PCA.py

#!/usr/bin/env python
# coding: utf-8

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import pandas as pd
from sklearn.datasets import load_digits


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dataset = load_digits()
dataset.keys()


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datasets.data[0]


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datasets.data[0].reshape(8,8)


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from matplotlib import pyplot as plt


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plt.gray()
plt.matshow(dataset.data[50].reshape(8,8))


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datasets.target


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df = pd.DataFrame(dataset.data)
df.describe()


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X = df
y= dataset.target


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y


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from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
x_scaled = scaler.fit_transform(X)
x_scaled


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from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x_scaled,y,  test_size = 0.2)


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from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)
model.score(X_test, y_test)


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from sklearn.decomposition import PCA
pca = PCA(0.95)

x_pca = pca.fit_transform(X)
x_pca.shape


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X.shape


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X_train_pca, X_test_pca, y_train, y_test = train_test_split(x_pca,y,  test_size = 0.2)


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model = LogisticRegression()
model.fit(X_train_pca, y_train)
model.score(X_test_pca, y_test)


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