ML05_8조.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GCCk8_dHpuNf"
   },
   "source": [
    "# Day05: CNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "- **Team ID** : 8조"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "- Write and run the code below in jupyter notebook\n",
    "- After completion, rename the file, and summit the file to e-class\n",
    "- Submit file name : **“ML01\\_<#조\\>.ipynb”**, send me 김동현 <donghyunkim1217@gmail.com>\n",
    "  - Ex) ML01\\_#조\\.ipynb\n",
    "- Due : **오늘 16:59pm**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "teh54kPruHeI"
   },
   "source": [
    "# 1. Convolutional Neural Netowks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "l6bvTAJB1mvT"
   },
   "source": [
    "### Image Classification with CNNs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mthideHn1bPR"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Z8Ncqy7jvenD"
   },
   "source": [
    "### Load and Preprocess the Fashion-MNIST Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-fyQZJQSuKBP"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the fashioin_mnist dataset\n",
    "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
    "\n",
    "(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-fyQZJQSuKBP"
   },
   "outputs": [],
   "source": [
    "# scaling X\n",
    "X_train, X_test = X_train / 255.0, X_test / 255.0\n",
    "num_classes = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ziqAmALKuRxL"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28, 1)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Expand dimension (Channel dim), and check shape\n",
    "X_train = X_train.reshape(X_train.shape[0], 28, 28, 1) \n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "heYH1Ssdu67x"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28, 1)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QfbdIbV2u7fm"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 1)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train = y_train.reshape(y_train.shape[0], 1) \n",
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fILY-2KMu75I"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 1)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test = y_test.reshape(y_test.shape[0], 1) \n",
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5Rxz6wkKvHGT"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9],\n",
       "       [0],\n",
       "       [0],\n",
       "       ...,\n",
       "       [3],\n",
       "       [0],\n",
       "       [5]], dtype=uint8)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show y_train\n",
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show first 10 images\n",
    "\n",
    "for i in range(10):\n",
    "    plt.subplot(2,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.imshow(X_train[i], cmap=plt.cm.binary)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZiVGDjjgvLol"
   },
   "source": [
    "### Build the CNN model\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WZW5kLyWv2UC"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_4 (Conv2D)           (None, 28, 28, 32)        320       \n",
      "                                                                 \n",
      " conv2d_5 (Conv2D)           (None, 26, 26, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 13, 13, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten_2 (Flatten)         (None, 10816)             0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 128)               1384576   \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 10)                1290      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,404,682\n",
      "Trainable params: 1,404,682\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# CNN model with 2 convolution layers \n",
    "#    1. 32 3x3 filters, padding=\"same\", input_shape = (28, 28, 1), activation=\"relu\"\n",
    "#    2. 64 3x3 filters, activation=\"relu\"\n",
    "# and max pooling layer with size=2\n",
    "# and 2 dense layers (128, 10)\n",
    "#    3. 128, activation=\"relu\"\n",
    "#    4. 10, activation=\"softmax\"\n",
    "layers = tf.keras.layers\n",
    "base_model = tf.keras.Sequential([\n",
    "    layers.Conv2D(32, (3, 3), padding=\"same\", input_shape = (28, 28, 1), activation=\"relu\"),\n",
    "    layers.Conv2D(64, (3, 3), activation=\"relu\"),\n",
    "    layers.MaxPooling2D(pool_size=2),\n",
    "    layers.Flatten(),\n",
    "    layers.Dense(128, activation=\"relu\"),\n",
    "    layers.Dense(10, activation=\"softmax\")\n",
    "])\n",
    "\n",
    "base_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_hGLF0IeItrj"
   },
   "source": [
    "### Compile the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rB3FZusjwGJk"
   },
   "outputs": [],
   "source": [
    "# compile the model with loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy']\n",
    "base_model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_hGLF0IeItrj"
   },
   "source": [
    "### Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VM5sHFb6wpEf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "   6/1875 [..............................] - ETA: 20s - loss: 2.0892 - accuracy: 0.2969 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-11 14:47:02.092187: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - ETA: 0s - loss: 0.3497 - accuracy: 0.8750"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-11 14:47:22.957847: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 23s 12ms/step - loss: 0.3497 - accuracy: 0.8750 - val_loss: 0.2639 - val_accuracy: 0.9065\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 22s 12ms/step - loss: 0.2139 - accuracy: 0.9219 - val_loss: 0.2686 - val_accuracy: 0.8983\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 22s 12ms/step - loss: 0.1599 - accuracy: 0.9410 - val_loss: 0.2270 - val_accuracy: 0.9200\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 22s 12ms/step - loss: 0.1189 - accuracy: 0.9564 - val_loss: 0.2406 - val_accuracy: 0.9231\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 23s 12ms/step - loss: 0.0841 - accuracy: 0.9693 - val_loss: 0.2697 - val_accuracy: 0.9189\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 22s 12ms/step - loss: 0.0604 - accuracy: 0.9785 - val_loss: 0.2845 - val_accuracy: 0.9235\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 23s 12ms/step - loss: 0.0428 - accuracy: 0.9845 - val_loss: 0.3526 - val_accuracy: 0.9229\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 22s 12ms/step - loss: 0.0324 - accuracy: 0.9880 - val_loss: 0.3830 - val_accuracy: 0.9207\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 22s 12ms/step - loss: 0.0281 - accuracy: 0.9901 - val_loss: 0.4348 - val_accuracy: 0.9227\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 22s 12ms/step - loss: 0.0206 - accuracy: 0.9924 - val_loss: 0.4599 - val_accuracy: 0.9230\n"
     ]
    }
   ],
   "source": [
    "# train the CNN model with X_train, y_train, 10 epochs, validation_data=(X_test, y_test)\n",
    "base_history = base_model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the loss. use history.history['loss']\n",
    "plt.plot(base_history.epoch, base_history.history['loss'])\n",
    "\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the base model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 15s 8ms/step - loss: 0.0145 - accuracy: 0.9947\n",
      "Train accuracy : 0.9947\n",
      "313/313 [==============================] - 3s 8ms/step - loss: 0.4599 - accuracy: 0.9230\n",
      "Test accuracy : 0.9230\n"
     ]
    }
   ],
   "source": [
    "# Train and test accuracy. use evaluate()\n",
    "\n",
    "loss, acc = base_model.evaluate(X_train, y_train)\n",
    "print('Train accuracy : %.4f' % acc)\n",
    "loss, acc = base_model.evaluate(X_test, y_test)\n",
    "print('Test accuracy : %.4f' % acc)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9-yYfjONzBjA"
   },
   "source": [
    "### The model with dropout regularization\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Pn1fV8iezTiv"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_6 (Conv2D)           (None, 28, 28, 32)        320       \n",
      "                                                                 \n",
      " conv2d_7 (Conv2D)           (None, 26, 26, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPooling  (None, 13, 13, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten_3 (Flatten)         (None, 10816)             0         \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 128)               1384576   \n",
      "                                                                 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " dropout_1 (Dropout)         (None, 128)               0         \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 10)                1290      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,404,682\n",
      "Trainable params: 1,404,682\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Base CNN model + dropout with 0.5 before the last layer\n",
    "dropout_model = tf.keras.Sequential([\n",
    "    layers.Conv2D(32, (3, 3), padding=\"same\", input_shape = (28, 28, 1), activation=\"relu\"),\n",
    "    layers.Conv2D(64, (3, 3), activation=\"relu\"),\n",
    "    layers.MaxPooling2D(pool_size=2),\n",
    "    layers.Flatten(),\n",
    "    layers.Dense(128, activation=\"relu\"),\n",
    "    layers.Dropout(0.5),  # Dropout layer with a dropout rate of 0.5\n",
    "    layers.Dense(10, activation=\"softmax\")\n",
    "])\n",
    "\n",
    "dropout_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_hGLF0IeItrj"
   },
   "source": [
    "### Compile the dropout model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Pn1fV8iezTiv"
   },
   "outputs": [],
   "source": [
    "# compile the model\n",
    "dropout_model.compile(loss='sparse_categorical_crossentropy',\n",
    "                   optimizer='adam',\n",
    "                   metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_hGLF0IeItrj"
   },
   "source": [
    "### Train the dropout model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0C22AQ_nzZG8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "   5/1875 [..............................] - ETA: 23s - loss: 2.2648 - accuracy: 0.1875 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-11 14:51:05.028288: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1870/1875 [============================>.] - ETA: 0s - loss: 0.4242 - accuracy: 0.8511"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-11 14:51:24.776683: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 22s 12ms/step - loss: 0.4242 - accuracy: 0.8512 - val_loss: 0.2956 - val_accuracy: 0.8932\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 22s 12ms/step - loss: 0.2686 - accuracy: 0.9037 - val_loss: 0.2553 - val_accuracy: 0.9068\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 22s 12ms/step - loss: 0.2182 - accuracy: 0.9206 - val_loss: 0.2382 - val_accuracy: 0.9120\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 22s 12ms/step - loss: 0.1862 - accuracy: 0.9313 - val_loss: 0.2367 - val_accuracy: 0.9138\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 21s 11ms/step - loss: 0.1621 - accuracy: 0.9397 - val_loss: 0.2115 - val_accuracy: 0.9241\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 22s 12ms/step - loss: 0.1372 - accuracy: 0.9494 - val_loss: 0.2450 - val_accuracy: 0.9120\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 22s 12ms/step - loss: 0.1187 - accuracy: 0.9565 - val_loss: 0.2259 - val_accuracy: 0.9246\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 22s 12ms/step - loss: 0.1041 - accuracy: 0.9609 - val_loss: 0.2308 - val_accuracy: 0.9254\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 22s 12ms/step - loss: 0.0953 - accuracy: 0.9650 - val_loss: 0.2441 - val_accuracy: 0.9275\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 22s 12ms/step - loss: 0.0845 - accuracy: 0.9683 - val_loss: 0.2947 - val_accuracy: 0.9244\n"
     ]
    }
   ],
   "source": [
    "# train the dropout model with X_train, y_train, 10 epochs, validation_data=(X_test, y_test)\n",
    "drop_history = dropout_model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the loss. use history.history['loss']\n",
    "plt.plot(drop_history.epoch, drop_history.history['loss'])\n",
    "\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the dropout model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 16s 9ms/step - loss: 0.0464 - accuracy: 0.9838\n",
      "Train accuracy : 0.9838\n",
      "313/313 [==============================] - 3s 8ms/step - loss: 0.2947 - accuracy: 0.9244\n",
      "Test accuracy : 0.9244\n"
     ]
    }
   ],
   "source": [
    "# Train and test accuracy. use evaluate()\n",
    "loss, acc = dropout_model.evaluate(X_train, y_train)\n",
    "print('Train accuracy : %.4f' % acc)\n",
    "loss, acc = dropout_model.evaluate(X_test, y_test)\n",
    "print('Test accuracy : %.4f' % acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hAT_RVIARomJ"
   },
   "source": [
    "### Plotting the learning curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UuzP0HSfyGw8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 9.0)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the train/validation losses for each model\n",
    "plt.plot(base_history.epoch, base_history.history['loss'], color='blue', label='Base CNN Train')\n",
    "plt.plot(base_history.epoch, base_history.history['val_loss'], color='red', label='Base CNN Val')\n",
    "\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.xlim([0,max(base_history.epoch)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UuzP0HSfyGw8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 9.0)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the train/validation losses for each model\n",
    "plt.plot(drop_history.epoch, drop_history.history['loss'], color='blue', label='Dropout CNN Train')\n",
    "plt.plot(drop_history.epoch, drop_history.history['val_loss'], color='red', label='Dropout CNN Val')\n",
    "\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.xlim([0,max(drop_history.epoch)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6zew6fk7zxJZ"
   },
   "source": [
    "# 2. Dealing with missing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2zhxZX-dzxJa"
   },
   "source": [
    "### Sample data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7X1OHsmNzxJb",
    "outputId": "fe01f32c-33a4-469d-b553-0784821e7bf0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A    B     C  label\n",
       "0  1  0.1  10.0      0\n",
       "1  2  0.2  20.0      0\n",
       "2  2  0.1   NaN      1\n",
       "3  3  0.3  20.0      1\n",
       "4  2  0.2   NaN      0"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_csv('sample-missing-data.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A        0\n",
       "B        0\n",
       "C        2\n",
       "label    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if there are missing values. use .isnull() and .sum()\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uoPn3IggzxJg"
   },
   "source": [
    "### Removing a feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "13eXyuKzzxJh",
    "outputId": "979f0239-2d63-469b-c22b-a9b5274f846d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A    B  label\n",
       "0  1  0.1      0\n",
       "1  2  0.2      0\n",
       "2  2  0.1      1\n",
       "3  3  0.3      1\n",
       "4  2  0.2      0"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove feature 'C'. use .drop()\n",
    "df.drop('C', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HmLhPVrHzxJo"
   },
   "source": [
    "### Removing data with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Rf_FOYjrzxJv",
    "outputId": "5bfee26f-d10b-4a76-e405-335fbe98f52a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A    B     C  label\n",
       "0  1  0.1  10.0      0\n",
       "1  2  0.2  20.0      0\n",
       "3  3  0.3  20.0      1"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove rows that contain missing values. use .dropna()\n",
    "df.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yQ_0JdVkzxJ1",
    "outputId": "3e007487-8b0a-4228-baec-6778c490fbc7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A    B  label\n",
       "0  1  0.1      0\n",
       "1  2  0.2      0\n",
       "2  2  0.1      1\n",
       "3  3  0.3      1\n",
       "4  2  0.2      0"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove colomns that contain missing values. use .dropna()\n",
    "df.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "C4-6aXAczxJ8"
   },
   "source": [
    "### Imputing missing values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "f_J280QizxJ9",
    "outputId": "54b91212-bbad-412e-bdac-f050a38a0ce6"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A    B          C  label\n",
       "0  1  0.1  10.000000      0\n",
       "1  2  0.2  20.000000      0\n",
       "2  2  0.1  16.666667      1\n",
       "3  3  0.3  20.000000      1\n",
       "4  2  0.2  16.666667      0"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Impute missing values to mean. use .fillna()\n",
    "df.fillna(df.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "C4-6aXAczxJ8"
   },
   "source": [
    "### Imputing missing values using scikit learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7pduFrOczxKD",
    "outputId": "00e62f54-2afe-460a-aa6e-711e36c0110c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.   0.1 10.   0. ]\n",
      " [ 2.   0.2 20.   0. ]\n",
      " [ 2.   0.1  nan  1. ]\n",
      " [ 3.   0.3 20.   1. ]\n",
      " [ 2.   0.2  nan  0. ]]\n"
     ]
    }
   ],
   "source": [
    "# get X array from df dataframe\n",
    "X = df.values\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZqLp2V5EzxKI",
    "outputId": "02cb5ab7-d3b0-4746-9787-982ead3b4468"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.          0.1        10.          0.        ]\n",
      " [ 2.          0.2        20.          0.        ]\n",
      " [ 2.          0.1        16.66666667  1.        ]\n",
      " [ 3.          0.3        20.          1.        ]\n",
      " [ 2.          0.2        16.66666667  0.        ]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# replace missing values, encoded as np.nan, using the mean value of the columns\n",
    "imr = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
    "imr.fit(X)\n",
    "\n",
    "imputed_X = imr.transform(X)\n",
    "print(imputed_X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CMpbkssKzxKN"
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jj8-CghVzxKO"
   },
   "source": [
    "# 2. Handling categorical feature values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Uf71KC7pzxKP"
   },
   "source": [
    "### Sample data \n",
    "- color : norminal\n",
    "- size : ordinal  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LUdCX2EPzxKR",
    "outputId": "7dabbf07-376c-4591-b0ee-b63ee45a3dad"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>size</th>\n",
       "      <th>price</th>\n",
       "      <th>classlabel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>green</td>\n",
       "      <td>M</td>\n",
       "      <td>10.1</td>\n",
       "      <td>class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>red</td>\n",
       "      <td>L</td>\n",
       "      <td>13.5</td>\n",
       "      <td>class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blue</td>\n",
       "      <td>XL</td>\n",
       "      <td>15.3</td>\n",
       "      <td>class3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>red</td>\n",
       "      <td>M</td>\n",
       "      <td>14.5</td>\n",
       "      <td>class2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   color size  price classlabel\n",
       "0  green    M   10.1     class1\n",
       "1    red    L   13.5     class1\n",
       "2   blue   XL   15.3     class3\n",
       "3    red    M   14.5     class2"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame([['green', 'M', 10.1, 'class1'],\n",
    "                   ['red', 'L', 13.5, 'class1'],\n",
    "                   ['blue', 'XL', 15.3, 'class3'],\n",
    "                   ['red', 'M', 14.5, 'class2']])\n",
    "\n",
    "df.columns = ['color', 'size', 'price', 'classlabel']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdf = df.copy()   # for pandas\n",
    "sdf = df.copy()   # for scikit learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "haM9k4tzzxKX"
   },
   "source": [
    "### Encoding class labels "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HABf1pj9zxKY",
    "outputId": "b6ccda59-e3b7-450f-e645-83f3b4cbccda"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>size</th>\n",
       "      <th>price</th>\n",
       "      <th>classlabel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>green</td>\n",
       "      <td>M</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>red</td>\n",
       "      <td>L</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blue</td>\n",
       "      <td>XL</td>\n",
       "      <td>15.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>red</td>\n",
       "      <td>M</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   color size  price  classlabel\n",
       "0  green    M   10.1           0\n",
       "1    red    L   13.5           0\n",
       "2   blue   XL   15.3           2\n",
       "3    red    M   14.5           1"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Label encoding using pandas. use .map()\n",
    "label_mapping = {'class1': 0, 'class2': 1, 'class3': 2 }\n",
    "\n",
    "pdf['classlabel'] = pdf['classlabel'].map(label_mapping)\n",
    "pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HABf1pj9zxKY",
    "outputId": "b6ccda59-e3b7-450f-e645-83f3b4cbccda"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>size</th>\n",
       "      <th>price</th>\n",
       "      <th>classlabel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>green</td>\n",
       "      <td>M</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>red</td>\n",
       "      <td>L</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blue</td>\n",
       "      <td>XL</td>\n",
       "      <td>15.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>red</td>\n",
       "      <td>M</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   color size  price  classlabel\n",
       "0  green    M   10.1           0\n",
       "1    red    L   13.5           0\n",
       "2   blue   XL   15.3           2\n",
       "3    red    M   14.5           1"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Label encoding using scikit learn. use LabelEncoder\n",
    "enc = LabelEncoder()\n",
    "\n",
    "sdf['classlabel'] = enc.fit_transform(sdf['classlabel'])\n",
    "sdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nYO7JCpUzxKe"
   },
   "source": [
    "### Encoding feature values "
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nYO7JCpUzxKe"
   },
   "source": [
    "### Encoding ordinal features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "o6SX3CwjzxKu",
    "outputId": "25d32332-c8ee-486a-c4e7-4e5a631b4324"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>size</th>\n",
       "      <th>price</th>\n",
       "      <th>classlabel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>green</td>\n",
       "      <td>0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>red</td>\n",
       "      <td>1</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blue</td>\n",
       "      <td>2</td>\n",
       "      <td>15.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>red</td>\n",
       "      <td>0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   color  size  price  classlabel\n",
       "0  green     0   10.1           0\n",
       "1    red     1   13.5           0\n",
       "2   blue     2   15.3           2\n",
       "3    red     0   14.5           1"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# encoding ordinal features using pandas\n",
    "size_mapping = {'M': 0, 'L': 1, 'XL': 2 }\n",
    "\n",
    "pdf['size'] = pdf['size'].map(size_mapping)\n",
    "pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>size</th>\n",
       "      <th>price</th>\n",
       "      <th>classlabel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>green</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>red</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blue</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>red</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   color  size  price  classlabel\n",
       "0  green   0.0   10.1           0\n",
       "1    red   1.0   13.5           0\n",
       "2   blue   2.0   15.3           2\n",
       "3    red   0.0   14.5           1"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "# encoding ordinal features  scikit learn. use OrdinalEncoder\n",
    "enc = OrdinalEncoder(categories=[['M', 'L', 'XL']])\n",
    "\n",
    "sdf[['size']] = enc.fit_transform(sdf[['size']])\n",
    "sdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "W-ThifDYzxKl"
   },
   "source": [
    "### One-hot encoding of nominal features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1zqt7RilzxKm",
    "outputId": "0ad8fb7d-8ddc-4db6-f7d5-114e60bf4cbc"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>price</th>\n",
       "      <th>classlabel</th>\n",
       "      <th>color_blue</th>\n",
       "      <th>color_green</th>\n",
       "      <th>color_red</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>15.3</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   size  price  classlabel  color_blue  color_green  color_red\n",
       "0     0   10.1           0       False         True      False\n",
       "1     1   13.5           0       False        False       True\n",
       "2     2   15.3           2        True        False      False\n",
       "3     0   14.5           1       False        False       True"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one-hot encoding 'color' using pandas. use get_dummies()\n",
    "pd.get_dummies(pdf, columns=['color'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color0</th>\n",
       "      <th>color1</th>\n",
       "      <th>color2</th>\n",
       "      <th>size</th>\n",
       "      <th>price</th>\n",
       "      <th>classlabel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   color0  color1  color2  size  price  classlabel\n",
       "0     0.0     1.0     0.0   0.0   10.1           0\n",
       "1     0.0     0.0     1.0   1.0   13.5           0\n",
       "2     1.0     0.0     0.0   2.0   15.3           2\n",
       "3     0.0     0.0     1.0   0.0   14.5           1"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "# one-hot encoding 'color' using scikit learn. use OneHotEncoder\n",
    "enc = OneHotEncoder(sparse_output=False)\n",
    "\n",
    "color_enc = enc.fit_transform(sdf[['color']])\n",
    "print(color_enc)\n",
    "\n",
    "# remake the data frame. use .concat()\n",
    "sdf_color_enc = pd.DataFrame(color_enc, columns=['color0', 'color1', 'color2'])\n",
    "sdf = pd.concat([sdf_color_enc, sdf[['size', 'price', 'classlabel']]], axis=1)\n",
    "sdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pgRF5C4ozxK9"
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_gbVPRcozxK-"
   },
   "source": [
    "# 3. Transformation of numerical feature values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lY5-sfH9zxK_"
   },
   "source": [
    "### Loading Wine Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1p9HJf8izxK_",
    "outputId": "8f7bf713-2087-4483-a996-bc7b45f074d8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class labels [1 2 3]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class label</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic acid</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity of ash</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Total phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoid phenols</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color intensity</th>\n",
       "      <th>Hue</th>\n",
       "      <th>OD280/OD315 of diluted wines</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Class label  Alcohol  Malic acid   Ash  Alcalinity of ash  Magnesium  \\\n",
       "0            1    14.23        1.71  2.43               15.6        127   \n",
       "1            1    13.20        1.78  2.14               11.2        100   \n",
       "2            1    13.16        2.36  2.67               18.6        101   \n",
       "3            1    14.37        1.95  2.50               16.8        113   \n",
       "4            1    13.24        2.59  2.87               21.0        118   \n",
       "\n",
       "   Total phenols  Flavanoids  Nonflavanoid phenols  Proanthocyanins  \\\n",
       "0           2.80        3.06                  0.28             2.29   \n",
       "1           2.65        2.76                  0.26             1.28   \n",
       "2           2.80        3.24                  0.30             2.81   \n",
       "3           3.85        3.49                  0.24             2.18   \n",
       "4           2.80        2.69                  0.39             1.82   \n",
       "\n",
       "   Color intensity   Hue  OD280/OD315 of diluted wines  Proline  \n",
       "0             5.64  1.04                          3.92     1065  \n",
       "1             4.38  1.05                          3.40     1050  \n",
       "2             5.68  1.03                          3.17     1185  \n",
       "3             7.80  0.86                          3.45     1480  \n",
       "4             4.32  1.04                          2.93      735  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df_wine = pd.read_csv('https://archive.ics.uci.edu/'\n",
    "                      'ml/machine-learning-databases/wine/wine.data',\n",
    "                      header=None)\n",
    "\n",
    "df_wine.columns = ['Class label', 'Alcohol', 'Malic acid', 'Ash',\n",
    "                   'Alcalinity of ash', 'Magnesium', 'Total phenols',\n",
    "                   'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins',\n",
    "                   'Color intensity', 'Hue', 'OD280/OD315 of diluted wines',\n",
    "                   'Proline']\n",
    "\n",
    "print('Class labels', np.unique(df_wine['Class label']))\n",
    "df_wine.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8acmoCcOzxLF"
   },
   "source": [
    "### Get X and y. Splitting data into 70% training, 30% test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iJac7ZrrzxLG"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Get X and y. use .iloc[ , ]. Class label is at column 0\n",
    "X = df_wine.iloc[:, 1:].values\n",
    "y = df_wine.iloc[:, 0].values\n",
    "\n",
    "# Get training and test data. use train_test_split()\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                                    test_size=0.3, random_state=0, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tHNRtp2hzxLI",
    "outputId": "0f1296f7-287f-4ca7-cbad-e8dd8997a4d6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(124, 13)\n",
      "(54, 13)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CfVibSmczxLN"
   },
   "source": [
    "### Normalization\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4KycnXsmzxLO"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# transform training and test data to min 0, max 1. use MinMaxScaler()\n",
    "mms = MinMaxScaler()\n",
    "X_train_norm = mms.fit_transform(X_train)\n",
    "X_test_norm = mms.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eNqIaVMHzxLR",
    "outputId": "e33c1bb4-7815-44b7-eb97-b8b399f34862"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 13.62   4.95   2.35  20.    92.  ]\n",
      " [ 13.76   1.53   2.7   19.5  132.  ]\n",
      " [ 13.73   1.5    2.7   22.5  101.  ]]\n",
      "[[0.64619883 0.83201581 0.4248366  0.46236559 0.27160494]\n",
      " [0.6871345  0.15612648 0.65359477 0.43548387 0.7654321 ]\n",
      " [0.67836257 0.15019763 0.65359477 0.59677419 0.38271605]]\n"
     ]
    }
   ],
   "source": [
    "# check normalized data\n",
    "print(X_train[0:3, 0:5])\n",
    "print(X_train_norm[0:3, 0:5])"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "12Z88fyGzxLT"
   },
   "source": [
    "### Standardization\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UL1aCZwvzxLU"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# transform training and test data to mean 0, variance 1\n",
    "stdsc = StandardScaler()\n",
    "X_train_std = stdsc.fit_transform(X_train)\n",
    "X_test_std = stdsc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "O_vEak3nzxLW",
    "outputId": "555786cc-194d-43d7-e7e0-d3597d9efdca",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 13.62   4.95   2.35  20.    92.  ]\n",
      " [ 13.76   1.53   2.7   19.5  132.  ]\n",
      " [ 13.73   1.5    2.7   22.5  101.  ]]\n",
      "mean[0] = 13.03 \n",
      "\n",
      "[[ 0.71225893  2.22048673 -0.13025864  0.05962872 -0.50432733]\n",
      " [ 0.88229214 -0.70457155  1.17533605 -0.09065504  2.34147876]\n",
      " [ 0.84585645 -0.73022996  1.17533605  0.81104754  0.13597904]]\n",
      "mean[0] = 0.00 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# check standardized data\n",
    "print(X_train[0:3, 0:5])\n",
    "print('mean[0] = %.2f \\n' % X_train[:,0].mean())\n",
    "print(X_train_std[0:3, 0:5])\n",
    "print('mean[0] = %.2f \\n' % X_train_std[:,0].mean())"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Week13_Deep_Learning_and_TensorFlow_2.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}