From 395e3be78b0047cc01d748f9a245b20186a136b0 Mon Sep 17 00:00:00 2001
From: EIMAN TAHIR <eimantahir027@gmail.com>
Date: Mon, 31 Aug 2026 11:56:41 +0500
Subject: [PATCH] Add files via upload

---
 code_files/5-LightGBM and CatBoost/day5_ex.py | 69 +++++++++++++++++++
 1 file changed, 69 insertions(+)
 create mode 100644 code_files/5-LightGBM and CatBoost/day5_ex.py

diff --git a/code_files/5-LightGBM and CatBoost/day5_ex.py b/code_files/5-LightGBM and CatBoost/day5_ex.py
new file mode 100644
index 0000000..72b86ef
--- /dev/null
+++ b/code_files/5-LightGBM and CatBoost/day5_ex.py	
@@ -0,0 +1,69 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+import lightgbm as lgb
+from sklearn.metrics import accuracy_score
+from catboost import CatBoostClassifier
+from xgboost import XGBClassifier
+
+# Load Titanic dataset
+url = "https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv"
+df = pd.read_csv(url)
+
+# Select features and target
+features = ['Pclass','Sex','Age','Fare', 'Embarked']
+target = 'Survived'
+
+# Handle missing values
+df.fillna({'Age': df['Age'].median()}, inplace=True)
+df.fillna({'Embarked': df['Embarked'].mode()[0]}, inplace=True)
+
+# Encode categorical variables
+label_encoders = {}
+for col in ['Sex', 'Embarked']:
+    le = LabelEncoder()
+    df[col] = le.fit_transform(df[col])
+    label_encoders[col] = le
+    
+# SPlit Data
+X = df[features]
+y = df[target]
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+print(f"Traing Data Shape: {X_train.shape}")
+print(f"Test Data Shape: {X_test.shape}")
+
+# Train LightGBM model
+lgb_model = lgb.LGBMClassifier()
+lgb_model.fit(X_train, y_train)
+
+# Predict and evaluate
+lgb_pred = lgb_model.predict(X_test)
+print(f"LightGBM Accuracy: {accuracy_score(y_test, lgb_pred):.4f}")
+
+# Train CatBoost model
+cat_features = ['Pclass', 'Sex', 'Embarked']
+cat_model = CatBoostClassifier(cat_features=cat_features, verbose=0)
+cat_model.fit(X_train, y_train)
+
+# Predict and evaluate
+cat_pred = cat_model.predict(X_test)
+print(f"CatBoost Accuracy: {accuracy_score(y_test, cat_pred):.4f}")
+
+# Train XGBoost model
+xgb_model = XGBClassifier(eval_metric='logloss')
+xgb_model.fit(X_train, y_train)
+
+# Predict and evaluate
+xgb_pred = xgb_model.predict(X_test)
+print(f"XGBoost Accuracy: {accuracy_score(y_test, xgb_pred):.4f}")
+
+
+# Train Catboost without encoding categorical features
+cat_model_native = CatBoostClassifier(cat_features=['Sex', 'Embarked'], verbose=0)
+cat_model_native.fit(X_train, y_train)
+
+# Predict and evaluate
+cat_preds_native = cat_model_native.predict(X_test)
+print(f"CatBoost Native Accuracy: {accuracy_score(y_test, cat_preds_native):.4f}")
+