path
stringlengths 13
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sequencelengths 1
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stringlengths 0
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|---|---|---|---|
72062410/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ramen = pd.read_csv('../input/ramen-ratings/ramen-ratings.csv')
ramen.info() | code |
72062410/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ramen = pd.read_csv('../input/ramen-ratings/ramen-ratings.csv')
ramen_drop_unrated = ramen.copy()
ramen_convert_unrated = ramen.copy()
ramen_drop_unrated = ramen_drop_unrated[ramen_drop_unrated['Stars'] != 'Unrated']
ramen_drop_unrated.groupby('Style')['rating'].mean() | code |
72062410/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 |
72062410/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ramen = pd.read_csv('../input/ramen-ratings/ramen-ratings.csv')
ramen['Stars'] | code |
72062410/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ramen = pd.read_csv('../input/ramen-ratings/ramen-ratings.csv')
ramen_drop_unrated = ramen.copy()
ramen_convert_unrated = ramen.copy()
ramen_drop_unrated = ramen_drop_unrated[ramen_drop_unrated['Stars'] != 'Unrated']
ramen_drop_unrated['rating'].max() | code |
72062410/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ramen = pd.read_csv('../input/ramen-ratings/ramen-ratings.csv')
ramen[['Stars']] | code |
72062410/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ramen = pd.read_csv('../input/ramen-ratings/ramen-ratings.csv')
ramen_drop_unrated = ramen.copy()
ramen_convert_unrated = ramen.copy()
ramen_drop_unrated = ramen_drop_unrated[ramen_drop_unrated['Stars'] != 'Unrated']
ramen_drop_unrated.groupby('Style')['rating'].mean()
ramen_drop_unrated.groupby('Country')['rating'].mean().sort_values()
ramen_drop_unrated.groupby('Brand')['rating'].count().sort_values(ascending=False)[:25] | code |
72062410/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ramen = pd.read_csv('../input/ramen-ratings/ramen-ratings.csv')
ramen['Style'].unique() | code |
72062410/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
ramen = pd.read_csv('../input/ramen-ratings/ramen-ratings.csv')
import seaborn as sns
sns.countplot(x='Style', data=ramen) | code |
72062410/cell_5 | [
"text_plain_output_1.png"
] | farbe = 'grün'
farbe = 'blau'
print(farbe) | code |
130014142/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv')
target = data.pop('income')
data = data.drop('relationship', axis=1)
data.hist(figsize=(10, 10)) | code |
130014142/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler,OneHotEncoder
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv')
target = data.pop('income')
data = data.drop('relationship', axis=1)
one_hot = OneHotEncoder()
ss = StandardScaler()
def trans_one_hot(column):
trans = one_hot.fit_transform(data[column].values.reshape(-1, 1))
array_name = trans.toarray().astype(int)
return array_name
def trans_normalize(column):
trans = ss.fit_transform(data[column].values.reshape(-1, 1))
return trans
txt_arr = np.concatenate((trans_one_hot('workclass'), trans_one_hot('education'), trans_one_hot('marital.status'), trans_one_hot('occupation'), trans_one_hot('relationship_change'), trans_one_hot('race'), trans_one_hot('sex'), trans_one_hot('native.country')), axis=1)
num_arr = np.concatenate((trans_normalize('age'), trans_normalize('fnlwgt'), trans_normalize('education.num'), trans_normalize('capital.gain'), trans_normalize('capital.loss'), trans_normalize('hours.per.week')), axis=1)
pred_data = np.concatenate((txt_arr, num_arr), axis=1)
pred_pd = pd.DataFrame(pred_data)
pred_pd | code |
130014142/cell_30 | [
"text_html_output_1.png"
] | from sklearn.metrics import confusion_matrix,precision_score,recall_score,f1_score,accuracy_score
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv')
def performance(y_test, pred, model_name):
precision = precision_score(y_test, pred, pos_label='<=50K')
recall = recall_score(y_test, pred, pos_label='<=50K')
F1 = f1_score(y_test, pred, pos_label='<=50K')
CM = confusion_matrix(y_test, pred)
accuracy = accuracy_score(y_test, pred)
svm = SVC()
svm.fit(x_train, y_train)
pred_svm = svm.predict(x_test)
performance(y_test, pred_svm, 'svm') | code |
130014142/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler,OneHotEncoder
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv')
target = data.pop('income')
data = data.drop('relationship', axis=1)
one_hot = OneHotEncoder()
ss = StandardScaler()
def trans_one_hot(column):
trans = one_hot.fit_transform(data[column].values.reshape(-1, 1))
array_name = trans.toarray().astype(int)
return array_name
def trans_normalize(column):
trans = ss.fit_transform(data[column].values.reshape(-1, 1))
return trans
txt_arr = np.concatenate((trans_one_hot('workclass'), trans_one_hot('education'), trans_one_hot('marital.status'), trans_one_hot('occupation'), trans_one_hot('relationship_change'), trans_one_hot('race'), trans_one_hot('sex'), trans_one_hot('native.country')), axis=1)
num_arr = np.concatenate((trans_normalize('age'), trans_normalize('fnlwgt'), trans_normalize('education.num'), trans_normalize('capital.gain'), trans_normalize('capital.loss'), trans_normalize('hours.per.week')), axis=1) | code |
130014142/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns | code |
130014142/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix,precision_score,recall_score,f1_score,accuracy_score
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv')
def performance(y_test, pred, model_name):
precision = precision_score(y_test, pred, pos_label='<=50K')
recall = recall_score(y_test, pred, pos_label='<=50K')
F1 = f1_score(y_test, pred, pos_label='<=50K')
CM = confusion_matrix(y_test, pred)
accuracy = accuracy_score(y_test, pred)
rf = RandomForestClassifier()
rf.fit(x_train, y_train)
pred_rf = rf.predict(x_test)
performance(y_test, pred_rf, 'rf') | code |
130014142/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.svm import SVC
svm = SVC()
svm.fit(x_train, y_train) | code |
130014142/cell_16 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | txt_col = ['workclass', 'education', 'marital.status', 'occupation', 'relationship_change', 'race', 'sex', 'native.country']
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, OneHotEncoder
num_col = ['age', 'fnlwgt', 'education.num', 'capital.gain', 'capital.loss', 'hours.per.week']
(num_col, txt_col) | code |
130014142/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv')
data | code |
130014142/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv')
sns.heatmap(data.corr(), cmap='Blues', annot=True) | code |
90108440/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 |
90108440/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
plt.imshow(num[0], cmap='inferno') | code |
324025/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import xgboost as xgb
dtrain = xgb.DMatrix(X_train, y_train)
dvalid = xgb.DMatrix(X_test, y_test)
watchlist = [(dtrain, 'train'), (dvalid, 'eval')]
params = {'objective': 'reg:linear', 'eval_metric': 'rmse', 'eta': 0.01, 'max_depth': 6, 'silent': 1, 'nthread': 1}
num_boost_round = 100
gbm = xgb.train(params, dtrain, num_boost_round, evals=watchlist, verbose_eval=True)
y_pred = gbm.predict(dvalid)
np.sqrt(np.mean((y_pred - y_test) ** 2)) | code |
324025/cell_2 | [
"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 |
324025/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import xgboost as xgb
from sklearn import datasets
from sklearn.cross_validation import train_test_split | code |
324025/cell_5 | [
"text_plain_output_1.png"
] | import xgboost as xgb
dtrain = xgb.DMatrix(X_train, y_train)
dvalid = xgb.DMatrix(X_test, y_test)
watchlist = [(dtrain, 'train'), (dvalid, 'eval')]
params = {'objective': 'reg:linear', 'eval_metric': 'rmse', 'eta': 0.01, 'max_depth': 6, 'silent': 1, 'nthread': 1}
num_boost_round = 100
gbm = xgb.train(params, dtrain, num_boost_round, evals=watchlist, verbose_eval=True)
y_pred = gbm.predict(dvalid) | code |
128035508/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.tree import DecisionTreeRegressor
tree_regressor = DecisionTreeRegressor(random_state=0)
tree_regressor.fit(X_train, Y_train) | code |
128035508/cell_6 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression,LogisticRegression
Logistic_R = LogisticRegression()
Logistic_R.fit(X_train, Y_train) | code |
128035508/cell_7 | [
"text_html_output_1.png"
] | from sklearn.svm import SVR
svr_regressor = SVR(kernel='rbf', gamma='auto')
svr_regressor.fit(X_train, Y_train) | code |
128035508/cell_18 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression,LogisticRegression
from sklearn.neighbors import KNeighborsRegressor
from sklearn.svm import SVR
from sklearn.tree import DecisionTreeRegressor
import pickle
knn = KNeighborsRegressor(n_neighbors=2)
knn.fit(X_train, Y_train)
Logistic_R = LogisticRegression()
Logistic_R.fit(X_train, Y_train)
svr_regressor = SVR(kernel='rbf', gamma='auto')
svr_regressor.fit(X_train, Y_train)
lr = LinearRegression()
lr.fit(X_train, Y_train)
tree_regressor = DecisionTreeRegressor(random_state=0)
tree_regressor.fit(X_train, Y_train)
forest_regressor = RandomForestRegressor(n_estimators=300, random_state=0)
forest_regressor.fit(X_train, Y_train)
knn_sav = pickle.dumps(knn)
knn_mod = pickle.loads(knn_sav)
lr_sav = pickle.dumps(lr)
lr_mod = pickle.loads(lr_sav)
Logistic_R_sav = pickle.dumps(Logistic_R)
log_mod = pickle.loads(Logistic_R_sav)
svr_sav = pickle.dumps(svr_regressor)
svr_mod = pickle.loads(svr_sav)
tree_regressor_sav = pickle.dumps(tree_regressor)
tree_mod = pickle.loads(tree_regressor_sav)
forest_regressor_sav = pickle.dumps(forest_regressor)
fr_mod = pickle.loads(forest_regressor_sav)
from tkinter import *
def predict(choice):
try:
a = float(int1.get())
b = float(int2.get())
if choice.get() == 1:
pred = lr_mod.predict([[a, b]])
if choice.get() == 2:
pred = log_mod.predict([[a, b]])
if choice.get() == 3:
pred = svr_mod.predict([[a, b]])
if choice.get() == 4:
pred = knn_mod.predict([[a, b]])
if choice.get() == 5:
pred = tree_mod.predict([[a, b]])
if choice.get() == 6:
pred = fr_mod.predict([[a, b]])
res = str(pred[0])
T.insert(END, bool(res))
except:
if choice.get() <= 0:
res = 'Choose the regression algorithm'
else:
res = 'INVALID INPUT'
T.insert(END, res)
root = Tk()
root.geometry('400x250')
frame = Frame(root)
frame.pack()
fr = Frame(root)
fr.pack()
int1 = StringVar()
int2 = StringVar()
choice = IntVar()
label1 = Label(frame, text='CGPA:').grid(row=0)
label2 = Label(frame, text='Placement Exam Marks:').grid(row=1)
e1 = Entry(frame, textvariable=int1)
e2 = Entry(frame, textvariable=int2)
e1.grid(row=0, column=1)
e2.grid(row=1, column=1)
r1 = Radiobutton(fr, text='LR', variable=choice, value=1)
r1.grid(row=0, column=0)
r2 = Radiobutton(fr, text='LOG', variable=choice, value=2)
r2.grid(row=0, column=1)
r3 = Radiobutton(fr, text='SVM', variable=choice, value=3)
r3.grid(row=0, column=2)
r4 = Radiobutton(fr, text='KNN', variable=choice, value=4)
r4.grid(row=1, column=0)
r5 = Radiobutton(fr, text='DTR', variable=choice, value=5)
r5.grid(row=1, column=1)
r6 = Radiobutton(fr, text='RFR', variable=choice, value=6)
r6.grid(row=1, column=2)
Button = Button(root, text='IS Placed', command=lambda: predict(choice), activebackground='red')
Button.pack()
T = Text(root, height=2, width=32)
T.pack()
root.mainloop() | code |
128035508/cell_8 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression,LogisticRegression
lr = LinearRegression()
lr.fit(X_train, Y_train) | code |
128035508/cell_10 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
forest_regressor = RandomForestRegressor(n_estimators=300, random_state=0)
forest_regressor.fit(X_train, Y_train) | code |
128035508/cell_5 | [
"text_html_output_1.png"
] | from sklearn.neighbors import KNeighborsRegressor
knn = KNeighborsRegressor(n_neighbors=2)
knn.fit(X_train, Y_train) | code |
90129425/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.head() | code |
90129425/cell_34 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T
columns_to_drop = []
columns_to_model = []
non_sparse_column_percentage = 90
for col in data.columns:
if data[data[col].notnull()][col].count() / len(data) * 100 != 100:
if data[data[col].notnull()][col].count() / len(data) * 100 < non_sparse_column_percentage:
columns_to_drop.append(col)
else:
columns_to_model.append(col)
else:
columns_to_model.append(col)
data = data[columns_to_model]
for col in columns_to_model:
data = data[data[col].notnull()]
numerical_features = []
categorical_features = []
for col in data:
if (data[col].dtype == int) | (data[col].dtype == float):
numerical_features.append(col)
else:
categorical_features.append(col)
categorical_numerical_features = []
for feature in categorical_numerical_features:
numerical_features.remove(feature)
catergorical_features.append(feature)
print(f'Numerical features:\n {numerical_features}\n')
print(f'Categorical features\n {categorical_features}') | code |
90129425/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T
columns_to_drop = []
columns_to_model = []
non_sparse_column_percentage = 90
for col in data.columns:
if data[data[col].notnull()][col].count() / len(data) * 100 != 100:
if data[data[col].notnull()][col].count() / len(data) * 100 < non_sparse_column_percentage:
columns_to_drop.append(col)
else:
columns_to_model.append(col)
else:
columns_to_model.append(col)
data = data[columns_to_model]
for col in columns_to_model:
data = data[data[col].notnull()]
numerical_features = []
categorical_features = []
for col in data:
if (data[col].dtype == int) | (data[col].dtype == float):
numerical_features.append(col)
else:
categorical_features.append(col)
print(f'Numerical features: {numerical_features}')
print(f'Categorical features {categorical_features}') | code |
90129425/cell_40 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T
columns_to_drop = []
columns_to_model = []
non_sparse_column_percentage = 90
for col in data.columns:
if data[data[col].notnull()][col].count() / len(data) * 100 != 100:
if data[data[col].notnull()][col].count() / len(data) * 100 < non_sparse_column_percentage:
columns_to_drop.append(col)
else:
columns_to_model.append(col)
else:
columns_to_model.append(col)
data = data[columns_to_model]
for col in columns_to_model:
data = data[data[col].notnull()]
data.corr()[data.corr()['SalePrice'] > 0][['SalePrice']].sort_values(by='SalePrice', ascending=False) | code |
90129425/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T
columns_to_drop = []
columns_to_model = []
non_sparse_column_percentage = 90
for col in data.columns:
if data[data[col].notnull()][col].count() / len(data) * 100 != 100:
if data[data[col].notnull()][col].count() / len(data) * 100 < non_sparse_column_percentage:
columns_to_drop.append(col)
else:
columns_to_model.append(col)
else:
columns_to_model.append(col)
data = data[columns_to_model]
for col in columns_to_model:
data = data[data[col].notnull()]
plt.figure(figsize=(24, 12))
sns.heatmap(test_data.isnull(), cmap='mako') | code |
90129425/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T
columns_to_drop = []
columns_to_model = []
non_sparse_column_percentage = 90
for col in data.columns:
if data[data[col].notnull()][col].count() / len(data) * 100 != 100:
print(f'{col}: {data[data[col].notnull()][col].count() / len(data) * 100:.4f}%')
if data[data[col].notnull()][col].count() / len(data) * 100 < non_sparse_column_percentage:
columns_to_drop.append(col)
else:
columns_to_model.append(col)
else:
columns_to_model.append(col) | code |
90129425/cell_32 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T
columns_to_drop = []
columns_to_model = []
non_sparse_column_percentage = 90
for col in data.columns:
if data[data[col].notnull()][col].count() / len(data) * 100 != 100:
if data[data[col].notnull()][col].count() / len(data) * 100 < non_sparse_column_percentage:
columns_to_drop.append(col)
else:
columns_to_model.append(col)
else:
columns_to_model.append(col)
data = data[columns_to_model]
for col in columns_to_model:
data = data[data[col].notnull()]
numerical_features = []
categorical_features = []
for col in data:
if (data[col].dtype == int) | (data[col].dtype == float):
numerical_features.append(col)
else:
categorical_features.append(col)
for feature in categorical_features:
print(f"{feature}: (Unique Count = {len(data[feature].unique())})\n\n{data[feature].unique()}\n\n{'*' * 75}") | code |
90129425/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T
columns_to_drop = []
columns_to_model = []
non_sparse_column_percentage = 90
for col in data.columns:
if data[data[col].notnull()][col].count() / len(data) * 100 != 100:
if data[data[col].notnull()][col].count() / len(data) * 100 < non_sparse_column_percentage:
columns_to_drop.append(col)
else:
columns_to_model.append(col)
else:
columns_to_model.append(col)
data = data[columns_to_model]
for col in columns_to_model:
data = data[data[col].notnull()]
plt.figure(figsize=(24, 12))
sns.heatmap(test_data[[col for col in columns_to_model if col != 'SalePrice']].isnull(), cmap='mako') | code |
90129425/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T
plt.figure(figsize=(24, 12))
sns.heatmap(data.isnull(), cmap='mako') | code |
90129425/cell_38 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T
columns_to_drop = []
columns_to_model = []
non_sparse_column_percentage = 90
for col in data.columns:
if data[data[col].notnull()][col].count() / len(data) * 100 != 100:
if data[data[col].notnull()][col].count() / len(data) * 100 < non_sparse_column_percentage:
columns_to_drop.append(col)
else:
columns_to_model.append(col)
else:
columns_to_model.append(col)
data = data[columns_to_model]
for col in columns_to_model:
data = data[data[col].notnull()]
plt.figure(figsize=(24, 12))
sns.heatmap(data.corr(), cmap='coolwarm', annot=True) | code |
90129425/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn import metrics
import itertools | code |
90129425/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T
columns_to_drop = []
columns_to_model = []
non_sparse_column_percentage = 90
for col in data.columns:
if data[data[col].notnull()][col].count() / len(data) * 100 != 100:
if data[data[col].notnull()][col].count() / len(data) * 100 < non_sparse_column_percentage:
columns_to_drop.append(col)
else:
columns_to_model.append(col)
else:
columns_to_model.append(col)
data = data[columns_to_model]
for col in columns_to_model:
data = data[data[col].notnull()]
data | code |
90129425/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T
columns_to_drop = []
columns_to_model = []
non_sparse_column_percentage = 90
for col in data.columns:
if data[data[col].notnull()][col].count() / len(data) * 100 != 100:
if data[data[col].notnull()][col].count() / len(data) * 100 < non_sparse_column_percentage:
columns_to_drop.append(col)
else:
columns_to_model.append(col)
else:
columns_to_model.append(col)
data = data[columns_to_model]
for col in columns_to_model:
data = data[data[col].notnull()]
plt.figure(figsize=(24, 12))
sns.heatmap(data.isnull(), cmap='mako') | code |
90129425/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.info() | code |
90129425/cell_27 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T
columns_to_drop = []
columns_to_model = []
non_sparse_column_percentage = 90
for col in data.columns:
if data[data[col].notnull()][col].count() / len(data) * 100 != 100:
if data[data[col].notnull()][col].count() / len(data) * 100 < non_sparse_column_percentage:
columns_to_drop.append(col)
else:
columns_to_model.append(col)
else:
columns_to_model.append(col)
data = data[columns_to_model]
for col in columns_to_model:
data = data[data[col].notnull()]
for col in test_data.columns:
if test_data[test_data[col].notnull()][col].count() / len(test_data) * 100 != 100:
print(f'{col}: {test_data[test_data[col].notnull()][col].count() / len(test_data) * 100:.4f}%') | code |
90129425/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T | code |
74051961/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
df.userid = df.userid.astype(str)
df.userid.dtypes
df.version.unique() | code |
74051961/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape | code |
74051961/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
df.userid = df.userid.astype(str)
df.userid.dtypes
df.version.unique()
treatment = df[df['version'] == 'gate_40']
treatment.shape[0] | code |
74051961/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
df.userid = df.userid.astype(str)
df.userid.dtypes
df.version.unique()
treatment = df[df['version'] == 'gate_40']
control = df[df['version'] == 'gate_30']
treatment.shape[0]
control.shape[0]
contr_d1 = '{0:.2f}%'.format(control.retention_1.mean() * 100)
trtm_d1 = '{0:.2f}%'.format(treatment.retention_1.mean() * 100)
print('Day 1 retention rate for the control group is {} and for the treatment group is {}'.format(contr_d1, trtm_d1)) | code |
74051961/cell_44 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
sns.set_style('white')
plt.rc('axes', titlesize=13)
plt.rc('axes', labelsize=12)
plt.rc('xtick', labelsize=11)
plt.rc('ytick', labelsize=11)
plt.rc('legend', fontsize=11)
plt.rc('font', size=10)
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
df.userid = df.userid.astype(str)
df.userid.dtypes
df.version.unique()
treatment = df[df['version'] == 'gate_40']
control = df[df['version'] == 'gate_30']
treatment.shape[0]
control.shape[0]
contr_d1 = '{0:.2f}%'.format(control.retention_1.mean() * 100)
trtm_d1 = '{0:.2f}%'.format(treatment.retention_1.mean() * 100)
contr_d7 = '{0:.2f}%'.format(control.retention_7.mean() * 100)
trtm_d7 = '{0:.2f}%'.format(treatment.retention_7.mean() * 100)
retention_1_30 = control.retention_1.mean()
retention_1_40 = treatment.retention_1.mean()
obs_diff_1 = retention_1_40 - retention_1_30
diffs_1 = []
for i in range(10000):
boot_sample = df.sample(df.shape[0], replace=True)
gate30_df_1 = boot_sample.query('version == "gate_30"')['retention_1'].mean()
gate40_df_1 = boot_sample.query('version == "gate_40"')['retention_1'].mean()
diffs_1.append(gate40_df_1 - gate30_df_1)
#plot the histogram of difference in 1 day retention
plt.figure(figsize=(16,5), tight_layout=True)
ax = sns.histplot(diffs_1, kde = True, kde_kws = {'bw_method':0.4})
ax.lines[0].set_color('black')
plt.ylim(0,600);
plt.title('Sampling Distribution for difference between control group\'s Day 1 Retention and treatment group\'s Day 1 Retention')
plt.ylabel('Frequency')
plt.xlabel('Difference');
null_vals_1 = np.random.normal(0, np.std(diffs_1), len(diffs_1))
p_val_1 = (null_vals_1 > obs_diff_1).mean()
p_form_1 = '{0:.2f}%'.format(p_val_1 * 100)
plt.figure(figsize=(16, 5), tight_layout=True)
ax = sns.histplot(null_vals_1, kde=True, kde_kws={'bw_method': 0.4})
ax.lines[0].set_color('black')
plt.axvline(obs_diff_1, color='r')
plt.text(0.008, 500, 'P-value = {}'.format(p_form_1), color='black', size=15)
plt.ylim(0, 600)
plt.title('Simulated Distribution under the Null Hypothesis')
plt.ylabel('Frequency') | code |
74051961/cell_40 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
sns.set_style('white')
plt.rc('axes', titlesize=13)
plt.rc('axes', labelsize=12)
plt.rc('xtick', labelsize=11)
plt.rc('ytick', labelsize=11)
plt.rc('legend', fontsize=11)
plt.rc('font', size=10)
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
df.userid = df.userid.astype(str)
df.userid.dtypes
df.version.unique()
diffs_1 = []
for i in range(10000):
boot_sample = df.sample(df.shape[0], replace=True)
gate30_df_1 = boot_sample.query('version == "gate_30"')['retention_1'].mean()
gate40_df_1 = boot_sample.query('version == "gate_40"')['retention_1'].mean()
diffs_1.append(gate40_df_1 - gate30_df_1)
plt.figure(figsize=(16, 5), tight_layout=True)
ax = sns.histplot(diffs_1, kde=True, kde_kws={'bw_method': 0.4})
ax.lines[0].set_color('black')
plt.ylim(0, 600)
plt.title("Sampling Distribution for difference between control group's Day 1 Retention and treatment group's Day 1 Retention")
plt.ylabel('Frequency')
plt.xlabel('Difference') | code |
74051961/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
df.userid = df.userid.astype(str)
df.userid.dtypes
df.version.unique()
control = df[df['version'] == 'gate_30']
control.shape[0] | code |
74051961/cell_48 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
df.userid = df.userid.astype(str)
df.userid.dtypes
df.version.unique()
treatment = df[df['version'] == 'gate_40']
control = df[df['version'] == 'gate_30']
treatment.shape[0]
control.shape[0]
contr_d1 = '{0:.2f}%'.format(control.retention_1.mean() * 100)
trtm_d1 = '{0:.2f}%'.format(treatment.retention_1.mean() * 100)
contr_d7 = '{0:.2f}%'.format(control.retention_7.mean() * 100)
trtm_d7 = '{0:.2f}%'.format(treatment.retention_7.mean() * 100)
retention_1_30 = control.retention_1.mean()
retention_1_40 = treatment.retention_1.mean()
obs_diff_1 = retention_1_40 - retention_1_30
retention_7_30 = control.retention_7.mean()
retention_7_40 = treatment.retention_7.mean()
obs_diff_7 = retention_7_40 - retention_7_30
print('Day 7 Retention rate in the control group is {}'.format(retention_7_30))
print('Day 7 Retention rate in the treatment group is {}'.format(retention_7_40))
print("The difference between control group's Day 7 Retention rate and treatment group's Day 7 retention is {}".format(obs_diff_7)) | code |
74051961/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
df.userid = df.userid.astype(str)
df.userid.dtypes
df.describe() | code |
74051961/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
df.userid = df.userid.astype(str)
df.userid.dtypes | code |
74051961/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
df.info() | code |
74051961/cell_43 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
df.userid = df.userid.astype(str)
df.userid.dtypes
df.version.unique()
treatment = df[df['version'] == 'gate_40']
control = df[df['version'] == 'gate_30']
treatment.shape[0]
control.shape[0]
contr_d1 = '{0:.2f}%'.format(control.retention_1.mean() * 100)
trtm_d1 = '{0:.2f}%'.format(treatment.retention_1.mean() * 100)
contr_d7 = '{0:.2f}%'.format(control.retention_7.mean() * 100)
trtm_d7 = '{0:.2f}%'.format(treatment.retention_7.mean() * 100)
retention_1_30 = control.retention_1.mean()
retention_1_40 = treatment.retention_1.mean()
obs_diff_1 = retention_1_40 - retention_1_30
diffs_1 = []
for i in range(10000):
boot_sample = df.sample(df.shape[0], replace=True)
gate30_df_1 = boot_sample.query('version == "gate_30"')['retention_1'].mean()
gate40_df_1 = boot_sample.query('version == "gate_40"')['retention_1'].mean()
diffs_1.append(gate40_df_1 - gate30_df_1)
null_vals_1 = np.random.normal(0, np.std(diffs_1), len(diffs_1))
p_val_1 = (null_vals_1 > obs_diff_1).mean()
p_form_1 = '{0:.2f}%'.format(p_val_1 * 100)
print('P-value is equal to {}'.format(p_form_1)) | code |
74051961/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
df.userid = df.userid.astype(str)
df.userid.dtypes
df.version.unique()
treatment = df[df['version'] == 'gate_40']
control = df[df['version'] == 'gate_30']
treatment.shape[0]
control.shape[0]
contr_d1 = '{0:.2f}%'.format(control.retention_1.mean() * 100)
trtm_d1 = '{0:.2f}%'.format(treatment.retention_1.mean() * 100)
contr_d7 = '{0:.2f}%'.format(control.retention_7.mean() * 100)
trtm_d7 = '{0:.2f}%'.format(treatment.retention_7.mean() * 100)
print('Day 7 retention rate for the control group is {} and for the treatment group is {}'.format(contr_d7, trtm_d7)) | code |
74051961/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
if df.userid.nunique() == df.shape[0]:
print('There are no duplicated user ids in the dataset')
else:
print('There are some duplicated user ids in the dataset') | code |
74051961/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
df.userid = df.userid.astype(str)
df.userid.dtypes
df.version.unique()
treatment = df[df['version'] == 'gate_40']
control = df[df['version'] == 'gate_30']
treatment.shape[0]
control.shape[0]
contr_d1 = '{0:.2f}%'.format(control.retention_1.mean() * 100)
trtm_d1 = '{0:.2f}%'.format(treatment.retention_1.mean() * 100)
contr_d7 = '{0:.2f}%'.format(control.retention_7.mean() * 100)
trtm_d7 = '{0:.2f}%'.format(treatment.retention_7.mean() * 100)
retention_1_30 = control.retention_1.mean()
retention_1_40 = treatment.retention_1.mean()
obs_diff_1 = retention_1_40 - retention_1_30
print('Day 1 Retention rate in the control group is {}'.format(retention_1_30))
print('Day 1 Retention rate in the treatment group is {}'.format(retention_1_40))
print('The difference in Day 1 Retention rate between control and treatment group is {}'.format(obs_diff_1)) | code |
74051961/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.head() | code |
122245085/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.shape
px.box(df['count'])
px.box(df, x='workingday', y='count') | code |
122245085/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.shape
Q1 = df['count'].quantile(0.25)
Q3 = df['count'].quantile(0.75)
IQR = Q3 - Q1
upper_fence = Q3 + 1.5 * IQR
lower_fence = Q1 - 1.5 * IQR
df = df[(df['count'] > lower_fence) & (df['count'] < upper_fence)]
df.shape
df.head() | code |
122245085/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.head() | code |
122245085/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.shape
Q1 = df['count'].quantile(0.25)
Q3 = df['count'].quantile(0.75)
IQR = Q3 - Q1
upper_fence = Q3 + 1.5 * IQR
lower_fence = Q1 - 1.5 * IQR
df = df[(df['count'] > lower_fence) & (df['count'] < upper_fence)]
df.shape
round(df.corr(), 2)
sns.heatmap(df.corr(), linewidths=0.5) | code |
122245085/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.describe() | code |
122245085/cell_1 | [
"text_plain_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode, download_plotlyjs, plot
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))
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
from plotly import tools
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode, download_plotlyjs, plot
init_notebook_mode(connected=True) | code |
122245085/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.shape | code |
122245085/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.shape
px.box(df['count'])
px.box(df, x='workingday', y='count')
Q1 = df['count'].quantile(0.25)
Q3 = df['count'].quantile(0.75)
IQR = Q3 - Q1
upper_fence = Q3 + 1.5 * IQR
lower_fence = Q1 - 1.5 * IQR
df = df[(df['count'] > lower_fence) & (df['count'] < upper_fence)]
df.shape
px.box(df['count']) | code |
122245085/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.shape
Q1 = df['count'].quantile(0.25)
Q3 = df['count'].quantile(0.75)
IQR = Q3 - Q1
upper_fence = Q3 + 1.5 * IQR
lower_fence = Q1 - 1.5 * IQR
df = df[(df['count'] > lower_fence) & (df['count'] < upper_fence)]
df.shape
sns.heatmap(df.isnull()) | code |
122245085/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.shape
px.box(df['count']) | code |
122245085/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.shape
Q1 = df['count'].quantile(0.25)
Q3 = df['count'].quantile(0.75)
IQR = Q3 - Q1
upper_fence = Q3 + 1.5 * IQR
lower_fence = Q1 - 1.5 * IQR
df = df[(df['count'] > lower_fence) & (df['count'] < upper_fence)]
df.shape | code |
122245085/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.shape
Q1 = df['count'].quantile(0.25)
Q3 = df['count'].quantile(0.75)
IQR = Q3 - Q1
upper_fence = Q3 + 1.5 * IQR
lower_fence = Q1 - 1.5 * IQR
df = df[(df['count'] > lower_fence) & (df['count'] < upper_fence)]
df.shape
round(df.corr(), 2) | code |
122245085/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.shape
Q1 = df['count'].quantile(0.25)
Q3 = df['count'].quantile(0.75)
IQR = Q3 - Q1
upper_fence = Q3 + 1.5 * IQR
lower_fence = Q1 - 1.5 * IQR
print('Interquartile range is', IQR)
print('upper fence is', upper_fence)
print('lower fence is', lower_fence) | code |
122245085/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.info() | code |
122245085/cell_36 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import seaborn as sns
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.shape
px.box(df['count'])
px.box(df, x='workingday', y='count')
Q1 = df['count'].quantile(0.25)
Q3 = df['count'].quantile(0.75)
IQR = Q3 - Q1
upper_fence = Q3 + 1.5 * IQR
lower_fence = Q1 - 1.5 * IQR
df = df[(df['count'] > lower_fence) & (df['count'] < upper_fence)]
df.shape
px.box(df['count'])
round(df.corr(), 2)
px.pie(df['season']) | code |
2014823/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare = all_data.Fare.fillna(all_data.Fare.median())
all_data.Embarked = all_data.Embarked.fillna(all_data.Embarked.mode()[0])
all_data['Title'] = all_data['Name'].str.split(', ', expand=True)[1].str.split('.', expand=True)[0]
stat_min = 10
title_names = all_data['Title'].value_counts() < stat_min
all_data['Title'] = all_data['Title'].apply(lambda x: 'Misc' if title_names.loc[x] == True else x)
all_data = all_data.drop(['Name'], axis=1)
all_data = pd.get_dummies(all_data)
train_cleared = all_data[:train.shape[0]]
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(train_cleared, train.Survived, random_state=0, test_size=0.1)
X_val = all_data[train.shape[0]:]
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier(random_state=0, max_depth=5)
model.fit(X_train, y_train)
print('Train score: {:.3f}'.format(model.score(X_train, y_train)))
print('Test score: {:.3f}'.format(model.score(X_test, y_test)))
decision_tree_predicts = model.predict(X_val) | code |
2014823/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.info() | code |
2014823/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare = all_data.Fare.fillna(all_data.Fare.median())
all_data.Embarked = all_data.Embarked.fillna(all_data.Embarked.mode()[0])
all_data['Title'] = all_data['Name'].str.split(', ', expand=True)[1].str.split('.', expand=True)[0]
stat_min = 10
title_names = all_data['Title'].value_counts() < stat_min
all_data['Title'] = all_data['Title'].apply(lambda x: 'Misc' if title_names.loc[x] == True else x)
print(all_data['Title'].value_counts())
all_data = all_data.drop(['Name'], axis=1)
all_data.info() | code |
2014823/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare = all_data.Fare.fillna(all_data.Fare.median())
all_data.Embarked = all_data.Embarked.fillna(all_data.Embarked.mode()[0])
all_data['Title'] = all_data['Name'].str.split(', ', expand=True)[1].str.split('.', expand=True)[0]
stat_min = 10
title_names = all_data['Title'].value_counts() < stat_min
all_data['Title'] = all_data['Title'].apply(lambda x: 'Misc' if title_names.loc[x] == True else x)
all_data = all_data.drop(['Name'], axis=1)
all_data = pd.get_dummies(all_data)
train_cleared = all_data[:train.shape[0]]
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(train_cleared, train.Survived, random_state=0, test_size=0.1)
X_val = all_data[train.shape[0]:]
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier(random_state=0, max_depth=5)
model.fit(X_train, y_train)
decision_tree_predicts = model.predict(X_val)
result = pd.DataFrame({'PassengerId': test.PassengerId, 'Survived': decision_tree_predicts})
result.to_csv('DecisionTree.csv', index=False)
result.info() | code |
2014823/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare = all_data.Fare.fillna(all_data.Fare.median())
all_data.Embarked = all_data.Embarked.fillna(all_data.Embarked.mode()[0])
all_data['Title'] = all_data['Name'].str.split(', ', expand=True)[1].str.split('.', expand=True)[0]
stat_min = 10
title_names = all_data['Title'].value_counts() < stat_min
all_data['Title'] = all_data['Title'].apply(lambda x: 'Misc' if title_names.loc[x] == True else x)
all_data = all_data.drop(['Name'], axis=1)
all_data = pd.get_dummies(all_data)
all_data.head() | code |
2014823/cell_10 | [
"text_plain_output_1.png"
] | from sklearn import tree
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import graphviz
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare = all_data.Fare.fillna(all_data.Fare.median())
all_data.Embarked = all_data.Embarked.fillna(all_data.Embarked.mode()[0])
all_data['Title'] = all_data['Name'].str.split(', ', expand=True)[1].str.split('.', expand=True)[0]
stat_min = 10
title_names = all_data['Title'].value_counts() < stat_min
all_data['Title'] = all_data['Title'].apply(lambda x: 'Misc' if title_names.loc[x] == True else x)
all_data = all_data.drop(['Name'], axis=1)
all_data = pd.get_dummies(all_data)
train_cleared = all_data[:train.shape[0]]
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(train_cleared, train.Survived, random_state=0, test_size=0.1)
X_val = all_data[train.shape[0]:]
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier(random_state=0, max_depth=5)
model.fit(X_train, y_train)
decision_tree_predicts = model.predict(X_val)
import graphviz
dot_data = tree.export_graphviz(model, out_file=None, feature_names=list(train_cleared), filled=True, rounded=True, special_characters=True)
graph = graphviz.Source(dot_data)
graph | code |
2014823/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare = all_data.Fare.fillna(all_data.Fare.median())
all_data.Embarked = all_data.Embarked.fillna(all_data.Embarked.mode()[0])
all_data.info() | code |
72073997/cell_42 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.copy()
y = train['target']
ss = StandardScaler()
X_train_scaled = ss.fit_transform(X_train)
X_test_scaled = ss.fit_transform(X_test)
lr = LinearRegression()
lr.fit(X_train_scaled, y_train)
y_preds_lr = lr.predict(X_val_scaled)
ridge = Ridge()
ridge.fit(X_train_scaled, y_train)
y_preds_ridge = ridge.predict(X_val_scaled)
tree_reg = DecisionTreeRegressor()
tree_reg.fit(X_train_scaled, y_train)
y_preds_tree = tree_reg.predict(X_val_scaled)
for_reg = RandomForestRegressor()
for_reg.fit(X_train_scaled, y_train)
y_preds_for = for_reg.predict(X_val_scaled)
target_pred_for = for_reg.predict(X_test_scaled)
target_pred_for | code |
72073997/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
sub | code |
72073997/cell_25 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.copy()
y = train['target']
ss = StandardScaler()
X_train_scaled = ss.fit_transform(X_train)
X_test_scaled = ss.fit_transform(X_test)
lr = LinearRegression()
lr.fit(X_train_scaled, y_train)
y_preds_lr = lr.predict(X_val_scaled)
print('RMSE for Linear Regression Model: ', np.sqrt(mse(y_val, y_preds_lr))) | code |
72073997/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.head() | code |
72073997/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.copy()
y = train['target']
ss = StandardScaler()
X_train_scaled = ss.fit_transform(X_train)
X_test_scaled = ss.fit_transform(X_test)
print('X_train data shape: ', X_train_scaled.shape)
print('X_val data shape: ', X_val_scaled.shape)
print('y_train shape: ', y_train.shape)
print('y_val shape: ', y_val.shape) | code |
72073997/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.copy()
y = train['target']
ss = StandardScaler()
X_train_scaled = ss.fit_transform(X_train)
X_test_scaled = ss.fit_transform(X_test)
lr = LinearRegression()
lr.fit(X_train_scaled, y_train)
y_preds_lr = lr.predict(X_val_scaled)
ridge = Ridge()
ridge.fit(X_train_scaled, y_train)
y_preds_ridge = ridge.predict(X_val_scaled)
target_pred_ridge = ridge.predict(X_test_scaled)
target_pred_ridge | code |
72073997/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
test.head() | code |
72073997/cell_29 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.copy()
y = train['target']
ss = StandardScaler()
X_train_scaled = ss.fit_transform(X_train)
X_test_scaled = ss.fit_transform(X_test)
lr = LinearRegression()
lr.fit(X_train_scaled, y_train)
y_preds_lr = lr.predict(X_val_scaled)
target_pred_lr = lr.predict(X_test_scaled)
sub_lr = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
sub_lr['target'] = target_pred_lr
sub_lr.to_csv('sub_lr.csv', index=False)
sub_lr.head() | code |
72073997/cell_48 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
import xgboost as xgb
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.copy()
y = train['target']
ss = StandardScaler()
X_train_scaled = ss.fit_transform(X_train)
X_test_scaled = ss.fit_transform(X_test)
lr = LinearRegression()
lr.fit(X_train_scaled, y_train)
y_preds_lr = lr.predict(X_val_scaled)
ridge = Ridge()
ridge.fit(X_train_scaled, y_train)
y_preds_ridge = ridge.predict(X_val_scaled)
tree_reg = DecisionTreeRegressor()
tree_reg.fit(X_train_scaled, y_train)
y_preds_tree = tree_reg.predict(X_val_scaled)
for_reg = RandomForestRegressor()
for_reg.fit(X_train_scaled, y_train)
y_preds_for = for_reg.predict(X_val_scaled)
xgb_reg = xgb.XGBRegressor(gpu_id=0, tree_method='gpu_hist')
xgb_reg.fit(X_train_scaled, y_train)
y_preds_xgb = xgb_reg.predict(X_val_scaled)
target_pred_xgb = xgb_reg.predict(X_test_scaled)
target_pred_xgb | code |
72073997/cell_41 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.copy()
y = train['target']
ss = StandardScaler()
X_train_scaled = ss.fit_transform(X_train)
X_test_scaled = ss.fit_transform(X_test)
lr = LinearRegression()
lr.fit(X_train_scaled, y_train)
y_preds_lr = lr.predict(X_val_scaled)
ridge = Ridge()
ridge.fit(X_train_scaled, y_train)
y_preds_ridge = ridge.predict(X_val_scaled)
tree_reg = DecisionTreeRegressor()
tree_reg.fit(X_train_scaled, y_train)
y_preds_tree = tree_reg.predict(X_val_scaled)
for_reg = RandomForestRegressor()
for_reg.fit(X_train_scaled, y_train)
y_preds_for = for_reg.predict(X_val_scaled)
print('RMSE for Random Forest Regressor: ', np.sqrt(mse(y_val, y_preds_for))) | code |
72073997/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.copy()
y = train['target']
X_test.info() | code |
72073997/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
test.info() | code |
72073997/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.copy()
y = train['target']
X_test.head() | code |
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