<jupyter_start><jupyter_text>Unsupervised Analysis of Days of WeekTreating crossings each day as features to learn about the relationships between various days.  Import Libraries<jupyter_code>%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn')

import pandas as pd
import numpy as np

from sklearn.decomposition import PCA
from sklearn.mixture import GaussianMixture<jupyter_output><empty_output><jupyter_text>Get Data<jupyter_code>from functions.data import get_fremont_data
data = get_fremont_data()
pivoted = data.pivot_table('Total', index=data.index.time, columns=data.index.date)
pivoted.plot(legend=False, alpha=0.01)<jupyter_output><empty_output><jupyter_text>Principal Componente Analysis<jupyter_code>## Check if NA value exist
data.isna().sum()
X = pivoted.fillna(0).T.values
X.shape
## Reduice dimension using PCA 24 => 2
X2 = PCA(2, svd_solver='full').fit_transform(X)
X2.shape
plt.scatter(X2[:, 0], X2[:, 1])<jupyter_output><empty_output><jupyter_text>Unsupervised Clustering<jupyter_code>gmm = GaussianMixture(2).fit(X)
labels = gmm.predict(X)
plt.scatter(X2[:, 0], X2[:, 1], c=labels, cmap='rainbow')
plt.colorbar()
fig, ax = plt.subplots(1, 2, figsize=(14, 6))

pivoted.T[labels == 0].T.plot(legend=False, alpha=0.1, ax=ax[0]);
pivoted.T[labels == 1].T.plot(legend=False, alpha=0.1, ax=ax[1]);

ax[0].set_title('Purple Cluster');
ax[1].set_title('Red Cluster');<jupyter_output><empty_output><jupyter_text>Comparing with Day of Week<jupyter_code>dayofweek = pd.DatetimeIndex(pivoted.columns).dayofweek
plt.scatter(X2[:, 0], X2[:, 1], c=dayofweek, cmap='rainbow')
plt.colorbar()<jupyter_output><empty_output><jupyter_text>Analyzing Outliers<jupyter_code>dates = pd.DatetimeIndex(pivoted.columns)
dates[(labels == 1) & (dayofweek < 5)]<jupyter_output><empty_output>
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