12-2- Adaline on Titanic Dataset.ipynb

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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from numpy.linalg import inv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78aaa62f-a3cb-4380-b518-cd6b6b7ae149",
   "metadata": {},
   "source": [
    "#### load and preprocessing data"
   ]
  },
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   "cell_type": "code",
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   "id": "1e0e8a31-162a-4965-ab76-25b5fe9112cd",
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     "text": [
      "(891, 1)\n"
     ]
    }
   ],
   "source": [
    "train_data = pd.read_csv('train.csv')\n",
    "\n",
    "train_data = train_data.replace(['female', 'male'], [0, 1])\n",
    "train_data = train_data.replace(['S', 'C', 'Q'], [0, 1, 2])\n",
    "train_data = train_data.fillna(0)\n",
    "\n",
    "Y_train = train_data[['Survived']]\n",
    "print(Y_train.shape)\n",
    "\n",
    "X_train = train_data[['Pclass', 'Sex', 'Fare', 'Age', 'SibSp', 'Parch', 'Embarked']]\n",
    "\n",
    "Y_train = np.array(Y_train)\n",
    "Y_train = Y_train.reshape(-1)\n",
    "X_train = np.array(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "556a0264-482c-402c-894b-0de056379d5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(418,)\n"
     ]
    }
   ],
   "source": [
    "test_data = pd.read_csv('test.csv')\n",
    "\n",
    "test_data = test_data.replace(['female', 'male'], [0, 1])\n",
    "test_data = test_data.replace(['S', 'C', 'Q'], [0, 1, 2])\n",
    "test_data = test_data.fillna(0)\n",
    "\n",
    "X_test = test_data[['Pclass', 'Sex', 'Fare', 'Age', 'SibSp', 'Parch', 'Embarked']]\n",
    "\n",
    "test_label = pd.read_csv('gender_submission.csv')\n",
    "Y_test = test_label[['Survived']]\n",
    "\n",
    "Y_test = np.array(Y_test)\n",
    "Y_test = Y_test.reshape(-1)\n",
    "print(Y_test.reshape(-1).shape)\n",
    "X_test = np.array(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "df7d217e-b8d9-47b2-a674-ca270af4b675",
   "metadata": {},
   "outputs": [],
   "source": [
    "class AdelineClassifier:\n",
    "    def __init__(self):\n",
    "        \"\"\"\n",
    "        Initialize the Adeline Classifier model.\n",
    "        \"\"\"\n",
    "        pass\n",
    "    \n",
    "    def fit(self, X_train, y_train):\n",
    "        \"\"\"\n",
    "        Fit the model to the training data.\n",
    "\n",
    "        Args:\n",
    "        X_train (numpy.ndarray): Input features for training.\n",
    "        y_train (numpy.ndarray): Output labels for training.\n",
    "        \"\"\"\n",
    "        self.w = np.matmul(inv(np.matmul(X_train.T, X_train)), np.matmul(X_train.T, y_train))\n",
    "        \n",
    "    def predict(self, X_test):\n",
    "        \"\"\"\n",
    "        Predict the output labels for test data.\n",
    "\n",
    "        Args:\n",
    "        X_test (numpy.ndarray): Input features for testing.\n",
    "\n",
    "        Returns:\n",
    "        numpy.ndarray: Predicted output labels for the test data.\n",
    "        \"\"\"\n",
    "        y_pred = np.matmul(X_test, self.w)\n",
    "        y_pred[y_pred > 0.5] = 1\n",
    "        y_pred[y_pred <= 0.5] = 0\n",
    "        \n",
    "        return y_pred\n",
    "    \n",
    "    def evaluate(self, X_test, y_test):\n",
    "        \"\"\"\n",
    "        Evaluate the performance of the model on the test data.\n",
    "\n",
    "        Args:\n",
    "        X_test (numpy.ndarray): Input features for testing.\n",
    "        y_test (numpy.ndarray): True output labels for testing.\n",
    "\n",
    "        Returns:\n",
    "        float: Accuracy of the model on the test data.\n",
    "        \"\"\"\n",
    "        y_pred = np.matmul(X_test, self.w)\n",
    "        y_pred[y_pred > 0.5] = 1\n",
    "        y_pred[y_pred <= 0.5] = 0\n",
    "    \n",
    "        accuracy = np.count_nonzero(y_pred == y_test) / len(y_test) * 100\n",
    "            \n",
    "        return accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "805dee6e-ee85-4fb3-a07e-3fdb8b980ff2",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AdelineClassifier()\n",
    "model.fit(X_train, Y_train)\n",
    "\n",
    "pred = model.predict(X_test)\n",
    "print(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1bdc8526-a10a-4c89-ae33-6a2826ed024b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 89.71291866028707\n"
     ]
    }
   ],
   "source": [
    "accuracy = model.evaluate(X_test, Y_test)\n",
    "print('accuracy', accuracy)"
   ]
  }
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