diff --git a/Predictions/Random Forest/readme.md b/Predictions/Random Forest/readme.md
index 76fa373..ae602b1 100644
--- a/Predictions/Random Forest/readme.md	
+++ b/Predictions/Random Forest/readme.md	
@@ -2,8 +2,21 @@
 ## Diabetes Prediction - `DONE`
 - No EDA
 - Using `RandomForestClassifier`, `SMOTE`, `sklearn`, `pandas` libraries
-- Attained Accuracy of 75.97%
-- Diabetes as Class `1` and No Diabetes as Class `0`
-- Precision (Class 0) = **0.84**, Precision (Class 1) = **0.64**
-- Recall (Class 0) = **0.77**, Recall (Class 1) = **0.75**
-- F1-score (Class 0) = **0.80**, F1-score (Class 1) = **0.69**
+- **Result:**
+    - Attained Accuracy of 75.97%
+    - Diabetes as Class `1` and No Diabetes as Class `0`
+    - Precision (Class 0) = **0.84**, Precision (Class 1) = **0.64**
+    - Recall (Class 0) = **0.77**, Recall (Class 1) = **0.75**
+    - F1-score (Class 0) = **0.80**, F1-score (Class 1) = **0.69**
+
+## Digital Marketing Expense Prediction - `DONE`
+- Performed Exploratory Data Analysis (EDA)
+- Using `RandomForestRegressor`, `sklearn`, `seaborn`, `pandas`, `matplotlib` libraries
+- Dataset contains features:
+  - `Impressions`, `Reach`, `Video_plays`, `Link_clicks`, `Engagement`, and `Live_time`
+- Target variable: `Amount_spent` (in IDR)
+- **Result:**
+   - Attained R² of **0.86**
+   - `live_time` contributes 80% to the prediction of `Amount_spent`
+   - Other features provide smaller but meaningful contributions
+   - The model demonstrates robust handling of non-linear relationships and multicollinearity.