ML_Project(Predicting_House_Prices_with_Regression) .ipynb

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      "name": "ML Project(Predicting House Prices with Regression).ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "# Task 1: Introduction\n",
        "\n",
        "---\n",
        "\n",
        "For this project, we are going to work on evaluating price of houses given the following features:\n",
        "\n",
        "1. Year of sale of the house\n",
        "2. The age of the house at the time of sale\n",
        "3. Distance from city center\n",
        "4. Number of stores in the locality\n",
        "5. The latitude\n",
        "6. The longitude\n",
        "\n",
        "\n",
        "Note: This notebook uses `python 3` and these packages: `tensorflow`, `pandas`, `matplotlib`, `scikit-learn`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RLCQLLvMPNNp",
        "colab_type": "text"
      },
      "source": [
        "## 1.1: Importing Libraries & Helper Functions\n",
        "\n",
        "First of all, we will need to import some libraries and helper functions. This includes TensorFlow and some utility functions that I've written to save time."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IWug-067PNNq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "cd0f5631-ed42-42d3-f1fa-c3b0d163b434"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "from tensorflow.keras.callbacks import EarlyStopping, LambdaCallback\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline\n",
        "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)\n",
        "\n",
        "print('Libraries imported.')"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Libraries imported.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IliNxds7PNNw",
        "colab_type": "text"
      },
      "source": [
        "# Task 2: Importing the Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j1PT0g7pPNNx",
        "colab_type": "text"
      },
      "source": [
        "## 2.1: Importing the Data\n",
        "\n",
        "The dataset is saved in a `data.csv` file. We will use `pandas` to take a look at some of the rows."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XoH8BerNPNNx",
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          "height": 202
        },
        "outputId": "62518e18-7b12-418d-b804-fbb0193144d4"
      },
      "source": [
        "df = pd.read_csv('data.csv', names= ['serial','date','age','distance','stores','latitude','longitude','price']) \n",
        "df.head()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>serial</th>\n",
              "      <th>date</th>\n",
              "      <th>age</th>\n",
              "      <th>distance</th>\n",
              "      <th>stores</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2009</td>\n",
              "      <td>21</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>84</td>\n",
              "      <td>121</td>\n",
              "      <td>14264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2007</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>86</td>\n",
              "      <td>121</td>\n",
              "      <td>12032</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2016</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>90</td>\n",
              "      <td>120</td>\n",
              "      <td>13560</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2002</td>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>80</td>\n",
              "      <td>128</td>\n",
              "      <td>12029</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2014</td>\n",
              "      <td>25</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>81</td>\n",
              "      <td>122</td>\n",
              "      <td>14157</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   serial  date  age  distance  stores  latitude  longitude  price\n",
              "0       0  2009   21         9       6        84        121  14264\n",
              "1       1  2007    4         2       3        86        121  12032\n",
              "2       2  2016   18         3       7        90        120  13560\n",
              "3       3  2002   13         2       2        80        128  12029\n",
              "4       4  2014   25         5       8        81        122  14157"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2tAndfwUPNN0",
        "colab_type": "text"
      },
      "source": [
        "## 2.2: Check Missing Data\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4kT2tkgIPNN1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "0fe5319f-6a1a-41e2-f07d-a0b0db7ffdf3"
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "serial       0\n",
              "date         0\n",
              "age          0\n",
              "distance     0\n",
              "stores       0\n",
              "latitude     0\n",
              "longitude    0\n",
              "price        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Byj-SFsjPNN3",
        "colab_type": "text"
      },
      "source": [
        "# Task 3: Data Normalization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RfP0M0uCPNN3",
        "colab_type": "text"
      },
      "source": [
        "## 3.1: Data Normalization\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tjl1ZqmnPNN4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "e814c134-14ac-4651-b283-dd975c578ccf"
      },
      "source": [
        "df = df.iloc[:,1:]\n",
        "df_norm = (df - df.mean()) / df.std()\n",
        "df_norm.head()"
      ],
      "execution_count": 41,
      "outputs": [
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          "output_type": "execute_result",
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              "      <th>stores</th>\n",
              "      <th>latitude</th>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.932135</td>\n",
              "      <td>0.534444</td>\n",
              "      <td>0.006938</td>\n",
              "      <td>0.981581</td>\n",
              "      <td>-1.255981</td>\n",
              "      <td>-0.945141</td>\n",
              "      <td>0.245266</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       date       age  distance    stores  latitude  longitude     price\n",
              "0  0.015978  0.181384  1.257002  0.345224 -0.307212  -1.260799  0.350088\n",
              "1 -0.350485 -1.319118 -0.930610 -0.609312  0.325301  -1.260799 -1.836486\n",
              "2  1.298598 -0.083410 -0.618094  0.663402  1.590328  -1.576456 -0.339584\n",
              "3 -1.266643 -0.524735 -0.930610 -0.927491 -1.572238   0.948803 -1.839425\n",
              "4  0.932135  0.534444  0.006938  0.981581 -1.255981  -0.945141  0.245266"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n3XREqAUPNN6",
        "colab_type": "text"
      },
      "source": [
        "## 3.2: Convert Label Value\n",
        "\n",
        "Because we are using normalized values for the labels, we will get the predictions back from a trained model in the same distribution. So, we need to convert the predicted values back to the original distribution if we want predicted prices.We write down a function here so that we can use this function to get back to original Label Value using this function. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QxBY4CLYPNN7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_mean = df['price'].mean()\n",
        "y_std = df['price'].std()\n",
        "\n",
        "def convert_label_value(pred):\n",
        "    return int(pred * y_std + y_mean)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XCElBqzbPNN9",
        "colab_type": "text"
      },
      "source": [
        "# Task 4: Create Training and Test Sets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fGF5sYgWPNN9",
        "colab_type": "text"
      },
      "source": [
        "## 4.1: Select Features\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hxp-_vwoPNN_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "0a87a8e0-50d5-4a87-c649-bfb379ebf0dd"
      },
      "source": [
        "X = df_norm.iloc[:, :6]\n",
        "X.head()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>age</th>\n",
              "      <th>distance</th>\n",
              "      <th>stores</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.015978</td>\n",
              "      <td>0.181384</td>\n",
              "      <td>1.257002</td>\n",
              "      <td>0.345224</td>\n",
              "      <td>-0.307212</td>\n",
              "      <td>-1.260799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.350485</td>\n",
              "      <td>-1.319118</td>\n",
              "      <td>-0.930610</td>\n",
              "      <td>-0.609312</td>\n",
              "      <td>0.325301</td>\n",
              "      <td>-1.260799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.298598</td>\n",
              "      <td>-0.083410</td>\n",
              "      <td>-0.618094</td>\n",
              "      <td>0.663402</td>\n",
              "      <td>1.590328</td>\n",
              "      <td>-1.576456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-1.266643</td>\n",
              "      <td>-0.524735</td>\n",
              "      <td>-0.930610</td>\n",
              "      <td>-0.927491</td>\n",
              "      <td>-1.572238</td>\n",
              "      <td>0.948803</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.932135</td>\n",
              "      <td>0.534444</td>\n",
              "      <td>0.006938</td>\n",
              "      <td>0.981581</td>\n",
              "      <td>-1.255981</td>\n",
              "      <td>-0.945141</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       date       age  distance    stores  latitude  longitude\n",
              "0  0.015978  0.181384  1.257002  0.345224 -0.307212  -1.260799\n",
              "1 -0.350485 -1.319118 -0.930610 -0.609312  0.325301  -1.260799\n",
              "2  1.298598 -0.083410 -0.618094  0.663402  1.590328  -1.576456\n",
              "3 -1.266643 -0.524735 -0.930610 -0.927491 -1.572238   0.948803\n",
              "4  0.932135  0.534444  0.006938  0.981581 -1.255981  -0.945141"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kxM9NKWtPNOC",
        "colab_type": "text"
      },
      "source": [
        "## 4.2: Select Labels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GgvA6zFePNOC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "9a53f69c-7f49-4aa5-c5c0-a402612c9ea1"
      },
      "source": [
        "Y = df_norm.iloc[:, -1]\n",
        "Y.head()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.350088\n",
              "1   -1.836486\n",
              "2   -0.339584\n",
              "3   -1.839425\n",
              "4    0.245266\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bDPaF912PNOF",
        "colab_type": "text"
      },
      "source": [
        "## 4.3: Feature and Label Values\n",
        "\n",
        "We will need to extract just the numeric values for the features and labels as the TensorFlow model will expect just numeric values as input."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lt9CrjUrPNOF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "12c9b233-0aba-4035-b192-67b9ca83e74b"
      },
      "source": [
        "X_arr = X.values\n",
        "Y_arr = Y.values\n",
        "\n",
        "print('X_arr shape: ', X_arr.shape)\n",
        "print('Y_arr shape: ', Y_arr.shape)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_arr shape:  (5000, 6)\n",
            "Y_arr shape:  (5000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HRmLgj1qPNOJ",
        "colab_type": "text"
      },
      "source": [
        "## 4.4: Train and Test Split\n",
        "\n",
        "We will keep some part of the data aside as a __test__ set. The model will not use this set during training and it will be used only for checking the performance of the model in trained and un-trained states. This way, we can make sure that we are going in the right direction with our model training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "osWDowTxPNOJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "e1732ec6-aee6-4044-aa28-fb4fd0681a1a"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_arr, Y_arr, test_size = 0.05, shuffle = True, random_state=0)\n",
        "\n",
        "print('X_train shape: ', X_train.shape)\n",
        "print('y_train shape: ', y_train.shape)\n",
        "print('X_test shape: ', X_test.shape)\n",
        "print('y_test shape: ', y_test.shape)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train shape:  (4750, 6)\n",
            "y_train shape:  (4750,)\n",
            "X_test shape:  (250, 6)\n",
            "y_test shape:  (250,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t_ZAuRwrPNOM",
        "colab_type": "text"
      },
      "source": [
        "# Task 5: Create the Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J7aub965PNOM",
        "colab_type": "text"
      },
      "source": [
        "## 5.1: Create the Model\n",
        "\n",
        "Now writing a function that returns an untrained model with three hidden layers which are densely connected."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4sgJc_i0PNON",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "e95effb4-91e2-47f9-f7c8-64f9e23582b0"
      },
      "source": [
        "def get_model():\n",
        "    \n",
        "    model = Sequential([\n",
        "        Dense(10, input_shape = (6,), activation = 'relu'),\n",
        "        Dense(20, activation = 'relu'),\n",
        "        Dense(5, activation = 'relu'),\n",
        "        Dense(1) \n",
        "    ])\n",
        "    #relu = Rectified Linear Activation Unit\n",
        "    model.compile(\n",
        "        loss='mse',\n",
        "        optimizer='adam',\n",
        "        metrics=[accuracy]    \n",
        "        )\n",
        "    #mse -> mean square error\n",
        "    \n",
        "    return model\n",
        "\n",
        "model = get_model()\n",
        "model.summary()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_19\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_76 (Dense)             (None, 10)                70        \n",
            "_________________________________________________________________\n",
            "dense_77 (Dense)             (None, 20)                220       \n",
            "_________________________________________________________________\n",
            "dense_78 (Dense)             (None, 5)                 105       \n",
            "_________________________________________________________________\n",
            "dense_79 (Dense)             (None, 1)                 6         \n",
            "=================================================================\n",
            "Total params: 401\n",
            "Trainable params: 401\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z9z87rlxPNOP",
        "colab_type": "text"
      },
      "source": [
        "# Task 6: Model Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1EhHbNtzPNOP",
        "colab_type": "text"
      },
      "source": [
        "## 6.1: Model Training\n",
        "\n",
        "We are using an `EarlyStopping` callback from Keras to stop the model training if the validation loss stops decreasing for a few epochs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fg2jjMDmPNOQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "outputId": "1925c701-8b40-4ba8-9962-fbbb3efe0262"
      },
      "source": [
        "early_stopping = EarlyStopping(monitor='val_loss', patience = 5)\n",
        "\n",
        "model = get_model()\n",
        "\n",
        "preds_on_untrained = model.predict(X_test)\n",
        "\n",
        "\n",
        "history = model.fit(\n",
        "    X_train, y_train,\n",
        "    validation_data = (X_test, y_test),\n",
        "    epochs = 100,\n",
        "    callbacks = [early_stopping]\n",
        ")\n",
        "\n",
        "\n",
        "#print(history.history['accuracy'])"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "149/149 [==============================] - 0s 2ms/step - loss: 0.6894 - soft_acc: 0.4499 - val_loss: 0.3943 - val_soft_acc: 0.5556\n",
            "Epoch 2/100\n",
            "149/149 [==============================] - 0s 1ms/step - loss: 0.3436 - soft_acc: 0.5612 - val_loss: 0.2510 - val_soft_acc: 0.5956\n",
            "Epoch 3/100\n",
            "149/149 [==============================] - 0s 1ms/step - loss: 0.2496 - soft_acc: 0.6052 - val_loss: 0.1989 - val_soft_acc: 0.6364\n",
            "Epoch 4/100\n",
            "149/149 [==============================] - 0s 1ms/step - loss: 0.2047 - soft_acc: 0.6330 - val_loss: 0.1737 - val_soft_acc: 0.6812\n",
            "Epoch 5/100\n",
            "149/149 [==============================] - 0s 1ms/step - loss: 0.1852 - soft_acc: 0.6444 - val_loss: 0.1665 - val_soft_acc: 0.6899\n",
            "Epoch 6/100\n",
            "149/149 [==============================] - 0s 1ms/step - loss: 0.1751 - soft_acc: 0.6503 - val_loss: 0.1619 - val_soft_acc: 0.6782\n",
            "Epoch 7/100\n",
            "149/149 [==============================] - 0s 1ms/step - loss: 0.1698 - soft_acc: 0.6543 - val_loss: 0.1578 - val_soft_acc: 0.6821\n",
            "Epoch 8/100\n",
            "149/149 [==============================] - 0s 1ms/step - loss: 0.1663 - soft_acc: 0.6548 - val_loss: 0.1529 - val_soft_acc: 0.6899\n",
            "Epoch 9/100\n",
            "149/149 [==============================] - 0s 1ms/step - loss: 0.1636 - soft_acc: 0.6588 - val_loss: 0.1555 - val_soft_acc: 0.6977\n",
            "Epoch 10/100\n",
            "149/149 [==============================] - 0s 1ms/step - loss: 0.1626 - soft_acc: 0.6550 - val_loss: 0.1491 - val_soft_acc: 0.6986\n",
            "Epoch 11/100\n",
            "149/149 [==============================] - 0s 1ms/step - loss: 0.1604 - soft_acc: 0.6598 - val_loss: 0.1485 - val_soft_acc: 0.7025\n",
            "Epoch 12/100\n",
            "149/149 [==============================] - 0s 1ms/step - loss: 0.1594 - soft_acc: 0.6628 - val_loss: 0.1520 - val_soft_acc: 0.6899\n",
            "Epoch 13/100\n",
            "149/149 [==============================] - 0s 1ms/step - loss: 0.1585 - soft_acc: 0.6598 - val_loss: 0.1505 - val_soft_acc: 0.6791\n",
            "Epoch 14/100\n",
            "149/149 [==============================] - 0s 1ms/step - loss: 0.1576 - soft_acc: 0.6648 - val_loss: 0.1467 - val_soft_acc: 0.6947\n",
            "Epoch 15/100\n",
            "149/149 [==============================] - 0s 1ms/step - loss: 0.1570 - soft_acc: 0.6678 - val_loss: 0.1458 - val_soft_acc: 0.7151\n",
            "Epoch 16/100\n",
            "149/149 [==============================] - 0s 1ms/step - loss: 0.1557 - soft_acc: 0.6625 - val_loss: 0.1491 - val_soft_acc: 0.6830\n",
            "Epoch 17/100\n",
            "149/149 [==============================] - 0s 1ms/step - loss: 0.1564 - soft_acc: 0.6653 - val_loss: 0.1469 - val_soft_acc: 0.7073\n",
            "Epoch 18/100\n",
            "149/149 [==============================] - 0s 1ms/step - loss: 0.1555 - soft_acc: 0.6664 - val_loss: 0.1474 - val_soft_acc: 0.6947\n",
            "Epoch 19/100\n",
            "149/149 [==============================] - 0s 1ms/step - loss: 0.1553 - soft_acc: 0.6634 - val_loss: 0.1465 - val_soft_acc: 0.6986\n",
            "Epoch 20/100\n",
            "149/149 [==============================] - 0s 1ms/step - loss: 0.1542 - soft_acc: 0.6699 - val_loss: 0.1469 - val_soft_acc: 0.6956\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z3h00rSIPNOS",
        "colab_type": "text"
      },
      "source": [
        "## 6.2: Plot Training Loss and Validation Loss\n",
        "\n",
        "Now, we will  use the `plot_loss`  function (written on  `utils.py` file) to take a look training and validation loss."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f12IlR9OPNOT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "961d8035-0edf-40da-96a3-8860b5dd4a57"
      },
      "source": [
        "def plot_loss(history):\n",
        "    h = history.history\n",
        "    x_lim = len(h['loss'])\n",
        "    plt.figure(figsize=(8, 8))\n",
        "    plt.plot(range(x_lim), h['val_loss'], label = 'Validation Loss')\n",
        "    plt.plot(range(x_lim), h['loss'], label = 'Training Loss')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "    return\n",
        "    \n",
        "plot_loss(history)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1McmhzQiPNOV",
        "colab_type": "text"
      },
      "source": [
        "# Task 7: Predictions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6FXW92k6jgqM",
        "colab_type": "text"
      },
      "source": [
        "Now, at first we are wrtting a function which `compare_predictions` helper function to compare predictions from the model when it was untrained and when it was trained."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZyBZqy6TjcRr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def compare_predictions(preds1, preds2, y_test):\n",
        "    plt.figure(figsize=(8, 8))\n",
        "    plt.plot(preds1, y_test, 'ro', label='Untrained Model')\n",
        "    plt.plot(preds2, y_test, 'go', label='Trained Model')\n",
        "    plt.xlabel('Preds')\n",
        "    plt.ylabel('Labels')\n",
        "    \n",
        "    y_min = min(min(y_test), min(preds1), min(preds2))\n",
        "    y_max = max(max(y_test), max(preds1), max(preds2))\n",
        "    \n",
        "    plt.xlim([y_min, y_max])\n",
        "    plt.ylim([y_min, y_max])\n",
        "    plt.plot([y_min, y_max], [y_min, y_max], 'b--')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "    return"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kt899jl_PNOW",
        "colab_type": "text"
      },
      "source": [
        "## 7.1: Plot Raw Predictions\n",
        "\n",
        "\n",
        "And than using this function we will show the comparison."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4j1Im8jUPNOW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "5d0a052c-5c83-4842-d06a-582f91890c5f"
      },
      "source": [
        "preds_on_trained = model.predict(X_test)\n",
        "\n",
        "compare_predictions(preds_on_untrained, preds_on_trained, y_test)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ALfgii2PNOY",
        "colab_type": "text"
      },
      "source": [
        "## 7.2: Plot Original Price Predictions\n",
        "\n",
        "The plot for price predictions and raw predictions will look the same with just one difference: The x and y axis scale is changed. Here we will first convert the label values( y value) to its original form using the `convert_label_values` function written on `section 3.2`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K6SLCTH6PNOY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "aa087a73-851f-4727-cab3-8b1ff806c9db"
      },
      "source": [
        "price_on_untrained = [convert_label_value(y) for y in preds_on_untrained]\n",
        "price_on_trained = [convert_label_value(y) for y in preds_on_trained]\n",
        "price_y_test = [convert_label_value(y) for y in y_test]\n",
        "\n",
        "\n",
        "\n",
        "compare_predictions(price_on_untrained, price_on_trained, price_y_test)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZGdAHKtokuj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "f5eb35b4-d4a5-48c8-dd50-da0bada25721"
      },
      "source": [
        "import numpy as np\n",
        "from sklearn import metrics\n",
        "y_pred = np.array(preds_on_trained)\n",
        "y_true = np.array(price_y_test)\n",
        "\n",
        "\n",
        "print(\"Mean Absolute Error (MAE): \",metrics.mean_absolute_error(y_true, y_pred))\n",
        "print(\"Mean Squared Error (MSE): \",metrics.mean_squared_error(y_true, y_pred))\n",
        "print(\"Root Mean Squared Error (RMSE): \",np.sqrt(metrics.mean_squared_error(y_true, y_pred)))"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error (MAE):  13945.525791986227\n",
            "Mean Squared Error (MSE):  195376690.70965487\n",
            "Root Mean Squared Error (RMSE):  13977.721227355154\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}