Here we will be checking the 10 ages and their counts.
Inference: Here we can see that the 58 age column has the highest frequency.
Let’s check the range of age in the dataset.
Min Age : 29 Max Age : 77 Mean Age : 54.366336633663366
We should divide the Age feature into three parts – “Young”, “Middle” and “Elder”
Young = data[(data.age>=29)&(data.age<40)]
Middle = data[(data.age>=40)&(data.age<55)]
Elder = data[(data.age>55)] plt.figure(figsize=(23,10))
sns.set_context('notebook',font_scale = 1.5)
sns.barplot(x=['young ages','middle ages','elderly ages'],y=[len(Young),len(Middle),len(Elder)])
plt.tight_layout()
Output:
Inference: Here we can see that elder people are the most affected by heart disease and young ones are the least affected.
To prove the above inference we will plot the pie chart.
colors = ['blue','green','yellow']
explode = [0,0,0.1]
plt.figure(figsize=(10,10))
sns.set_context('notebook',font_scale = 1.2)
plt.pie([len(Young),len(Middle),len(Elder)],labels=['young ages','middle ages','elderly ages'],explode=explode,colors=colors, autopct='%1.1f%%')
plt.tight_layout()
Output:
Sex(“sex”) Feature Analysis
plt.figure(figsize=(18,9))
sns.set_context('notebook',font_scale = 1.5)
sns.countplot(data['sex'])
plt.tight_layout()
Output:
Inference: Here it is clearly visible that, Ratio of Male to Female is approx 2:1.
Now let’s plot the relation between sex and slope.
plt.figure(figsize=(18,9))
sns.set_context('notebook',font_scale = 1.5)
sns.countplot(data['sex'],hue=data["slope"])
plt.tight_layout()
Output:
Inference: Here it is clearly visible that the slope value is higher in the case of males(1).
Chest Pain Type(“cp”) Analysis
plt.figure(figsize=(18,9))
sns.set_context('notebook',font_scale = 1.5)
sns.countplot(data['cp'])
plt.tight_layout()
Output:
Inference: As seen, there are 4 types of chest pain
- status at least
- condition slightly distressed
- condition medium problem
- condition too bad
Analyzing cp vs target column
Inference: From the above graph we can make some inferences,
- People having the least chest pain are not likely to have heart disease.
- People having severe chest pain are likely to have heart disease.
Elderly people are more likely to have chest pain.
Thal Analysis
plt.figure(figsize=(18,9))
sns.set_context('notebook',font_scale = 1.5)
sns.countplot(data['thal'])
plt.tight_layout()
Output:
Target
plt.figure(figsize=(18,9))
sns.set_context('notebook',font_scale = 1.5)
sns.countplot(data['target'])
plt.tight_layout()
Output:
Inference: The ratio between 1 and 0 is much less than 1.5 which indicates that the target feature is not imbalanced. So for a balanced dataset, we can use accuracy_score as evaluation metrics for our model.
Feature Engineering
Now we will see the complete description of the continuous data as well as the categorical data
categorical_val = []
continous_val = []
for column in data.columns: print("--------------------") print(f"{column} : {data[column].unique()}") if len(data[column].unique()) <= 10: categorical_val.append(column) else: continous_val.append(column)
Output:
Now here first we will be removing the target column from our set of features then we will categorize all the categorical variables using the get dummies method which will create a separate column for each category suppose X variable contains 2 types of unique values then it will create 2 different columns for the X variable.
categorical_val.remove('target')
dfs = pd.get_dummies(data, columns = categorical_val)
dfs.head(6)
Output:
Now we will be using the standard scaler method to scale down the data so that it won’t raise the outliers also dataset which is scaled to general units leads to having better accuracy.
sc = StandardScaler() col_to_scale = ['age', 'trestbps', 'chol', 'thalach', 'oldpeak'] dfs[col_to_scale] = sc.fit_transform(dfs[col_to_scale]) dfs.head(6)
Output:
Modeling
Splitting our Dataset
X = dfs.drop('target', axis=1)
y = dfs.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
The KNN Machine Learning Algorithm
knn = KNeighborsClassifier(n_neighbors = 10) knn.fit(X_train,y_train) y_pred1 = knn.predict(X_test) print(accuracy_score(y_test,y_pred1))
Output:
0.8571428571428571
1. We did data visualization and data analysis of the target variable, age features, and whatnot along with its univariate analysis and bivariate analysis.
2. We also did a complete feature engineering part in this article which summons all the valid steps needed for further steps i.e.
model building.
3. From the above model accuracy, KNN is giving us the accuracy which is 89%.
Endnotes
Here’s the repo link to this article. Hope you liked my article on Heart disease detection using ML. If you have any opinions or questions, then comment below.
Read on AV Blog about various predictions using Machine Learning.
About Me
Greeting to everyone, I’m currently working in TCS and previously, I worked as a Data Science Analyst in Zorba Consulting India. Along with full-time work, I’ve got an immense interest in the same field, i.e. Data Science, along with its other subsets of Artificial Intelligence such as Computer Vision, Machine Learning, and Deep learning; feel free to collaborate with me on any project on the domains mentioned above (LinkedIn).
Hope you liked my article on Heart Disease Prediction? You can access my other articles, which are published on Analytics Vidhya as a part of the Blogathon link.
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