fnn-titanic-competition.ipynb

{
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    "tags": []
   },
   "source": [
    "# Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "33c2d726",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a1c26d2",
   "metadata": {
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     "end_time": "2022-12-25T15:34:01.917629",
     "exception": false,
     "start_time": "2022-12-25T15:34:01.911326",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "252fcb9b",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "train_dataset = pd.read_csv('/kaggle/input/titanic/train.csv')\n",
    "test_dataset = pd.read_csv('/kaggle/input/titanic/test.csv')\n",
    "gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')\n",
    "\n",
    "Y_train = train_dataset.iloc[:, 1:2].values.flatten()\n",
    "x_train_names = train_dataset.iloc[:, 3:4].values\n",
    "x_train = train_dataset.drop(columns = [\"Survived\", \"Cabin\", \"PassengerId\", \"Name\", \"Ticket\"]).iloc[:, :].values\n",
    "x_test_ids = test_dataset.iloc[:,0:1].values\n",
    "x_test_names = test_dataset.iloc[:, 2:3].values\n",
    "x_test = test_dataset.drop(columns = [\"Cabin\", \"Name\", \"Ticket\"]).iloc[:,1:].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cc791da",
   "metadata": {
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     "start_time": "2022-12-25T15:34:02.001394",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Filling of missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e6bbc0e0",
   "metadata": {
    "execution": {
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    },
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   },
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# Filling the age column of x_train\n",
    "\n",
    "imputer = SimpleImputer(missing_values = np.nan, strategy = 'mean')\n",
    "imputer.fit(x_train[:,2:3])\n",
    "x_train[:,2:3] = imputer.transform(x_train[:,2:3])\n",
    "\n",
    "#Filling the embarked column of x_train\n",
    "\n",
    "x_train[:, -1]=['S' if x is np.nan else x for x in x_train[:, -1]]\n",
    "\n",
    "#Filling the age column of x_test\n",
    "\n",
    "imputer = SimpleImputer(missing_values = np.nan, strategy = 'mean')\n",
    "imputer.fit(x_test[:,2:3])\n",
    "x_test[:,2:3] = imputer.transform(x_test[:,2:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8132346",
   "metadata": {
    "papermill": {
     "duration": 0.00633,
     "end_time": "2022-12-25T15:34:03.170637",
     "exception": false,
     "start_time": "2022-12-25T15:34:03.164307",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Encoding some of the categorical features at training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6253eb73",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "ct_x_train_sex = ColumnTransformer(transformers=[('encoder', OneHotEncoder(),[1])], \n",
    "                           remainder='passthrough')\n",
    "x_train = np.array(ct_x_train_sex.fit_transform(x_train))\n",
    "\n",
    "\n",
    "ct_x_train_Pclass = ColumnTransformer(transformers=[('encoder', OneHotEncoder(),[2])], \n",
    "                           remainder='passthrough')\n",
    "x_train = np.array(ct_x_train_Pclass.fit_transform(x_train))\n",
    "\n",
    "\n",
    "ct_x_train_embarked = ColumnTransformer(transformers=[('encoder', OneHotEncoder(),[9])], \n",
    "                           remainder='passthrough')\n",
    "x_train = np.array(ct_x_train_embarked.fit_transform(x_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "498b8d13",
   "metadata": {
    "papermill": {
     "duration": 0.006457,
     "end_time": "2022-12-25T15:34:03.226014",
     "exception": false,
     "start_time": "2022-12-25T15:34:03.219557",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Encoding at test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f9e82fcb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-25T15:34:03.241085Z",
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     "exception": false,
     "start_time": "2022-12-25T15:34:03.232709",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "ct_x_test_sex = ColumnTransformer(transformers=[('encoder', OneHotEncoder(),[1])], \n",
    "                           remainder='passthrough')\n",
    "x_test = np.array(ct_x_test_sex.fit_transform(x_test))\n",
    "\n",
    "ct_x_test_Pclass = ColumnTransformer(transformers=[('encoder', OneHotEncoder(),[2])], \n",
    "                           remainder='passthrough')\n",
    "x_test = np.array(ct_x_test_Pclass.fit_transform(x_test))\n",
    "\n",
    "ct_x_test_embarked = ColumnTransformer(transformers=[('encoder', OneHotEncoder(),[9])], \n",
    "                           remainder='passthrough')\n",
    "x_test = np.array(ct_x_test_embarked.fit_transform(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aa997e0",
   "metadata": {
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     "end_time": "2022-12-25T15:34:03.271901",
     "exception": false,
     "start_time": "2022-12-25T15:34:03.265435",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Scaling some of the features of training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e77d2feb",
   "metadata": {
    "execution": {
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     "exception": false,
     "start_time": "2022-12-25T15:34:03.278472",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "\n",
    "x_train[:, 8:9] = sc.fit_transform(x_train[:, 8:9])\n",
    "x_train[:, 9:10] = sc.fit_transform(x_train[:, 9:10])\n",
    "x_train[:, 10:11] = sc.fit_transform(x_train[:, 10:11])\n",
    "x_train[:, 11:12] = sc.fit_transform(x_train[:, 11:12])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cda532e4",
   "metadata": {
    "papermill": {
     "duration": 0.006727,
     "end_time": "2022-12-25T15:34:03.311957",
     "exception": false,
     "start_time": "2022-12-25T15:34:03.305230",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Scaling some features of the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "48fd12cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-25T15:34:03.327372Z",
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     "duration": 0.021368,
     "end_time": "2022-12-25T15:34:03.340276",
     "exception": false,
     "start_time": "2022-12-25T15:34:03.318908",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_test[:, 8:9] = sc.fit_transform(x_test[:, 8:9])\n",
    "x_test[:, 9:10] = sc.fit_transform(x_test[:, 9:10])\n",
    "x_test[:, 10:11] = sc.fit_transform(x_test[:, 10:11])\n",
    "x_test[:, 11:12] = sc.fit_transform(x_test[:, 11:12])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d728ed0c",
   "metadata": {
    "papermill": {
     "duration": 0.006267,
     "end_time": "2022-12-25T15:34:03.353200",
     "exception": false,
     "start_time": "2022-12-25T15:34:03.346933",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Importing tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4c1cdb1a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-25T15:34:03.368103Z",
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     "start_time": "2022-12-25T15:34:03.359690",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.6.4\n",
      "(891, 12)\n",
      "(891,)\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.__version__)\n",
    "print(x_train.shape)\n",
    "print(Y_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be3fe50e",
   "metadata": {
    "papermill": {
     "duration": 0.00638,
     "end_time": "2022-12-25T15:34:07.872357",
     "exception": false,
     "start_time": "2022-12-25T15:34:07.865977",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Converting numpy arrays into tensors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bba94165",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-25T15:34:07.887953Z",
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     "exception": false,
     "start_time": "2022-12-25T15:34:07.879008",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 12)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-25 15:34:07.909069: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n"
     ]
    }
   ],
   "source": [
    "x_train= tf.convert_to_tensor(x_train,dtype=tf.float32)\n",
    "x_test= tf.convert_to_tensor(x_test,dtype=tf.float32)\n",
    "Y_train= tf.convert_to_tensor(Y_train,dtype=tf.float32)\n",
    "\n",
    "print(x_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5a0eacc",
   "metadata": {
    "papermill": {
     "duration": 0.006728,
     "end_time": "2022-12-25T15:34:07.945637",
     "exception": false,
     "start_time": "2022-12-25T15:34:07.938909",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Creating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "41047ab7",
   "metadata": {
    "execution": {
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     "exception": false,
     "start_time": "2022-12-25T15:34:07.952369",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Dense(256,input_shape=(12,), activation='relu'), # first layer\n",
    "    tf.keras.layers.Dense(128, activation='relu'),\n",
    "    tf.keras.layers.Dense(64, activation='relu'),\n",
    "    tf.keras.layers.Dense(32, activation='relu'),\n",
    "    tf.keras.layers.Dense(1, activation='sigmoid') # second layer\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efe0789b",
   "metadata": {
    "papermill": {
     "duration": 0.006832,
     "end_time": "2022-12-25T15:34:09.396914",
     "exception": false,
     "start_time": "2022-12-25T15:34:09.390082",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Compiling the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f3a7655e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-25T15:34:09.413029Z",
     "iopub.status.busy": "2022-12-25T15:34:09.412572Z",
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     "shell.execute_reply": "2022-12-25T15:34:09.427949Z"
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    "papermill": {
     "duration": 0.027365,
     "end_time": "2022-12-25T15:34:09.431449",
     "exception": false,
     "start_time": "2022-12-25T15:34:09.404084",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77399839",
   "metadata": {
    "papermill": {
     "duration": 0.006666,
     "end_time": "2022-12-25T15:34:09.445425",
     "exception": false,
     "start_time": "2022-12-25T15:34:09.438759",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "be5c4fcf",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-25 15:34:09.546220: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "28/28 [==============================] - 1s 4ms/step - loss: 0.5409 - accuracy: 0.7430\n",
      "Epoch 2/50\n",
      "28/28 [==============================] - 0s 4ms/step - loss: 0.4420 - accuracy: 0.8148\n",
      "Epoch 3/50\n",
      "28/28 [==============================] - 0s 4ms/step - loss: 0.4105 - accuracy: 0.8272\n",
      "Epoch 4/50\n",
      "28/28 [==============================] - 0s 4ms/step - loss: 0.4024 - accuracy: 0.8305\n",
      "Epoch 5/50\n",
      "28/28 [==============================] - 0s 4ms/step - loss: 0.3927 - accuracy: 0.8384\n",
      "Epoch 6/50\n",
      "28/28 [==============================] - 0s 4ms/step - loss: 0.3824 - accuracy: 0.8418\n",
      "Epoch 7/50\n",
      "28/28 [==============================] - 0s 4ms/step - loss: 0.3843 - accuracy: 0.8451\n",
      "Epoch 8/50\n",
      "28/28 [==============================] - 0s 5ms/step - loss: 0.3743 - accuracy: 0.8474\n",
      "Epoch 9/50\n",
      "28/28 [==============================] - 0s 4ms/step - loss: 0.3778 - accuracy: 0.8429\n",
      "Epoch 10/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3688 - accuracy: 0.8462\n",
      "Epoch 11/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3643 - accuracy: 0.8586\n",
      "Epoch 12/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3643 - accuracy: 0.8496\n",
      "Epoch 13/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3570 - accuracy: 0.8541\n",
      "Epoch 14/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3596 - accuracy: 0.8563\n",
      "Epoch 15/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3534 - accuracy: 0.8552\n",
      "Epoch 16/50\n",
      "28/28 [==============================] - 0s 4ms/step - loss: 0.3512 - accuracy: 0.8597\n",
      "Epoch 17/50\n",
      "28/28 [==============================] - 0s 4ms/step - loss: 0.3460 - accuracy: 0.8608\n",
      "Epoch 18/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3423 - accuracy: 0.8597\n",
      "Epoch 19/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3521 - accuracy: 0.8474\n",
      "Epoch 20/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3389 - accuracy: 0.8575\n",
      "Epoch 21/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3525 - accuracy: 0.8474\n",
      "Epoch 22/50\n",
      "28/28 [==============================] - 0s 4ms/step - loss: 0.3416 - accuracy: 0.8620\n",
      "Epoch 23/50\n",
      "28/28 [==============================] - 0s 4ms/step - loss: 0.3359 - accuracy: 0.8608\n",
      "Epoch 24/50\n",
      "28/28 [==============================] - 0s 4ms/step - loss: 0.3473 - accuracy: 0.8563\n",
      "Epoch 25/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3510 - accuracy: 0.8519\n",
      "Epoch 26/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3322 - accuracy: 0.8676\n",
      "Epoch 27/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3204 - accuracy: 0.8687\n",
      "Epoch 28/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3286 - accuracy: 0.8653\n",
      "Epoch 29/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3247 - accuracy: 0.8676\n",
      "Epoch 30/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3239 - accuracy: 0.8676\n",
      "Epoch 31/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3141 - accuracy: 0.8687\n",
      "Epoch 32/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3226 - accuracy: 0.8620\n",
      "Epoch 33/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3155 - accuracy: 0.8732\n",
      "Epoch 34/50\n",
      "28/28 [==============================] - 0s 5ms/step - loss: 0.3091 - accuracy: 0.8732\n",
      "Epoch 35/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3213 - accuracy: 0.8608\n",
      "Epoch 36/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3172 - accuracy: 0.8631\n",
      "Epoch 37/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3210 - accuracy: 0.8698\n",
      "Epoch 38/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3066 - accuracy: 0.8754\n",
      "Epoch 39/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3030 - accuracy: 0.8799\n",
      "Epoch 40/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3126 - accuracy: 0.8754\n",
      "Epoch 41/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3028 - accuracy: 0.8765\n",
      "Epoch 42/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3009 - accuracy: 0.8721\n",
      "Epoch 43/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3085 - accuracy: 0.8709\n",
      "Epoch 44/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3026 - accuracy: 0.8799\n",
      "Epoch 45/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.2976 - accuracy: 0.8743\n",
      "Epoch 46/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.3036 - accuracy: 0.8799\n",
      "Epoch 47/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.2897 - accuracy: 0.8777\n",
      "Epoch 48/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.2857 - accuracy: 0.8822\n",
      "Epoch 49/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.2999 - accuracy: 0.8765\n",
      "Epoch 50/50\n",
      "28/28 [==============================] - 0s 3ms/step - loss: 0.2864 - accuracy: 0.8810\n"
     ]
    }
   ],
   "source": [
    "r = model.fit(x_train, Y_train, epochs=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10f0b293",
   "metadata": {
    "papermill": {
     "duration": 0.01723,
     "end_time": "2022-12-25T15:34:15.634302",
     "exception": false,
     "start_time": "2022-12-25T15:34:15.617072",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Plotting the loss per epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e821c498",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-25T15:34:15.672016Z",
     "iopub.status.busy": "2022-12-25T15:34:15.671560Z",
     "iopub.status.idle": "2022-12-25T15:34:15.885824Z",
     "shell.execute_reply": "2022-12-25T15:34:15.884699Z"
    },
    "papermill": {
     "duration": 0.236294,
     "end_time": "2022-12-25T15:34:15.888557",
     "exception": false,
     "start_time": "2022-12-25T15:34:15.652263",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fef0309b1d0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(r.history['loss'], label='training loss')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a1e1adc",
   "metadata": {
    "papermill": {
     "duration": 0.017927,
     "end_time": "2022-12-25T15:34:15.925077",
     "exception": false,
     "start_time": "2022-12-25T15:34:15.907150",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Plotting the accuracy per epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "70c451aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-25T15:34:15.963360Z",
     "iopub.status.busy": "2022-12-25T15:34:15.962936Z",
     "iopub.status.idle": "2022-12-25T15:34:16.202128Z",
     "shell.execute_reply": "2022-12-25T15:34:16.200956Z"
    },
    "papermill": {
     "duration": 0.26154,
     "end_time": "2022-12-25T15:34:16.204814",
     "exception": false,
     "start_time": "2022-12-25T15:34:15.943274",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fef02784f90>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(r.history['accuracy'], label='accuracy')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab8ef9b4",
   "metadata": {
    "papermill": {
     "duration": 0.018409,
     "end_time": "2022-12-25T15:34:16.241812",
     "exception": false,
     "start_time": "2022-12-25T15:34:16.223403",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Predicting the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "57e28f96",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-25T15:34:16.280596Z",
     "iopub.status.busy": "2022-12-25T15:34:16.280153Z",
     "iopub.status.idle": "2022-12-25T15:34:16.462175Z",
     "shell.execute_reply": "2022-12-25T15:34:16.460255Z"
    },
    "papermill": {
     "duration": 0.205362,
     "end_time": "2022-12-25T15:34:16.465532",
     "exception": false,
     "start_time": "2022-12-25T15:34:16.260170",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 1. 1. 0. 0. 0. 0. 1. 1. 1. 1.\n",
      " 1. 0. 1. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 0.\n",
      " 1. 1. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 1. 0. 0. 1. 1. 0.\n",
      " 0. 0. 1. 0. 0. 1. 0. 1. 1. 1. 0. 0. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0.\n",
      " 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 0. 1. 0. 1.\n",
      " 1. 0. 1. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0.\n",
      " 1. 0. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0.\n",
      " 1. 1. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 0. 1. 1. 0. 1. 0. 1. 0. 0. 0. 0. 0.\n",
      " 1. 0. 1. 0. 1. 0. 0. 0. 1. 1. 0. 1. 0. 0. 1. 0. 1. 0. 0. 0. 0. 1. 0. 0.\n",
      " 1. 0. 1. 0. 1. 0. 1. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 0. 1. 0. 1. 0. 1. 1.\n",
      " 0. 1. 0. 0. 0. 0. 1. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 1.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 1. 0. 0. 0. 0. 1. 1. 1. 0. 1. 0. 0. 0.\n",
      " 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 1. 0. 0. 0. 0.\n",
      " 0. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 1. 0. 0. 0. 1. 0. 0. 1. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 1. 0. 1. 0. 0. 1.\n",
      " 0. 1. 1. 0. 1. 0. 0. 1. 1. 0. 0. 1. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 1. 0. 1. 0. 0. 1. 0. 1. 0. 1. 0. 0. 0.\n",
      " 1. 1. 1. 1. 0. 0. 1. 0. 0. 1.]\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(x_test).round().flatten()\n",
    "x_test_ids = x_test_ids.flatten()\n",
    "print(predictions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e80c10d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-25T15:34:16.505876Z",
     "iopub.status.busy": "2022-12-25T15:34:16.505151Z",
     "iopub.status.idle": "2022-12-25T15:34:16.520557Z",
     "shell.execute_reply": "2022-12-25T15:34:16.519328Z"
    },
    "papermill": {
     "duration": 0.038276,
     "end_time": "2022-12-25T15:34:16.523148",
     "exception": false,
     "start_time": "2022-12-25T15:34:16.484872",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     PassengerId  Survived\n",
      "0            892         0\n",
      "1            893         0\n",
      "2            894         0\n",
      "3            895         0\n",
      "4            896         0\n",
      "..           ...       ...\n",
      "413         1305         0\n",
      "414         1306         1\n",
      "415         1307         0\n",
      "416         1308         0\n",
      "417         1309         1\n",
      "\n",
      "[418 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({'PassengerId':x_test_ids, 'Survived':predictions.astype(int)})\n",
    "df.to_csv('Titanic-Submission-V.1.csv', index=False)\n",
    "print(df)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 27.854671,
   "end_time": "2022-12-25T15:34:19.743727",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-12-25T15:33:51.889056",
   "version": "2.3.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}