Update README.md

README.md

@@ -10,7 +10,7 @@ The Bayesian networks in our analysis incorporate several key risk factors as no
 
 ### Data Preprocessing
 
-Before diving into machine learning models, it's essential to preprocess the data to ensure its quality and suitability for analysis. The following steps have been taken:
+Before diving into the predictions, it's essential to preprocess the data to ensure its quality and suitability for analysis. The following steps have been taken:
 
 1. **Resampling Techniques:** 
    - Random Under Sampler (Undersampling)
@@ -40,7 +40,7 @@ Here are the models implemented:
 
 ### Model Evaluation
 
-To assess the performance of the machine learning models, the following evaluation metrics have been utilized:
+To assess the performance of the models, the following evaluation metrics have been utilized:
 
 1. **Precision**
 
@@ -50,13 +50,35 @@ To assess the performance of the machine learning models, the following evaluati
 
 4. **Specificity**
 
+<hr></hr>
 
 ## Files
 
 1. Read_data.R
+   - Read original data
+   - Outlier Detection
+   - Discretization
+   - Write to CSV to use in python
 2. Preprocessing.R
+   - Check for null values
+   - Plot the correlation of each feature with the target variable
 3. Resampling_and_RFE.ipynb
+   - Resampling
+   - RFECV Recursive feature elimination with cross-validation
+   - Write to CSV to use in R
 4. read_new_data.R
+   - Read resampled data 
+   - Drop discarted variables
 5. PC_and_HC_for_BN.R
+   - PC algorithm to create dag
+   - Hill climbing algorithm to create dag 
 6. BN_final.R
+   - Create two manual dags, dag 3 and 4 in report 
+   - Plot the dags
+   - Fit the models on the training data
+   - Predict on the testing set
+   - Evaluate the predictions
 7. RandomForest.R
+   - Fit the Random Forest model on the training set
+   - Predict on the test set 
+   - Evaluate the results