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|---|---|---|---|
73074345/cell_13 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor, BaggingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, accuracy_score
def score_model(model, X_t=X_train, X_v=X_valid, y_t=y_train, y_v=y_valid):
model.fit(X_t, y_t)
preds = model.predict(X_v)
return mean_absolute_error(y_v, preds)
model_1 = RandomForestRegressor(n_estimators=50, random_state=69)
model_2 = RandomForestRegressor(n_estimators=100, random_state=69)
model_3 = RandomForestRegressor(n_estimators=100, criterion='mae', random_state=69)
model_4 = RandomForestRegressor(n_estimators=200, min_samples_split=20, random_state=69)
model_5 = RandomForestRegressor(n_estimators=100, max_depth=7, random_state=69)
models = [model_1, model_2, model_3, model_4, model_5]
mae1 = score_model(model_1, X_t=X_train, X_v=X_valid, y_t=y_train, y_v=y_valid)
print('model 1 MAE is :')
print(mae1) | code |
73074345/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X = X_full.copy()
X_test = X_test_full.copy()
X.drop(['target'], axis=1, inplace=True)
X_numeric = X.select_dtypes(exclude=['object'])
X_test_numeric = X_test_full.select_dtypes(exclude=['object'])
X_numeric.head() | code |
73074345/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X = X_full.copy()
X_test = X_test_full.copy()
y = X_full.target
y.head() | code |
73074345/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 |
73074345/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X = X_full.copy()
X_test = X_test_full.copy()
X.drop(['target'], axis=1, inplace=True)
X.head() | code |
73074345/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X = X_full.copy()
X_test = X_test_full.copy()
X_full.head() | code |
73074345/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X = X_full.copy()
X_test = X_test_full.copy()
y = X_full.target
X_full.info()
print('*' * 100)
X_full.isna().sum() | code |
325098/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
df = pd.read_csv('../input/nflplaybyplay2015.csv', low_memory=False)
df.columns | code |
325098/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier
df = df[df['posteam'] == 'CHI']
df = df[df['DefensiveTeam'] == 'GB']
used_downs = [1, 2, 3]
df = df[df['down'].isin(used_downs)]
valid_plays = ['Pass', 'Run', 'Sack']
df = df[df['PlayType'].isin(valid_plays)]
pass_plays = ['Pass', 'Sack']
df['is_pass'] = df['PlayType'].isin(pass_plays).astype('int')
df = df[['down', 'yrdline100', 'ScoreDiff', 'ydstogo', 'TimeSecs', 'is_pass']]
X, test = train_test_split(df, test_size=0.2)
y = X.pop('is_pass')
rf = RandomForestClassifier(n_estimators=1000)
rf.fit(X, y)
test_y = test.pop('is_pass')
rf.score(test, test_y) | code |
325098/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier
df = df[df['posteam'] == 'CHI']
df = df[df['DefensiveTeam'] == 'GB']
used_downs = [1, 2, 3]
df = df[df['down'].isin(used_downs)]
valid_plays = ['Pass', 'Run', 'Sack']
df = df[df['PlayType'].isin(valid_plays)]
pass_plays = ['Pass', 'Sack']
df['is_pass'] = df['PlayType'].isin(pass_plays).astype('int')
df = df[['down', 'yrdline100', 'ScoreDiff', 'ydstogo', 'TimeSecs', 'is_pass']]
X, test = train_test_split(df, test_size=0.2)
y = X.pop('is_pass')
rf = RandomForestClassifier(n_estimators=1000)
rf.fit(X, y) | code |
1008127/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from fig_code import plot_iris_knn
from fig_code import plot_iris_knn
plot_iris_knn() | code |
1008127/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
data = load_iris()
n_samples, n_features = data.data.shape
x_index = 1
y_index = 2
formatter = plt.FuncFormatter(lambda i, *args: data.target_names[int(i)])
plt.scatter(data.data[:, x_index], data.data[:, y_index], c=data.target, cmap=plt.cm.get_cmap('RdYlBu', 3))
plt.colorbar(ticks=[0, 1, 2], format=formatter)
plt.clim(-0.5, 2.5)
plt.xlabel(data.feature_names[x_index])
plt.ylabel(data.feature_names[y_index]) | code |
1008127/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1008127/cell_8 | [
"text_plain_output_1.png"
] | from sklearn import neighbors, datasets
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
data = load_iris()
n_samples, n_features = data.data.shape
x_index = 1
y_index = 2
formatter = plt.FuncFormatter(lambda i, *args: data.target_names[int(i)])
plt.colorbar(ticks=[0, 1, 2], format=formatter)
plt.clim(-0.5, 2.5)
from sklearn import neighbors, datasets
data = datasets.load_iris()
X, y = (data.data, data.target)
clf = neighbors.KNeighborsClassifier(n_neighbors=5, weights='uniform')
clf.fit(X, y)
X_test = [3, 4, 2, 5]
y_pred = clf.predict([X_test])
print(y_pred)
print(data.target_names[y_pred])
print(data.target_names)
print(clf.predict_proba([X_test])) | code |
1008127/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from IPython.display import Image
from IPython.display import Image
Image('http://scikit-learn.org/dev/_static/ml_map.png', width=800) | code |
1008127/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_iris
from sklearn.datasets import load_iris
data = load_iris()
n_samples, n_features = data.data.shape
print(data.keys())
print(n_samples, n_features)
print(data.data.shape)
print(data.target.shape)
print(data.target_names)
print(data.feature_names) | code |
2025162/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
sb.set()
cols = df[['price', 'sqft_living', 'grade', 'sqft_above', 'bathrooms', 'sqft_living15']]
#saleprice correlation matrix
k = 10 #number of variables for heatmap
corrmat = df.corr()
cols = corrmat.nlargest(k, 'price')['price'].index
cm = np.corrcoef(df[cols].values.T)
sb.set(font_scale=1.25)
hm = sb.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
plt.show()
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)
var1 = 'grade'
data = pd.concat([df['price'], df[var1]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sb.boxplot(x=var1, y='price', data=data)
fig.axis(ymin=0, ymax=8000000) | code |
2025162/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/kc_house_data.csv')
df.describe() | code |
2025162/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
sb.set()
cols = df[['price', 'sqft_living', 'grade', 'sqft_above', 'bathrooms', 'sqft_living15']]
k = 10
corrmat = df.corr()
cols = corrmat.nlargest(k, 'price')['price'].index
cm = np.corrcoef(df[cols].values.T)
sb.set(font_scale=1.25)
hm = sb.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
plt.show() | code |
2025162/cell_19 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
import statsmodels.api as sm
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
sb.set()
cols = df[['price', 'sqft_living', 'grade', 'sqft_above', 'bathrooms', 'sqft_living15']]
#saleprice correlation matrix
k = 10 #number of variables for heatmap
corrmat = df.corr()
cols = corrmat.nlargest(k, 'price')['price'].index
cm = np.corrcoef(df[cols].values.T)
sb.set(font_scale=1.25)
hm = sb.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
plt.show()
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)
#boxplot 'grade'/'price'
var1 = 'grade'
data = pd.concat([df['price'], df[var1]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sb.boxplot(x=var1, y="price", data=data)
fig.axis(ymin=0, ymax=8000000);
var2 = 'sqft_living15'
data = pd.concat([df['price'], df[var2]], axis=1)
var3 = 'sqft_above'
data = pd.concat([df['price'], df[var3]], axis=1)
var4 = 'bathrooms'
data = pd.concat([df['price'], df[var4]], axis=1)
X = df[[var, var1, var2, var3, var4, 'view']]
y = df['price']
est = sm.OLS(y, X).fit()
est.summary() | code |
2025162/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/kc_house_data.csv')
df.info() | code |
2025162/cell_18 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
sb.set()
cols = df[['price', 'sqft_living', 'grade', 'sqft_above', 'bathrooms', 'sqft_living15']]
#saleprice correlation matrix
k = 10 #number of variables for heatmap
corrmat = df.corr()
cols = corrmat.nlargest(k, 'price')['price'].index
cm = np.corrcoef(df[cols].values.T)
sb.set(font_scale=1.25)
hm = sb.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
plt.show()
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)
#boxplot 'grade'/'price'
var1 = 'grade'
data = pd.concat([df['price'], df[var1]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sb.boxplot(x=var1, y="price", data=data)
fig.axis(ymin=0, ymax=8000000);
var2 = 'sqft_living15'
data = pd.concat([df['price'], df[var2]], axis=1)
var3 = 'sqft_above'
data = pd.concat([df['price'], df[var3]], axis=1)
var4 = 'bathrooms'
data = pd.concat([df['price'], df[var4]], axis=1)
X = df[[var, var1, var2, var3, var4, 'view']]
y = df['price']
LinReg = LinearRegression(normalize=True)
LinReg.fit(X, y)
print(LinReg.score(X, y)) | code |
2025162/cell_8 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
f, ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=0.5, fmt='.1f', ax=ax) | code |
2025162/cell_15 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
sb.set()
cols = df[['price', 'sqft_living', 'grade', 'sqft_above', 'bathrooms', 'sqft_living15']]
#saleprice correlation matrix
k = 10 #number of variables for heatmap
corrmat = df.corr()
cols = corrmat.nlargest(k, 'price')['price'].index
cm = np.corrcoef(df[cols].values.T)
sb.set(font_scale=1.25)
hm = sb.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
plt.show()
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)
#boxplot 'grade'/'price'
var1 = 'grade'
data = pd.concat([df['price'], df[var1]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sb.boxplot(x=var1, y="price", data=data)
fig.axis(ymin=0, ymax=8000000);
var2 = 'sqft_living15'
data = pd.concat([df['price'], df[var2]], axis=1)
var3 = 'sqft_above'
data = pd.concat([df['price'], df[var3]], axis=1)
data.plot.scatter(x=var3, y='price', ylim=(0, 8000000)) | code |
2025162/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
sb.set()
cols = df[['price', 'sqft_living', 'grade', 'sqft_above', 'bathrooms', 'sqft_living15']]
#saleprice correlation matrix
k = 10 #number of variables for heatmap
corrmat = df.corr()
cols = corrmat.nlargest(k, 'price')['price'].index
cm = np.corrcoef(df[cols].values.T)
sb.set(font_scale=1.25)
hm = sb.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
plt.show()
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)
#boxplot 'grade'/'price'
var1 = 'grade'
data = pd.concat([df['price'], df[var1]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sb.boxplot(x=var1, y="price", data=data)
fig.axis(ymin=0, ymax=8000000);
var2 = 'sqft_living15'
data = pd.concat([df['price'], df[var2]], axis=1)
var3 = 'sqft_above'
data = pd.concat([df['price'], df[var3]], axis=1)
var4 = 'bathrooms'
data = pd.concat([df['price'], df[var4]], axis=1)
data.plot.scatter(x=var4, y='price', ylim=(0, 8000000)) | code |
2025162/cell_3 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import scale
import statsmodels.api as sm
from sklearn.preprocessing import StandardScaler
scale = StandardScaler()
from scipy import stats | code |
2025162/cell_14 | [
"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 sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
sb.set()
cols = df[['price', 'sqft_living', 'grade', 'sqft_above', 'bathrooms', 'sqft_living15']]
#saleprice correlation matrix
k = 10 #number of variables for heatmap
corrmat = df.corr()
cols = corrmat.nlargest(k, 'price')['price'].index
cm = np.corrcoef(df[cols].values.T)
sb.set(font_scale=1.25)
hm = sb.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
plt.show()
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)
#boxplot 'grade'/'price'
var1 = 'grade'
data = pd.concat([df['price'], df[var1]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sb.boxplot(x=var1, y="price", data=data)
fig.axis(ymin=0, ymax=8000000);
var2 = 'sqft_living15'
data = pd.concat([df['price'], df[var2]], axis=1)
data.plot.scatter(x=var2, y='price', ylim=(0, 8000000)) | code |
2025162/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
sb.set()
cols = df[['price', 'sqft_living', 'grade', 'sqft_above', 'bathrooms', 'sqft_living15']]
sb.pairplot(cols, size=2.5)
plt.show() | code |
2025162/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
sb.set()
cols = df[['price', 'sqft_living', 'grade', 'sqft_above', 'bathrooms', 'sqft_living15']]
#saleprice correlation matrix
k = 10 #number of variables for heatmap
corrmat = df.corr()
cols = corrmat.nlargest(k, 'price')['price'].index
cm = np.corrcoef(df[cols].values.T)
sb.set(font_scale=1.25)
hm = sb.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
plt.show()
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)
data.plot.scatter(x=var, y='price', ylim=(0, 8000000)) | code |
2025162/cell_5 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/kc_house_data.csv')
df.head() | code |
329250/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
img = cv2.imread('../input/train_sm/set107_1.jpeg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.imshow(img) | code |
329250/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
329250/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sub = pd.read_csv('../input/sample_submission.csv')
df_train = pd.read_csv('../input/train_sm/') | code |
329250/cell_5 | [
"text_plain_output_1.png"
] | import cv2
img = cv2.imread('../input/train_sm/set107_1.jpeg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img.shape | code |
129036266/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/real-estate-sales-2001-2020-gl/Real_Estate_Sales_2001-2020_GL.csv')
df.sample(2)
df = df.drop('OPM remarks', axis=1)
df = df.dropna()
df.sample(3) | code |
129036266/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/real-estate-sales-2001-2020-gl/Real_Estate_Sales_2001-2020_GL.csv')
df.describe() | code |
129036266/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/real-estate-sales-2001-2020-gl/Real_Estate_Sales_2001-2020_GL.csv')
df.sample(2) | code |
129036266/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/real-estate-sales-2001-2020-gl/Real_Estate_Sales_2001-2020_GL.csv') | code |
129036266/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/real-estate-sales-2001-2020-gl/Real_Estate_Sales_2001-2020_GL.csv')
df.sample(2)
df = df.drop('OPM remarks', axis=1)
df = df.dropna()
df['Date Recorded'].values | code |
129036266/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px | code |
129036266/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/real-estate-sales-2001-2020-gl/Real_Estate_Sales_2001-2020_GL.csv')
df.sample(2)
df['OPM remarks'].value_counts() | code |
129036266/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/real-estate-sales-2001-2020-gl/Real_Estate_Sales_2001-2020_GL.csv')
df.sample(2)
df = df.drop('OPM remarks', axis=1)
df = df.dropna()
df['Date Recorded'].values | code |
129036266/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/real-estate-sales-2001-2020-gl/Real_Estate_Sales_2001-2020_GL.csv')
df.info() | code |
129036266/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/real-estate-sales-2001-2020-gl/Real_Estate_Sales_2001-2020_GL.csv')
df.sample(2)
df = df.drop('OPM remarks', axis=1)
df = df.dropna()
df.info() | code |
129036266/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/real-estate-sales-2001-2020-gl/Real_Estate_Sales_2001-2020_GL.csv')
df.describe(include='all') | code |
129040633/cell_4 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_9.png",
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_6.png",
"application_vnd.jupyter.stderr_output_12.png",
"application_vnd.jupyter.stderr_output_8.png",
"application_vnd.jupyter.stderr_output_10.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_1.png",
"text_plain_output_11.png"
] | from skimage import io
from torchvision import datasets, transforms
import os
import pandas as pd
def fetch_dataset(path, attrs_name='lfw_attributes.txt', images_name='lfw-deepfunneled', dx=80, dy=80, dimx=64, dimy=64):
if not os.path.exists(images_name):
os.system('wget http://vis-www.cs.umass.edu/lfw/lfw-deepfunneled.tgz -O tmp.tgz')
os.system('tar xvzf tmp.tgz && rm tmp.tgz')
assert os.path.exists(images_name)
if not os.path.exists(attrs_name):
os.system('wget http://www.cs.columbia.edu/CAVE/databases/pubfig/download/%s' % attrs_name)
transform = transforms.Compose([transforms.ToPILImage(), transforms.CenterCrop((dx, dy)), transforms.Resize((dimx, dimy)), transforms.ToTensor()])
df_attrs = pd.read_csv(os.path.join(path, attrs_name), sep='\t', skiprows=1)
df_attrs = pd.DataFrame(df_attrs.iloc[:, :-1].values, columns=df_attrs.columns[1:])
photo_ids = []
for dirpath, dirnames, filenames in os.walk(os.path.join(path, images_name)):
for fname in filenames:
if fname.endswith('.jpg'):
fpath = os.path.join(dirpath, fname)
photo_id = fname[:-4].replace('_', ' ').split()
person_id = ' '.join(photo_id[:-1])
photo_number = int(photo_id[-1])
photo_ids.append({'person': person_id, 'imagenum': photo_number, 'photo_path': fpath})
photo_ids = pd.DataFrame(photo_ids)
df = pd.merge(df_attrs, photo_ids, on=('person', 'imagenum'))
assert len(df) == len(df_attrs), 'lost some data when merging dataframes'
all_photos = df['photo_path'].apply(io.imread).apply(transform)
all_photos = all_photos.values
all_attrs = df.drop(['photo_path', 'person', 'imagenum'], axis=1)
return (all_photos, all_attrs)
img_size = 64
path = os.path.abspath('')
data, attrs = fetch_dataset(path=path, dimx=img_size, dimy=img_size) | code |
129040633/cell_6 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
batch_size = 32
train_photos, val_photos, train_attrs, val_attrs = train_test_split(data, attrs, train_size=0.8, shuffle=False)
print('Training input shape: ', train_photos.shape)
data_tr = torch.utils.data.DataLoader(train_photos, batch_size=batch_size)
data_val = torch.utils.data.DataLoader(val_photos, batch_size=batch_size) | code |
129040633/cell_2 | [
"image_output_1.png"
] | import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(device) | code |
129040633/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
from torch.autograd import Variable
from torchvision import datasets, transforms
from skimage import io
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as data_utils
import torch
import matplotlib.pyplot as plt
import os
import pandas as pd
from skimage.transform import resize
from IPython.display import clear_output | code |
129040633/cell_7 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
batch_size = 32
train_photos, val_photos, train_attrs, val_attrs = train_test_split(data, attrs, train_size=0.8, shuffle=False)
data_tr = torch.utils.data.DataLoader(train_photos, batch_size=batch_size)
data_val = torch.utils.data.DataLoader(val_photos, batch_size=batch_size)
plt.figure(figsize=(18, 6))
for i in range(12):
plt.subplot(2, 6, i + 1)
plt.axis('off')
plt.imshow(data_tr.dataset[i].permute(1, 2, 0))
plt.show() | code |
129040633/cell_16 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from IPython.display import clear_output
from skimage import io
from sklearn.model_selection import train_test_split
from time import time
from torch.optim import lr_scheduler
from torchvision import datasets, transforms
from tqdm.autonotebook import tqdm
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import torch
import torch.nn as nn
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def fetch_dataset(path, attrs_name='lfw_attributes.txt', images_name='lfw-deepfunneled', dx=80, dy=80, dimx=64, dimy=64):
if not os.path.exists(images_name):
os.system('wget http://vis-www.cs.umass.edu/lfw/lfw-deepfunneled.tgz -O tmp.tgz')
os.system('tar xvzf tmp.tgz && rm tmp.tgz')
assert os.path.exists(images_name)
if not os.path.exists(attrs_name):
os.system('wget http://www.cs.columbia.edu/CAVE/databases/pubfig/download/%s' % attrs_name)
transform = transforms.Compose([transforms.ToPILImage(), transforms.CenterCrop((dx, dy)), transforms.Resize((dimx, dimy)), transforms.ToTensor()])
df_attrs = pd.read_csv(os.path.join(path, attrs_name), sep='\t', skiprows=1)
df_attrs = pd.DataFrame(df_attrs.iloc[:, :-1].values, columns=df_attrs.columns[1:])
photo_ids = []
for dirpath, dirnames, filenames in os.walk(os.path.join(path, images_name)):
for fname in filenames:
if fname.endswith('.jpg'):
fpath = os.path.join(dirpath, fname)
photo_id = fname[:-4].replace('_', ' ').split()
person_id = ' '.join(photo_id[:-1])
photo_number = int(photo_id[-1])
photo_ids.append({'person': person_id, 'imagenum': photo_number, 'photo_path': fpath})
photo_ids = pd.DataFrame(photo_ids)
df = pd.merge(df_attrs, photo_ids, on=('person', 'imagenum'))
assert len(df) == len(df_attrs), 'lost some data when merging dataframes'
all_photos = df['photo_path'].apply(io.imread).apply(transform)
all_photos = all_photos.values
all_attrs = df.drop(['photo_path', 'person', 'imagenum'], axis=1)
return (all_photos, all_attrs)
batch_size = 32
train_photos, val_photos, train_attrs, val_attrs = train_test_split(data, attrs, train_size=0.8, shuffle=False)
data_tr = torch.utils.data.DataLoader(train_photos, batch_size=batch_size)
data_val = torch.utils.data.DataLoader(val_photos, batch_size=batch_size)
for i in range(12):
plt.axis('off')
dim_code = 32
class CVAE(nn.Module):
def __init__(self, base_channel_size: int, latent_dim: int, num_classes: int, num_input_channels: int=3, act_fn=nn.ReLU):
super().__init__()
self.dummy_param = nn.Parameter(torch.empty(0))
self.latent_dim = latent_dim
self.c_hid = base_channel_size
self.num_classes = num_classes
conv_size = int(np.exp2(np.log2(self.c_hid) - 3))
ln_size = 2 * self.c_hid * conv_size * conv_size
self.encoder = nn.Sequential(nn.Conv2d(num_input_channels, self.c_hid, kernel_size=3, padding=1, stride=2), act_fn(), nn.Conv2d(self.c_hid, self.c_hid, kernel_size=3, padding=1), act_fn(), nn.Conv2d(self.c_hid, 2 * self.c_hid, kernel_size=3, padding=1, stride=2), act_fn(), nn.Conv2d(2 * self.c_hid, 2 * self.c_hid, kernel_size=3, padding=1), act_fn(), nn.Conv2d(2 * self.c_hid, 2 * self.c_hid, kernel_size=3, padding=1, stride=2), act_fn())
self.flatten = nn.Flatten(start_dim=1)
self.linear_mu = nn.Sequential(nn.Linear(ln_size, latent_dim))
self.linear_logvar = nn.Sequential(nn.Linear(ln_size, latent_dim))
self.linear_decoder = nn.Sequential(nn.Linear(latent_dim + num_classes, ln_size), act_fn())
self.unflatten = nn.Sequential(nn.Unflatten(dim=1, unflattened_size=(2 * self.c_hid, conv_size, conv_size)))
self.decoder = nn.Sequential(nn.ConvTranspose2d(2 * self.c_hid, 2 * self.c_hid, kernel_size=3, output_padding=1, padding=1, stride=2), act_fn(), nn.Conv2d(2 * self.c_hid, 2 * self.c_hid, kernel_size=3, padding=1), act_fn(), nn.ConvTranspose2d(2 * self.c_hid, self.c_hid, kernel_size=3, output_padding=1, padding=1, stride=2), act_fn(), nn.Conv2d(self.c_hid, self.c_hid, kernel_size=3, padding=1), act_fn(), nn.ConvTranspose2d(self.c_hid, num_input_channels, kernel_size=3, output_padding=1, padding=1, stride=2), nn.Sigmoid())
def encode(self, x):
x = self.encoder(x)
x = self.flatten(x)
mu = self.linear_mu(x)
logvar = self.linear_logvar(x)
return (mu, logvar)
def reparameterize(self, mu, logvar):
if self.training:
std = torch.exp(logvar / 2)
eps = torch.randn_like(std)
return eps * std + mu
else:
return mu
def decode(self, x):
x = self.linear_decoder(x)
x = self.unflatten(x)
x = self.decoder(x)
return x
def forward(self, x, **kwargs):
y = kwargs['labels']
y = torch.nn.functional.one_hot(y, num_classes=self.num_classes)
mu, logvar = self.encode(x)
z = self.reparameterize(mu, logvar)
z = torch.cat([z, y], dim=1)
z = self.decode(z)
return (mu, logvar, z)
def sample(self, labels: list):
y = torch.tensor(labels, dtype=torch.int64).to(self.dummy_param.device)
y = torch.nn.functional.one_hot(y, num_classes=self.num_classes)
z = torch.randn(y.size()[0], 32).to(self.dummy_param.device)
z = torch.cat([z, y], dim=1)
return self.decode(z)
def loss_vae(x, mu, logsigma, reconstruction):
kl = KL_divergence(mu, logsigma)
ll = log_likelihood(x, reconstruction)
return kl + ll
def KL_divergence(mu, logvar):
"""
часть функции потерь, которая отвечает за "близость" латентных представлений разных людей
"""
loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
return loss
def log_likelihood(x, reconstruction):
"""
часть функции потерь, которая отвечает за качество реконструкции (как mse в обычном autoencoder)
"""
loss = nn.BCELoss(reduction='sum')
return loss(reconstruction, x)
def loss_vae(x, mu, logsigma, reconstruction):
kl = KL_divergence(mu, logsigma)
ll = log_likelihood(x, reconstruction)
return kl + ll
batch_size = 32
size = 32
transform = transforms.Compose([transforms.Resize(size), transforms.ToTensor()])
train_dataset = datasets.MNIST(root='./mnist_data/', transform=transform, train=True, download=True)
test_dataset = datasets.MNIST(root='./mnist_data/', transform=transform, train=False, download=False)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)
criterion = loss_vae
autoencoder = CVAE(num_input_channels=1, base_channel_size=32, num_classes=train_dataset.targets.unique().size()[0], latent_dim=dim_code)
optimizer = torch.optim.Adam(autoencoder.parameters(), lr=0.001)
def train(model, opt, loss_fn, epochs, data_tr, data_val, scheduler=None, device='cpu', show=True, show_num=3):
from time import time
from tqdm.autonotebook import tqdm
model = model.to(device)
X_val, Y_val = next(iter(data_val))
train_losses = []
val_losses = []
log_template = 'Epoch {ep:03d}/{epochs:03d} train loss: {t_loss:0.4f} val loss {v_loss:0.4f}'
with tqdm(desc='epoch', total=epochs) as pbar_outer:
for epoch in range(epochs):
tic = time()
avg_loss = 0
model.train()
for X_batch, Y_batch in data_tr:
X_batch = X_batch.to(device, dtype=torch.float32)
Y_batch = Y_batch.to(device)
opt.zero_grad()
mu, logvar, X_pred = model(X_batch, labels=Y_batch)
loss = loss_fn(X_batch, mu, logvar, X_pred)
loss.backward()
opt.step()
avg_loss += loss / len(data_tr)
toc = time()
model.eval()
mu, logvar, X_hat = model(X_val.to(device, dtype=torch.float32), labels=Y_val.to(device))
X_hat = X_hat.detach().to('cpu')
train_losses.append(avg_loss.item())
val_losses.append(loss_fn(X_val, mu, logvar, X_hat).item())
nums = np.random.randint(10, size=show_num)
output_nums = model.sample(nums).detach()
output_nums = output_nums.detach().to('cpu')
if scheduler:
scheduler.step()
pbar_outer.update(1)
if show:
clear_output(wait=True)
plt.clf()
for k in range(show_num):
plt.axis('off')
plt.axis('off')
plt.axis('off')
else:
tqdm.write(log_template.format(ep=epoch + 1, epochs=epochs, t_loss=train_losses[-1], v_loss=val_losses[-1]))
return (train_losses, val_losses, X_hat, mu, logvar)
from torch.optim import lr_scheduler
lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)
max_epochs = 20
cvae_train_loss, cvae_val_loss, cvae_predict_img_val, cvae_mu, cvae_logvar = train(model=autoencoder, opt=optimizer, loss_fn=criterion, epochs=max_epochs, data_tr=train_loader, data_val=test_loader, device=device, scheduler=lr_scheduler, show=True) | code |
129040633/cell_12 | [
"text_plain_output_1.png"
] | from skimage import io
from sklearn.model_selection import train_test_split
from torchvision import datasets, transforms
import numpy as np
import os
import pandas as pd
import torch
import torch.nn as nn
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def fetch_dataset(path, attrs_name='lfw_attributes.txt', images_name='lfw-deepfunneled', dx=80, dy=80, dimx=64, dimy=64):
if not os.path.exists(images_name):
os.system('wget http://vis-www.cs.umass.edu/lfw/lfw-deepfunneled.tgz -O tmp.tgz')
os.system('tar xvzf tmp.tgz && rm tmp.tgz')
assert os.path.exists(images_name)
if not os.path.exists(attrs_name):
os.system('wget http://www.cs.columbia.edu/CAVE/databases/pubfig/download/%s' % attrs_name)
transform = transforms.Compose([transforms.ToPILImage(), transforms.CenterCrop((dx, dy)), transforms.Resize((dimx, dimy)), transforms.ToTensor()])
df_attrs = pd.read_csv(os.path.join(path, attrs_name), sep='\t', skiprows=1)
df_attrs = pd.DataFrame(df_attrs.iloc[:, :-1].values, columns=df_attrs.columns[1:])
photo_ids = []
for dirpath, dirnames, filenames in os.walk(os.path.join(path, images_name)):
for fname in filenames:
if fname.endswith('.jpg'):
fpath = os.path.join(dirpath, fname)
photo_id = fname[:-4].replace('_', ' ').split()
person_id = ' '.join(photo_id[:-1])
photo_number = int(photo_id[-1])
photo_ids.append({'person': person_id, 'imagenum': photo_number, 'photo_path': fpath})
photo_ids = pd.DataFrame(photo_ids)
df = pd.merge(df_attrs, photo_ids, on=('person', 'imagenum'))
assert len(df) == len(df_attrs), 'lost some data when merging dataframes'
all_photos = df['photo_path'].apply(io.imread).apply(transform)
all_photos = all_photos.values
all_attrs = df.drop(['photo_path', 'person', 'imagenum'], axis=1)
return (all_photos, all_attrs)
batch_size = 32
train_photos, val_photos, train_attrs, val_attrs = train_test_split(data, attrs, train_size=0.8, shuffle=False)
data_tr = torch.utils.data.DataLoader(train_photos, batch_size=batch_size)
data_val = torch.utils.data.DataLoader(val_photos, batch_size=batch_size)
class CVAE(nn.Module):
def __init__(self, base_channel_size: int, latent_dim: int, num_classes: int, num_input_channels: int=3, act_fn=nn.ReLU):
super().__init__()
self.dummy_param = nn.Parameter(torch.empty(0))
self.latent_dim = latent_dim
self.c_hid = base_channel_size
self.num_classes = num_classes
conv_size = int(np.exp2(np.log2(self.c_hid) - 3))
ln_size = 2 * self.c_hid * conv_size * conv_size
self.encoder = nn.Sequential(nn.Conv2d(num_input_channels, self.c_hid, kernel_size=3, padding=1, stride=2), act_fn(), nn.Conv2d(self.c_hid, self.c_hid, kernel_size=3, padding=1), act_fn(), nn.Conv2d(self.c_hid, 2 * self.c_hid, kernel_size=3, padding=1, stride=2), act_fn(), nn.Conv2d(2 * self.c_hid, 2 * self.c_hid, kernel_size=3, padding=1), act_fn(), nn.Conv2d(2 * self.c_hid, 2 * self.c_hid, kernel_size=3, padding=1, stride=2), act_fn())
self.flatten = nn.Flatten(start_dim=1)
self.linear_mu = nn.Sequential(nn.Linear(ln_size, latent_dim))
self.linear_logvar = nn.Sequential(nn.Linear(ln_size, latent_dim))
self.linear_decoder = nn.Sequential(nn.Linear(latent_dim + num_classes, ln_size), act_fn())
self.unflatten = nn.Sequential(nn.Unflatten(dim=1, unflattened_size=(2 * self.c_hid, conv_size, conv_size)))
self.decoder = nn.Sequential(nn.ConvTranspose2d(2 * self.c_hid, 2 * self.c_hid, kernel_size=3, output_padding=1, padding=1, stride=2), act_fn(), nn.Conv2d(2 * self.c_hid, 2 * self.c_hid, kernel_size=3, padding=1), act_fn(), nn.ConvTranspose2d(2 * self.c_hid, self.c_hid, kernel_size=3, output_padding=1, padding=1, stride=2), act_fn(), nn.Conv2d(self.c_hid, self.c_hid, kernel_size=3, padding=1), act_fn(), nn.ConvTranspose2d(self.c_hid, num_input_channels, kernel_size=3, output_padding=1, padding=1, stride=2), nn.Sigmoid())
def encode(self, x):
x = self.encoder(x)
x = self.flatten(x)
mu = self.linear_mu(x)
logvar = self.linear_logvar(x)
return (mu, logvar)
def reparameterize(self, mu, logvar):
if self.training:
std = torch.exp(logvar / 2)
eps = torch.randn_like(std)
return eps * std + mu
else:
return mu
def decode(self, x):
x = self.linear_decoder(x)
x = self.unflatten(x)
x = self.decoder(x)
return x
def forward(self, x, **kwargs):
y = kwargs['labels']
y = torch.nn.functional.one_hot(y, num_classes=self.num_classes)
mu, logvar = self.encode(x)
z = self.reparameterize(mu, logvar)
z = torch.cat([z, y], dim=1)
z = self.decode(z)
return (mu, logvar, z)
def sample(self, labels: list):
y = torch.tensor(labels, dtype=torch.int64).to(self.dummy_param.device)
y = torch.nn.functional.one_hot(y, num_classes=self.num_classes)
z = torch.randn(y.size()[0], 32).to(self.dummy_param.device)
z = torch.cat([z, y], dim=1)
return self.decode(z)
def loss_vae(x, mu, logsigma, reconstruction):
kl = KL_divergence(mu, logsigma)
ll = log_likelihood(x, reconstruction)
return kl + ll
def KL_divergence(mu, logvar):
"""
часть функции потерь, которая отвечает за "близость" латентных представлений разных людей
"""
loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
return loss
def log_likelihood(x, reconstruction):
"""
часть функции потерь, которая отвечает за качество реконструкции (как mse в обычном autoencoder)
"""
loss = nn.BCELoss(reduction='sum')
return loss(reconstruction, x)
def loss_vae(x, mu, logsigma, reconstruction):
kl = KL_divergence(mu, logsigma)
ll = log_likelihood(x, reconstruction)
return kl + ll
batch_size = 32
size = 32
transform = transforms.Compose([transforms.Resize(size), transforms.ToTensor()])
train_dataset = datasets.MNIST(root='./mnist_data/', transform=transform, train=True, download=True)
test_dataset = datasets.MNIST(root='./mnist_data/', transform=transform, train=False, download=False)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False) | code |
122248863/cell_4 | [
"text_plain_output_1.png"
] | import random
import random
list = []
for i in range(5):
list.append(random.randint(1, 10))
list.sort()
list = []
for i in range(1, 11, 2):
list.append(i)
buah = ['Anggur', 'Jambu', 'Apel', 'Pisang', 'Semangka']
print('List with Slicing = ', buah[2:5])
print('Panjang List ini =', len(list)) | code |
122248863/cell_2 | [
"text_plain_output_1.png"
] | import random
import random
list = []
for i in range(5):
list.append(random.randint(1, 10))
print('Contoh list acak :', list)
list.sort()
print('Lalu diurutkan :', list) | code |
122248863/cell_3 | [
"text_plain_output_1.png"
] | import random
import random
list = []
for i in range(5):
list.append(random.randint(1, 10))
list.sort()
list = []
for i in range(1, 11, 2):
list.append(i)
print('Contoh list dengan angka ganjil : \n', list) | code |
122248863/cell_5 | [
"text_plain_output_1.png"
] | mytupple = ((1, 2, 3, 4, 5, 6), ('A', 'N', 'G', 'G', 'U', 'R'))
for i in mytupple:
for j in i:
print(j) | code |
16129261/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sklearn as sklearn #machine learning
test = pd.read_csv('../input/test.csv', low_memory=False)
train = pd.read_csv('../input/train.csv', low_memory=False)
loans_in_default = train.default.value_counts(True)
default_by_zip = train.default.groupby(train.ZIP).mean()
default_by_year = train.default.groupby(train.year).mean()
y_train = train['default']
x_train = train[['rent', 'education', 'income', 'loan_size', 'payment_timing', 'job_stability', 'ZIP', 'occupation']]
x_train = pd.get_dummies(x_train)
clf = sklearn.ensemble.RandomForestClassifier(n_estimators=100, max_depth=4, random_state=42, oob_score=True, n_jobs=-1)
clf.fit(x_train, y_train.values.ravel()) | code |
16129261/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/test.csv', low_memory=False)
train = pd.read_csv('../input/train.csv', low_memory=False)
loans_in_default = train.default.value_counts(True)
default_by_zip = train.default.groupby(train.ZIP).mean()
print('Question 2', '\n', '\n')
print('ZIP code with highest default rate:', default_by_zip.idxmax()) | code |
16129261/cell_20 | [
"text_plain_output_1.png"
] | print('The criterion of demographic parity allows us to examine whether the fraction of applicants getting loans is the same across groups.')
print('As the above data shows, the model estimates substantially higher default rates for minority applicants (4.6%) compared to non-minority applicants (0.1%).')
print('We also observe a discrepancy between female (2.8%) and male applicants (1.9%), though to a lesser degree.')
print('Differences in the “positive rate” across groups indicates that the loan granting scheme is not making loans to each group at the same rate.')
print('This means that the criteria of demographic parity has not been achieved.') | code |
16129261/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/test.csv', low_memory=False)
train = pd.read_csv('../input/train.csv', low_memory=False)
loans_in_default = train.default.value_counts(True)
default_by_zip = train.default.groupby(train.ZIP).mean()
default_by_year = train.default.groupby(train.year).mean()
print('Question 4', '\n', '\n')
print('Correlation between age and income:', train['income'].corr(train['age']) * 100, '%') | code |
16129261/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sklearn as sklearn #machine learning
test = pd.read_csv('../input/test.csv', low_memory=False)
train = pd.read_csv('../input/train.csv', low_memory=False)
loans_in_default = train.default.value_counts(True)
default_by_zip = train.default.groupby(train.ZIP).mean()
default_by_year = train.default.groupby(train.year).mean()
y_train = train['default']
x_train = train[['rent', 'education', 'income', 'loan_size', 'payment_timing', 'job_stability', 'ZIP', 'occupation']]
x_train = pd.get_dummies(x_train)
clf = sklearn.ensemble.RandomForestClassifier(n_estimators=100, max_depth=4, random_state=42, oob_score=True, n_jobs=-1)
clf.fit(x_train, y_train.values.ravel())
print('Question 6', '\n', '\n')
print('Out of bag score:', clf.oob_score_ * 100, '%') | code |
16129261/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sklearn as sklearn #machine learning
test = pd.read_csv('../input/test.csv', low_memory=False)
train = pd.read_csv('../input/train.csv', low_memory=False)
loans_in_default = train.default.value_counts(True)
default_by_zip = train.default.groupby(train.ZIP).mean()
default_by_year = train.default.groupby(train.year).mean()
y_train = train['default']
x_train = train[['rent', 'education', 'income', 'loan_size', 'payment_timing', 'job_stability', 'ZIP', 'occupation']]
x_train = pd.get_dummies(x_train)
clf = sklearn.ensemble.RandomForestClassifier(n_estimators=100, max_depth=4, random_state=42, oob_score=True, n_jobs=-1)
clf.fit(x_train, y_train.values.ravel())
y_test = test[['default']]
x_test = test[['rent', 'education', 'income', 'loan_size', 'payment_timing', 'job_stability', 'ZIP', 'occupation']]
x_test = pd.get_dummies(data=x_test)
out_sample_pred = clf.predict(x_test)
test['out_sample_pred'] = out_sample_pred
minority_default = test.out_sample_pred.groupby(test.minority).mean()
female = test[test.sex == 1]
male = test[test.sex == 0]
minority = test[test.minority == 1]
non_minority = test[test.minority == 0]
print('Question 11', '\n', '\n')
print('Percentage of accepted and rejected - Minority applicants', '\n', '\n', minority.out_sample_pred.value_counts(True) * 100, '\n')
print('Percentage of accepted and rejected - Non-minority applicants', '\n', '\n', non_minority.out_sample_pred.value_counts(True) * 100, '\n')
print('Percentage of accepted and rejected - Female applicants', '\n', '\n', female.out_sample_pred.value_counts(True) * 100, '\n')
print('Percentage of accepted and rejected - Male applicants', '\n', '\n', male.out_sample_pred.value_counts(True) * 100, '\n') | code |
16129261/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import numpy as np
import pandas as pd
import sklearn as sklearn
import sklearn.model_selection as sklearn_model_selection
import sklearn.ensemble as sklearn_ensemble
import os
print(os.listdir('../input')) | code |
16129261/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sklearn as sklearn #machine learning
test = pd.read_csv('../input/test.csv', low_memory=False)
train = pd.read_csv('../input/train.csv', low_memory=False)
loans_in_default = train.default.value_counts(True)
default_by_zip = train.default.groupby(train.ZIP).mean()
default_by_year = train.default.groupby(train.year).mean()
y_train = train['default']
x_train = train[['rent', 'education', 'income', 'loan_size', 'payment_timing', 'job_stability', 'ZIP', 'occupation']]
x_train = pd.get_dummies(x_train)
clf = sklearn.ensemble.RandomForestClassifier(n_estimators=100, max_depth=4, random_state=42, oob_score=True, n_jobs=-1)
clf.fit(x_train, y_train.values.ravel())
y_test = test[['default']]
x_test = test[['rent', 'education', 'income', 'loan_size', 'payment_timing', 'job_stability', 'ZIP', 'occupation']]
x_test = pd.get_dummies(data=x_test)
out_sample_pred = clf.predict(x_test)
test['out_sample_pred'] = out_sample_pred
minority_default = test.out_sample_pred.groupby(test.minority).mean()
print('Question 8', '\n', '\n')
print('Default rate for non-minorities:', minority_default[0] * 100, '%') | code |
16129261/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sklearn as sklearn #machine learning
test = pd.read_csv('../input/test.csv', low_memory=False)
train = pd.read_csv('../input/train.csv', low_memory=False)
loans_in_default = train.default.value_counts(True)
default_by_zip = train.default.groupby(train.ZIP).mean()
default_by_year = train.default.groupby(train.year).mean()
y_train = train['default']
x_train = train[['rent', 'education', 'income', 'loan_size', 'payment_timing', 'job_stability', 'ZIP', 'occupation']]
x_train = pd.get_dummies(x_train)
clf = sklearn.ensemble.RandomForestClassifier(n_estimators=100, max_depth=4, random_state=42, oob_score=True, n_jobs=-1)
clf.fit(x_train, y_train.values.ravel())
y_test = test[['default']]
x_test = test[['rent', 'education', 'income', 'loan_size', 'payment_timing', 'job_stability', 'ZIP', 'occupation']]
x_test = pd.get_dummies(data=x_test)
out_sample_pred = clf.predict(x_test)
test['out_sample_pred'] = out_sample_pred
minority_default = test.out_sample_pred.groupby(test.minority).mean()
print('Question 9', '\n', '\n')
print('Default rate for minorities:', minority_default[1] * 100, '%') | code |
16129261/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/test.csv', low_memory=False)
train = pd.read_csv('../input/train.csv', low_memory=False)
loans_in_default = train.default.value_counts(True)
print('Question 1:', '\n', '\n', 'Percentage of training set loans in default:', loans_in_default[1] * 100, '%') | code |
16129261/cell_17 | [
"text_plain_output_1.png"
] | print('Question 10', '\n', '\n')
print('The loan granting scheme is group unaware. The model calculates the default probability of each applicants and then applies the same cut-off (50%) to all groups') | code |
16129261/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sklearn as sklearn #machine learning
test = pd.read_csv('../input/test.csv', low_memory=False)
train = pd.read_csv('../input/train.csv', low_memory=False)
loans_in_default = train.default.value_counts(True)
default_by_zip = train.default.groupby(train.ZIP).mean()
default_by_year = train.default.groupby(train.year).mean()
y_train = train['default']
x_train = train[['rent', 'education', 'income', 'loan_size', 'payment_timing', 'job_stability', 'ZIP', 'occupation']]
x_train = pd.get_dummies(x_train)
clf = sklearn.ensemble.RandomForestClassifier(n_estimators=100, max_depth=4, random_state=42, oob_score=True, n_jobs=-1)
clf.fit(x_train, y_train.values.ravel())
y_test = test[['default']]
x_test = test[['rent', 'education', 'income', 'loan_size', 'payment_timing', 'job_stability', 'ZIP', 'occupation']]
x_test = pd.get_dummies(data=x_test)
out_sample_pred = clf.predict(x_test)
print('Question 7', '\n', '\n')
print('Out-of-sample accuracy:', sklearn.metrics.accuracy_score(out_sample_pred, y_test) * 100, '%') | code |
16129261/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sklearn as sklearn #machine learning
test = pd.read_csv('../input/test.csv', low_memory=False)
train = pd.read_csv('../input/train.csv', low_memory=False)
loans_in_default = train.default.value_counts(True)
default_by_zip = train.default.groupby(train.ZIP).mean()
default_by_year = train.default.groupby(train.year).mean()
y_train = train['default']
x_train = train[['rent', 'education', 'income', 'loan_size', 'payment_timing', 'job_stability', 'ZIP', 'occupation']]
x_train = pd.get_dummies(x_train)
clf = sklearn.ensemble.RandomForestClassifier(n_estimators=100, max_depth=4, random_state=42, oob_score=True, n_jobs=-1)
clf.fit(x_train, y_train.values.ravel())
print('Question 5', '\n', '\n')
print('In-sample accuracy:', sklearn.metrics.accuracy_score(clf.predict(x_train), y_train) * 100, '%') | code |
16129261/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/test.csv', low_memory=False)
train = pd.read_csv('../input/train.csv', low_memory=False)
loans_in_default = train.default.value_counts(True)
default_by_zip = train.default.groupby(train.ZIP).mean()
default_by_year = train.default.groupby(train.year).mean()
print('Question 3', '\n', '\n')
print('Default rate in the first year for which we have data:', default_by_year[0] * 100, '%') | code |
90105070/cell_4 | [
"image_output_11.png",
"image_output_17.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_18.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_16.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_15.png",
"image_output_9.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id')
train.time = pd.to_datetime(train.time)
train['daytime_id'] = ((train.time.dt.hour * 60 + train.time.dt.minute) / 20).astype(int)
train = train.set_index('row_id', drop=True)
train['roadway'] = train.x.astype('str') + '_' + train.y.astype('str') + '_' + train.direction.astype('str')
train['day_of_week'] = train.time.dt.dayofweek
test.time = pd.to_datetime(test.time)
test['roadway'] = test.x.astype('str') + '_' + test.y.astype('str') + '_' + test.direction.astype('str')
test['day_of_week'] = test.time.dt.dayofweek
plot = sns.histplot(train['congestion'])
plot.set_title('Congestion Histogram')
plt.show() | code |
90105070/cell_6 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id')
train.time = pd.to_datetime(train.time)
train['daytime_id'] = ((train.time.dt.hour * 60 + train.time.dt.minute) / 20).astype(int)
train = train.set_index('row_id', drop=True)
train['roadway'] = train.x.astype('str') + '_' + train.y.astype('str') + '_' + train.direction.astype('str')
train['day_of_week'] = train.time.dt.dayofweek
test.time = pd.to_datetime(test.time)
test['roadway'] = test.x.astype('str') + '_' + test.y.astype('str') + '_' + test.direction.astype('str')
test['day_of_week'] = test.time.dt.dayofweek
# Histogram of all congestions
plot = sns.histplot(train['congestion'])
plot.set_title('Congestion Histogram')
plt.show()
rw = train.roadway.unique()
i = 0
while i < len(rw):
fig, axs = plt.subplots(1, 5, figsize=(10, 3))
sns.histplot(data=train, x=train.congestion[train.roadway == rw[i]], kde=True, color='skyblue', ax=axs[0])
axs[0].set_title(f'{rw[i]}')
sns.histplot(data=train, x=train.congestion[train.roadway == rw[i + 1]], kde=True, color='skyblue', ax=axs[1])
axs[1].set_title(f'{rw[i + 1]}')
sns.histplot(data=train, x=train.congestion[train.roadway == rw[i + 2]], kde=True, color='skyblue', ax=axs[2])
axs[2].set_title(f'{rw[i + 2]}')
sns.histplot(data=train, x=train.congestion[train.roadway == rw[i + 3]], kde=True, color='skyblue', ax=axs[3])
axs[3].set_title(f'{rw[i + 3]}')
sns.histplot(data=train, x=train.congestion[train.roadway == rw[i + 4]], kde=True, color='skyblue', ax=axs[4])
axs[4].set_title(f'{rw[i + 4]}')
i += 5
plt.tight_layout()
plt.show() | code |
90105070/cell_8 | [
"image_output_11.png",
"image_output_17.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_18.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_16.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_15.png",
"image_output_9.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id')
train.time = pd.to_datetime(train.time)
train['daytime_id'] = ((train.time.dt.hour * 60 + train.time.dt.minute) / 20).astype(int)
train = train.set_index('row_id', drop=True)
train['roadway'] = train.x.astype('str') + '_' + train.y.astype('str') + '_' + train.direction.astype('str')
train['day_of_week'] = train.time.dt.dayofweek
test.time = pd.to_datetime(test.time)
test['roadway'] = test.x.astype('str') + '_' + test.y.astype('str') + '_' + test.direction.astype('str')
test['day_of_week'] = test.time.dt.dayofweek
# Histogram of all congestions
plot = sns.histplot(train['congestion'])
plot.set_title('Congestion Histogram')
plt.show()
rw = train.roadway.unique()
i=0
while i < len(rw):
fig, axs = plt.subplots(1,5,figsize=(10, 3))
sns.histplot(data=train,x=train.congestion[train.roadway==rw[i]], kde=True, color="skyblue",ax=axs[0])
axs[0].set_title(f'{rw[i]}')
sns.histplot(data=train,x=train.congestion[train.roadway==rw[i+1]], kde=True, color="skyblue",ax=axs[1])
axs[1].set_title(f'{rw[i+1]}')
sns.histplot(data=train,x=train.congestion[train.roadway==rw[i+2]], kde=True, color="skyblue",ax=axs[2])
axs[2].set_title(f'{rw[i+2]}')
sns.histplot(data=train,x=train.congestion[train.roadway==rw[i+3]], kde=True, color="skyblue",ax=axs[3])
axs[3].set_title(f'{rw[i+3]}')
sns.histplot(data=train,x=train.congestion[train.roadway==rw[i+4]], kde=True, color="skyblue",ax=axs[4])
axs[4].set_title(f'{rw[i+4]}')
i+=5
plt.tight_layout()
plt.show()
dt = train.daytime_id.unique()
i = 0
while i < len(dt):
fig, axs = plt.subplots(1, 4, figsize=(10, 3))
sns.histplot(data=train, x=train.congestion[train.daytime_id == dt[i]], kde=True, color='skyblue', ax=axs[0])
axs[0].set_title(f'{dt[i]}')
sns.histplot(data=train, x=train.congestion[train.daytime_id == dt[i + 1]], kde=True, color='skyblue', ax=axs[1])
axs[1].set_title(f'{dt[i + 1]}')
sns.histplot(data=train, x=train.congestion[train.daytime_id == dt[i + 2]], kde=True, color='skyblue', ax=axs[2])
axs[2].set_title(f'{dt[i + 2]}')
sns.histplot(data=train, x=train.congestion[train.daytime_id == dt[i + 3]], kde=True, color='skyblue', ax=axs[3])
axs[3].set_title(f'{dt[i + 3]}')
i += 4
plt.tight_layout()
plt.show() | code |
90105070/cell_10 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id')
train.time = pd.to_datetime(train.time)
train['daytime_id'] = ((train.time.dt.hour * 60 + train.time.dt.minute) / 20).astype(int)
train = train.set_index('row_id', drop=True)
train['roadway'] = train.x.astype('str') + '_' + train.y.astype('str') + '_' + train.direction.astype('str')
train['day_of_week'] = train.time.dt.dayofweek
test.time = pd.to_datetime(test.time)
test['roadway'] = test.x.astype('str') + '_' + test.y.astype('str') + '_' + test.direction.astype('str')
test['day_of_week'] = test.time.dt.dayofweek
# Histogram of all congestions
plot = sns.histplot(train['congestion'])
plot.set_title('Congestion Histogram')
plt.show()
rw = train.roadway.unique()
i=0
while i < len(rw):
fig, axs = plt.subplots(1,5,figsize=(10, 3))
sns.histplot(data=train,x=train.congestion[train.roadway==rw[i]], kde=True, color="skyblue",ax=axs[0])
axs[0].set_title(f'{rw[i]}')
sns.histplot(data=train,x=train.congestion[train.roadway==rw[i+1]], kde=True, color="skyblue",ax=axs[1])
axs[1].set_title(f'{rw[i+1]}')
sns.histplot(data=train,x=train.congestion[train.roadway==rw[i+2]], kde=True, color="skyblue",ax=axs[2])
axs[2].set_title(f'{rw[i+2]}')
sns.histplot(data=train,x=train.congestion[train.roadway==rw[i+3]], kde=True, color="skyblue",ax=axs[3])
axs[3].set_title(f'{rw[i+3]}')
sns.histplot(data=train,x=train.congestion[train.roadway==rw[i+4]], kde=True, color="skyblue",ax=axs[4])
axs[4].set_title(f'{rw[i+4]}')
i+=5
plt.tight_layout()
plt.show()
dt = train.daytime_id.unique()
i=0
while i < len(dt):
fig, axs = plt.subplots(1,4,figsize=(10, 3))
sns.histplot(data=train,x=train.congestion[train.daytime_id==dt[i]], kde=True, color="skyblue",ax=axs[0])
axs[0].set_title(f'{dt[i]}')
sns.histplot(data=train,x=train.congestion[train.daytime_id==dt[i+1]], kde=True, color="skyblue",ax=axs[1])
axs[1].set_title(f'{dt[i+1]}')
sns.histplot(data=train,x=train.congestion[train.daytime_id==dt[i+2]], kde=True, color="skyblue",ax=axs[2])
axs[2].set_title(f'{dt[i+2]}')
sns.histplot(data=train,x=train.congestion[train.daytime_id==dt[i+3]], kde=True, color="skyblue",ax=axs[3])
axs[3].set_title(f'{dt[i+3]}')
i+=4
plt.tight_layout()
plt.show()
dow = train.time.dt.dayofweek.unique()
i = 0
while i < len(dow):
sns.histplot(data=train, x=train.congestion[train.time.dt.dayofweek == dow[i]], kde=True, color='skyblue').set(title=f'{dow[i]}')
i += 1
plt.show() | code |
2029345/cell_9 | [
"image_output_1.png"
] | from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
y = data_train.SalePrice
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF', 'LotShape', 'SaleType', 'SaleCondition']
one_hot_encoded_training_predictors = pd.get_dummies(predicators)
one_hot_encoded_training_predictors
X = data_train[predicators]
X
from sklearn.tree import DecisionTreeRegressor
housing_model = DecisionTreeRegressor()
housing_model.fit(X, y) | code |
2029345/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF', 'LotShape', 'SaleType', 'SaleCondition']
one_hot_encoded_training_predictors = pd.get_dummies(predicators)
one_hot_encoded_training_predictors
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
train = pd.read_csv('../input/train.csv')
train_y = train.SalePrice
predictor_cols = ['LotArea', 'OverallQual', 'YearBuilt', 'TotRmsAbvGrd']
train_X = train[predictor_cols]
my_model = RandomForestRegressor()
my_model.fit(train_X, train_y)
test = pd.read_csv('../input/test.csv')
test_X = test[predictor_cols]
predict_prices = my_model.predict(test_X)
my_submission = pd.DataFrame({'Id': test.Id, 'SalePrice': predict_prices})
my_submission.to_csv('submission.csv', index=False)
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.dropna(axis=0, subset=['SalePrice'], inplace=True)
target = train_data.SalePrice
cols_with_missing = [col for col in train_data.columns if train_data[col].isnull().any()]
candidate_train_predictors = train_data.drop(['Id', 'SalePrice'] + cols_with_missing, axis=1)
candidate_test_predictors = test_data.drop(['Id'] + cols_with_missing, axis=1)
low_cardinality_cols = [cname for cname in candidate_train_predictors.columns if candidate_train_predictors[cname].nunique() < 10 and candidate_train_predictors[cname].dtype == 'object']
numeric_cols = [cname for cname in candidate_train_predictors.columns if candidate_train_predictors[cname].dtype in ['int64', 'float64']]
my_cols = low_cardinality_cols + numeric_cols
train_predictors = candidate_train_predictors[my_cols]
test_predictors = candidate_test_predictors[my_cols]
train_predictors.dtypes.sample(10) | code |
2029345/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
col_interest = ['ScreenPorch', 'MoSold', 'LotShape', 'SaleType', 'SaleCondition']
sa = data_train[col_interest]
sa.describe() | code |
2029345/cell_34 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import Imputer
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
y = data_train.SalePrice
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF', 'LotShape', 'SaleType', 'SaleCondition']
one_hot_encoded_training_predictors = pd.get_dummies(predicators)
one_hot_encoded_training_predictors
X = data_train[predicators]
X
from sklearn.tree import DecisionTreeRegressor
housing_model = DecisionTreeRegressor()
housing_model.fit(X, y)
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
def get_mae(max_leaf_nodes, predictors_train, predictors_val, targ_train, targ_val):
model = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes, random_state=0)
model.fit(predictors_train, targ_train)
preds_val = model.predict(predictors_val)
mae = mean_absolute_error(targ_val, preds_val)
return mae
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
train = pd.read_csv('../input/train.csv')
train_y = train.SalePrice
predictor_cols = ['LotArea', 'OverallQual', 'YearBuilt', 'TotRmsAbvGrd']
train_X = train[predictor_cols]
my_model = RandomForestRegressor()
my_model.fit(train_X, train_y)
test = pd.read_csv('../input/test.csv')
test_X = test[predictor_cols]
predict_prices = my_model.predict(test_X)
my_submission = pd.DataFrame({'Id': test.Id, 'SalePrice': predict_prices})
my_submission.to_csv('submission.csv', index=False)
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.dropna(axis=0, subset=['SalePrice'], inplace=True)
target = train_data.SalePrice
cols_with_missing = [col for col in train_data.columns if train_data[col].isnull().any()]
candidate_train_predictors = train_data.drop(['Id', 'SalePrice'] + cols_with_missing, axis=1)
candidate_test_predictors = test_data.drop(['Id'] + cols_with_missing, axis=1)
low_cardinality_cols = [cname for cname in candidate_train_predictors.columns if candidate_train_predictors[cname].nunique() < 10 and candidate_train_predictors[cname].dtype == 'object']
numeric_cols = [cname for cname in candidate_train_predictors.columns if candidate_train_predictors[cname].dtype in ['int64', 'float64']]
my_cols = low_cardinality_cols + numeric_cols
train_predictors = candidate_train_predictors[my_cols]
test_predictors = candidate_test_predictors[my_cols]
train_predictors.dtypes.sample(10)
one_hot_encoded_training_predictors = pd.get_dummies(train_predictors)
one_hot_encoded_training_predictors
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestRegressor
def get_mae(X, y):
return -1 * cross_val_score(RandomForestRegressor(50), X, y, scoring='neg_mean_absolute_error').mean()
predictors_without_categoricals = train_predictors.select_dtypes(exclude=['object'])
mae_without_categoricals = get_mae(one_hot_encoded_training_predictors, target)
mae_one_hot_encoded = get_mae(one_hot_encoded_training_predictors, target)
one_hot_encoded_training_predictors = pd.get_dummies(train_predictors)
one_hot_encoded_test_predictors = pd.get_dummies(test_predictors)
final_train, final_test = one_hot_encoded_training_predictors.align(one_hot_encoded_test_predictors, join='left', axis=1)
final_train
final_test
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import Imputer
data_train = pd.read_csv('../input/train.csv')
data_train.dropna(axis=0, subset=['SalePrice'], inplace=True)
y = data_train.SalePrice
X = data_train.drop(['SalePrice'], axis=1).select_dtypes(exclude=['object'])
train_X, test_X, train_Y, test_y = train_test_split(X.as_matrix(), y.as_matrix(), test_size=0.25)
my_imputer = Imputer()
train_X = my_imputer.fit_transform(train_X)
test_X = my_imputer.transform(test_X)
data_train.columns | code |
2029345/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import Imputer
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
from xgboost import XGBRegressor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
y = data_train.SalePrice
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF', 'LotShape', 'SaleType', 'SaleCondition']
one_hot_encoded_training_predictors = pd.get_dummies(predicators)
one_hot_encoded_training_predictors
X = data_train[predicators]
X
from sklearn.tree import DecisionTreeRegressor
housing_model = DecisionTreeRegressor()
housing_model.fit(X, y)
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
def get_mae(max_leaf_nodes, predictors_train, predictors_val, targ_train, targ_val):
model = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes, random_state=0)
model.fit(predictors_train, targ_train)
preds_val = model.predict(predictors_val)
mae = mean_absolute_error(targ_val, preds_val)
return mae
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
train = pd.read_csv('../input/train.csv')
train_y = train.SalePrice
predictor_cols = ['LotArea', 'OverallQual', 'YearBuilt', 'TotRmsAbvGrd']
train_X = train[predictor_cols]
my_model = RandomForestRegressor()
my_model.fit(train_X, train_y)
test = pd.read_csv('../input/test.csv')
test_X = test[predictor_cols]
predict_prices = my_model.predict(test_X)
my_submission = pd.DataFrame({'Id': test.Id, 'SalePrice': predict_prices})
my_submission.to_csv('submission.csv', index=False)
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.dropna(axis=0, subset=['SalePrice'], inplace=True)
target = train_data.SalePrice
cols_with_missing = [col for col in train_data.columns if train_data[col].isnull().any()]
candidate_train_predictors = train_data.drop(['Id', 'SalePrice'] + cols_with_missing, axis=1)
candidate_test_predictors = test_data.drop(['Id'] + cols_with_missing, axis=1)
low_cardinality_cols = [cname for cname in candidate_train_predictors.columns if candidate_train_predictors[cname].nunique() < 10 and candidate_train_predictors[cname].dtype == 'object']
numeric_cols = [cname for cname in candidate_train_predictors.columns if candidate_train_predictors[cname].dtype in ['int64', 'float64']]
my_cols = low_cardinality_cols + numeric_cols
train_predictors = candidate_train_predictors[my_cols]
test_predictors = candidate_test_predictors[my_cols]
train_predictors.dtypes.sample(10)
one_hot_encoded_training_predictors = pd.get_dummies(train_predictors)
one_hot_encoded_training_predictors
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestRegressor
def get_mae(X, y):
return -1 * cross_val_score(RandomForestRegressor(50), X, y, scoring='neg_mean_absolute_error').mean()
predictors_without_categoricals = train_predictors.select_dtypes(exclude=['object'])
mae_without_categoricals = get_mae(one_hot_encoded_training_predictors, target)
mae_one_hot_encoded = get_mae(one_hot_encoded_training_predictors, target)
one_hot_encoded_training_predictors = pd.get_dummies(train_predictors)
one_hot_encoded_test_predictors = pd.get_dummies(test_predictors)
final_train, final_test = one_hot_encoded_training_predictors.align(one_hot_encoded_test_predictors, join='left', axis=1)
final_train
final_test
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import Imputer
data_train = pd.read_csv('../input/train.csv')
data_train.dropna(axis=0, subset=['SalePrice'], inplace=True)
y = data_train.SalePrice
X = data_train.drop(['SalePrice'], axis=1).select_dtypes(exclude=['object'])
train_X, test_X, train_Y, test_y = train_test_split(X.as_matrix(), y.as_matrix(), test_size=0.25)
my_imputer = Imputer()
train_X = my_imputer.fit_transform(train_X)
test_X = my_imputer.transform(test_X)
from xgboost import XGBRegressor
my_model = XGBRegressor()
my_model.fit(train_X, train_Y, verbose=False)
predictions = my_model.predict(test_X)
from sklearn.metrics import mean_absolute_error
my_model = XGBRegressor(n_estimators=1000, learning_rate=0.5)
my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], verbose=False) | code |
2029345/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
import pandas as pd
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF', 'LotShape', 'SaleType', 'SaleCondition']
one_hot_encoded_training_predictors = pd.get_dummies(predicators)
one_hot_encoded_training_predictors
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
train = pd.read_csv('../input/train.csv')
train_y = train.SalePrice
predictor_cols = ['LotArea', 'OverallQual', 'YearBuilt', 'TotRmsAbvGrd']
train_X = train[predictor_cols]
my_model = RandomForestRegressor()
my_model.fit(train_X, train_y)
test = pd.read_csv('../input/test.csv')
test_X = test[predictor_cols]
predict_prices = my_model.predict(test_X)
print(predict_prices) | code |
2029345/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF', 'LotShape', 'SaleType', 'SaleCondition']
one_hot_encoded_training_predictors = pd.get_dummies(predicators)
one_hot_encoded_training_predictors | code |
2029345/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF', 'LotShape', 'SaleType', 'SaleCondition']
one_hot_encoded_training_predictors = pd.get_dummies(predicators)
one_hot_encoded_training_predictors
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
train = pd.read_csv('../input/train.csv')
train_y = train.SalePrice
predictor_cols = ['LotArea', 'OverallQual', 'YearBuilt', 'TotRmsAbvGrd']
train_X = train[predictor_cols]
my_model = RandomForestRegressor()
my_model.fit(train_X, train_y)
test = pd.read_csv('../input/test.csv')
test_X = test[predictor_cols]
predict_prices = my_model.predict(test_X)
my_submission = pd.DataFrame({'Id': test.Id, 'SalePrice': predict_prices})
my_submission.to_csv('submission.csv', index=False)
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.dropna(axis=0, subset=['SalePrice'], inplace=True)
target = train_data.SalePrice
cols_with_missing = [col for col in train_data.columns if train_data[col].isnull().any()]
candidate_train_predictors = train_data.drop(['Id', 'SalePrice'] + cols_with_missing, axis=1)
candidate_test_predictors = test_data.drop(['Id'] + cols_with_missing, axis=1)
low_cardinality_cols = [cname for cname in candidate_train_predictors.columns if candidate_train_predictors[cname].nunique() < 10 and candidate_train_predictors[cname].dtype == 'object']
numeric_cols = [cname for cname in candidate_train_predictors.columns if candidate_train_predictors[cname].dtype in ['int64', 'float64']]
my_cols = low_cardinality_cols + numeric_cols
train_predictors = candidate_train_predictors[my_cols]
test_predictors = candidate_test_predictors[my_cols]
train_predictors.dtypes.sample(10)
one_hot_encoded_training_predictors = pd.get_dummies(train_predictors)
one_hot_encoded_training_predictors | code |
2029345/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
print(data_train.columns) | code |
2029345/cell_11 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
y = data_train.SalePrice
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF', 'LotShape', 'SaleType', 'SaleCondition']
one_hot_encoded_training_predictors = pd.get_dummies(predicators)
one_hot_encoded_training_predictors
X = data_train[predicators]
X
from sklearn.tree import DecisionTreeRegressor
housing_model = DecisionTreeRegressor()
housing_model.fit(X, y)
from sklearn.metrics import mean_absolute_error
predicted_Home_prices = housing_model.predict(X)
mean_absolute_error(y, predicted_Home_prices) | code |
2029345/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
import pandas as pd
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF', 'LotShape', 'SaleType', 'SaleCondition']
one_hot_encoded_training_predictors = pd.get_dummies(predicators)
one_hot_encoded_training_predictors
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
train = pd.read_csv('../input/train.csv')
train_y = train.SalePrice
predictor_cols = ['LotArea', 'OverallQual', 'YearBuilt', 'TotRmsAbvGrd']
train_X = train[predictor_cols]
my_model = RandomForestRegressor()
my_model.fit(train_X, train_y) | code |
2029345/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
y = data_train.SalePrice
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF', 'LotShape', 'SaleType', 'SaleCondition']
one_hot_encoded_training_predictors = pd.get_dummies(predicators)
one_hot_encoded_training_predictors
X = data_train[predicators]
X | code |
2029345/cell_32 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import Imputer
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
from xgboost import XGBRegressor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
y = data_train.SalePrice
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF', 'LotShape', 'SaleType', 'SaleCondition']
one_hot_encoded_training_predictors = pd.get_dummies(predicators)
one_hot_encoded_training_predictors
X = data_train[predicators]
X
from sklearn.tree import DecisionTreeRegressor
housing_model = DecisionTreeRegressor()
housing_model.fit(X, y)
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
def get_mae(max_leaf_nodes, predictors_train, predictors_val, targ_train, targ_val):
model = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes, random_state=0)
model.fit(predictors_train, targ_train)
preds_val = model.predict(predictors_val)
mae = mean_absolute_error(targ_val, preds_val)
return mae
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
train = pd.read_csv('../input/train.csv')
train_y = train.SalePrice
predictor_cols = ['LotArea', 'OverallQual', 'YearBuilt', 'TotRmsAbvGrd']
train_X = train[predictor_cols]
my_model = RandomForestRegressor()
my_model.fit(train_X, train_y)
test = pd.read_csv('../input/test.csv')
test_X = test[predictor_cols]
predict_prices = my_model.predict(test_X)
my_submission = pd.DataFrame({'Id': test.Id, 'SalePrice': predict_prices})
my_submission.to_csv('submission.csv', index=False)
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.dropna(axis=0, subset=['SalePrice'], inplace=True)
target = train_data.SalePrice
cols_with_missing = [col for col in train_data.columns if train_data[col].isnull().any()]
candidate_train_predictors = train_data.drop(['Id', 'SalePrice'] + cols_with_missing, axis=1)
candidate_test_predictors = test_data.drop(['Id'] + cols_with_missing, axis=1)
low_cardinality_cols = [cname for cname in candidate_train_predictors.columns if candidate_train_predictors[cname].nunique() < 10 and candidate_train_predictors[cname].dtype == 'object']
numeric_cols = [cname for cname in candidate_train_predictors.columns if candidate_train_predictors[cname].dtype in ['int64', 'float64']]
my_cols = low_cardinality_cols + numeric_cols
train_predictors = candidate_train_predictors[my_cols]
test_predictors = candidate_test_predictors[my_cols]
train_predictors.dtypes.sample(10)
one_hot_encoded_training_predictors = pd.get_dummies(train_predictors)
one_hot_encoded_training_predictors
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestRegressor
def get_mae(X, y):
return -1 * cross_val_score(RandomForestRegressor(50), X, y, scoring='neg_mean_absolute_error').mean()
predictors_without_categoricals = train_predictors.select_dtypes(exclude=['object'])
mae_without_categoricals = get_mae(one_hot_encoded_training_predictors, target)
mae_one_hot_encoded = get_mae(one_hot_encoded_training_predictors, target)
one_hot_encoded_training_predictors = pd.get_dummies(train_predictors)
one_hot_encoded_test_predictors = pd.get_dummies(test_predictors)
final_train, final_test = one_hot_encoded_training_predictors.align(one_hot_encoded_test_predictors, join='left', axis=1)
final_train
final_test
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import Imputer
data_train = pd.read_csv('../input/train.csv')
data_train.dropna(axis=0, subset=['SalePrice'], inplace=True)
y = data_train.SalePrice
X = data_train.drop(['SalePrice'], axis=1).select_dtypes(exclude=['object'])
train_X, test_X, train_Y, test_y = train_test_split(X.as_matrix(), y.as_matrix(), test_size=0.25)
my_imputer = Imputer()
train_X = my_imputer.fit_transform(train_X)
test_X = my_imputer.transform(test_X)
from xgboost import XGBRegressor
my_model = XGBRegressor()
my_model.fit(train_X, train_Y, verbose=False)
predictions = my_model.predict(test_X)
from sklearn.metrics import mean_absolute_error
print('Mean absolute error :' + str(mean_absolute_error(predictions, test_y))) | code |
2029345/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
y = data_train.SalePrice
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF', 'LotShape', 'SaleType', 'SaleCondition']
one_hot_encoded_training_predictors = pd.get_dummies(predicators)
one_hot_encoded_training_predictors
X = data_train[predicators]
X
from sklearn.tree import DecisionTreeRegressor
housing_model = DecisionTreeRegressor()
housing_model.fit(X, y)
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
def get_mae(max_leaf_nodes, predictors_train, predictors_val, targ_train, targ_val):
model = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes, random_state=0)
model.fit(predictors_train, targ_train)
preds_val = model.predict(predictors_val)
mae = mean_absolute_error(targ_val, preds_val)
return mae
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
train = pd.read_csv('../input/train.csv')
train_y = train.SalePrice
predictor_cols = ['LotArea', 'OverallQual', 'YearBuilt', 'TotRmsAbvGrd']
train_X = train[predictor_cols]
my_model = RandomForestRegressor()
my_model.fit(train_X, train_y)
test = pd.read_csv('../input/test.csv')
test_X = test[predictor_cols]
predict_prices = my_model.predict(test_X)
my_submission = pd.DataFrame({'Id': test.Id, 'SalePrice': predict_prices})
my_submission.to_csv('submission.csv', index=False)
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.dropna(axis=0, subset=['SalePrice'], inplace=True)
target = train_data.SalePrice
cols_with_missing = [col for col in train_data.columns if train_data[col].isnull().any()]
candidate_train_predictors = train_data.drop(['Id', 'SalePrice'] + cols_with_missing, axis=1)
candidate_test_predictors = test_data.drop(['Id'] + cols_with_missing, axis=1)
low_cardinality_cols = [cname for cname in candidate_train_predictors.columns if candidate_train_predictors[cname].nunique() < 10 and candidate_train_predictors[cname].dtype == 'object']
numeric_cols = [cname for cname in candidate_train_predictors.columns if candidate_train_predictors[cname].dtype in ['int64', 'float64']]
my_cols = low_cardinality_cols + numeric_cols
train_predictors = candidate_train_predictors[my_cols]
test_predictors = candidate_test_predictors[my_cols]
train_predictors.dtypes.sample(10)
one_hot_encoded_training_predictors = pd.get_dummies(train_predictors)
one_hot_encoded_training_predictors
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestRegressor
def get_mae(X, y):
return -1 * cross_val_score(RandomForestRegressor(50), X, y, scoring='neg_mean_absolute_error').mean()
predictors_without_categoricals = train_predictors.select_dtypes(exclude=['object'])
mae_without_categoricals = get_mae(one_hot_encoded_training_predictors, target)
mae_one_hot_encoded = get_mae(one_hot_encoded_training_predictors, target)
one_hot_encoded_training_predictors = pd.get_dummies(train_predictors)
one_hot_encoded_test_predictors = pd.get_dummies(test_predictors)
final_train, final_test = one_hot_encoded_training_predictors.align(one_hot_encoded_test_predictors, join='left', axis=1)
final_train
final_test | code |
2029345/cell_15 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
def get_mae(max_leaf_nodes, predictors_train, predictors_val, targ_train, targ_val):
model = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes, random_state=0)
model.fit(predictors_train, targ_train)
preds_val = model.predict(predictors_val)
mae = mean_absolute_error(targ_val, preds_val)
return mae
for max_leaf_nodes in [5, 50, 500, 5000]:
my_mae = get_mae(max_leaf_nodes, train_X, val_X, train_y, val_y)
print('Max leaf nodes :%d \t\t Mean Absolute Error: %d' % (max_leaf_nodes, my_mae)) | code |
2029345/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
data_train | code |
2029345/cell_17 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
forest_model = RandomForestRegressor()
forest_model.fit(train_X, train_y)
predict_vals = forest_model.predict(val_X)
print(mean_absolute_error(val_y, predict_vals)) | code |
2029345/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import Imputer
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
from xgboost import XGBRegressor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
y = data_train.SalePrice
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF', 'LotShape', 'SaleType', 'SaleCondition']
one_hot_encoded_training_predictors = pd.get_dummies(predicators)
one_hot_encoded_training_predictors
X = data_train[predicators]
X
from sklearn.tree import DecisionTreeRegressor
housing_model = DecisionTreeRegressor()
housing_model.fit(X, y)
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
def get_mae(max_leaf_nodes, predictors_train, predictors_val, targ_train, targ_val):
model = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes, random_state=0)
model.fit(predictors_train, targ_train)
preds_val = model.predict(predictors_val)
mae = mean_absolute_error(targ_val, preds_val)
return mae
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
train = pd.read_csv('../input/train.csv')
train_y = train.SalePrice
predictor_cols = ['LotArea', 'OverallQual', 'YearBuilt', 'TotRmsAbvGrd']
train_X = train[predictor_cols]
my_model = RandomForestRegressor()
my_model.fit(train_X, train_y)
test = pd.read_csv('../input/test.csv')
test_X = test[predictor_cols]
predict_prices = my_model.predict(test_X)
my_submission = pd.DataFrame({'Id': test.Id, 'SalePrice': predict_prices})
my_submission.to_csv('submission.csv', index=False)
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.dropna(axis=0, subset=['SalePrice'], inplace=True)
target = train_data.SalePrice
cols_with_missing = [col for col in train_data.columns if train_data[col].isnull().any()]
candidate_train_predictors = train_data.drop(['Id', 'SalePrice'] + cols_with_missing, axis=1)
candidate_test_predictors = test_data.drop(['Id'] + cols_with_missing, axis=1)
low_cardinality_cols = [cname for cname in candidate_train_predictors.columns if candidate_train_predictors[cname].nunique() < 10 and candidate_train_predictors[cname].dtype == 'object']
numeric_cols = [cname for cname in candidate_train_predictors.columns if candidate_train_predictors[cname].dtype in ['int64', 'float64']]
my_cols = low_cardinality_cols + numeric_cols
train_predictors = candidate_train_predictors[my_cols]
test_predictors = candidate_test_predictors[my_cols]
train_predictors.dtypes.sample(10)
one_hot_encoded_training_predictors = pd.get_dummies(train_predictors)
one_hot_encoded_training_predictors
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestRegressor
def get_mae(X, y):
return -1 * cross_val_score(RandomForestRegressor(50), X, y, scoring='neg_mean_absolute_error').mean()
predictors_without_categoricals = train_predictors.select_dtypes(exclude=['object'])
mae_without_categoricals = get_mae(one_hot_encoded_training_predictors, target)
mae_one_hot_encoded = get_mae(one_hot_encoded_training_predictors, target)
one_hot_encoded_training_predictors = pd.get_dummies(train_predictors)
one_hot_encoded_test_predictors = pd.get_dummies(test_predictors)
final_train, final_test = one_hot_encoded_training_predictors.align(one_hot_encoded_test_predictors, join='left', axis=1)
final_train
final_test
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import Imputer
data_train = pd.read_csv('../input/train.csv')
data_train.dropna(axis=0, subset=['SalePrice'], inplace=True)
y = data_train.SalePrice
X = data_train.drop(['SalePrice'], axis=1).select_dtypes(exclude=['object'])
train_X, test_X, train_Y, test_y = train_test_split(X.as_matrix(), y.as_matrix(), test_size=0.25)
my_imputer = Imputer()
train_X = my_imputer.fit_transform(train_X)
test_X = my_imputer.transform(test_X)
from xgboost import XGBRegressor
my_model = XGBRegressor()
my_model.fit(train_X, train_Y, verbose=False) | code |
2029345/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
y = data_train.SalePrice
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF', 'LotShape', 'SaleType', 'SaleCondition']
one_hot_encoded_training_predictors = pd.get_dummies(predicators)
one_hot_encoded_training_predictors
X = data_train[predicators]
X
from sklearn.tree import DecisionTreeRegressor
housing_model = DecisionTreeRegressor()
housing_model.fit(X, y)
print(' making predictions for the following 5 houses:')
print(X.head())
print('The prediction are')
print(housing_model.predict(X.head())) | code |
2029345/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
y = data_train.SalePrice
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF', 'LotShape', 'SaleType', 'SaleCondition']
one_hot_encoded_training_predictors = pd.get_dummies(predicators)
one_hot_encoded_training_predictors
X = data_train[predicators]
X
from sklearn.tree import DecisionTreeRegressor
housing_model = DecisionTreeRegressor()
housing_model.fit(X, y)
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
def get_mae(max_leaf_nodes, predictors_train, predictors_val, targ_train, targ_val):
model = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes, random_state=0)
model.fit(predictors_train, targ_train)
preds_val = model.predict(predictors_val)
mae = mean_absolute_error(targ_val, preds_val)
return mae
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
train = pd.read_csv('../input/train.csv')
train_y = train.SalePrice
predictor_cols = ['LotArea', 'OverallQual', 'YearBuilt', 'TotRmsAbvGrd']
train_X = train[predictor_cols]
my_model = RandomForestRegressor()
my_model.fit(train_X, train_y)
test = pd.read_csv('../input/test.csv')
test_X = test[predictor_cols]
predict_prices = my_model.predict(test_X)
my_submission = pd.DataFrame({'Id': test.Id, 'SalePrice': predict_prices})
my_submission.to_csv('submission.csv', index=False)
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.dropna(axis=0, subset=['SalePrice'], inplace=True)
target = train_data.SalePrice
cols_with_missing = [col for col in train_data.columns if train_data[col].isnull().any()]
candidate_train_predictors = train_data.drop(['Id', 'SalePrice'] + cols_with_missing, axis=1)
candidate_test_predictors = test_data.drop(['Id'] + cols_with_missing, axis=1)
low_cardinality_cols = [cname for cname in candidate_train_predictors.columns if candidate_train_predictors[cname].nunique() < 10 and candidate_train_predictors[cname].dtype == 'object']
numeric_cols = [cname for cname in candidate_train_predictors.columns if candidate_train_predictors[cname].dtype in ['int64', 'float64']]
my_cols = low_cardinality_cols + numeric_cols
train_predictors = candidate_train_predictors[my_cols]
test_predictors = candidate_test_predictors[my_cols]
train_predictors.dtypes.sample(10)
one_hot_encoded_training_predictors = pd.get_dummies(train_predictors)
one_hot_encoded_training_predictors
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestRegressor
def get_mae(X, y):
return -1 * cross_val_score(RandomForestRegressor(50), X, y, scoring='neg_mean_absolute_error').mean()
predictors_without_categoricals = train_predictors.select_dtypes(exclude=['object'])
mae_without_categoricals = get_mae(one_hot_encoded_training_predictors, target)
mae_one_hot_encoded = get_mae(one_hot_encoded_training_predictors, target)
print('Mean Absolute Error when Dropping Categoricals:' + str(int(mae_without_categoricals)))
print('Mean Absolute Error with One-Hot Encoding:' + str(int(mae_one_hot_encoded))) | code |
2029345/cell_12 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data_train = pd.read_csv(main_file_path)
y = data_train.SalePrice
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF', 'LotShape', 'SaleType', 'SaleCondition']
one_hot_encoded_training_predictors = pd.get_dummies(predicators)
one_hot_encoded_training_predictors
X = data_train[predicators]
X
from sklearn.tree import DecisionTreeRegressor
housing_model = DecisionTreeRegressor()
housing_model.fit(X, y)
from sklearn.metrics import mean_absolute_error
predicted_Home_prices = housing_model.predict(X)
mean_absolute_error(y, predicted_Home_prices)
from sklearn.model_selection import train_test_split
train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=0)
housing_model = DecisionTreeRegressor()
housing_model.fit(train_X, train_y)
val_predictions = housing_model.predict(val_X)
print(mean_absolute_error(val_y, val_predictions)) | code |
73067465/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler, RobustScaler, StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split, cross_val_score, StratifiedKFold
from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import KNNImputer, IterativeImputer
from sklearn.feature_selection import SelectKBest, chi2, mutual_info_classif
from sklearn.linear_model import LogisticRegression
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.decomposition import PCA
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier
sns.set_style('whitegrid')
from sklearn.metrics import accuracy_score
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
submit = pd.DataFrame(test['PassengerId'])
train['title'] = 0
for i in range(0, len(train)):
train.loc[i, 'title'] = train['Name'].iloc[i].split(',')[1].split('.')[0][1:]
train['title'].replace({'Mr': 1, 'Miss': 2, 'Mrs': 2, 'Master': 3, 'Dr': 4, 'Rev': 5}, inplace=True)
train['title'].replace(['Major', 'Mlle', 'Col', 'Don', 'the Countess', 'Sir', 'Capt', 'Mme', 'Lady', 'Jonkheer', 'Ms'], 6, inplace=True)
for i in range(len(train)):
if not pd.isnull(train['Cabin'].iloc[i]):
train.loc[i, 'Cabin'] = train['Cabin'].loc[i][0]
train['Cabin'].replace({'C': 1, 'B': 2, 'D': 3, 'E': 4, 'A': 5, 'F': 6, 'G': 7, 'T': 8}, inplace=True)
train['Fare'] = np.sqrt(train['Fare'])
train.drop(['Name', 'SibSp', 'Parch', 'Ticket', 'PassengerId', 'Cabin'], axis=1, inplace=True)
fig,ax=plt.subplots(3,1,figsize=(15,13))
sns.heatmap(train.corr('spearman'),annot=True,ax=ax[0],label='spearman') #spearman
sns.heatmap(train.corr('kendall'),annot=True,ax=ax[1],label='kendall') #Kendall
sns.heatmap(train.corr('pearson'),annot=True,ax=ax[2],label='pearson') #pearson
sns.catplot(x='Embarked', data=train, kind='count', hue='Survived', col='Sex') | code |
73067465/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier,VotingClassifier
from sklearn.impute import KNNImputer,IterativeImputer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split,cross_val_score,StratifiedKFold
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler,RobustScaler,StandardScaler
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler, RobustScaler, StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split, cross_val_score, StratifiedKFold
from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import KNNImputer, IterativeImputer
from sklearn.feature_selection import SelectKBest, chi2, mutual_info_classif
from sklearn.linear_model import LogisticRegression
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.decomposition import PCA
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier
sns.set_style('whitegrid')
from sklearn.metrics import accuracy_score
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
submit = pd.DataFrame(test['PassengerId'])
train['title'] = 0
for i in range(0, len(train)):
train.loc[i, 'title'] = train['Name'].iloc[i].split(',')[1].split('.')[0][1:]
train['title'].replace({'Mr': 1, 'Miss': 2, 'Mrs': 2, 'Master': 3, 'Dr': 4, 'Rev': 5}, inplace=True)
train['title'].replace(['Major', 'Mlle', 'Col', 'Don', 'the Countess', 'Sir', 'Capt', 'Mme', 'Lady', 'Jonkheer', 'Ms'], 6, inplace=True)
for i in range(len(train)):
if not pd.isnull(train['Cabin'].iloc[i]):
train.loc[i, 'Cabin'] = train['Cabin'].loc[i][0]
train['Cabin'].replace({'C': 1, 'B': 2, 'D': 3, 'E': 4, 'A': 5, 'F': 6, 'G': 7, 'T': 8}, inplace=True)
train['Fare'] = np.sqrt(train['Fare'])
train.drop(['Name', 'SibSp', 'Parch', 'Ticket', 'PassengerId', 'Cabin'], axis=1, inplace=True)
fig,ax=plt.subplots(3,1,figsize=(15,13))
sns.heatmap(train.corr('spearman'),annot=True,ax=ax[0],label='spearman') #spearman
sns.heatmap(train.corr('kendall'),annot=True,ax=ax[1],label='kendall') #Kendall
sns.heatmap(train.corr('pearson'),annot=True,ax=ax[2],label='pearson') #pearson
train = pd.get_dummies(train, columns=['Pclass', 'Embarked', 'title', 'family'], drop_first=True)
impute = KNNImputer(n_neighbors=13)
train = pd.DataFrame(impute.fit_transform(train), columns=train.columns)
model = []
model.append(('Logistic Regression', LogisticRegression(max_iter=1000)))
model.append(('LDA', LinearDiscriminantAnalysis()))
model.append(('SVC', SVC(kernel='rbf')))
model.append(('DTC', DecisionTreeClassifier()))
model.append(('GBC', GradientBoostingClassifier()))
model.append(('RFC', RandomForestClassifier()))
model.append(('Kneig', KNeighborsClassifier()))
x = train.drop('Survived', axis=1)
y = train['Survived']
xtrain, xvalid, ytrain, yvalid = train_test_split(x, y, test_size=0.3)
scores = []
for name, models in model:
pipeline = Pipeline(steps=[('scale', MinMaxScaler()), ('model', models)])
cv = StratifiedKFold(n_splits=10, random_state=21, shuffle=True)
score = cross_val_score(pipeline, x, y, cv=cv, scoring='accuracy', n_jobs=-1)
scores.append((name, np.mean(score)))
scores | code |
73067465/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
submit = pd.DataFrame(test['PassengerId'])
train['title'] = 0
for i in range(0, len(train)):
train.loc[i, 'title'] = train['Name'].iloc[i].split(',')[1].split('.')[0][1:]
train['title'].replace({'Mr': 1, 'Miss': 2, 'Mrs': 2, 'Master': 3, 'Dr': 4, 'Rev': 5}, inplace=True)
train['title'].replace(['Major', 'Mlle', 'Col', 'Don', 'the Countess', 'Sir', 'Capt', 'Mme', 'Lady', 'Jonkheer', 'Ms'], 6, inplace=True)
for i in range(len(train)):
if not pd.isnull(train['Cabin'].iloc[i]):
train.loc[i, 'Cabin'] = train['Cabin'].loc[i][0]
train['Cabin'].replace({'C': 1, 'B': 2, 'D': 3, 'E': 4, 'A': 5, 'F': 6, 'G': 7, 'T': 8}, inplace=True)
train['Fare'] = np.sqrt(train['Fare'])
train.drop(['Name', 'SibSp', 'Parch', 'Ticket', 'PassengerId', 'Cabin'], axis=1, inplace=True)
train.hist(figsize=(15, 10))
plt.show() | code |
73067465/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler, RobustScaler, StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split, cross_val_score, StratifiedKFold
from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import KNNImputer, IterativeImputer
from sklearn.feature_selection import SelectKBest, chi2, mutual_info_classif
from sklearn.linear_model import LogisticRegression
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.decomposition import PCA
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier
sns.set_style('whitegrid')
from sklearn.metrics import accuracy_score
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
submit = pd.DataFrame(test['PassengerId'])
train['title'] = 0
for i in range(0, len(train)):
train.loc[i, 'title'] = train['Name'].iloc[i].split(',')[1].split('.')[0][1:]
train['title'].replace({'Mr': 1, 'Miss': 2, 'Mrs': 2, 'Master': 3, 'Dr': 4, 'Rev': 5}, inplace=True)
train['title'].replace(['Major', 'Mlle', 'Col', 'Don', 'the Countess', 'Sir', 'Capt', 'Mme', 'Lady', 'Jonkheer', 'Ms'], 6, inplace=True)
for i in range(len(train)):
if not pd.isnull(train['Cabin'].iloc[i]):
train.loc[i, 'Cabin'] = train['Cabin'].loc[i][0]
train['Cabin'].replace({'C': 1, 'B': 2, 'D': 3, 'E': 4, 'A': 5, 'F': 6, 'G': 7, 'T': 8}, inplace=True)
train['Fare'] = np.sqrt(train['Fare'])
train.drop(['Name', 'SibSp', 'Parch', 'Ticket', 'PassengerId', 'Cabin'], axis=1, inplace=True)
fig,ax=plt.subplots(3,1,figsize=(15,13))
sns.heatmap(train.corr('spearman'),annot=True,ax=ax[0],label='spearman') #spearman
sns.heatmap(train.corr('kendall'),annot=True,ax=ax[1],label='kendall') #Kendall
sns.heatmap(train.corr('pearson'),annot=True,ax=ax[2],label='pearson') #pearson
sns.countplot(x='family', data=train, hue='Survived') | code |
73067465/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 |
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