Regression_HW_Week_3.ipynb

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    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNQ+UgR3hq+RXb9y3z/SGIA",
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      "name": "python3",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/theskyvalker/matchboxdao-mlcourse/blob/main/Regression_HW_Week_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fqJMWIBQpZmV",
        "outputId": "99b52093-6d06-446f-b584-31c480926c2d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  archive (1).zip\n",
            "replace rent_all.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: rent_all.csv            \n"
          ]
        }
      ],
      "source": [
        "#!unzip \"archive (1).zip\""
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Singapore Condo Rental Dataset\n",
        "\n",
        "First, import all needed libraries"
      ],
      "metadata": {
        "id": "DhWCMPg0uieN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline \n",
        "import seaborn as sns\n",
        "sns.set(style=\"white\")\n",
        "from datetime import datetime"
      ],
      "metadata": {
        "id": "9qeX_GiauXoy"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "n1RzKtybuoeu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('rent_all.csv')\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ae6i_j6qugEy",
        "outputId": "88b18daf-8e9f-4514-9dc5-38045045cbdb"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id                                    building              street  \\\n",
              "0   1                          SKYLINE RESIDENCES  TELOK BLANGAH ROAD   \n",
              "1   2                   REFLECTIONS AT KEPPEL BAY     KEPPEL BAY VIEW   \n",
              "2   3                                 ONE SHENTON         SHENTON WAY   \n",
              "3   4                    THE ROCHESTER RESIDENCES     ROCHESTER DRIVE   \n",
              "4   5  THE RESIDENCES AT W SINGAPORE SENTOSA COVE           OCEAN WAY   \n",
              "\n",
              "   district  room  rent  rentk          area  lower  year  month  \n",
              "0         4     3  7300    7.3  1300 to 1400   1300    22     12  \n",
              "1         4     3  5800    5.8  1500 to 1600   1500    22     12  \n",
              "2         1     2  7300    7.3  1100 to 1200   1100    22     12  \n",
              "3         5     2  6500    6.5  1300 to 1400   1300    22     12  \n",
              "4         4     2  7000    7.0  1200 to 1300   1200    22     12  "
            ],
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              "      <th>id</th>\n",
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              "      <th>room</th>\n",
              "      <th>rent</th>\n",
              "      <th>rentk</th>\n",
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              "      <th>month</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>SKYLINE RESIDENCES</td>\n",
              "      <td>TELOK BLANGAH ROAD</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>7300</td>\n",
              "      <td>7.3</td>\n",
              "      <td>1300 to 1400</td>\n",
              "      <td>1300</td>\n",
              "      <td>22</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>REFLECTIONS AT KEPPEL BAY</td>\n",
              "      <td>KEPPEL BAY VIEW</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>5800</td>\n",
              "      <td>5.8</td>\n",
              "      <td>1500 to 1600</td>\n",
              "      <td>1500</td>\n",
              "      <td>22</td>\n",
              "      <td>12</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>ONE SHENTON</td>\n",
              "      <td>SHENTON WAY</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>7300</td>\n",
              "      <td>7.3</td>\n",
              "      <td>1100 to 1200</td>\n",
              "      <td>1100</td>\n",
              "      <td>22</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>THE ROCHESTER RESIDENCES</td>\n",
              "      <td>ROCHESTER DRIVE</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>6500</td>\n",
              "      <td>6.5</td>\n",
              "      <td>1300 to 1400</td>\n",
              "      <td>1300</td>\n",
              "      <td>22</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>THE RESIDENCES AT W SINGAPORE SENTOSA COVE</td>\n",
              "      <td>OCEAN WAY</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>7000</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1200 to 1300</td>\n",
              "      <td>1200</td>\n",
              "      <td>22</td>\n",
              "      <td>12</td>\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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        "# We can get a quick overview of all the numeric columns of our data with this\n",
        "\n",
        "data.describe()"
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        "colab": {
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        "outputId": "a86f28b2-a8ac-48fe-dc24-665544ca8870"
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      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  id       district           room           rent  \\\n",
              "count  432191.000000  432191.000000  432191.000000  432191.000000   \n",
              "mean   216096.000000      13.005363       6.376861    3764.759535   \n",
              "std    124762.939433       6.470458      13.410442    2324.058875   \n",
              "min         1.000000       1.000000       0.000000     307.000000   \n",
              "25%    108048.500000       9.000000       2.000000    2450.000000   \n",
              "50%    216096.000000      13.000000       2.000000    3100.000000   \n",
              "75%    324143.500000      18.000000       3.000000    4250.000000   \n",
              "max    432191.000000      28.000000      50.000000  170000.000000   \n",
              "\n",
              "               rentk          lower           year          month  \n",
              "count  432191.000000  432191.000000  432191.000000  432191.000000  \n",
              "mean        3.764760    1030.388648      20.029779       6.576759  \n",
              "std         2.324059     539.619998       1.393993       3.374170  \n",
              "min         0.307000     100.000000      18.000000       1.000000  \n",
              "25%         2.450000     600.000000      19.000000       4.000000  \n",
              "50%         3.100000     900.000000      20.000000       7.000000  \n",
              "75%         4.250000    1300.000000      21.000000       9.000000  \n",
              "max       170.000000    3000.000000      22.000000      12.000000  "
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              "      <th>count</th>\n",
              "      <td>432191.000000</td>\n",
              "      <td>432191.000000</td>\n",
              "      <td>432191.000000</td>\n",
              "      <td>432191.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>216096.000000</td>\n",
              "      <td>13.005363</td>\n",
              "      <td>6.376861</td>\n",
              "      <td>3764.759535</td>\n",
              "      <td>3.764760</td>\n",
              "      <td>1030.388648</td>\n",
              "      <td>20.029779</td>\n",
              "      <td>6.576759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>124762.939433</td>\n",
              "      <td>6.470458</td>\n",
              "      <td>13.410442</td>\n",
              "      <td>2324.058875</td>\n",
              "      <td>2.324059</td>\n",
              "      <td>539.619998</td>\n",
              "      <td>1.393993</td>\n",
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              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>307.000000</td>\n",
              "      <td>0.307000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>108048.500000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2450.000000</td>\n",
              "      <td>2.450000</td>\n",
              "      <td>600.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>4.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>216096.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3100.000000</td>\n",
              "      <td>3.100000</td>\n",
              "      <td>900.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>7.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>324143.500000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4250.000000</td>\n",
              "      <td>4.250000</td>\n",
              "      <td>1300.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>9.000000</td>\n",
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              "      <th>max</th>\n",
              "      <td>432191.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>170000.000000</td>\n",
              "      <td>170.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>22.000000</td>\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-934a33dc-e66e-461a-91c1-31afbbc0c6bc button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-934a33dc-e66e-461a-91c1-31afbbc0c6bc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# group rent data by year and district to check the pattern year on year\n",
        "# we expect this to be an ascending pattern overall\n",
        "\n",
        "grouped_data = data.groupby(['year', 'district']).mean()['rent'].unstack()\n",
        "\n",
        "# plot the data\n",
        "grouped_data.plot(kind='line', figsize=(12, 8))\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Average Rent')\n",
        "plt.title('Average Rent by District by Year')\n",
        "plt.legend(loc='best')\n",
        "plt.xticks(range(18, 23))\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "myEXPNmrA9BH",
        "outputId": "3d8cdd75-792a-4e93-e369-68a71a3a8486"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute the correlation matrix using the current features\n",
        "corr = data.corr()\n",
        "\n",
        "# Draw the correlation matrix\n",
        "sns.heatmap(corr, square=True, linewidths=.4)\n",
        "\n",
        "plt.title(\"Rent Correlation\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "-B7nRxJiCDhz",
        "outputId": "069ecd6a-9c3e-4f01-e4cf-5f0bf45a5d53"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Version 1: Baseline\n",
        "\n",
        "The easiest model to do would be to take just the numeric features from the dataset which includes the disctrict as a measure of the locality data"
      ],
      "metadata": {
        "id": "fY1puRqi3Eyq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check how many have room=50 (This represents missing or N/A room data)\n",
        "\n",
        "print(\"No. of data points with N/A room data {}\".format(\n",
        "    len(data[data['room'] == 50])))\n",
        "\n",
        "# what is the % of these data points from our total data\n",
        "\n",
        "print(\"% of datapoints with N/A room data {}\".format(\n",
        "    len(data[data['room'] == 50])/len(data) * 100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MD3qZZ91vXba",
        "outputId": "4501a875-8451-4ac2-a20f-5b1fe5ef37ad"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of data points with N/A room data 37171\n",
            "% of datapoints with N/A room data 8.600595570014184\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We can remove these data points for now since this is less than 10% of our\n",
        "# data\n",
        "\n",
        "data = data.drop(data[data['room'] == 50].index)\n",
        "print(\"Data points left after removing room=N/A data: {}\".format(len(data)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XLlBFWaXvZBA",
        "outputId": "03627248-751e-4242-c670-976627325e8e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data points left after removing room=N/A data: 395020\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# separate the data into train and test sets - we use years 2018-2021 as train\n",
        "# and try to predict the rentals for 2022\n",
        "train_data = data.loc[data['year'] != 22]\n",
        "test_data = data.loc[data['year'] == 22]\n",
        "\n",
        "# print the total number of data points in the train and test sets\n",
        "print(f\"Number of data points in train set: {len(train_data)}\")\n",
        "print(f\"Number of data points in test set: {len(test_data)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Vph9om8Et56",
        "outputId": "b2f7a514-4b62-4a1e-ae8c-f615b1fa1390"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of data points in train set: 318522\n",
            "Number of data points in test set: 76498\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_absolute_error, r2_score\n",
        "\n",
        "# extract the features and target variables\n",
        "X_train = train_data[['year', 'district', 'room', 'lower']]\n",
        "y_train = train_data['rent']\n",
        "X_test = test_data[['year', 'district', 'room', 'lower']]\n",
        "y_test = test_data['rent']\n",
        "\n",
        "# create a linear regression model and fit it to the training data\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# predict the target variable on the test data\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# evaluate the model on the test data and print the metrics\n",
        "mae = mean_absolute_error(y_test, y_pred)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "print(f\"MAE: {mae:.3f}\")\n",
        "print(f\"R2-score: {r2:.3f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BxAVpSP5FxdK",
        "outputId": "4fc0e232-fbb0-4277-824c-c53490eb6ff4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 1171.286\n",
            "R2-score: 0.475\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Results of the Baseline Model\n",
        "\n",
        "Our baseline model has an MAE of 1171 meaning that any prediction made by the model can be off by +- 1171 and we can try to do better\n",
        "\n",
        "# Next Steps\n",
        "\n",
        "Let's try to do some feature engineering to try and improve the result"
      ],
      "metadata": {
        "id": "1AP0gWErHjPW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# psf -> per square feet -> rent normalized by the area of the place\n",
        "data['psf'] = data['rent'] / data['lower']\n",
        "\n",
        "# district_psf_cummean -> \n",
        "district_psf_cummean = data.groupby(['district', 'year'])['psf'].mean().groupby('district').cumsum().reset_index()\n",
        "district_psf_cummean.rename(columns={'psf': 'district_mean_psf'}, inplace=True)\n",
        "data = data.merge(district_psf_cummean, on=['district', 'year'])"
      ],
      "metadata": {
        "id": "JhUvCwjUGUY6"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "1Cpz2FZpLWwt",
        "outputId": "fa97737e-4231-47f0-b365-f1b610783dc5"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  id       district           room           rent  \\\n",
              "count  395020.000000  395020.000000  395020.000000  395020.000000   \n",
              "mean   215617.820252      12.990342       2.271966    3804.981373   \n",
              "std    125278.213608       6.489465       0.918521    2324.811249   \n",
              "min         1.000000       1.000000       0.000000     307.000000   \n",
              "25%    106870.750000       9.000000       2.000000    2500.000000   \n",
              "50%    213415.500000      13.000000       2.000000    3150.000000   \n",
              "75%    325556.250000      18.000000       3.000000    4300.000000   \n",
              "max    432191.000000      28.000000       8.000000  170000.000000   \n",
              "\n",
              "               rentk          lower           year          month  \\\n",
              "count  395020.000000  395020.000000  395020.000000  395020.000000   \n",
              "mean        3.804981    1027.826692      20.025247       6.581525   \n",
              "std         2.324811     532.637635       1.391645       3.376423   \n",
              "min         0.307000     100.000000      18.000000       1.000000   \n",
              "25%         2.500000     600.000000      19.000000       4.000000   \n",
              "50%         3.150000     900.000000      20.000000       7.000000   \n",
              "75%         4.300000    1200.000000      21.000000       9.000000   \n",
              "max       170.000000    3000.000000      22.000000      12.000000   \n",
              "\n",
              "                 psf  district_mean_psf  \n",
              "count  395020.000000      395020.000000  \n",
              "mean        3.965569          11.585343  \n",
              "std         1.437632           6.121037  \n",
              "min         0.500000           2.335456  \n",
              "25%         2.900000           6.602611  \n",
              "50%         3.714286          11.023135  \n",
              "75%         4.750000          15.868243  \n",
              "max        56.666667          42.574763  "
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>district</th>\n",
              "      <th>room</th>\n",
              "      <th>rent</th>\n",
              "      <th>rentk</th>\n",
              "      <th>lower</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>psf</th>\n",
              "      <th>district_mean_psf</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>395020.000000</td>\n",
              "      <td>395020.000000</td>\n",
              "      <td>395020.000000</td>\n",
              "      <td>395020.000000</td>\n",
              "      <td>395020.000000</td>\n",
              "      <td>395020.000000</td>\n",
              "      <td>395020.000000</td>\n",
              "      <td>395020.000000</td>\n",
              "      <td>395020.000000</td>\n",
              "      <td>395020.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>215617.820252</td>\n",
              "      <td>12.990342</td>\n",
              "      <td>2.271966</td>\n",
              "      <td>3804.981373</td>\n",
              "      <td>3.804981</td>\n",
              "      <td>1027.826692</td>\n",
              "      <td>20.025247</td>\n",
              "      <td>6.581525</td>\n",
              "      <td>3.965569</td>\n",
              "      <td>11.585343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>125278.213608</td>\n",
              "      <td>6.489465</td>\n",
              "      <td>0.918521</td>\n",
              "      <td>2324.811249</td>\n",
              "      <td>2.324811</td>\n",
              "      <td>532.637635</td>\n",
              "      <td>1.391645</td>\n",
              "      <td>3.376423</td>\n",
              "      <td>1.437632</td>\n",
              "      <td>6.121037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>307.000000</td>\n",
              "      <td>0.307000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>2.335456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>106870.750000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2500.000000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>600.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>6.602611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>213415.500000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3150.000000</td>\n",
              "      <td>3.150000</td>\n",
              "      <td>900.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>3.714286</td>\n",
              "      <td>11.023135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>325556.250000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4300.000000</td>\n",
              "      <td>4.300000</td>\n",
              "      <td>1200.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>4.750000</td>\n",
              "      <td>15.868243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>432191.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>170000.000000</td>\n",
              "      <td>170.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>56.666667</td>\n",
              "      <td>42.574763</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# we do a new split with the new data\n",
        "train_data = data.loc[data['year'] != 22]\n",
        "test_data = data.loc[data['year'] == 22]\n",
        "\n",
        "# extract the features and target variables\n",
        "X_train = train_data[['year', 'district_mean_psf', 'room', 'lower']]\n",
        "y_train = train_data['rent']\n",
        "X_test = test_data[['year', 'district_mean_psf', 'room', 'lower']]\n",
        "y_test = test_data['rent']\n",
        "\n",
        "# create a linear regression model and fit it to the training data\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# predict the target variable on the test data\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# evaluate the model on the test data and print the metrics\n",
        "mae = mean_absolute_error(y_test, y_pred)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "print(f\"MAE: {mae:.3f}\")\n",
        "print(f\"R2-score: {r2:.3f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xVOVr92xLsfT",
        "outputId": "88f81576-e433-4356-c2b0-77cee753f482"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 1137.176\n",
            "R2-score: 0.519\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We see that we have some improvement. However, we can try to also encode the building and street information in a similar way by taking the per-square-foot pricing at a building and street level into consideration as well in addition to the psf by district"
      ],
      "metadata": {
        "id": "mLdi6YUIBNm8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "building_psf_cummean = data.groupby(['building', 'year'])['psf'].mean().groupby('building').cumsum().reset_index()\n",
        "building_psf_cummean.rename(columns={'psf': 'building_mean_psf'}, inplace=True)\n",
        "data = data.merge(building_psf_cummean, on=['building', 'year'])\n",
        "\n",
        "street_psf_cummean = data.groupby(['street', 'year'])['psf'].mean().groupby('street').cumsum().reset_index()\n",
        "street_psf_cummean.rename(columns={'psf': 'street_mean_psf'}, inplace=True)\n",
        "data = data.merge(street_psf_cummean, on=['street', 'year'])"
      ],
      "metadata": {
        "id": "Qf1TmmoOBKb3"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# we do a new split with the new data\n",
        "train_data = data.loc[data['year'] != 22]\n",
        "test_data = data.loc[data['year'] == 22]\n",
        "\n",
        "# extract the features and target variables\n",
        "X_train = train_data[['year', 'district_mean_psf', 'street_mean_psf', 'building_mean_psf', 'room', 'lower']]\n",
        "y_train = train_data['rent']\n",
        "X_test = test_data[['year', 'district_mean_psf', 'street_mean_psf', 'building_mean_psf', 'room', 'lower']]\n",
        "y_test = test_data['rent']\n",
        "\n",
        "# create a linear regression model and fit it to the training data\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# predict the target variable on the test data\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# evaluate the model on the test data and print the metrics\n",
        "mae = mean_absolute_error(y_test, y_pred)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "print(f\"MAE: {mae:.3f}\")\n",
        "print(f\"R2-score: {r2:.3f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2kKVPQJbB6aH",
        "outputId": "6f4ec9a1-72e3-4b33-8ee9-2f53dc257665"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 1155.579\n",
            "R2-score: 0.563\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We see that we lose MAE but the R2-score improves. We can try other regression techniques and see if a non-linear method can improve the model further as opposed to linear regression."
      ],
      "metadata": {
        "id": "BrxI0RqWDDIh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Lasso, Ridge, ElasticNet\n",
        "from sklearn import neighbors, tree\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "# initialize different regression models\n",
        "models = {\n",
        "    'Lasso Regression': Lasso(),\n",
        "    'Ridge Regression': Ridge(),\n",
        "    'ElasticNet Regression': ElasticNet(),\n",
        "    'KNN': neighbors.KNeighborsRegressor(10),\n",
        "    'Decision Tree': tree.DecisionTreeRegressor(),\n",
        "    'Random Forest': RandomForestRegressor()\n",
        "}\n",
        "\n",
        "# train and evaluate each model\n",
        "for name, model in models.items():\n",
        "    model.fit(X_train, y_train)\n",
        "    y_pred = model.predict(X_test)\n",
        "    mae = mean_absolute_error(y_test, y_pred)\n",
        "    r2 = r2_score(y_test, y_pred)\n",
        "    print(f\"{name}: MAE = {mae:.2f}, R2 Score = {r2:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IzAVFsnkDSIv",
        "outputId": "beb1cb95-405a-48b5-c936-c6d2ce2735b3"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Lasso Regression: MAE = 1153.67, R2 Score = 0.56\n",
            "Ridge Regression: MAE = 1155.58, R2 Score = 0.56\n",
            "ElasticNet Regression: MAE = 991.27, R2 Score = 0.61\n",
            "KNN: MAE = 860.66, R2 Score = 0.75\n",
            "Decision Tree: MAE = 1022.85, R2 Score = 0.52\n",
            "Random Forest: MAE = 839.41, R2 Score = 0.74\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We see that KNN and random forest techniques provide a better model, but there is still scope for improvement. External features may help improve this further but what we can do without that is to try polynomial features.\n",
        "\n",
        "We should also apply a scaling technique on the features which we can do using the standard scaling method since it matters for a subset of our selected approaches even if it doesn't matter to the tree based approaches."
      ],
      "metadata": {
        "id": "1qbK9mnBEXMk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures, StandardScaler\n",
        "\n",
        "# add polynomial features\n",
        "poly = PolynomialFeatures(degree=2, include_bias=True)\n",
        "X_train_poly = poly.fit_transform(X_train)\n",
        "X_test_poly = poly.transform(X_test)\n",
        "\n",
        "# concatenate original and polynomial features\n",
        "X_train_all = pd.DataFrame(X_train_poly, columns=poly.get_feature_names_out(X_train.columns))\n",
        "X_test_all = pd.DataFrame(X_test_poly, columns=poly.get_feature_names_out(X_test.columns))\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train_all)\n",
        "\n",
        "# assume 'X_test' is the test data\n",
        "X_test_scaled = scaler.transform(X_test_all)\n",
        "\n",
        "# train and evaluate each model\n",
        "for name, model in models.items():\n",
        "    model.fit(X_train_scaled, y_train)\n",
        "    y_pred = model.predict(X_test_scaled)\n",
        "    mae = mean_absolute_error(y_test, y_pred)\n",
        "    r2 = r2_score(y_test, y_pred)\n",
        "    print(f\"{name}: MAE = {mae:.2f}, R2 Score = {r2:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lLM3Ob7ZET_D",
        "outputId": "adffbb08-f42e-493e-b7b3-3564f27c5f3b"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.121e+10, tolerance: 1.473e+08\n",
            "  model = cd_fast.enet_coordinate_descent(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Lasso Regression: MAE = 1128.53, R2 Score = 0.66\n",
            "Ridge Regression: MAE = 1035.58, R2 Score = 0.69\n",
            "ElasticNet Regression: MAE = 850.49, R2 Score = 0.72\n",
            "KNN: MAE = 913.82, R2 Score = 0.72\n",
            "Decision Tree: MAE = 1006.39, R2 Score = 0.45\n",
            "Random Forest: MAE = 821.04, R2 Score = 0.73\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We see that KNN without polynomial features remains our best model so far and perhaps we don't have enough data to be able to make use of the additionally generated features. Let's take a look at how our predicted rental prices differ from the actual rental prices."
      ],
      "metadata": {
        "id": "EDs8EuDlOyFc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "KNN_model = neighbors.KNeighborsRegressor(10)\n",
        "KNN_model.fit(X_train, y_train)\n",
        "y_pred = KNN_model.predict(X_test)"
      ],
      "metadata": {
        "id": "54p0WwYvE07P"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(y_test.values, label='Actual')\n",
        "plt.plot(y_pred, label='Predicted')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "vjbTYSSvPcgt",
        "outputId": "96f655b3-3820-4e8e-b88e-30dd2a9b22bc"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VhpHWwSfZe_1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SmpPgCBRZYaV"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}