Kaggle ( Titanic ML competition) - Kapil Sharma (Class-103/S2022)

import numpy as np # linear algebra

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

from sklearn.ensemble import RandomForestClassifier

train_data = pd.read_csv("/kaggle/input/titanic/train.csv") 

test_data = pd.read_csv("/kaggle/input/titanic/test.csv")

train_data.head()

women = train_data.loc[train_data.Sex == 'female']["Survived"]

rate_women = sum(women)/len(women)

print("% of women who survived:", rate_women)

train_data[train_data.Sex == 'male']

men = train_data.loc[train_data.Sex == 'male']["Survived"]

rate_men = sum(men)/len(men)

print("% of men who survived:", rate_men)

target = train_data["Survived"]

features = ["Sex","Parch","SibSp"]

X = pd.get_dummies(train_data[features])

X_test = pd.get_dummies(test_data[features])

model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)

model.fit(X, target)

predictions = model.predict(X_test)

output = pd.DataFrame({"PassengerId": test_data.PassengerId, "Survived": predictions})

output.to_csv("my_submission.csv", index=False)

output.head()