Merge branch 'feature/model-training' into development

main.py

@@ -3,18 +3,68 @@ import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
 
+from sklearn.preprocessing import MinMaxScaler, LabelEncoder
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
+
+
 data_path = "student_lifestyle_dataset.csv"
 
 def main():
-    #loading
+    # loading
     df = load_data()
     
-    #preprocessing
-    preprocess_data(df)
+    # preprocessing
+    df_clean = preprocess_data(df)
     
-    #exploratory data analysis
-    draw_plots(df)
+    # exploratory data analysis
+    # draw_graphs(df_clean)
     
+    # separate features and target
+    X = df_clean.drop('Stress_Level', axis=1)
+    y_raw = df_clean['Stress_Level']
+        
+    # encode target
+    le = LabelEncoder()
+    y = le.fit_transform(y_raw)
+    
+    # split into train and test data
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.2, stratify=y, random_state=0
+    )   
+    
+    # feature engineering
+    X_train_normalized, X_test_normalized = normalize_features(X_train, X_test)
+    
+    feature_names = X.columns
+    model = train_logistic_regression(X_train_normalized, X_test_normalized, y_train, y_test, le, feature_names)
+    
+    
+    y_pred = model.predict(X_test)
+    
+    # Evaluate
+    print("Accuracy:", accuracy_score(y_test, y_pred))
+    print("\nClassification Report:")
+    print(classification_report(y_test, y_pred, target_names=le.classes_))
+    
+    print("\nConfusion Matrix:")
+    print(confusion_matrix(y_test, y_pred))
+    
+    feature_importance = pd.DataFrame({
+        'Feature': feature_names,
+        'Coefficient': model.coef_[0]
+    })
+    print(feature_importance.sort_values(by='Coefficient', ascending=False))
+
+def train_logistic_regression(X_train, X_test, y_train, y_test, le, feature_names):
+    model = LogisticRegression(
+        solver='lbfgs',
+        max_iter=10000
+    )
+    model.fit(X_train, y_train)
+    return model  
+ 
 def load_data():
     df = pd.read_csv(data_path, encoding="ascii", delimiter=",")
     #removing uneeded feature
@@ -35,11 +85,12 @@ def inspect_data(df):
     print("\n")
 
 def clean_data(df):
-    print("Missing values:")
-    print(df.isnull().sum())
-    print("\n")
+    # print("Missing values:")
+    # print(df.isnull().sum())
+    # print("\n")
     
-    df.dropna(inplace=True)
+    df.dropna(inplace=False)
+    return df
 
 def order_data_stress_level(df):
     df["Stress_Level"] = pd.Categorical(
@@ -70,14 +121,21 @@ def display_feature_boxplots(df):
         plt.title(f"{col} by Stress Level")
         plt.show()
 
-def draw_plots(df):
+def draw_graphs(df):
     display_feature_distributions_histogram(df)
     display_scatter_plot_matrix(df)
     display_correlation_heatmap(df)
     display_feature_boxplots(df)
 
 def preprocess_data(df):
-    clean_data(df)
-    order_data_stress_level(df)
+    df_clean = clean_data(df)
+    order_data_stress_level(df_clean)
+    return df_clean
+
+def normalize_features(X_train, X_test):
+    scaler = MinMaxScaler()
+    X_train_scaled = scaler.fit_transform(X_train)
+    X_test_scaled = scaler.transform(X_test)    
+    return X_train_scaled, X_test_scaled
 
 main()