Cereberal-Stroke-Analysis

# Followed Process

Read Data:

The script starts by importing necessary libraries (pandas, numpy, seaborn, matplotlib.pyplot) and reading a CSV file into a DataFrame (df).

Exploratory Data Analysis (EDA):

Basic exploration of the dataset using head(), describe(), and checking for missing values using isnull().sum().

Handling Categorical Variables:

One-hot encoding is performed on categorical variables using pd.get_dummies().

Handling Missing Values:

Missing values are imputed using the k-nearest neighbors algorithm (KNNImputer from sklearn.impute).

Feature Scaling and Train-Test Split:

Features are scaled using MinMaxScaler, and the dataset is split into training and testing sets.

Model Selection:

Several classification models are chosen (KNeighborsClassifier, GaussianNB, DecisionTreeClassifier, and RandomForestClassifier) for initial testing.

Model Evaluation Without Resampling:

Classification reports are generated for each model to evaluate their performance on the imbalanced dataset.

OverSampling (SMOTE):

The script uses the Synthetic Minority Over-sampling Technique (SMOTE) to oversample the minority class.

Model Evaluation After OverSampling:

The same models are re-trained and evaluated on the oversampled dataset.

UnderSampling:

Random under-sampling is performed to balance the class distribution.

Model Evaluation After UnderSampling:

The models are re-trained and evaluated on the undersampled dataset.

Combining OverSampling and UnderSampling (SMOTEENN):

The SMOTEENN technique, which combines SMOTE and Edited Nearest Neighbours (ENN), is applied.

Model Evaluation After Combining OverSampling and UnderSampling:

The models are re-trained and evaluated on the combined dataset.

Conclusion:

• The script provides classification reports for each model after different resampling techniques.
• It highlights that resampling techniques, particularly SMOTEENN, improve the model's ability to identify cases positive for stroke.