Task Isolation Forest Model on ODDS Shuttle dataset

PULL_REQUEST_TEMPLATE.md

@@ -2,7 +2,7 @@
 
 ## Related Issue
 
-Closes #
+Closes #16
 
 ---
 
@@ -11,14 +11,17 @@ Closes #
 Provide a brief overview of your implementation.
 
 - What did you implement?
+- I trained a model on the shuttle ODDS dataset using the isolation forest to detect the the anomalies in the dataset. 
 - What approach did you follow?
+- Firstly after importing the data and converted the data into the DataFrame i looked for the missing valeus(NaN) luckily there weren't any so there was almost no preprocessing required.
+  Major Challenge i faced  during training of the model was recalling the correct syntax and some libs, I took some help from the medium page.To decide the best parameters values like         n_estimators,contamination and sample size i did some hard yakka, by running the loops to decide the n_estimators in range(1,700,50) luckily i got the highest f1 scores for values 101 and 151 but i took 100 because it had higher f1_score as well as auc_value both tending to one if i took approximaiton upto 2 decimal places.
 
 ---
 
 ## Dataset
 
 - [ ] Mammography
-- [ ] Shuttle
+- [x] Shuttle
 
 Dataset Source:
 
@@ -28,11 +31,10 @@ Dataset Source:
 
 Describe any preprocessing performed.
 
-Examples:
-- Missing value handling
-- Feature scaling
-- Encoding
-- Feature selection
+- Missing value handling - No,It has nil NaN values confirmed it by df.isnull().sum()==0
+- Feature scaling - No isolation forest can handle without scaling.
+- Encoding - No data contained the numeric values so not needed.
+- Feature selection - No verly low dimensionality.
 
 ---
 
@@ -42,37 +44,34 @@ List the important hyperparameters used.
 
 | Hyperparameter | Value |
 |---------------|-------|
-| n_estimators | |
-| contamination | |
-| max_samples | |
-| max_features | |
-| random_state | |
+| n_estimators |100|
+| contamination |0.075|
+| max_samples |256|
+| max_features |1|
+| random_state |42|
 
 ---
 
 ## Evaluation Results
 
 | Metric | Value |
 |--------|-------|
-| Precision | |
-| Recall | |
-| F1-score | |
-| ROC-AUC (Optional) | |
+| Precision |0.9332|
+| Recall |0.9789|
+| F1-score |0.955|
+| ROC-AUC (Optional) |0.9975|
 
 ---
 
 ## Visualizations
 
 Attach **at least 2 plots** from your analysis.
 
-Examples:
-- PCA visualization
-- Anomaly score distribution
-- Confusion Matrix
-- Correlation heatmap
-- Feature distributions
-- Hyperparameter comparison
-- Precision/Recall/F1 comparison
+<img width="3063" height="2957" alt="anomalyscore" src="https://github.com/user-attachments/assets/435f65ba-3713-4eee-a5f8-229dcdef624f" />
+<img width="1591" height="1403" alt="confusionmatrix" src="https://github.com/user-attachments/assets/54dbf8b6-1e82-4dab-97b6-4145b6f24558" />
+<img width="1872" height="1403" alt="roc_auc_curve" src="https://github.com/user-attachments/assets/c5072c8e-060b-46ea-9f78-674df0df1436" />
+<img width="1191" height="180" alt="Screenshot From 2026-07-13 02-28-36" src="https://github.com/user-attachments/assets/7cacdab4-2405-4c2f-97e0-99eb5f9eb483" />
+
 
 ---
 
@@ -81,17 +80,19 @@ Examples:
 Briefly summarize:
 
 - What worked well?
+ - Preprocessing there wasn't any.
 - Which hyperparameter had the biggest impact?
-- Any interesting findings?
+ - Contamination value.increasing it slightly helped me achieved the good f1_score,recall slight more auc_val.
 - Challenges faced (if any)
+ - Since new to Isolation forest faced slight issues with syntax and libs.
 
 ---
 
 ## Checklist
 
-- [ ] Code runs successfully
-- [ ] Notebook (`.ipynb`) included
-- [ ] Code is well-commented
-- [ ] README/documentation updated
-- [ ] At least **2 plots** included
-- [ ] PR is linked to the corresponding issue
+- [x] Code runs successfully
+- [x] Notebook (`.ipynb`) included
+- [x] Code is well-commented
+- [x] README/documentation updated
+- [x] At least **2 plots** included
+- [x] PR is linked to the corresponding issue

main.py

@@ -0,0 +1,6 @@
+def main():
+    print("Hello from cognios!")
+
+
+if __name__ == "__main__":
+    main()