import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

df = pd.read_csv("C:/Users/akash/titanic.csv")

df.head()

NAs = pd.concat([df.isnull().sum()], axis = 1, keys = ['Train'])

NAs[NAs.sum(axis = 1)>0]

df['Age'] = df['Age'].fillna(df['Age'].mean())

df['Embarked'] = df['Embarked'].fillna(df['Embarked'].mode()[0])

df['Cabin'] = df['Cabin'].fillna(df['Cabin'].mode()[0])

df['Pclass'] = df['Pclass'].apply(str)

for col in df.dtypes[df.dtypes == 'object'].index:

for_dummy = df.pop(col)

df = pd.concat([df, pd.get_dummies(for_dummy, prefix = col)], axis = 1)

df.head()

labels = df.pop("Survived")

from sklearn.ensemble import RandomForestClassifier

from sklearn.model_selection import train_test_split

x_train, x_test, y_train, y_test = train_test_split(df, labels, test_size = 0.25)

rf = RandomForestClassifier()

rf.fit(x_train, y_train)

y_pred = rf.predict(x_test)

from sklearn.metrics import roc_curve, auc

fpr, tpr, thresholds = roc_curve(y_test, y_pred)

roc_auc = auc(fpr, tpr)

n_estimators = [1, 2, 4, 8, 16, 32, 64, 100, 200]

train_results = []

test_results = []

for estimator in n_estimators:

rf = RandomForestClassifier(n_estimators = estimator, n_jobs=-1)

rf.fit(x_train, y_train)

df_pred = rf.predict(x_train)

fpr, tpr, thresholds = roc_curve(y_test, y_pred)

roc_auc = auc(fpr, tpr)

train_results.append(roc_auc)

y_pred = rf.predict(x_test)

fpr, tpr, thresholds = roc_curve(y_test, y_pred)

roc_auc = auc(fpr, tpr)

test_results.append(roc_auc)

from matplotlib.legend_handler import HandlerLine2D

line1, = plt.plot(n_estimators, train_results, 'b', label = "Train AUC")

line2, = plt.plot(n_estimators, test_results, 'r', label = "Test AUC")

plt.legend(handler_map= {line1: HandlerLine2D(numpoints = 2)})

plt.ylabel("AUC score")

plt.xlabel('n_estimators')

plt.show()