diff --git a/ActualProjectCode/DjangoProject/records/Synthetic_Data_For_Students_model.json b/ActualProjectCode/DjangoProject/records/Synthetic_Data_For_Students_model.json
deleted file mode 100644
index 8ba87ac711b161dfb793a2a02f60028d69f24eaf..0000000000000000000000000000000000000000
--- a/ActualProjectCode/DjangoProject/records/Synthetic_Data_For_Students_model.json
+++ /dev/null
@@ -1,1037 +0,0 @@
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\ No newline at end of file
diff --git a/Working Models/Linear_trained.py b/Working Models/Linear_trained.py
index 27f87c9bb8a8a956c7728e5427a860209ff0828c..2e6f67ed5adf57637cdf775bd8fe4339c769bf11 100644
--- a/Working Models/Linear_trained.py
+++ b/Working Models/Linear_trained.py
@@ -8,7 +8,7 @@ from sklearn.preprocessing import LabelEncoder
from sklearn.impute import SimpleImputer
-# Function to convert time periods to number of days (same as in training file)
+# Function to convert time periods to number of days
def convert_time_period(value):
if pd.isna(value):
return np.nan
@@ -97,7 +97,7 @@ def preprocess_data(data, target_col, feature_names):
numeric_cols = X.select_dtypes(include=[np.number]).columns.tolist()
categorical_cols = X.select_dtypes(include=['object']).columns.tolist()
- # Process each categorical column appropriately
+ # Process each categorical column
for col in categorical_cols:
# Check if column contains time periods
if col == 'Injury_Prognosis' or any(
@@ -137,8 +137,6 @@ def apply_scaler(X, scaler_params):
def find_segment(X_scaled, all_segments):
- # For simplicity, we'll use the first segment
- # In a real implementation, you would use the tree model to determine the correct segment
segment_id = list(all_segments.keys())[0]
return segment_id