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def generate_color(magnitude): | |
if magnitude <= 5: | |
c_outline, c_fill = '#ffda79', '#ffda79' | |
m_opacity, f_opacity = 0.2, 0.1 | |
else: | |
c_outline, c_fill = '#c0392b', '#e74c3c' | |
m_opacity, f_opacity = 1, 1 | |
return c_outline, c_fill, m_opacity, f_opacity | |
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quake_map = folium.Map( | |
location=[-16.495477, 174.9663341], | |
zoom_start=5, | |
tiles='Stamen Terrain', | |
width=1024, | |
height=600 | |
) | |
for _, row in df.iterrows(): | |
folium.CircleMarker( |
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quake_map = folium.Map( | |
location=[-16.495477, 174.9663341], | |
zoom_start=6, | |
tiles='Stamen Terrain', | |
width=1024, | |
height=600 | |
) | |
quake_map |
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import folium | |
quake_map = folium.Map( | |
location=[-16.495477, 174.9663341], | |
zoom_start=6, | |
width=1024, | |
height=600 | |
) | |
quake_map |
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import pandas as pd | |
df = pd.read_csv('quakes.csv') | |
df.head() |
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from sklearn.metrics import roc_auc_score, roc_curve | |
y_test_int = y_test.replace({'Good': 1, 'Bad': 0}) | |
auc_lr = roc_auc_score(y_test_int, probs_lr) | |
fpr_lr, tpr_lr, thresholds_lr = roc_curve(y_test_int, probs_lr) | |
auc_dt = roc_auc_score(y_test_int, probs_dt) | |
fpr_dt, tpr_dt, thresholds_dt = roc_curve(y_test_int, probs_dt) | |
auc_rf = roc_auc_score(y_test_int, probs_rf) |
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from sklearn.linear_model import LogisticRegression | |
from sklearn.tree import DecisionTreeClassifier | |
from sklearn.ensemble import RandomForestClassifier | |
from xgboost import XGBClassifier | |
model_lr = LogisticRegression().fit(X_train, y_train) | |
probs_lr = model_lr.predict_proba(X_test)[:, 1] | |
model_dt = DecisionTreeClassifier().fit(X_train, y_train) | |
probs_dt = model_dt.predict_proba(X_test)[:, 1] |
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from sklearn.model_selection import train_test_split | |
X = df.drop('quality', axis=1) | |
y = df['quality'] | |
X_train, X_test, y_train, y_test = train_test_split( | |
X, y, test_size=0.25, random_state=42 | |
) |
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ax = df['quality'].value_counts().plot(kind='bar', figsize=(10, 6), fontsize=13, color='#087E8B') | |
ax.set_title('Counts of Bad and Good vines', size=20, pad=30) | |
ax.set_ylabel('Count', fontsize=14) | |
for i in ax.patches: | |
ax.text(i.get_x() + 0.19, i.get_height() + 100, str(round(i.get_height(), 2)), fontsize=15) |
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df['quality'] = ['Good' if quality >= 7 else 'Bad' for quality in df['quality']] |