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Update README.md

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@@ -40,36 +40,48 @@ train_test_split(df_train,
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  random_state=1111)
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  ```
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- ## Training Log
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
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  Epoch Training Loss Validation Loss
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- 1 No log 0.150276
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- 2 0.195100 0.132906
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- 3 0.107700 0.128633
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- 4 0.107700 0.127795
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  ```
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  ## Threshold Optimization
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- - **Best Threshold:** 0.1
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- - **F1 @ Threshold:** 0.734
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  ## Eval Results
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  ```
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  precision recall f1-score support
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- Alakasiz 0.90 0.87 0.89 734
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- Barinma 0.85 0.80 0.83 207
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- Elektronik 0.73 0.78 0.75 130
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- Giysi 0.83 0.66 0.73 94
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- Kurtarma 0.86 0.79 0.82 362
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- Lojistik 0.73 0.51 0.60 112
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- Saglik 0.74 0.74 0.74 108
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- Su 0.64 0.60 0.62 78
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- Yagma 0.68 0.55 0.61 31
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- Yemek 0.80 0.83 0.81 117
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- micro avg 0.84 0.79 0.81 1973
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- macro avg 0.78 0.71 0.74 1973
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- weighted avg 0.84 0.79 0.81 1973
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- samples avg 0.84 0.82 0.82 1973
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  ```
 
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  random_state=1111)
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  ```
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+ ## Class Loss Weights
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+ - **Alakasiz:** 1.0
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+ - **Barinma:** 1.5167249178108022
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+ - **Elektronik:** 1.7547338578655642
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+ - **Giysi:** 1.9610520059358458
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+ - **Kurtarma:** 1.269341370129623
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+ - **Lojistik:** 1.8684086209021484
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+ - **Saglik:** 1.8019018017117145
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+ - **Su:** 2.110648663094536
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+ - **Yagma:** 3.081208739200435
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+ - **Yemek:** 1.7994815143101963
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+
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+ ## Training Log (Class-Scaled)
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  ```
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  Epoch Training Loss Validation Loss
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+ 1 No log 0.216295
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+ 2 0.260000 0.171498
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+ 3 0.142700 0.175608
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+ 4 0.142700 0.169851
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  ```
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  ## Threshold Optimization
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+ - **Best Threshold:** 0.15
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+ - **F1 @ Threshold:** 0.7503
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  ## Eval Results
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  ```
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  precision recall f1-score support
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+ Alakasiz 0.91 0.87 0.89 734
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+ Barinma 0.85 0.81 0.83 207
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+ Elektronik 0.72 0.78 0.75 130
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+ Giysi 0.73 0.67 0.70 94
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+ Kurtarma 0.86 0.81 0.83 362
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+ Lojistik 0.68 0.56 0.62 112
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+ Saglik 0.72 0.81 0.76 108
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+ Su 0.61 0.69 0.65 78
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+ Yagma 0.67 0.65 0.66 31
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+ Yemek 0.79 0.85 0.82 117
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+ micro avg 0.82 0.81 0.81 1973
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+ macro avg 0.75 0.75 0.75 1973
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+ weighted avg 0.83 0.81 0.81 1973
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+ samples avg 0.84 0.84 0.83 1973
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  ```