Fixes: #6551

machine_learning/xgboostclassifier.py

@@ -0,0 +1,88 @@
+import matplotlib.pyplot as plt
+
+"""
+The Url for the algorithm
+https://xgboost.readthedocs.io/en/stable/
+"""
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from xgboost import XGBClassifier
+
+"""
+    You have to download the dataset from kaggle in order to run this
+    https://www.kaggle.com/competitions/titanic/data
+    This is the link from where you can get the data.
+"""
+
+training = pd.read_csv("../input/titanic/train.csv")
+test = pd.read_csv("../input/titanic/test.csv")
+
+# Commented out IPython magic to ensure Python compatibility.
+training["train_test"] = 1
+test["train_test"] = 0
+test["Survived"] = np.NaN
+all_data = pd.concat([training, test])
+# %matplotlib inline
+all_data.columns
+
+all_data.describe()
+
+all_data["cabin_mul"] = all_data.Cabin.apply(
+    lambda cabin_class: 0 if pd.isna(cabin_class) else len(x.split(" "))
+)
+all_data["cabin_adv"] = all_data.Cabin.apply(lambda cabin_number: str(cabin_number)[0])
+all_data["name_title"] = all_data.Name.apply(
+    lambda name: name.split(",")[1].split(".")[0].strip()
+)
+all_data.Age = all_data.Age.fillna(training.Age.median())
+all_data.Fare = all_data.Fare.fillna(training.Fare.median())
+all_data.dropna(subset=["Embarked"], inplace=True)
+all_data["norm_fare"] = np.log(all_data.Fare + 1)
+all_data.Pclass = all_data.Pclass.astype(str)
+all_data["Age"] = all_data["Age"].apply(np.int64)
+all_dummies = pd.get_dummies(
+    all_data[
+        [
+            "Pclass",
+            "Sex",
+            "Age",
+            "SibSp",
+            "Parch",
+            "norm_fare",
+            "Embarked",
+            "cabin_adv",
+            "cabin_mul",
+            "name_title",
+            "train_test",
+        ]
+    ]
+)
+
+from sklearn.preprocessing import StandardScaler
+
+scale = StandardScaler()
+all_dummies_scaled = all_dummies.copy()
+all_dummies_scaled[["Age", "SibSp", "Parch", "norm_fare"]] = scale.fit_transform(
+    all_dummies_scaled[["Age", "SibSp", "Parch", "norm_fare"]]
+)
+all_dummies_scaled.head()
+
+x_train_scaled = all_dummies_scaled[all_dummies_scaled.train_test == 1].drop(
+    ["train_test"], axis=1
+)
+x_test_scaled = all_dummies_scaled[all_dummies_scaled.train_test == 0].drop(
+    ["train_test"], axis=1
+)
+
+y_train = all_data[all_data.train_test == 1].Survived
+
+from xgboost import XGBClassifier
+
+xgb = XGBClassifier()
+xgb.fit(X_train_scaled, y_train)
+
+y_hat_base_vc = xgb.predict(x_test_scaled).astype(int)
+basic_submission = {"PassengerId": test.PassengerId, "Survived": y_hat_base_vc}
+base_submission = pd.DataFrame(data=basic_submission)
+base_submission.to_csv("xgb_submission.csv", index=False)