Stroke Prediction

1. EDA
   • Show info of dataset

 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   gender             4000 non-null   object 
 1   age                3893 non-null   float64
 2   hypertension       3770 non-null   float64
 3   heart_disease      3910 non-null   float64
 4   ever_married       4000 non-null   object 
 5   work_type          4000 non-null   object 
 6   Residence_type     4000 non-null   object 
 7   avg_glucose_level  3829 non-null   float64
 8   bmi                2884 non-null   float64
 9   smoking_status     4000 non-null   object 
 10  blood              3990 non-null   object 
 11  height             2928 non-null   float64
 12  stroke             4000 non-null   int64  

• Draw missing data table

	Total	Percent
height	1072	0.26800
hypertension	230	0.05750
avg_glucose_level	171	0.04275
heart_disease	90	0.02250
blood	10	0.00250
gender	0	0.00000
age	0	0.00000
ever_married	0	0.00000
work_type	0	0.00000
Residence_type	0	0.00000
bmi	0	0.00000
smoking_status	0	0.00000
stroke	0	0.00000
age_bin	0	0.00000

• Pairplot
• Show correlation

stroke               1.000000
age                  0.196161
heart_disease        0.116838
avg_glucose_level    0.109547
hypertension         0.084755
bmi                  0.030560
height               0.029482
Name: stroke, dtype: float64

• Boxplot BMI

Ta thấy dữ liệu có nhiều người BMI lạ kì nên đã quyết định loại bỏ những dữ liệu có BMI lớn hơn 45

• Count unique value

Value counts: 

Female    2218 
Male      1625
Other      157
Name: gender, dtype: int64

0.0    3402
1.0     368
Name: hypertension, dtype: int64


0.0    3694
1.0     216
Name: heart_disease, dtype: int64

Yes    2626
No     1374
Name: ever_married, dtype: int64

Private          2305
Self-employed     631
children          539
Govt_job          508
Never_worked       17
Name: work_type, dtype: int64

Unknown    1987
Urban      1018
Rural       995
Name: Residence_type, dtype: int64

never smoked       1467
Unknown            1226
formerly smoked     686
smokes              621
Name: smoking_status, dtype: int64

AB    1544
A      841
B      829
O      776
Name: blood, dtype: int64

0    3870
1     130
Name: stroke, dtype: int64

• Show value percent

Female    55.450
Male      40.625
Other      3.925
Name: gender, dtype: float64


0.0    90.238727
1.0     9.761273
Name: hypertension, dtype: float64


0.0    94.475703
1.0     5.524297
Name: heart_disease, dtype: float64


Yes    65.65
No     34.35
Name: ever_married, dtype: float64


Private          57.625
Self-employed    15.775
children         13.475
Govt_job         12.700
Never_worked      0.425
Name: work_type, dtype: float64


Unknown    49.675
Urban      25.450
Rural      24.875
Name: Residence_type, dtype: float64


never smoked       36.675
Unknown            30.650
formerly smoked    17.150
smokes             15.525
Name: smoking_status, dtype: float64


AB    38.696742
A     21.077694
B     20.776942
O     19.448622
Name: blood, dtype: float64


0    96.75
1     3.25
Name: stroke, dtype: float64

Ta thấy smoking_status và Residence_type có dữ liệu Unknown khá cao (Đang tìm cách xử lí)

1. Preprocessing data
   • Drop data with 'bmi' > 45
   • Obtain the mean value of bmi for the age class
   • Fill NaN with 0 for hypertension and heart_disease
   • Fill NaN with AB for blood which is the highest distribution

• Use One hot encoder for ['gender', 'blood', 'smoking_status', 'Residence_type', 'work_type']
• Use label ecoder for ever_married which {'No': 0, 'Yes': 1}
• Use StandardScaler for ['avg_glucose_level','bmi','age']