Brainstroke Prediction

Kaggle dataset link : https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset

---

Libraries used for dataset processing

• Numpy
• Pandas

Libraries used for graphical representation

• Matplotlib
• Seaborn

Libraries used for Scaling and Oversampling

• Sklearn.preprocessing
• Imblearn

---

PREPROCESSING

---

• Removed the id column – decreasing the dimension – did not add to insights in the data analysis.

df = df.drop(['id'],axis=1)

• Count for NULL values are checked among the attributes of the dataset

print(df.isna().sum())

• Only BMI-Attribute had NULL values
  
• Plotted BMI's value distribution - looked skewed - therefore imputed the missing values using the median.
  
• Didn’t eliminate the records due to dataset being highly skewed on the target attribute – stroke and a good portion of the missing BMI values had accounted for positive stroke

• The dataset was skewed because there were only few records which had a positive value for stroke-target attribute

• In the gender attribute, there were 3 types - Male, Female and Other. There was only 1 record of the type "other", Hence it was converted to the majority type – decrease the dimension

• Most of the attributes in the dataset were binary values – converting the numeric bin values into string bin values for dummy encoding.

  • Dummy encoding similar to one-hot encoding – Values in the binary ecoded columns are 1/0 – Additional attributes/columns created.

• Random oversampling done on the dataset to balance the skew in the target attributes.

  • Boosting the number of records in the minority class – records

---

EDA - Exploratory Data Analysis

---

• Plotted plots of each attribute - Analyse trends if any – plots: pie, histogram.
• Plotted relation of target attribute to other attributes to find any correlation.
• Plotted the heatmap – correlation plot between the attributes.
  • Heatmap showed very less correlation between the attribute values.

---

MODEL BUILDING

---

• Creating a train and test split of the oversampled dataset. (80-20)

Applied various Machine learning models for predictive analysis

1. Decision tree
2. KNN
3. XG-Boost
4. Random forest
5. Logistic regression

Analysed the results generated using confusion matrix - accuracy, precision, recall and plotting the ROC plot and generating the AUC scores.

Accuracies calculated:

1. Decision tree : 97.89%
2. KNN : 97.22%
3. XG-Boost : 97.48%
4. Random forest : 99.48%
5. Logistic regression : 76.34%

Chosen model - RANDOM FOREST

Results were validated using the k fold (20 splits) validation for overfitting

• Accuracy: 95.01
  For Random Forest