PCA Tutorial

PCA Tutorial: Heart disease prediction 
import pandas as pd

# https://www.kaggle.com/fedesoriano/heart-failure-prediction
df = pd.read_csv("Data/heart.csv")
df.head()
Age Sex ChestPainType RestingBP Cholesterol FastingBS RestingECG MaxHR ExerciseAngina Oldpeak ST_Slope HeartDisease
0 40 M ATA 140 289 0 Normal 172 N 0.0 Up 0
1 49 F NAP 160 180 0 Normal 156 N 1.0 Flat 1
2 37 M ATA 130 283 0 ST 98 N 0.0 Up 0
3 48 F ASY 138 214 0 Normal 108 Y 1.5 Flat 1
4 54 M NAP 150 195 0 Normal 122 N 0.0 Up 0 
df.shape
(918, 12)
df.describe()
Age RestingBP Cholesterol FastingBS MaxHR Oldpeak HeartDisease
count 918.000000 918.000000 918.000000 918.000000 918.000000 918.000000 918.000000
mean 53.510893 132.396514 198.799564 0.233115 136.809368 0.887364 0.553377
std 9.432617 18.514154 109.384145 0.423046 25.460334 1.066570 0.497414
min 28.000000 0.000000 0.000000 0.000000 60.000000 -2.600000 0.000000
25% 47.000000 120.000000 173.250000 0.000000 120.000000 0.000000 0.000000
50% 54.000000 130.000000 223.000000 0.000000 138.000000 0.600000 1.000000
75% 60.000000 140.000000 267.000000 0.000000 156.000000 1.500000 1.000000
max 77.000000 200.000000 603.000000 1.000000 202.000000 6.200000 1.000000

Treat Outliers 

df[df.Cholesterol>(df.Cholesterol.mean()+3*df.Cholesterol.std())]
Age Sex ChestPainType RestingBP Cholesterol FastingBS RestingECG MaxHR ExerciseAngina Oldpeak ST_Slope HeartDisease
76 32 M ASY 118 529 0 Normal 130 N 0.0 Flat 1
149 54 M ASY 130 603 1 Normal 125 Y 1.0 Flat 1
616 67 F NAP 115 564 0 LVH 160 N 1.6 Flat 0 
df.shape
(918, 12)
df1 = df[df.Cholesterol<=(df.Cholesterol.mean()+3*df.Cholesterol.std())]
df1.shape
(915, 12)
df[df.MaxHR>(df.MaxHR.mean()+3*df.MaxHR.std())]
Age Sex ChestPainType RestingBP Cholesterol FastingBS RestingECG MaxHR ExerciseAngina Oldpeak ST_Slope HeartDisease 
df[df.FastingBS>(df.FastingBS.mean()+3*df.FastingBS.std())]
Age Sex ChestPainType RestingBP Cholesterol FastingBS RestingECG MaxHR ExerciseAngina Oldpeak ST_Slope HeartDisease 
df[df.Oldpeak>(df.Oldpeak.mean()+3*df.Oldpeak.std())]
Age Sex ChestPainType RestingBP Cholesterol FastingBS RestingECG MaxHR ExerciseAngina Oldpeak ST_Slope HeartDisease
166 50 M ASY 140 231 0 ST 140 Y 5.0 Flat 1
702 59 M TA 178 270 0 LVH 145 N 4.2 Down 0
771 55 M ASY 140 217 0 Normal 111 Y 5.6 Down 1
791 51 M ASY 140 298 0 Normal 122 Y 4.2 Flat 1
850 62 F ASY 160 164 0 LVH 145 N 6.2 Down 1
900 58 M ASY 114 318 0 ST 140 N 4.4 Down 1 
df2 = df1[df1.Oldpeak<=(df1.Oldpeak.mean()+3*df1.Oldpeak.std())]
df2.shape
(909, 12)
df[df.RestingBP>(df.RestingBP.mean()+3*df.RestingBP.std())]
Age Sex ChestPainType RestingBP Cholesterol FastingBS RestingECG MaxHR ExerciseAngina Oldpeak ST_Slope HeartDisease
109 39 M ATA 190 241 0 Normal 106 N 0.0 Up 0
241 54 M ASY 200 198 0 Normal 142 Y 2.0 Flat 1
365 64 F ASY 200 0 0 Normal 140 Y 1.0 Flat 1
399 61 M NAP 200 0 1 ST 70 N 0.0 Flat 1
592 61 M ASY 190 287 1 LVH 150 Y 2.0 Down 1
732 56 F ASY 200 288 1 LVH 133 Y 4.0 Down 1
759 54 M ATA 192 283 0 LVH 195 N 0.0 Up 1 
df3 = df2[df2.RestingBP<=(df2.RestingBP.mean()+3*df2.RestingBP.std())]
df3.shape
(902, 12)
df.ChestPainType.unique()
array(['ATA', 'NAP', 'ASY', 'TA'], dtype=object)
df.RestingECG.unique()
array(['Normal', 'ST', 'LVH'], dtype=object)
df.ExerciseAngina.unique()
array(['N', 'Y'], dtype=object)
df.ST_Slope.unique()
array(['Up', 'Flat', 'Down'], dtype=object)
df4 = df3.copy()
df4.ExerciseAngina.replace(
    {
        'N': 0,
        'Y': 1
    },
    inplace=True)

df4.ST_Slope.replace(
    {
        'Down': 1,
        'Flat': 2,
        'Up': 3
    },
    inplace=True
)

df4.RestingECG.replace(
    {
        'Normal': 1,
        'ST': 2,
        'LVH': 3
    },
    inplace=True)

df4.head()
Age Sex ChestPainType RestingBP Cholesterol FastingBS RestingECG MaxHR ExerciseAngina Oldpeak ST_Slope HeartDisease
0 40 M ATA 140 289 0 1 172 0 0.0 3 0
1 49 F NAP 160 180 0 1 156 0 1.0 2 1
2 37 M ATA 130 283 0 2 98 0 0.0 3 0
3 48 F ASY 138 214 0 1 108 1 1.5 2 1
4 54 M NAP 150 195 0 1 122 0 0.0 3 0 
df5 = pd.get_dummies(df4, drop_first=True)
df5.head()
Age RestingBP Cholesterol FastingBS RestingECG MaxHR ExerciseAngina Oldpeak ST_Slope HeartDisease Sex_M ChestPainType_ATA ChestPainType_NAP ChestPainType_TA
0 40 140 289 0 1 172 0 0.0 3 0 1 1 0 0
1 49 160 180 0 1 156 0 1.0 2 1 0 0 1 0
2 37 130 283 0 2 98 0 0.0 3 0 1 1 0 0
3 48 138 214 0 1 108 1 1.5 2 1 0 0 0 0
4 54 150 195 0 1 122 0 0.0 3 0 1 0 1 0 
X = df5.drop("HeartDisease",axis='columns')
y = df5.HeartDisease

X.head()
Age RestingBP Cholesterol FastingBS RestingECG MaxHR ExerciseAngina Oldpeak ST_Slope Sex_M ChestPainType_ATA ChestPainType_NAP ChestPainType_TA
0 40 140 289 0 1 172 0 0.0 3 1 1 0 0
1 49 160 180 0 1 156 0 1.0 2 0 0 1 0
2 37 130 283 0 2 98 0 0.0 3 1 1 0 0
3 48 138 214 0 1 108 1 1.5 2 0 0 0 0
4 54 150 195 0 1 122 0 0.0 3 1 0 1 0 
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
X_scaled
array([[-1.42896269,  0.46089071,  0.85238015, ...,  2.06757196,
        -0.53547478, -0.22914788],
       [-0.47545956,  1.5925728 , -0.16132855, ..., -0.4836591 ,
         1.86750159, -0.22914788],
       [-1.74679706, -0.10495034,  0.79657967, ...,  2.06757196,
        -0.53547478, -0.22914788],
       ...,
       [ 0.37209878, -0.10495034, -0.61703246, ..., -0.4836591 ,
        -0.53547478, -0.22914788],
       [ 0.37209878, -0.10495034,  0.35947592, ...,  2.06757196,
        -0.53547478, -0.22914788],
       [-1.64085227,  0.3477225 , -0.20782894, ..., -0.4836591 ,
         1.86750159, -0.22914788]])
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, random_state=30)
X_train.shape
(721, 13)
X_test.shape
(181, 13)
from sklearn.ensemble import RandomForestClassifier

model_rf = RandomForestClassifier()
model_rf.fit(X_train, y_train)
model_rf.score(X_test, y_test)
0.8674033149171271

Use PCA to reduce dimensions 

X
Age RestingBP Cholesterol FastingBS RestingECG MaxHR ExerciseAngina Oldpeak ST_Slope Sex_M ChestPainType_ATA ChestPainType_NAP ChestPainType_TA
0 40 140 289 0 1 172 0 0.0 3 1 1 0 0
1 49 160 180 0 1 156 0 1.0 2 0 0 1 0
2 37 130 283 0 2 98 0 0.0 3 1 1 0 0
3 48 138 214 0 1 108 1 1.5 2 0 0 0 0
4 54 150 195 0 1 122 0 0.0 3 1 0 1 0
... ... ... ... ... ... ... ... ... ... ... ... ... ...
913 45 110 264 0 1 132 0 1.2 2 1 0 0 1
914 68 144 193 1 1 141 0 3.4 2 1 0 0 0
915 57 130 131 0 1 115 1 1.2 2 1 0 0 0
916 57 130 236 0 3 174 0 0.0 2 0 1 0 0
917 38 138 175 0 1 173 0 0.0 3 1 0 1 0

902 rows × 13 columns

from sklearn.decomposition import PCA

pca = PCA(0.95)
X_pca = pca.fit_transform(X)
X_pca
array([[ 93.82465373, -29.40099458],
       [-15.58422331, -14.10909233],
       [ 83.29606634,  38.6867453 ],
       ...,
       [-67.57318721,  17.61319354],
       [ 40.70458237, -33.38750602],
       [-19.91368346, -37.29085722]])
X_train_pca, X_test_pca, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=30)
from sklearn.ensemble import RandomForestClassifier

model_rf = RandomForestClassifier()
model_rf.fit(X_train_pca, y_train)
model_rf.score(X_test_pca, y_test)
0.7182320441988951
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