main.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Titanic survival percentage
X_train = pd.read_csv("train_X.csv")
Y_train = pd.read_csv("train_Y.csv")
X_test = pd.read_csv("test_X.csv")
Y_test = pd.read_csv("test_Y.csv")

X_train = X_train.drop('Id', axis=1)
Y_train = Y_train.drop('Id', axis=1)
X_test = X_test.drop('Id', axis=1)
Y_test = Y_test.drop('Id', axis=1)

X_train = X_train.values
Y_train = Y_train.values
X_test = X_test.values
Y_test = Y_test.values

X_train = X_train.T
Y_train = Y_train.reshape(1, X_train.shape[1])

X_test = X_test.T
Y_test = Y_test.reshape(1, X_test.shape[1])


def sigmoid(x):
    return 1 / (1 + np.exp(-x))


def model(X, Y, learning_rate, iterations):
    m = X_train.shape[1]
    n = X_train.shape[0]

    W = np.zeros((n, 1))
    B = 0
    cost_list = []

    for i in range(iterations):
        Z = np.dot(W.T, X) + B
        A = sigmoid(Z)

        cost = -(1 / m) * np.sum(Y * np.log(A) + (1 - Y) * np.log(1 - A))
        dW = (1 / m) * np.dot(A - Y, X.T)
        dB = (1 / m) * np.sum(A - Y)

        W -= learning_rate * dW.T
        B -= learning_rate * dB

        cost_list.append(cost)

        if (i % (iterations/10)) == 0:
            print("cost after ", i, "iteration is : ", cost)

    return W, B, cost_list


def accuracy(X, Y, W, B):
    Z = np.dot(W.T, X) + B
    A = sigmoid(Z)

    A = np.array(A > 0.5, dtype='int64')

    acc = (1 - np.sum(abs(A - Y)) / Y.shape[1]) * 100

    print("Accuracy of the model is : ", acc, "%")


iterations = 100000
learning_rate = 0.0015
W, B, cost_list = model(X_train, Y_train, learning_rate=learning_rate, iterations=iterations)
accuracy(X_test, Y_test, W, B)

plt.plot(np.arange(iterations), cost_list)
plt.show()