = train_data.drop(train_data[train_data['Id'] == 1299].index)
model_data = pd.concat([train_data, test_data], axis=0, sort=None, ignore_index=True)
model_data.shape
numeric_feats = model_data.dtypes[model_data.dtypes != 'object'].index
numeric_feats = [elem for elem in numeric_feats if elem not in ('Id', 'SalePrice')]
from scipy.special import boxcox1p
lam = 0.15
for feat in numeric_feats:
model_data[feat] = boxcox1p(model_data[feat], lam)
model_data_new = model_data.copy()
model_data_new = model_data_new.drop(['Id'], axis=1)
model_data_new = pd.get_dummies(model_data_new)
model_data_updated = pd.concat([model_data['Id'], model_data_new], axis=1)
model_train_data = model_data_updated[model_data_updated.Id < 1461]
model_test_data = model_data_updated[model_data_updated.Id > 1460]
print('Train Data Shape: ', model_train_data.shape)
print('Test Data Shape : ', model_test_data.shape) | code |
34147377/cell_21 | [
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
cat_data = train_data.select_dtypes(include='object')
cat_cols = cat_data.columns
num_data = train_data.select_dtypes(exclude='object')
num_cols = num_data.columns
num_to_cat_cols = ['MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond']
num_cols = [i for i in num_cols if not i in num_to_cat_cols]
num_cols = [i for i in num_cols if not i in ['Id']]
num_data = num_data.drop(['Id', 'MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond'], axis=1)
print('There are %d Num , %d Cat, %d Num-Cat columns.' % (len(num_cols), len(cat_cols), len(num_to_cat_cols))) | code |
34147377/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
test_data.shape | code |
34147377/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(figsize=(10,5))
sns.distplot(train_data['SalePrice'])
plt.tight_layout()
plt.show()
fig = plt.figure(figsize=(10,5))
sns.distplot(np.log1p(train_data['SalePrice']))
plt.tight_layout()
plt.show()
cat_data = train_data.select_dtypes(include='object')
cat_cols = cat_data.columns
num_data = train_data.select_dtypes(exclude='object')
num_cols = num_data.columns
num_to_cat_cols = ['MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond']
num_cols = [i for i in num_cols if not i in num_to_cat_cols]
num_cols = [i for i in num_cols if not i in ['Id']]
num_data = num_data.drop(['Id', 'MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond'], axis=1)
for i in range(len(cat_data.columns)):
f, ax = plt.subplots(figsize=(7, 4))
fig = sns.countplot(cat_data.iloc[:, i].dropna())
plt.xlabel(cat_data.columns[i])
plt.xticks(rotation=60) | code |
34147377/cell_30 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(figsize=(10,5))
sns.distplot(train_data['SalePrice'])
plt.tight_layout()
plt.show()
fig = plt.figure(figsize=(10,5))
sns.distplot(np.log1p(train_data['SalePrice']))
plt.tight_layout()
plt.show()
cat_data = train_data.select_dtypes(include='object')
cat_cols = cat_data.columns
num_data = train_data.select_dtypes(exclude='object')
num_cols = num_data.columns
num_to_cat_cols = ['MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond']
num_cols = [i for i in num_cols if not i in num_to_cat_cols]
num_cols = [i for i in num_cols if not i in ['Id']]
num_data = num_data.drop(['Id', 'MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond'], axis=1)
for i in range(len(cat_data.columns)):
f, ax = plt.subplots(figsize=(7, 4))
fig = sns.countplot(cat_data.iloc[:,i].dropna())
plt.xlabel(cat_data.columns[i])
plt.xticks(rotation=60)
for i in range(len(num_data.columns)):
f, ax = plt.subplots(figsize=(7, 4))
fig = sns.distplot(num_data.iloc[:,i].dropna(), rug=False, hist=False, kde_kws={'bw':0.1})
plt.xlabel(num_data.columns[i])
fig = plt.figure(figsize=(12, 18))
for i in range(len(num_data.columns)):
fig.add_subplot(9, 4, i + 1)
sns.scatterplot(num_data.iloc[:, i], num_data['SalePrice'])
plt.tight_layout()
plt.show() | code |
34147377/cell_33 | [
"image_output_11.png",
"image_output_24.png",
"image_output_25.png",
"image_output_17.png",
"image_output_30.png",
"image_output_14.png",
"image_output_28.png",
"image_output_23.png",
"image_output_13.png",
"image_output_5.png",
"image_output_18.png",
"image_output_21.png",
"image_output_7.png",
"image_output_31.png",
"image_output_20.png",
"image_output_32.png",
"image_output_4.png",
"image_output_8.png",
"image_output_16.png",
"image_output_27.png",
"image_output_6.png",
"image_output_12.png",
"image_output_22.png",
"image_output_3.png",
"image_output_29.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_15.png",
"image_output_9.png",
"image_output_19.png",
"image_output_26.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(figsize=(10,5))
sns.distplot(train_data['SalePrice'])
plt.tight_layout()
plt.show()
fig = plt.figure(figsize=(10,5))
sns.distplot(np.log1p(train_data['SalePrice']))
plt.tight_layout()
plt.show()
cat_data = train_data.select_dtypes(include='object')
cat_cols = cat_data.columns
num_data = train_data.select_dtypes(exclude='object')
num_cols = num_data.columns
num_to_cat_cols = ['MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond']
num_cols = [i for i in num_cols if not i in num_to_cat_cols]
num_cols = [i for i in num_cols if not i in ['Id']]
num_data = num_data.drop(['Id', 'MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond'], axis=1)
for i in range(len(cat_data.columns)):
f, ax = plt.subplots(figsize=(7, 4))
fig = sns.countplot(cat_data.iloc[:,i].dropna())
plt.xlabel(cat_data.columns[i])
plt.xticks(rotation=60)
for i in range(len(num_data.columns)):
f, ax = plt.subplots(figsize=(7, 4))
fig = sns.distplot(num_data.iloc[:,i].dropna(), rug=False, hist=False, kde_kws={'bw':0.1})
plt.xlabel(num_data.columns[i])
fig = plt.figure(figsize=(12,18))
for i in range(len(num_data.columns)):
fig.add_subplot(9, 4, i+1)
sns.scatterplot(num_data.iloc[:, i], num_data['SalePrice'])
plt.tight_layout()
plt.show()
corr_matrix = train_data[num_cols].corr()
corr_matrix['SalePrice'].sort_values(ascending=False)
sns.heatmap(corr_matrix) | code |
34147377/cell_26 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(figsize=(10,5))
sns.distplot(train_data['SalePrice'])
plt.tight_layout()
plt.show()
fig = plt.figure(figsize=(10,5))
sns.distplot(np.log1p(train_data['SalePrice']))
plt.tight_layout()
plt.show()
cat_data = train_data.select_dtypes(include='object')
cat_cols = cat_data.columns
num_data = train_data.select_dtypes(exclude='object')
num_cols = num_data.columns
num_to_cat_cols = ['MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond']
num_cols = [i for i in num_cols if not i in num_to_cat_cols]
num_cols = [i for i in num_cols if not i in ['Id']]
num_data = num_data.drop(['Id', 'MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond'], axis=1)
for i in range(len(cat_data.columns)):
f, ax = plt.subplots(figsize=(7, 4))
fig = sns.countplot(cat_data.iloc[:,i].dropna())
plt.xlabel(cat_data.columns[i])
plt.xticks(rotation=60)
for i in range(len(num_data.columns)):
f, ax = plt.subplots(figsize=(7, 4))
fig = sns.distplot(num_data.iloc[:, i].dropna(), rug=False, hist=False, kde_kws={'bw': 0.1})
plt.xlabel(num_data.columns[i]) | code |
34147377/cell_48 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
test_data.shape
cat_data = train_data.select_dtypes(include='object')
cat_cols = cat_data.columns
num_data = train_data.select_dtypes(exclude='object')
num_cols = num_data.columns
num_to_cat_cols = ['MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond']
num_cols = [i for i in num_cols if not i in num_to_cat_cols]
num_cols = [i for i in num_cols if not i in ['Id']]
num_data = num_data.drop(['Id', 'MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond'], axis=1)
skew_dict = {}
for cols in num_cols:
skew_dict[cols] = {'Skewness': train_data[cols].skew()}
skew_df = pd.DataFrame(skew_dict).transpose()
skew_df.columns = ['Skewness']
skew_df.sort_values(by=['Skewness'], ascending=False)
train_data = train_data.drop(train_data[train_data['Id'] == 524].index)
train_data = train_data.drop(train_data[train_data['Id'] == 1299].index)
model_data = pd.concat([train_data, test_data], axis=0, sort=None, ignore_index=True)
model_data.tail() | code |
34147377/cell_61 | [
"image_output_1.png"
] | from scipy.special import boxcox1p
import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
test_data.shape
cat_data = train_data.select_dtypes(include='object')
cat_cols = cat_data.columns
num_data = train_data.select_dtypes(exclude='object')
num_cols = num_data.columns
num_to_cat_cols = ['MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond']
num_cols = [i for i in num_cols if not i in num_to_cat_cols]
num_cols = [i for i in num_cols if not i in ['Id']]
num_data = num_data.drop(['Id', 'MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond'], axis=1)
skew_dict = {}
for cols in num_cols:
skew_dict[cols] = {'Skewness': train_data[cols].skew()}
skew_df = pd.DataFrame(skew_dict).transpose()
skew_df.columns = ['Skewness']
skew_df.sort_values(by=['Skewness'], ascending=False)
train_data = train_data.drop(train_data[train_data['Id'] == 524].index)
train_data = train_data.drop(train_data[train_data['Id'] == 1299].index)
model_data = pd.concat([train_data, test_data], axis=0, sort=None, ignore_index=True)
model_data.shape
numeric_feats = model_data.dtypes[model_data.dtypes != 'object'].index
numeric_feats = [elem for elem in numeric_feats if elem not in ('Id', 'SalePrice')]
from scipy.special import boxcox1p
lam = 0.15
for feat in numeric_feats:
model_data[feat] = boxcox1p(model_data[feat], lam)
model_data_new = model_data.copy()
model_data_new = model_data_new.drop(['Id'], axis=1)
model_data_new = pd.get_dummies(model_data_new)
model_data_updated = pd.concat([model_data['Id'], model_data_new], axis=1)
model_data_updated.head() | code |
34147377/cell_54 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
test_data.shape
cat_data = train_data.select_dtypes(include='object')
cat_cols = cat_data.columns
num_data = train_data.select_dtypes(exclude='object')
num_cols = num_data.columns
num_to_cat_cols = ['MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond']
num_cols = [i for i in num_cols if not i in num_to_cat_cols]
num_cols = [i for i in num_cols if not i in ['Id']]
num_data = num_data.drop(['Id', 'MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond'], axis=1)
skew_dict = {}
for cols in num_cols:
skew_dict[cols] = {'Skewness': train_data[cols].skew()}
skew_df = pd.DataFrame(skew_dict).transpose()
skew_df.columns = ['Skewness']
skew_df.sort_values(by=['Skewness'], ascending=False)
train_data = train_data.drop(train_data[train_data['Id'] == 524].index)
train_data = train_data.drop(train_data[train_data['Id'] == 1299].index)
model_data = pd.concat([train_data, test_data], axis=0, sort=None, ignore_index=True)
model_data.shape | code |
34147377/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape | code |
34147377/cell_18 | [
"text_plain_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(figsize=(10,5))
sns.distplot(train_data['SalePrice'])
plt.tight_layout()
plt.show()
from scipy import stats
fig = plt.figure(figsize=(10,5))
sns.distplot(np.log1p(train_data['SalePrice']))
plt.tight_layout()
plt.show()
stats.probplot(np.log1p(train_data['SalePrice']), plot=plt) | code |
34147377/cell_32 | [
"image_output_11.png",
"image_output_24.png",
"image_output_25.png",
"image_output_17.png",
"image_output_30.png",
"image_output_14.png",
"image_output_39.png",
"image_output_28.png",
"image_output_23.png",
"image_output_34.png",
"image_output_13.png",
"image_output_40.png",
"image_output_5.png",
"image_output_18.png",
"image_output_21.png",
"image_output_7.png",
"image_output_31.png",
"image_output_20.png",
"image_output_32.png",
"image_output_4.png",
"image_output_42.png",
"image_output_35.png",
"image_output_41.png",
"image_output_36.png",
"image_output_8.png",
"image_output_37.png",
"image_output_16.png",
"image_output_27.png",
"image_output_6.png",
"image_output_12.png",
"image_output_22.png",
"image_output_3.png",
"image_output_29.png",
"image_output_43.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_33.png",
"image_output_15.png",
"image_output_9.png",
"image_output_19.png",
"image_output_38.png",
"image_output_26.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
cat_data = train_data.select_dtypes(include='object')
cat_cols = cat_data.columns
num_data = train_data.select_dtypes(exclude='object')
num_cols = num_data.columns
num_to_cat_cols = ['MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond']
num_cols = [i for i in num_cols if not i in num_to_cat_cols]
num_cols = [i for i in num_cols if not i in ['Id']]
num_data = num_data.drop(['Id', 'MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond'], axis=1)
corr_matrix = train_data[num_cols].corr()
corr_matrix['SalePrice'].sort_values(ascending=False) | code |
34147377/cell_51 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
test_data.shape
fig = plt.figure(figsize=(10,5))
sns.distplot(train_data['SalePrice'])
plt.tight_layout()
plt.show()
fig = plt.figure(figsize=(10,5))
sns.distplot(np.log1p(train_data['SalePrice']))
plt.tight_layout()
plt.show()
cat_data = train_data.select_dtypes(include='object')
cat_cols = cat_data.columns
num_data = train_data.select_dtypes(exclude='object')
num_cols = num_data.columns
num_to_cat_cols = ['MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond']
num_cols = [i for i in num_cols if not i in num_to_cat_cols]
num_cols = [i for i in num_cols if not i in ['Id']]
num_data = num_data.drop(['Id', 'MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond'], axis=1)
for i in range(len(cat_data.columns)):
f, ax = plt.subplots(figsize=(7, 4))
fig = sns.countplot(cat_data.iloc[:,i].dropna())
plt.xlabel(cat_data.columns[i])
plt.xticks(rotation=60)
for i in range(len(num_data.columns)):
f, ax = plt.subplots(figsize=(7, 4))
fig = sns.distplot(num_data.iloc[:,i].dropna(), rug=False, hist=False, kde_kws={'bw':0.1})
plt.xlabel(num_data.columns[i])
skew_dict = {}
for cols in num_cols:
skew_dict[cols] = {'Skewness': train_data[cols].skew()}
skew_df = pd.DataFrame(skew_dict).transpose()
skew_df.columns = ['Skewness']
skew_df.sort_values(by=['Skewness'], ascending=False)
fig = plt.figure(figsize=(12,18))
for i in range(len(num_data.columns)):
fig.add_subplot(9, 4, i+1)
sns.scatterplot(num_data.iloc[:, i], num_data['SalePrice'])
plt.tight_layout()
plt.show()
corr_matrix = train_data[num_cols].corr()
corr_matrix['SalePrice'].sort_values(ascending=False)
train_data = train_data.drop(train_data[train_data['Id'] == 524].index)
train_data = train_data.drop(train_data[train_data['Id'] == 1299].index)
model_data = pd.concat([train_data, test_data], axis=0, sort=None, ignore_index=True)
sns.lmplot('Age', 'SalePrice', data=model_data, height=8)
plt.title('Age vs SalePrice')
plt.show() | code |
34147377/cell_59 | [
"text_html_output_1.png"
] | from scipy.special import boxcox1p
import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
test_data.shape
cat_data = train_data.select_dtypes(include='object')
cat_cols = cat_data.columns
num_data = train_data.select_dtypes(exclude='object')
num_cols = num_data.columns
num_to_cat_cols = ['MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond']
num_cols = [i for i in num_cols if not i in num_to_cat_cols]
num_cols = [i for i in num_cols if not i in ['Id']]
num_data = num_data.drop(['Id', 'MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond'], axis=1)
skew_dict = {}
for cols in num_cols:
skew_dict[cols] = {'Skewness': train_data[cols].skew()}
skew_df = pd.DataFrame(skew_dict).transpose()
skew_df.columns = ['Skewness']
skew_df.sort_values(by=['Skewness'], ascending=False)
train_data = train_data.drop(train_data[train_data['Id'] == 524].index)
train_data = train_data.drop(train_data[train_data['Id'] == 1299].index)
model_data = pd.concat([train_data, test_data], axis=0, sort=None, ignore_index=True)
model_data.shape
numeric_feats = model_data.dtypes[model_data.dtypes != 'object'].index
numeric_feats = [elem for elem in numeric_feats if elem not in ('Id', 'SalePrice')]
from scipy.special import boxcox1p
lam = 0.15
for feat in numeric_feats:
model_data[feat] = boxcox1p(model_data[feat], lam)
model_data.head() | code |
34147377/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
train_data.info() | code |
34147377/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(figsize=(10,5))
sns.distplot(train_data['SalePrice'])
plt.tight_layout()
plt.show()
fig = plt.figure(figsize=(10, 5))
sns.distplot(np.log1p(train_data['SalePrice']))
plt.tight_layout()
plt.show() | code |
34147377/cell_35 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(figsize=(10,5))
sns.distplot(train_data['SalePrice'])
plt.tight_layout()
plt.show()
fig = plt.figure(figsize=(10,5))
sns.distplot(np.log1p(train_data['SalePrice']))
plt.tight_layout()
plt.show()
cat_data = train_data.select_dtypes(include='object')
cat_cols = cat_data.columns
num_data = train_data.select_dtypes(exclude='object')
num_cols = num_data.columns
num_to_cat_cols = ['MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond']
num_cols = [i for i in num_cols if not i in num_to_cat_cols]
num_cols = [i for i in num_cols if not i in ['Id']]
num_data = num_data.drop(['Id', 'MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond'], axis=1)
for i in range(len(cat_data.columns)):
f, ax = plt.subplots(figsize=(7, 4))
fig = sns.countplot(cat_data.iloc[:,i].dropna())
plt.xlabel(cat_data.columns[i])
plt.xticks(rotation=60)
for i in range(len(num_data.columns)):
f, ax = plt.subplots(figsize=(7, 4))
fig = sns.distplot(num_data.iloc[:,i].dropna(), rug=False, hist=False, kde_kws={'bw':0.1})
plt.xlabel(num_data.columns[i])
fig = plt.figure(figsize=(12,18))
for i in range(len(num_data.columns)):
fig.add_subplot(9, 4, i+1)
sns.scatterplot(num_data.iloc[:, i], num_data['SalePrice'])
plt.tight_layout()
plt.show()
corr_matrix = train_data[num_cols].corr()
corr_matrix['SalePrice'].sort_values(ascending=False)
sns.lmplot('GrLivArea', 'SalePrice', data=train_data, height=8)
plt.title('GrLivArea vs SalePrice')
plt.show() | code |
34147377/cell_14 | [
"text_plain_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(figsize=(10,5))
sns.distplot(train_data['SalePrice'])
plt.tight_layout()
plt.show()
from scipy import stats
stats.probplot(train_data['SalePrice'], plot=plt) | code |
34147377/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
test_data.shape
test_data.info() | code |
34147377/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
cat_data = train_data.select_dtypes(include='object')
cat_cols = cat_data.columns
num_data = train_data.select_dtypes(exclude='object')
num_cols = num_data.columns
num_to_cat_cols = ['MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond']
num_cols = [i for i in num_cols if not i in num_to_cat_cols]
num_cols = [i for i in num_cols if not i in ['Id']]
num_data = num_data.drop(['Id', 'MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond'], axis=1)
skew_dict = {}
for cols in num_cols:
skew_dict[cols] = {'Skewness': train_data[cols].skew()}
skew_df = pd.DataFrame(skew_dict).transpose()
skew_df.columns = ['Skewness']
skew_df.sort_values(by=['Skewness'], ascending=False) | code |
34147377/cell_37 | [
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
cat_data = train_data.select_dtypes(include='object')
cat_cols = cat_data.columns
num_data = train_data.select_dtypes(exclude='object')
num_cols = num_data.columns
num_to_cat_cols = ['MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond']
num_cols = [i for i in num_cols if not i in num_to_cat_cols]
num_cols = [i for i in num_cols if not i in ['Id']]
num_data = num_data.drop(['Id', 'MSSubClass', 'MoSold', 'YrSold', 'OverallQual', 'OverallCond'], axis=1)
train_data[(train_data['GrLivArea'] > 4000) & (train_data['SalePrice'] < 300000)] | code |
34147377/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(figsize=(10, 5))
sns.distplot(train_data['SalePrice'])
plt.tight_layout()
plt.show() | code |
34147377/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.head() | code |
106191058/cell_8 | [
"image_output_1.png"
] | import json
import matplotlib.pyplot as plt
import pandas as pd
import requests
import seaborn as sns
token_address = '0xd7efb00d12c2c13131fd319336fdf952525da2af'
base_url = 'https://api.ethplorer.io'
url = base_url + f'/getTokenInfo/{token_address}?apiKey=freekey'
response = requests.get(url)
if response.status_code == 200:
token_info_response = json.loads(response.text)
token_info_response
url = base_url + f'/getTopTokenHolders/{token_address}?apiKey=freekey&limit=100'
response = requests.get(url)
if response.status_code == 200:
token_holders_response = json.loads(response.text)
token_holders_df = pd.DataFrame(token_holders_response['holders'])
n_top_holders_list = ['1', '2', '3', '5', '10', '25', '100']
shares_list = [round(token_holders_df['share'].values[:int(n)].sum(), 2) for n in n_top_holders_list]
plt.figure(figsize=(12, 6))
ax = sns.barplot(x=n_top_holders_list, y=shares_list, alpha=0.8, color=color[3])
ax.bar_label(ax.containers[0])
plt.xlabel('Top N Wallets', fontsize=12)
plt.ylabel('Cumulative percentage of Token share', fontsize=12)
plt.title('Percentage of tokens hodl by top N wallets', fontsize=16)
plt.show() | code |
106191058/cell_5 | [
"text_plain_output_1.png"
] | import json
import requests
token_address = '0xd7efb00d12c2c13131fd319336fdf952525da2af'
base_url = 'https://api.ethplorer.io'
url = base_url + f'/getTokenInfo/{token_address}?apiKey=freekey'
response = requests.get(url)
if response.status_code == 200:
token_info_response = json.loads(response.text)
token_info_response | code |
106194498/cell_2 | [
"text_plain_output_1.png"
] | a = 'Functions'
len(a) | code |
106194498/cell_5 | [
"text_plain_output_1.png"
] | def my_first_function():
pass
my_first_function() | code |
17096880/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
import numpy as np
def multiclass_logloss(actual, predicted, eps=1e-15):
"""Multi class version of Logarithmic Loss metric.
:param actual: Array containing the actual target classes
:param predicted: Matrix with class predictions, one probability per class
"""
if len(actual.shape) == 1:
actual2 = np.zeros((actual.shape[0], predicted.shape[1]))
for i, val in enumerate(actual):
actual2[i, val] = 1
actual = actual2
clip = np.clip(predicted, eps, 1 - eps)
rows = actual.shape[0]
vsota = np.sum(actual * np.log(clip))
return -1.0 / rows * vsota
tfv = TfidfVectorizer(min_df=3, max_features=None, strip_accents='unicode', analyzer='word', token_pattern='\\w{1,}', ngram_range=(1, 3), use_idf=1, smooth_idf=1, sublinear_tf=1, stop_words='english')
tfv.fit(list(xtrain) + list(xvalid))
xtrain_tfv = tfv.transform(xtrain)
xvalid_tfv = tfv.transform(xvalid)
clf = LogisticRegression(C=1.0)
clf.fit(xtrain_tfv, ytrain)
predictions = clf.predict_proba(xvalid_tfv)
ctv = CountVectorizer(analyzer='word', token_pattern='\\w{1,}', ngram_range=(1, 3), stop_words='english')
ctv.fit(list(xtrain) + list(xvalid))
xtrain_ctv = ctv.transform(xtrain)
xvalid_ctv = ctv.transform(xvalid)
clf = MultinomialNB()
clf.fit(xtrain_ctv, ytrain)
predictions = clf.predict_proba(xvalid_ctv)
print('logloss: %0.3f ' % multiclass_logloss(yvalid, predictions)) | code |
17096880/cell_25 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing, decomposition, model_selection, metrics, pipeline
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import SVC
import numpy as np
import pandas as pd
import xgboost as xgb
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sample = pd.read_csv('../input/sample_submission.csv')
train.author.nunique()
def multiclass_logloss(actual, predicted, eps=1e-15):
"""Multi class version of Logarithmic Loss metric.
:param actual: Array containing the actual target classes
:param predicted: Matrix with class predictions, one probability per class
"""
if len(actual.shape) == 1:
actual2 = np.zeros((actual.shape[0], predicted.shape[1]))
for i, val in enumerate(actual):
actual2[i, val] = 1
actual = actual2
clip = np.clip(predicted, eps, 1 - eps)
rows = actual.shape[0]
vsota = np.sum(actual * np.log(clip))
return -1.0 / rows * vsota
lbl_enc = preprocessing.LabelEncoder()
y = lbl_enc.fit_transform(train.author.values)
tfv = TfidfVectorizer(min_df=3, max_features=None, strip_accents='unicode', analyzer='word', token_pattern='\\w{1,}', ngram_range=(1, 3), use_idf=1, smooth_idf=1, sublinear_tf=1, stop_words='english')
tfv.fit(list(xtrain) + list(xvalid))
xtrain_tfv = tfv.transform(xtrain)
xvalid_tfv = tfv.transform(xvalid)
clf = LogisticRegression(C=1.0)
clf.fit(xtrain_tfv, ytrain)
predictions = clf.predict_proba(xvalid_tfv)
ctv = CountVectorizer(analyzer='word', token_pattern='\\w{1,}', ngram_range=(1, 3), stop_words='english')
ctv.fit(list(xtrain) + list(xvalid))
xtrain_ctv = ctv.transform(xtrain)
xvalid_ctv = ctv.transform(xvalid)
clf = MultinomialNB()
clf.fit(xtrain_ctv, ytrain)
predictions = clf.predict_proba(xvalid_ctv)
svd = decomposition.TruncatedSVD(n_components=120)
svd.fit(xtrain_tfv)
xtrain_svd = svd.transform(xtrain_tfv)
xvalid_svd = svd.transform(xvalid_tfv)
scl = preprocessing.StandardScaler()
scl.fit(xtrain_svd)
xtrain_svd_scl = scl.transform(xtrain_svd)
xvalid_svd_scl = scl.transform(xvalid_svd)
clf = SVC(C=1.0, probability=True)
clf.fit(xtrain_svd_scl, ytrain)
predictions = clf.predict_proba(xvalid_svd_scl)
clf = xgb.XGBClassifier(max_depth=7, n_estimators=200, colsample_bytree=0.8, subsample=0.8, nthread=10, learning_rate=0.1)
clf.fit(xtrain_tfv.tocsc(), ytrain)
predictions = clf.predict_proba(xvalid_tfv.tocsc())
clf = xgb.XGBClassifier(max_depth=7, n_estimators=200, colsample_bytree=0.8, subsample=0.8, nthread=10, learning_rate=0.1)
clf.fit(xtrain_ctv.tocsc(), ytrain)
predictions = clf.predict_proba(xvalid_ctv.tocsc())
clf = xgb.XGBClassifier(max_depth=7, n_estimators=200, colsample_bytree=0.8, subsample=0.8, nthread=10, learning_rate=0.1)
clf.fit(xtrain_svd, ytrain)
predictions = clf.predict_proba(xvalid_svd)
mll_scorer = metrics.make_scorer(multiclass_logloss, greater_is_better=False, needs_proba=True)
svd = TruncatedSVD()
scl = preprocessing.StandardScaler()
lr_model = LogisticRegression()
clf = pipeline.Pipeline([('svd', svd), ('scl', scl), ('lr', lr_model)])
param_grid = {'svd__n_components': [120, 180], 'lr__C': [0.1, 1.0, 10], 'lr__penalty': ['l1', 'l2']}
model = GridSearchCV(estimator=clf, param_grid=param_grid, scoring=mll_scorer, verbose=1, n_jobs=-1, iid=True, refit=True, cv=2)
model.fit(xtrain_tfv, ytrain)
print('Best score: %0.3f' % model.best_score_)
print('Best parameters set:')
best_parameters = model.best_estimator_.get_params()
for param_name in sorted(param_grid.keys()):
print('\t%s: %r' % (param_name, best_parameters[param_name])) | code |
17096880/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sample = pd.read_csv('../input/sample_submission.csv')
train.author.nunique() | code |
17096880/cell_20 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing, decomposition, model_selection, metrics, pipeline
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import SVC
import numpy as np
import pandas as pd
import xgboost as xgb
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sample = pd.read_csv('../input/sample_submission.csv')
train.author.nunique()
def multiclass_logloss(actual, predicted, eps=1e-15):
"""Multi class version of Logarithmic Loss metric.
:param actual: Array containing the actual target classes
:param predicted: Matrix with class predictions, one probability per class
"""
if len(actual.shape) == 1:
actual2 = np.zeros((actual.shape[0], predicted.shape[1]))
for i, val in enumerate(actual):
actual2[i, val] = 1
actual = actual2
clip = np.clip(predicted, eps, 1 - eps)
rows = actual.shape[0]
vsota = np.sum(actual * np.log(clip))
return -1.0 / rows * vsota
lbl_enc = preprocessing.LabelEncoder()
y = lbl_enc.fit_transform(train.author.values)
tfv = TfidfVectorizer(min_df=3, max_features=None, strip_accents='unicode', analyzer='word', token_pattern='\\w{1,}', ngram_range=(1, 3), use_idf=1, smooth_idf=1, sublinear_tf=1, stop_words='english')
tfv.fit(list(xtrain) + list(xvalid))
xtrain_tfv = tfv.transform(xtrain)
xvalid_tfv = tfv.transform(xvalid)
clf = LogisticRegression(C=1.0)
clf.fit(xtrain_tfv, ytrain)
predictions = clf.predict_proba(xvalid_tfv)
ctv = CountVectorizer(analyzer='word', token_pattern='\\w{1,}', ngram_range=(1, 3), stop_words='english')
ctv.fit(list(xtrain) + list(xvalid))
xtrain_ctv = ctv.transform(xtrain)
xvalid_ctv = ctv.transform(xvalid)
clf = MultinomialNB()
clf.fit(xtrain_ctv, ytrain)
predictions = clf.predict_proba(xvalid_ctv)
svd = decomposition.TruncatedSVD(n_components=120)
svd.fit(xtrain_tfv)
xtrain_svd = svd.transform(xtrain_tfv)
xvalid_svd = svd.transform(xvalid_tfv)
scl = preprocessing.StandardScaler()
scl.fit(xtrain_svd)
xtrain_svd_scl = scl.transform(xtrain_svd)
xvalid_svd_scl = scl.transform(xvalid_svd)
clf = SVC(C=1.0, probability=True)
clf.fit(xtrain_svd_scl, ytrain)
predictions = clf.predict_proba(xvalid_svd_scl)
clf = xgb.XGBClassifier(max_depth=7, n_estimators=200, colsample_bytree=0.8, subsample=0.8, nthread=10, learning_rate=0.1)
clf.fit(xtrain_tfv.tocsc(), ytrain)
predictions = clf.predict_proba(xvalid_tfv.tocsc())
clf = xgb.XGBClassifier(max_depth=7, n_estimators=200, colsample_bytree=0.8, subsample=0.8, nthread=10, learning_rate=0.1)
clf.fit(xtrain_ctv.tocsc(), ytrain)
predictions = clf.predict_proba(xvalid_ctv.tocsc())
clf = xgb.XGBClassifier(max_depth=7, n_estimators=200, colsample_bytree=0.8, subsample=0.8, nthread=10, learning_rate=0.1)
clf.fit(xtrain_svd, ytrain)
predictions = clf.predict_proba(xvalid_svd)
print('logloss: %0.3f ' % multiclass_logloss(yvalid, predictions)) | code |
17096880/cell_19 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing, decomposition, model_selection, metrics, pipeline
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import SVC
import numpy as np
import pandas as pd
import xgboost as xgb
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sample = pd.read_csv('../input/sample_submission.csv')
train.author.nunique()
def multiclass_logloss(actual, predicted, eps=1e-15):
"""Multi class version of Logarithmic Loss metric.
:param actual: Array containing the actual target classes
:param predicted: Matrix with class predictions, one probability per class
"""
if len(actual.shape) == 1:
actual2 = np.zeros((actual.shape[0], predicted.shape[1]))
for i, val in enumerate(actual):
actual2[i, val] = 1
actual = actual2
clip = np.clip(predicted, eps, 1 - eps)
rows = actual.shape[0]
vsota = np.sum(actual * np.log(clip))
return -1.0 / rows * vsota
lbl_enc = preprocessing.LabelEncoder()
y = lbl_enc.fit_transform(train.author.values)
tfv = TfidfVectorizer(min_df=3, max_features=None, strip_accents='unicode', analyzer='word', token_pattern='\\w{1,}', ngram_range=(1, 3), use_idf=1, smooth_idf=1, sublinear_tf=1, stop_words='english')
tfv.fit(list(xtrain) + list(xvalid))
xtrain_tfv = tfv.transform(xtrain)
xvalid_tfv = tfv.transform(xvalid)
clf = LogisticRegression(C=1.0)
clf.fit(xtrain_tfv, ytrain)
predictions = clf.predict_proba(xvalid_tfv)
ctv = CountVectorizer(analyzer='word', token_pattern='\\w{1,}', ngram_range=(1, 3), stop_words='english')
ctv.fit(list(xtrain) + list(xvalid))
xtrain_ctv = ctv.transform(xtrain)
xvalid_ctv = ctv.transform(xvalid)
clf = MultinomialNB()
clf.fit(xtrain_ctv, ytrain)
predictions = clf.predict_proba(xvalid_ctv)
svd = decomposition.TruncatedSVD(n_components=120)
svd.fit(xtrain_tfv)
xtrain_svd = svd.transform(xtrain_tfv)
xvalid_svd = svd.transform(xvalid_tfv)
scl = preprocessing.StandardScaler()
scl.fit(xtrain_svd)
xtrain_svd_scl = scl.transform(xtrain_svd)
xvalid_svd_scl = scl.transform(xvalid_svd)
clf = SVC(C=1.0, probability=True)
clf.fit(xtrain_svd_scl, ytrain)
predictions = clf.predict_proba(xvalid_svd_scl)
clf = xgb.XGBClassifier(max_depth=7, n_estimators=200, colsample_bytree=0.8, subsample=0.8, nthread=10, learning_rate=0.1)
clf.fit(xtrain_tfv.tocsc(), ytrain)
predictions = clf.predict_proba(xvalid_tfv.tocsc())
clf = xgb.XGBClassifier(max_depth=7, n_estimators=200, colsample_bytree=0.8, subsample=0.8, nthread=10, learning_rate=0.1)
clf.fit(xtrain_ctv.tocsc(), ytrain)
predictions = clf.predict_proba(xvalid_ctv.tocsc())
print('logloss: %0.3f ' % multiclass_logloss(yvalid, predictions)) | code |
17096880/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.corpus import stopwords
import os
import pandas as pd
import numpy as np
import xgboost as xgb
from tqdm import tqdm
from sklearn.svm import SVC
from keras.models import Sequential
from keras.layers.recurrent import LSTM, GRU
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.embeddings import Embedding
from keras.layers.normalization import BatchNormalization
from keras.utils import np_utils
from sklearn import preprocessing, decomposition, model_selection, metrics, pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.decomposition import TruncatedSVD
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
from keras.layers import GlobalMaxPooling1D, Conv1D, MaxPooling1D, Flatten, Bidirectional, SpatialDropout1D
from keras.preprocessing import sequence, text
from keras.callbacks import EarlyStopping
from nltk import word_tokenize
from nltk.corpus import stopwords
stop_words = stopwords.words('english')
import os
print(os.listdir('../input')) | code |
17096880/cell_18 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing, decomposition, model_selection, metrics, pipeline
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import SVC
import numpy as np
import pandas as pd
import xgboost as xgb
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sample = pd.read_csv('../input/sample_submission.csv')
train.author.nunique()
def multiclass_logloss(actual, predicted, eps=1e-15):
"""Multi class version of Logarithmic Loss metric.
:param actual: Array containing the actual target classes
:param predicted: Matrix with class predictions, one probability per class
"""
if len(actual.shape) == 1:
actual2 = np.zeros((actual.shape[0], predicted.shape[1]))
for i, val in enumerate(actual):
actual2[i, val] = 1
actual = actual2
clip = np.clip(predicted, eps, 1 - eps)
rows = actual.shape[0]
vsota = np.sum(actual * np.log(clip))
return -1.0 / rows * vsota
lbl_enc = preprocessing.LabelEncoder()
y = lbl_enc.fit_transform(train.author.values)
tfv = TfidfVectorizer(min_df=3, max_features=None, strip_accents='unicode', analyzer='word', token_pattern='\\w{1,}', ngram_range=(1, 3), use_idf=1, smooth_idf=1, sublinear_tf=1, stop_words='english')
tfv.fit(list(xtrain) + list(xvalid))
xtrain_tfv = tfv.transform(xtrain)
xvalid_tfv = tfv.transform(xvalid)
clf = LogisticRegression(C=1.0)
clf.fit(xtrain_tfv, ytrain)
predictions = clf.predict_proba(xvalid_tfv)
ctv = CountVectorizer(analyzer='word', token_pattern='\\w{1,}', ngram_range=(1, 3), stop_words='english')
ctv.fit(list(xtrain) + list(xvalid))
xtrain_ctv = ctv.transform(xtrain)
xvalid_ctv = ctv.transform(xvalid)
clf = MultinomialNB()
clf.fit(xtrain_ctv, ytrain)
predictions = clf.predict_proba(xvalid_ctv)
svd = decomposition.TruncatedSVD(n_components=120)
svd.fit(xtrain_tfv)
xtrain_svd = svd.transform(xtrain_tfv)
xvalid_svd = svd.transform(xvalid_tfv)
scl = preprocessing.StandardScaler()
scl.fit(xtrain_svd)
xtrain_svd_scl = scl.transform(xtrain_svd)
xvalid_svd_scl = scl.transform(xvalid_svd)
clf = SVC(C=1.0, probability=True)
clf.fit(xtrain_svd_scl, ytrain)
predictions = clf.predict_proba(xvalid_svd_scl)
clf = xgb.XGBClassifier(max_depth=7, n_estimators=200, colsample_bytree=0.8, subsample=0.8, nthread=10, learning_rate=0.1)
clf.fit(xtrain_tfv.tocsc(), ytrain)
predictions = clf.predict_proba(xvalid_tfv.tocsc())
print('logloss: %0.3f ' % multiclass_logloss(yvalid, predictions)) | code |
17096880/cell_16 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import preprocessing, decomposition, model_selection, metrics, pipeline
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import SVC
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sample = pd.read_csv('../input/sample_submission.csv')
train.author.nunique()
def multiclass_logloss(actual, predicted, eps=1e-15):
"""Multi class version of Logarithmic Loss metric.
:param actual: Array containing the actual target classes
:param predicted: Matrix with class predictions, one probability per class
"""
if len(actual.shape) == 1:
actual2 = np.zeros((actual.shape[0], predicted.shape[1]))
for i, val in enumerate(actual):
actual2[i, val] = 1
actual = actual2
clip = np.clip(predicted, eps, 1 - eps)
rows = actual.shape[0]
vsota = np.sum(actual * np.log(clip))
return -1.0 / rows * vsota
lbl_enc = preprocessing.LabelEncoder()
y = lbl_enc.fit_transform(train.author.values)
tfv = TfidfVectorizer(min_df=3, max_features=None, strip_accents='unicode', analyzer='word', token_pattern='\\w{1,}', ngram_range=(1, 3), use_idf=1, smooth_idf=1, sublinear_tf=1, stop_words='english')
tfv.fit(list(xtrain) + list(xvalid))
xtrain_tfv = tfv.transform(xtrain)
xvalid_tfv = tfv.transform(xvalid)
clf = LogisticRegression(C=1.0)
clf.fit(xtrain_tfv, ytrain)
predictions = clf.predict_proba(xvalid_tfv)
ctv = CountVectorizer(analyzer='word', token_pattern='\\w{1,}', ngram_range=(1, 3), stop_words='english')
ctv.fit(list(xtrain) + list(xvalid))
xtrain_ctv = ctv.transform(xtrain)
xvalid_ctv = ctv.transform(xvalid)
clf = MultinomialNB()
clf.fit(xtrain_ctv, ytrain)
predictions = clf.predict_proba(xvalid_ctv)
svd = decomposition.TruncatedSVD(n_components=120)
svd.fit(xtrain_tfv)
xtrain_svd = svd.transform(xtrain_tfv)
xvalid_svd = svd.transform(xvalid_tfv)
scl = preprocessing.StandardScaler()
scl.fit(xtrain_svd)
xtrain_svd_scl = scl.transform(xtrain_svd)
xvalid_svd_scl = scl.transform(xvalid_svd)
clf = SVC(C=1.0, probability=True)
clf.fit(xtrain_svd_scl, ytrain)
predictions = clf.predict_proba(xvalid_svd_scl)
print('logloss: %0.3f ' % multiclass_logloss(yvalid, predictions)) | code |
17096880/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sample = pd.read_csv('../input/sample_submission.csv')
train.head(3) | code |
17096880/cell_10 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.linear_model import LogisticRegression
import numpy as np
def multiclass_logloss(actual, predicted, eps=1e-15):
"""Multi class version of Logarithmic Loss metric.
:param actual: Array containing the actual target classes
:param predicted: Matrix with class predictions, one probability per class
"""
if len(actual.shape) == 1:
actual2 = np.zeros((actual.shape[0], predicted.shape[1]))
for i, val in enumerate(actual):
actual2[i, val] = 1
actual = actual2
clip = np.clip(predicted, eps, 1 - eps)
rows = actual.shape[0]
vsota = np.sum(actual * np.log(clip))
return -1.0 / rows * vsota
tfv = TfidfVectorizer(min_df=3, max_features=None, strip_accents='unicode', analyzer='word', token_pattern='\\w{1,}', ngram_range=(1, 3), use_idf=1, smooth_idf=1, sublinear_tf=1, stop_words='english')
tfv.fit(list(xtrain) + list(xvalid))
xtrain_tfv = tfv.transform(xtrain)
xvalid_tfv = tfv.transform(xvalid)
clf = LogisticRegression(C=1.0)
clf.fit(xtrain_tfv, ytrain)
predictions = clf.predict_proba(xvalid_tfv)
print('logloss: %0.3f ' % multiclass_logloss(yvalid, predictions)) | code |
122264561/cell_42 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_react.isnull().sum()
df_react = df_react.dropna()
df_react_users = df_react.drop(['Unnamed: 0', 'Content ID', 'Type'], axis=1)
plt.figure(figsize=(10, 6), dpi=200)
sns.countplot(x='type_sentiment', data=df_react)
plt.xlabel('Reaction_Type')
plt.ylabel('Count')
plt.title('Number Of Diff Reactions')
plt.savefig('Number Of Diff Reactions2.jpeg') | code |
122264561/cell_63 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_session = pd.read_csv('/kaggle/input/a360-internship-practice/Session.csv')
df_user = pd.read_csv('/kaggle/input/a360-internship-practice/User.csv')
df_react['Datetime'] = pd.to_datetime(df_react['Datetime'])
df_content = df_content.drop(['Unnamed: 0'], axis=1)
df_react.isnull().sum()
df_react = df_react.dropna()
df_react_users = df_react.drop(['Unnamed: 0', 'Content ID', 'Type'], axis=1)
df_cont_score = pd.DataFrame(df_react.groupby('Content ID')['Type_score'].mean())
mode_counts = df_react.groupby('Content ID')['type_sentiment'].agg(lambda x: x.mode().iloc[0])
selected_mode = mode_counts.index[0]
df_cont_sentiment = pd.DataFrame(mode_counts)
df_cont_sentiment = df_cont_sentiment.reset_index()
df_cont_score = df_cont_score.reset_index()
df_content = pd.merge(df_content, df_cont_score, how='outer')
df_content = pd.merge(df_content, df_cont_sentiment, how='outer')
df_content.fillna(0)
df_content['type_sentiment'] | code |
122264561/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_content = df_content.drop(['Unnamed: 0'], axis=1)
df_content | code |
122264561/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_session = pd.read_csv('/kaggle/input/a360-internship-practice/Session.csv')
df_user = pd.read_csv('/kaggle/input/a360-internship-practice/User.csv')
df_user | code |
122264561/cell_83 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_session = pd.read_csv('/kaggle/input/a360-internship-practice/Session.csv')
df_user = pd.read_csv('/kaggle/input/a360-internship-practice/User.csv')
df_user | code |
122264561/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_react.isnull().sum()
df_react[df_react['Type_score'].isnull()].isnull().sum() | code |
122264561/cell_87 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_profile = df_profile.drop('Unnamed: 0', axis=1)
type(df_profile['Interests'][2]) | code |
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