BostonHousingDataset.ipynb

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    "# Regression Analysis with the Boston Housing Dataset in Keras\n",
    "\n",
    "In this notebook we will cover the following issues:\n",
    "\n",
    "* How to create a simple regressor NN model and work with continuous numerical data\n",
    "* Using K-Fold cross-validation in order to get more general results for our models (Sklearn library used for that)\n",
    "    * Generally, we will use 10-Fold cross-validation as 9 fold for training, and 1 fold for validation-set.;\n",
    "<img src=\"images/KFoldCrossValidation.png\" width=600 height=400>\n",
    "    \n",
    "* Mean Squared Error (MSE) and Mean Absolute Error (MAE)\n",
    "    * We can use both of them as loss function, and evaluation matrices for regression analysis;\n",
    "    * Formulas;\n",
    "<img src=\"images/MSE.png\" width=400 height=400>   \n",
    "\n",
    "* Vusualizing and observing feature correlation on heatmap\n",
    "* Feature Selection for more generalized data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7t63OCoI8OrG"
   },
   "source": [
    "### Required Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   "source": [
    "from keras.datasets import boston_housing\n",
    "import numpy as np\n",
    "from keras import models, layers\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import KFold\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iwUoabjZ8SHg"
   },
   "source": [
    "### Loading and Exploring Dataset"
   ]
  },
  {
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   "execution_count": 2,
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     "base_uri": "https://localhost:8080/"
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    "id": "r2tBxv0m5NZc",
    "outputId": "386a69d2-3cf0-4d8d-c310-6a1f50728c17"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/boston_housing.npz\n",
      "57344/57026 [==============================] - 0s 0us/step\n"
     ]
    }
   ],
   "source": [
    "(train_data, train_labels), (test_data, test_labels) = boston_housing.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dhVAXphw-Qla"
   },
   "source": [
    "* In this case, we have a limited dataset 404 samples for training and 102 samples for testing."
   ]
  },
  {
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   "execution_count": 3,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 13)"
      ]
     },
     "execution_count": 3,
     "metadata": {
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     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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    {
     "data": {
      "text/plain": [
       "(102, 13)"
      ]
     },
     "execution_count": 4,
     "metadata": {
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   ],
   "source": [
    "test_data.shape"
   ]
  },
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    "id": "CJCtXhhB-kg1"
   },
   "source": [
    "* Labels consist of prices of the houses in Boston in the mid of 1970s.\n",
    "* They were written in short form like 50, which represents 50000$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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    "executionInfo": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15.2, 42.3, 50. , 21.1, 17.7, 18.5, 11.3, 15.6, 15.6, 14.4])"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
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   ],
   "source": [
    "train_labels[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LAR5N-lnr5Wa"
   },
   "source": [
    "### Normalizing Data\n",
    "\n",
    "* StandardScaler in Scikit-learn library normalizes data in feature-based instead of sample base."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
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   "source": [
    "std = StandardScaler()\n",
    "\n",
    "train_data = std.fit_transform(train_data)\n",
    "test_data = std.transform(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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    "executionInfo": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.27224633, -0.48361547, -0.43576161, -0.25683275, -0.1652266 ,\n",
       "       -0.1764426 ,  0.81306188,  0.1166983 , -0.62624905, -0.59517003,\n",
       "        1.14850044,  0.44807713,  0.8252202 ])"
      ]
     },
     "execution_count": 7,
     "metadata": {
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     "output_type": "execute_result"
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   ],
   "source": [
    "train_data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3QmTdMLvChH9"
   },
   "source": [
    "* Before start the fitting process of the model, let's separate 100 samples as validation data from the training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "b4zgRpdqyOJL"
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   "outputs": [],
   "source": [
    "X_train = train_data[100:]\n",
    "y_train = train_labels[100:]\n",
    "X_val = train_data[:100]\n",
    "y_val = train_labels[:100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8zyPe8xhC2MV"
   },
   "source": [
    "### Building and Fitting Model\n",
    "\n",
    "Since we will be using the same model many times later, we have built it inside a function:\n",
    "\n",
    "* Our model consists of 4 hidden layers with 128, 64, 16, and 4 units. Rectified Linear Unit `ReLu` activation function is used.\n",
    "* `Rmsprop` is used as the optimizer. However, you might try `Adam` or `SGD` as well. Besides, we can count `Tanh` activation as another option."
   ]
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   "execution_count": null,
   "metadata": {
    "id": "J_O4IM3q7JpL"
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   "source": [
    "def build_model():\n",
    "  model = models.Sequential()\n",
    "  model.add(layers.Dense(128, activation='relu', input_shape=(train_data.shape[1],)))\n",
    "  model.add(layers.Dense(64, activation='relu'))\n",
    "  model.add(layers.Dense(16, activation='relu'))\n",
    "  model.add(layers.Dense(4, activation='relu'))\n",
    "  model.add(layers.Dense(1)) # For regression, there is no activation function \n",
    "                             # and a single unit should be used in the layer.\n",
    "\n",
    "  model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])\n",
    "  return model"
   ]
  },
  {
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   "execution_count": null,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "76/76 [==============================] - 0s 6ms/step - loss: 200.7920 - mae: 10.9542 - val_loss: 34.8257 - val_mae: 3.9821\n",
      "Epoch 2/20\n",
      "76/76 [==============================] - 0s 2ms/step - loss: 31.2657 - mae: 3.9388 - val_loss: 22.4652 - val_mae: 3.3547\n",
      "Epoch 3/20\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 21.0626 - mae: 3.1302 - val_loss: 16.4121 - val_mae: 2.8201\n",
      "Epoch 4/20\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 18.1595 - mae: 2.8867 - val_loss: 15.8029 - val_mae: 2.7889\n",
      "Epoch 5/20\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 15.7975 - mae: 2.7387 - val_loss: 11.3968 - val_mae: 2.2820\n",
      "Epoch 6/20\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 13.4953 - mae: 2.5800 - val_loss: 11.2366 - val_mae: 2.2701\n",
      "Epoch 7/20\n",
      "76/76 [==============================] - 0s 4ms/step - loss: 13.4130 - mae: 2.5274 - val_loss: 9.8178 - val_mae: 2.1030\n",
      "Epoch 8/20\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 12.0948 - mae: 2.4020 - val_loss: 9.3299 - val_mae: 2.1104\n",
      "Epoch 9/20\n",
      "76/76 [==============================] - 0s 4ms/step - loss: 11.3894 - mae: 2.3949 - val_loss: 10.4932 - val_mae: 2.4091\n",
      "Epoch 10/20\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 10.8076 - mae: 2.3158 - val_loss: 10.3316 - val_mae: 2.1055\n",
      "Epoch 11/20\n",
      "76/76 [==============================] - 0s 2ms/step - loss: 10.5169 - mae: 2.2215 - val_loss: 9.4314 - val_mae: 2.2133\n",
      "Epoch 12/20\n",
      "76/76 [==============================] - 0s 2ms/step - loss: 10.2677 - mae: 2.1976 - val_loss: 9.5232 - val_mae: 2.0619\n",
      "Epoch 13/20\n",
      "76/76 [==============================] - 0s 2ms/step - loss: 9.5174 - mae: 2.1841 - val_loss: 9.0677 - val_mae: 2.0037\n",
      "Epoch 14/20\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 9.5246 - mae: 2.1679 - val_loss: 8.8564 - val_mae: 2.0838\n",
      "Epoch 15/20\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 8.5810 - mae: 2.0324 - val_loss: 11.9337 - val_mae: 2.6505\n",
      "Epoch 16/20\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 8.9526 - mae: 2.0774 - val_loss: 8.7399 - val_mae: 2.1499\n",
      "Epoch 17/20\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 9.0899 - mae: 2.1315 - val_loss: 11.3549 - val_mae: 2.4972\n",
      "Epoch 18/20\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 8.9507 - mae: 2.0769 - val_loss: 10.5244 - val_mae: 2.1637\n",
      "Epoch 19/20\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 8.8349 - mae: 2.0666 - val_loss: 8.6087 - val_mae: 2.1143\n",
      "Epoch 20/20\n",
      "76/76 [==============================] - 0s 2ms/step - loss: 7.9484 - mae: 1.9909 - val_loss: 8.0303 - val_mae: 1.9691\n"
     ]
    }
   ],
   "source": [
    "model = build_model()\n",
    "history = model.fit(X_train, y_train, epochs=20, batch_size=4, validation_data=(X_val, y_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xv3ioOtjDTpD"
   },
   "source": [
    "* At the end of 20 epoch, we got roughly 1.97 validation mean absolute error, which equivalent to 1970\\\\$ error in the validation set.\n",
    "* However, in the test case, we have a 2720\\\\$ error. That makes a 750\\\\$ gap between validation and test scores. That means the model tends to overfit the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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     "height": 54
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    "executionInfo": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 2ms/step - loss: 19.0536 - mae: 2.7212\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[19.053613662719727, 2.7211787700653076]"
      ]
     },
     "execution_count": 11,
     "metadata": {
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     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_data, test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZAQhinOGDfCU"
   },
   "source": [
    "### Validating Model Using KFold\n",
    "\n",
    "* Previously, we used 100 samples as validation data, and we observed that data tends to overfit validation data. During K-Fold, we are splitting data in 10 portion that makes 364 training and 40 test sample for each fold.\n",
    "* Now, let's use 10-Fold cross-validation to obtain average test error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 201
    },
    "executionInfo": {
     "elapsed": 48517,
     "status": "ok",
     "timestamp": 1599921301487,
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    "id": "Yuc-XMVW8sqJ",
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 7ms/step - loss: 20.9725 - mae: 2.8901\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 21.1001 - mae: 2.8806\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 19.1039 - mae: 2.7260\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 17.1655 - mae: 2.7352\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 19.2768 - mae: 2.8365\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 23.5524 - mae: 3.2920\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 20.7972 - mae: 2.9213\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 18.9536 - mae: 2.6662\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 19.5892 - mae: 2.8592\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 18.6407 - mae: 2.7264\n"
     ]
    }
   ],
   "source": [
    "kf = KFold(n_splits=10, shuffle=True, random_state=1)\n",
    "val_scores = []\n",
    "test_scores = []\n",
    "\n",
    "for train_index, test_index in kf.split(train_data, train_labels):\n",
    "  X_train, X_val = train_data[train_index], train_data[test_index]\n",
    "  y_train, y_val = train_labels[train_index], train_labels[test_index]\n",
    "\n",
    "  model = build_model()\n",
    "  history = model.fit(X_train, y_train, epochs=20, batch_size=4, verbose=0, validation_data=(X_val, y_val))\n",
    "  val_scores.append(history.history['val_mae'][-1])\n",
    "  test_scores.append(model.evaluate(test_data, test_labels)[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DWMDMTj0xWpb"
   },
   "source": [
    "As a new result, we have a 500\\\\$ gap between validation and test data since we have decreased the amount of validation data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5034162402153015"
      ]
     },
     "execution_count": 13,
     "metadata": {
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   ],
   "source": [
    "np.mean(np.abs(np.ravel(test_scores)-np.ravel(val_scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5HIt0QImQ2_m"
   },
   "source": [
    "* We have roughly 2850\\\\$ test error on average. That means we hurt the test performance a bit."
   ]
  },
  {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.8533445835113525"
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     },
     "execution_count": 14,
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   ],
   "source": [
    "np.mean(test_scores)"
   ]
  },
  {
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   },
   "source": [
    "Now, since we have limited data, let's repeat the same process without using validation data. But in this case, we will concatenate the train and test samples in order to evaluate our model with a different portion of the whole data-set using 5-fold cross-validation.\n",
    "\n",
    "The main reason we chose 5-fold cross-validation is 1 portion of it comes across almost 100 samples as test data, which is the same amount as the previous case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HmwMv1RSDwjS"
   },
   "outputs": [],
   "source": [
    "X = np.concatenate((train_data, test_data), axis=0)\n",
    "y = np.concatenate((train_labels, test_labels), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "executionInfo": {
     "elapsed": 48467,
     "status": "ok",
     "timestamp": 1599921301517,
     "user": {
      "displayName": "Huzeyfe Ayaz",
      "photoUrl": "",
      "userId": "04212809444872264866"
     },
     "user_tz": -180
    },
    "id": "s8SQTpbV5pxp",
    "outputId": "4e29c07b-85c8-4317-bce4-3ab3c52cef4c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 13)"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 109
    },
    "executionInfo": {
     "elapsed": 63054,
     "status": "ok",
     "timestamp": 1599921316127,
     "user": {
      "displayName": "Huzeyfe Ayaz",
      "photoUrl": "",
      "userId": "04212809444872264866"
     },
     "user_tz": -180
    },
    "id": "CqVGXzTyDX5o",
    "outputId": "88340082-a115-4f1b-be37-6e335b1c43d6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 2ms/step - loss: 18.3048 - mae: 2.7950\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 12.2931 - mae: 2.3391\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 11.6508 - mae: 2.6909\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 21.0789 - mae: 2.5324\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 10.5325 - mae: 2.4890\n"
     ]
    }
   ],
   "source": [
    "kf = KFold(n_splits=5, shuffle=True, random_state=1)\n",
    "scores = []\n",
    "\n",
    "for train_index, test_index in kf.split(X, y):\n",
    "  X_train, X_test = X[train_index], X[test_index]\n",
    "  y_train, y_test = y[train_index], y[test_index]\n",
    "\n",
    "  model = build_model()\n",
    "  model.fit(X_train, y_train, epochs=20, batch_size=4, verbose=0)\n",
    "  scores.append(model.evaluate(X_test, y_test)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 109
    },
    "executionInfo": {
     "elapsed": 63038,
     "status": "ok",
     "timestamp": 1599921316132,
     "user": {
      "displayName": "Huzeyfe Ayaz",
      "photoUrl": "",
      "userId": "04212809444872264866"
     },
     "user_tz": -180
    },
    "id": "_NYK3cjVEPKf",
    "outputId": "d1d0f91b-1129-47db-da70-958fc294e0e3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2.794982671737671,\n",
       " 2.3391175270080566,\n",
       " 2.690911054611206,\n",
       " 2.5324416160583496,\n",
       " 2.4890496730804443]"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WtiqN6RbRp7k"
   },
   "source": [
    "Now, we have obtained a 2.57 MAE score and 2570\\\\$ overall deviation which is better than our previous cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "executionInfo": {
     "elapsed": 63019,
     "status": "ok",
     "timestamp": 1599921316137,
     "user": {
      "displayName": "Huzeyfe Ayaz",
      "photoUrl": "",
      "userId": "04212809444872264866"
     },
     "user_tz": -180
    },
    "id": "3TnHXHf_EUvT",
    "outputId": "2a74fd09-7944-414e-b5c4-2b1169c060d1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5693005084991456"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YWYkWT-IShT7"
   },
   "source": [
    "### One More Thing!\n",
    "\n",
    "In Kaggle's competition of [Boston Housing](https://www.kaggle.com/c/boston-housing/overview), they were looking at Root Mean Squared Error `RMSE`. Let's change our evaluation matrices as RMSE and re-train our model.\n",
    "\n",
    "* For the formula of RMSE;\n",
    "\n",
    "<img src=\"images/RMSE.png\" width=480 height=360>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "executionInfo": {
     "elapsed": 1125,
     "status": "ok",
     "timestamp": 1602050673975,
     "user": {
      "displayName": "Huzeyfe Ayaz",
      "photoUrl": "",
      "userId": "04212809444872264866"
     },
     "user_tz": -180
    },
    "id": "Pldnml_BbHWI"
   },
   "outputs": [],
   "source": [
    "from keras import backend as K\n",
    "import tensorflow as tf\n",
    "\n",
    "@tf.function\n",
    "def RMSE(y_true, y_pred):\n",
    "    return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1)) \n",
    "\n",
    "def build_model():\n",
    "  model = models.Sequential()\n",
    "  model.add(layers.Dense(128, activation='relu', input_shape=(train_data.shape[1],)))\n",
    "  model.add(layers.Dense(64, activation='relu'))\n",
    "  model.add(layers.Dense(16, activation='relu'))\n",
    "  model.add(layers.Dense(4, activation='relu'))\n",
    "  model.add(layers.Dense(1))\n",
    "\n",
    "  model.compile(optimizer='rmsprop', loss='mse', metrics=RMSE)\n",
    "  return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PQqau6q_b5bp"
   },
   "source": [
    "Since TensorFlow creates randomly initialized weights each time, let's repeat the training process 10 times and take the mean of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 38916,
     "status": "ok",
     "timestamp": 1602050713227,
     "user": {
      "displayName": "Huzeyfe Ayaz",
      "photoUrl": "",
      "userId": "04212809444872264866"
     },
     "user_tz": -180
    },
    "id": "Qil0solFbHWk",
    "outputId": "1de55eb6-f25d-4f99-b187-eca1e92cc8fd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 2ms/step - loss: 21.1999 - RMSE: 2.8791\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 18.1231 - RMSE: 2.7726\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 18.5403 - RMSE: 2.7231\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 19.5673 - RMSE: 2.7538\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 24.1006 - RMSE: 3.0847\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 17.3106 - RMSE: 2.6433\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 18.4001 - RMSE: 2.7455\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 22.5769 - RMSE: 2.9208\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 18.6691 - RMSE: 2.8329\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 20.3492 - RMSE: 2.6892\n"
     ]
    }
   ],
   "source": [
    "rmse_scores = []\n",
    "for step in range(10):\n",
    "  model = build_model()\n",
    "  model.fit(train_data, train_labels, epochs=20, batch_size=4, verbose=0)\n",
    "  rmse_scores.append(model.evaluate(test_data, test_labels)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 36289,
     "status": "ok",
     "timestamp": 1602050713238,
     "user": {
      "displayName": "Huzeyfe Ayaz",
      "photoUrl": "",
      "userId": "04212809444872264866"
     },
     "user_tz": -180
    },
    "id": "40XYA-7ocKbR",
    "outputId": "97cbcf77-c33e-46e4-b0e0-f852921f9882"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Score: 2.8044910430908203\n"
     ]
    }
   ],
   "source": [
    "print('RMSE Score:',np.mean(rmse_scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q7SYywNIeAv5"
   },
   "source": [
    "We got a 2.8 RMSE score on average, which places us within the top 5 in the leaderboard even without making any feature engineering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9yFI36R1nYiY"
   },
   "source": [
    "### Future Engineering\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nLEJlUdovZqO"
   },
   "source": [
    "#### Feature Correlations\n",
    "\n",
    "Observing the correlation between parameters is important to detect useful ones, and eliminate others, which add some bias, and may mislead the model. For this reason, the heatmap of the correlation between parameters is plotted below. In order to observe better the relationship between output and parameters, targets are also inserted inside correlation as the 13th index. Thus, we can select the most correlated columns with targets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 378
    },
    "executionInfo": {
     "elapsed": 2597,
     "status": "ok",
     "timestamp": 1602050792864,
     "user": {
      "displayName": "Huzeyfe Ayaz",
      "photoUrl": "",
      "userId": "04212809444872264866"
     },
     "user_tz": -180
    },
    "id": "CySZXEmeFm7f",
    "outputId": "6ed02790-007c-4d9e-c6a7-a38f4eeb9b42"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined_train = np.concatenate((train_data, train_labels.reshape((-1,1))), axis=1)\n",
    "df = pd.DataFrame(combined_train)\n",
    "\n",
    "fig = plt.figure(figsize=(10,6))\n",
    "sns.heatmap(df.corr(), annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cIDK2vorXSFX"
   },
   "source": [
    "When we look at the 13th row, the 12th parameter seems the strongest-correlated one negatively, then the 5th column following it. Also, we should exclude highly correlated features since they yield variance, and models will be highly sensitive to training data. But, we should generalize it to get a better score on test data. For instance, the 8th and 9th features have a 0.92 correlation, which is pretty high. In this case, we can extract the 8th parameter since the 9th one is more correlated with the target."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0fXtzA_XndqW"
   },
   "source": [
    "#### Feature Selection\n",
    "\n",
    "After all, it is may hard to detect all anomalies from correlations. For this reason, we may use feature selection techniques with a Random Forest algorithm in Sklearn library, which is quite popular in the ML area.\n",
    "\n",
    "10000 estimators are used for training RandomForesRegressor model. Then, importances of features are returned by `feature_importances_` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "executionInfo": {
     "elapsed": 38487,
     "status": "ok",
     "timestamp": 1602092738140,
     "user": {
      "displayName": "Huzeyfe Ayaz",
      "photoUrl": "",
      "userId": "04212809444872264866"
     },
     "user_tz": -180
    },
    "id": "qfAZ6nPhNt7B"
   },
   "outputs": [],
   "source": [
    "rf = RandomForestRegressor(n_estimators=10000, n_jobs=-1)\n",
    "rf.fit(train_data, train_labels)\n",
    "feature_importance = rf.feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0rSpLBvapZ1Q"
   },
   "source": [
    "You can see the probability of feature importances, and corresponding indices in sorted order below. For this case, we will only consider the first 9 features since others have less than 1% importance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 37563,
     "status": "ok",
     "timestamp": 1602092738144,
     "user": {
      "displayName": "Huzeyfe Ayaz",
      "photoUrl": "",
      "userId": "04212809444872264866"
     },
     "user_tz": -180
    },
    "id": "38w1R8nPfi7F",
    "outputId": "1c97c89b-8388-4072-d5d6-8f50044149d3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.41543101047382563,\n",
       " 0.38765565617116654,\n",
       " 0.06695360817617603,\n",
       " 0.04643026292834846,\n",
       " 0.018391822652997856,\n",
       " 0.016937947022275624,\n",
       " 0.014196346454481033,\n",
       " 0.011876463158528007,\n",
       " 0.010789064707926142,\n",
       " 0.005342345252706388,\n",
       " 0.004075733256904156,\n",
       " 0.001021874334716227,\n",
       " 0.0008978654099477418]"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(feature_importance, reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1341,
     "status": "ok",
     "timestamp": 1602050799618,
     "user": {
      "displayName": "Huzeyfe Ayaz",
      "photoUrl": "",
      "userId": "04212809444872264866"
     },
     "user_tz": -180
    },
    "id": "KJhQcmceOBj0",
    "outputId": "afd1e50e-96b3-463c-c132-29ffb0ce5728"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12,  5,  7,  0,  4,  6, 10,  9, 11,  8,  2,  3,  1])"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_idxs = np.argsort(feature_importance)[::-1]\n",
    "sorted_idxs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "executionInfo": {
     "elapsed": 930,
     "status": "ok",
     "timestamp": 1602051803468,
     "user": {
      "displayName": "Huzeyfe Ayaz",
      "photoUrl": "",
      "userId": "04212809444872264866"
     },
     "user_tz": -180
    },
    "id": "gZfqsGEFGlyK"
   },
   "outputs": [],
   "source": [
    "from keras import backend as K\n",
    "import tensorflow as tf\n",
    "\n",
    "@tf.function\n",
    "def RMSE(y_true, y_pred):\n",
    "    return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1)) \n",
    "\n",
    "def build_model():\n",
    "  model = models.Sequential()\n",
    "  model.add(layers.Dense(128, activation='relu', input_shape=(9,)))\n",
    "  model.add(layers.Dense(64, activation='relu'))\n",
    "  model.add(layers.Dense(16, activation='relu'))\n",
    "  model.add(layers.Dense(4, activation='relu'))\n",
    "  model.add(layers.Dense(1))\n",
    "\n",
    "  model.compile(optimizer='rmsprop', loss='mse', metrics=RMSE)\n",
    "  return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 40676,
     "status": "ok",
     "timestamp": 1602051972702,
     "user": {
      "displayName": "Huzeyfe Ayaz",
      "photoUrl": "",
      "userId": "04212809444872264866"
     },
     "user_tz": -180
    },
    "id": "Il1bCJrEGMiC",
    "outputId": "a0b873da-4882-4318-e171-60d17903ffcc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 3ms/step - loss: 18.2219 - RMSE: 2.6565\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 15.8207 - RMSE: 2.6226\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 18.4171 - RMSE: 2.8106\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 14.4205 - RMSE: 2.7120\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 16.7071 - RMSE: 2.6969\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 16.4052 - RMSE: 2.5547\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 16.5790 - RMSE: 2.5308\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 17.6878 - RMSE: 2.6500\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 17.1926 - RMSE: 2.6908\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 17.1858 - RMSE: 2.7560\n"
     ]
    }
   ],
   "source": [
    "rmse_scores = []\n",
    "for step in range(10):\n",
    "  model = build_model()\n",
    "  model.fit(train_data[:, sorted_idxs[:9]], train_labels, epochs=20, batch_size=4, verbose=0)\n",
    "  rmse_scores.append(model.evaluate(test_data[:, sorted_idxs[:9]], test_labels)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 988,
     "status": "ok",
     "timestamp": 1602051983995,
     "user": {
      "displayName": "Huzeyfe Ayaz",
      "photoUrl": "",
      "userId": "04212809444872264866"
     },
     "user_tz": -180
    },
    "id": "aKCvtNVFGMjI",
    "outputId": "1c7c284a-9526-4df7-a508-67300b99069b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Score: 2.668097186088562\n"
     ]
    }
   ],
   "source": [
    "print('RMSE Score:',np.mean(rmse_scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8enPxvvhLAy1"
   },
   "source": [
    "Now, we got a better RMSE score than ever."
   ]
  }
 ],
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   "name": "BostonHousingDataset.ipynb",
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