project_1_level_2o.ipynb

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        "#**MEHWISH ZAMAN (DATA ANALYST)**\n",
        "AS INTERN AT OASIS INFOBYTE"
      ],
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        "# **Idea: Predicting House Prices with Linear Regression**\n",
        "\n",
        "# DESCRIPTION:\n",
        "The objective of this project is to build a predictive model using linear regression to estimate a\n",
        "numerical outcome based on a dataset with relevant features. Linear regression is a\n",
        "fundamental machine learning algorithm, and this project provides hands-on experience in\n",
        "developing, evaluating, and interpreting a predictive model.\n",
        "\n",
        "\n",
        "# **Key Concepts and Challenges:**\n",
        "\n",
        "1. Data Collection: Obtain a dataset with numerical features and a target variable for prediction.\n",
        "\n",
        "2. Data Exploration and Cleaning: Explore the dataset to understand its structure, handle missing values, and ensure data quality.\n",
        "\n",
        "3. Feature Selection: Identify relevant features that may contribute to the predictive model.\n",
        "\n",
        "4. Model Training: Implement linear regression using a machine learning library (e.g., Scikit-\n",
        "Learn).\n",
        "\n",
        "5. Model Evaluation: Evaluate the model's performance on a separate test dataset using metrics such as Mean Squared Error or R-squared.\n",
        "\n",
        "6. Visualization: Create visualizations to illustrate the relationship between the predicted and actual values."
      ],
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      "source": [
        "# IMPORTING LIBRARIES.\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "from sklearn.metrics import mean_squared_error, r2_score"
      ]
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      "source": [
        "# LOADING DATA.\n",
        "housing = pd.DataFrame(pd.read_csv(\"/content/level_2_project_1.zip\"))"
      ],
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      "source": [
        "# DATA EXPLORATION.\n",
        "housing.head()"
      ],
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              "      price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
              "0  13300000  7420         4          2        3      yes        no       no   \n",
              "1  12250000  8960         4          4        4      yes        no       no   \n",
              "2  12250000  9960         3          2        2      yes        no      yes   \n",
              "3  12215000  7500         4          2        2      yes        no      yes   \n",
              "4  11410000  7420         4          1        2      yes       yes      yes   \n",
              "\n",
              "  hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
              "0              no             yes        2      yes        furnished  \n",
              "1              no             yes        3       no        furnished  \n",
              "2              no              no        2      yes   semi-furnished  \n",
              "3              no             yes        3      yes        furnished  \n",
              "4              no             yes        2       no        furnished  "
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              "(545, 13)"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 545 entries, 0 to 544\n",
            "Data columns (total 13 columns):\n",
            " #   Column            Non-Null Count  Dtype \n",
            "---  ------            --------------  ----- \n",
            " 0   price             545 non-null    int64 \n",
            " 1   area              545 non-null    int64 \n",
            " 2   bedrooms          545 non-null    int64 \n",
            " 3   bathrooms         545 non-null    int64 \n",
            " 4   stories           545 non-null    int64 \n",
            " 5   mainroad          545 non-null    object\n",
            " 6   guestroom         545 non-null    object\n",
            " 7   basement          545 non-null    object\n",
            " 8   hotwaterheating   545 non-null    object\n",
            " 9   airconditioning   545 non-null    object\n",
            " 10  parking           545 non-null    int64 \n",
            " 11  prefarea          545 non-null    object\n",
            " 12  furnishingstatus  545 non-null    object\n",
            "dtypes: int64(6), object(7)\n",
            "memory usage: 55.5+ KB\n"
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      "source": [
        "# describing dataset.\n",
        "housing.describe()"
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              "              price          area    bedrooms   bathrooms     stories  \\\n",
              "count  5.450000e+02    545.000000  545.000000  545.000000  545.000000   \n",
              "mean   4.766729e+06   5150.541284    2.965138    1.286239    1.805505   \n",
              "std    1.870440e+06   2170.141023    0.738064    0.502470    0.867492   \n",
              "min    1.750000e+06   1650.000000    1.000000    1.000000    1.000000   \n",
              "25%    3.430000e+06   3600.000000    2.000000    1.000000    1.000000   \n",
              "50%    4.340000e+06   4600.000000    3.000000    1.000000    2.000000   \n",
              "75%    5.740000e+06   6360.000000    3.000000    2.000000    2.000000   \n",
              "max    1.330000e+07  16200.000000    6.000000    4.000000    4.000000   \n",
              "\n",
              "          parking  \n",
              "count  545.000000  \n",
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              "std      0.861586  \n",
              "min      0.000000  \n",
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              "    .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>price</th>\n",
              "      <th>area</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>bathrooms</th>\n",
              "      <th>stories</th>\n",
              "      <th>parking</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5.450000e+02</td>\n",
              "      <td>545.000000</td>\n",
              "      <td>545.000000</td>\n",
              "      <td>545.000000</td>\n",
              "      <td>545.000000</td>\n",
              "      <td>545.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.766729e+06</td>\n",
              "      <td>5150.541284</td>\n",
              "      <td>2.965138</td>\n",
              "      <td>1.286239</td>\n",
              "      <td>1.805505</td>\n",
              "      <td>0.693578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.870440e+06</td>\n",
              "      <td>2170.141023</td>\n",
              "      <td>0.738064</td>\n",
              "      <td>0.502470</td>\n",
              "      <td>0.867492</td>\n",
              "      <td>0.861586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.750000e+06</td>\n",
              "      <td>1650.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.430000e+06</td>\n",
              "      <td>3600.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.340000e+06</td>\n",
              "      <td>4600.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5.740000e+06</td>\n",
              "      <td>6360.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.330000e+07</td>\n",
              "      <td>16200.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cf088795-cead-4e37-ba83-8c252ca8e725')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cf088795-cead-4e37-ba83-8c252ca8e725 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-cf088795-cead-4e37-ba83-8c252ca8e725');\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",
              "\n",
              "\n",
              "<div id=\"df-930e78bc-7a20-4f3a-b4a3-66846ee9513f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-930e78bc-7a20-4f3a-b4a3-66846ee9513f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-930e78bc-7a20-4f3a-b4a3-66846ee9513f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking unique values for each variable.\n",
        "unique_values = housing.nunique()\n",
        "unique_values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wb9lhjbasfhH",
        "outputId": "645a3b63-c139-487f-e5f6-29a23673a1b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price               219\n",
              "area                284\n",
              "bedrooms              6\n",
              "bathrooms             4\n",
              "stories               4\n",
              "mainroad              2\n",
              "guestroom             2\n",
              "basement              2\n",
              "hotwaterheating       2\n",
              "airconditioning       2\n",
              "parking               4\n",
              "prefarea              2\n",
              "furnishingstatus      3\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking Null values\n",
        "housing.isnull().sum()*100/housing.shape[0]\n",
        "# There are no NULL values in the dataset, hence it is clean."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8uP-dW4U_YEZ",
        "outputId": "58883f4b-e657-463e-f763-6b1e4c142fd2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price               0.0\n",
              "area                0.0\n",
              "bedrooms            0.0\n",
              "bathrooms           0.0\n",
              "stories             0.0\n",
              "mainroad            0.0\n",
              "guestroom           0.0\n",
              "basement            0.0\n",
              "hotwaterheating     0.0\n",
              "airconditioning     0.0\n",
              "parking             0.0\n",
              "prefarea            0.0\n",
              "furnishingstatus    0.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualizing numerical variables.\n",
        "sns.pairplot(housing)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 951
        },
        "id": "gyK0UATpp-9l",
        "outputId": "acdb9d57-d2e1-4ac2-e3ce-8d9b750bfb49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 42 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a scatter plot of price vs area\n",
        "housing.plot.scatter(x='price', y='area')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "ukw9eH8D4M6G",
        "outputId": "8fa5a5f4-7afa-45fc-8f9b-389b8bf252f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='price', ylabel='area'>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a scatter plot of area vs price\n",
        "import altair as alt\n",
        "chart = alt.Chart(housing).mark_point().encode(\n",
        "    x='area',\n",
        "    y='price'\n",
        ").interactive()\n",
        "chart\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "SYiTP-P34VZW",
        "outputId": "ee05032a-1d78-4e16-e70c-cfdac7567bad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "<div id=\"altair-viz-8c53916832994706b29d3620596e4f63\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
              "  (function(spec, embedOpt){\n",
              "    let outputDiv = document.currentScript.previousElementSibling;\n",
              "    if (outputDiv.id !== \"altair-viz-8c53916832994706b29d3620596e4f63\") {\n",
              "      outputDiv = document.getElementById(\"altair-viz-8c53916832994706b29d3620596e4f63\");\n",
              "    }\n",
              "    const paths = {\n",
              "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
              "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
              "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
              "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
              "    };\n",
              "\n",
              "    function maybeLoadScript(lib, version) {\n",
              "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
              "      return (VEGA_DEBUG[key] == version) ?\n",
              "        Promise.resolve(paths[lib]) :\n",
              "        new Promise(function(resolve, reject) {\n",
              "          var s = document.createElement('script');\n",
              "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
              "          s.async = true;\n",
              "          s.onload = () => {\n",
              "            VEGA_DEBUG[key] = version;\n",
              "            return resolve(paths[lib]);\n",
              "          };\n",
              "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
              "          s.src = paths[lib];\n",
              "        });\n",
              "    }\n",
              "\n",
              "    function showError(err) {\n",
              "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
              "      throw err;\n",
              "    }\n",
              "\n",
              "    function displayChart(vegaEmbed) {\n",
              "      vegaEmbed(outputDiv, spec, embedOpt)\n",
              "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
              "    }\n",
              "\n",
              "    if(typeof define === \"function\" && define.amd) {\n",
              "      requirejs.config({paths});\n",
              "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
              "    } else {\n",
              "      maybeLoadScript(\"vega\", \"5\")\n",
              "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
              "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
              "        .catch(showError)\n",
              "        .then(() => displayChart(vegaEmbed));\n",
              "    }\n",
              "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-275c1909d757c2f679490eb080c1e7c7\"}, \"mark\": \"point\", \"encoding\": {\"x\": {\"field\": \"area\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"price\", \"type\": \"quantitative\"}}, \"selection\": {\"selector001\": {\"type\": \"interval\", \"bind\": \"scales\", \"encodings\": [\"x\", \"y\"]}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-275c1909d757c2f679490eb080c1e7c7\": [{\"price\": 13300000, \"area\": 7420, \"bedrooms\": 4, \"bathrooms\": 2, \"stories\": 3, \"mainroad\": \"yes\", \"guestroom\": \"no\", \"basement\": \"no\", \"hotwaterheating\": \"no\", \"airconditioning\": \"yes\", \"parking\": 2, \"prefarea\": \"yes\", \"furnishingstatus\": \"furnished\", \"main_guest\": \"yesno\"}, {\"price\": 12250000, \"area\": 8960, \"bedrooms\": 4, \"bathrooms\": 4, \"stories\": 4, \"mainroad\": \"yes\", 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\"unfurnished\", \"main_guest\": \"yesno\"}, {\"price\": 1767150, \"area\": 2400, \"bedrooms\": 3, \"bathrooms\": 1, \"stories\": 1, \"mainroad\": \"no\", \"guestroom\": \"no\", \"basement\": \"no\", \"hotwaterheating\": \"no\", \"airconditioning\": \"no\", \"parking\": 0, \"prefarea\": \"no\", \"furnishingstatus\": \"semi-furnished\", \"main_guest\": \"nono\"}, {\"price\": 1750000, \"area\": 3620, \"bedrooms\": 2, \"bathrooms\": 1, \"stories\": 1, \"mainroad\": \"yes\", \"guestroom\": \"no\", \"basement\": \"no\", \"hotwaterheating\": \"no\", \"airconditioning\": \"no\", \"parking\": 0, \"prefarea\": \"no\", \"furnishingstatus\": \"unfurnished\", \"main_guest\": \"yesno\"}, {\"price\": 1750000, \"area\": 2910, \"bedrooms\": 3, \"bathrooms\": 1, \"stories\": 1, \"mainroad\": \"no\", \"guestroom\": \"no\", \"basement\": \"no\", \"hotwaterheating\": \"no\", \"airconditioning\": \"no\", \"parking\": 0, \"prefarea\": \"no\", \"furnishingstatus\": \"furnished\", \"main_guest\": \"nono\"}, {\"price\": 1750000, \"area\": 3850, \"bedrooms\": 3, \"bathrooms\": 1, \"stories\": 2, \"mainroad\": \"yes\", \"guestroom\": \"no\", \"basement\": \"no\", \"hotwaterheating\": \"no\", \"airconditioning\": \"no\", \"parking\": 0, \"prefarea\": \"no\", \"furnishingstatus\": \"unfurnished\", \"main_guest\": \"yesno\"}]}}, {\"mode\": \"vega-lite\"});\n",
              "</script>"
            ],
            "text/plain": [
              "alt.Chart(...)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a pivot table with bedrooms as rows and bathrooms as columns\n",
        "pivot_table = housing.pivot_table(values='price', index='bedrooms', columns='bathrooms')\n",
        "\n",
        "# Print the pivot table\n",
        "print(pivot_table)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yhpeSggn4hBl",
        "outputId": "ebdee26b-1732-4932-e98a-49d79fde4607"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "bathrooms             1             2          3           4\n",
            "bedrooms                                                    \n",
            "1          2.712500e+06           NaN        NaN         NaN\n",
            "2          3.596852e+06  4.194750e+06        NaN         NaN\n",
            "3          4.463484e+06  6.379569e+06  6807500.0         NaN\n",
            "4          4.710583e+06  6.358990e+06  7250250.0  12250000.0\n",
            "5          4.996250e+06  5.405750e+06  8295000.0         NaN\n",
            "6          3.500000e+06  6.083000e+06        NaN         NaN\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "housing.groupby(['furnishingstatus', 'prefarea']).mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "MoHE91qf4odO",
        "outputId": "79f44e98-c9b3-4ecf-f1fb-8fb62851cd72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-29-8cbe8a0a40c4>:1: FutureWarning:\n",
            "\n",
            "The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  price         area  bedrooms  bathrooms  \\\n",
              "furnishingstatus prefarea                                                   \n",
              "furnished        no        5.121072e+06  5321.979381  3.020619   1.371134   \n",
              "                 yes       6.340779e+06  6514.000000  3.162791   1.395349   \n",
              "semi-furnished   no        4.614918e+06  4911.977143  2.994286   1.297143   \n",
              "                 yes       5.892257e+06  6022.365385  3.057692   1.326923   \n",
              "unfurnished      no        3.731000e+06  4512.717241  2.800000   1.165517   \n",
              "                 yes       5.256576e+06  5563.878788  2.969697   1.303030   \n",
              "\n",
              "                            stories   parking  \n",
              "furnishingstatus prefarea                      \n",
              "furnished        no        1.896907  0.824742  \n",
              "                 yes       2.046512  1.023256  \n",
              "semi-furnished   no        1.840000  0.748571  \n",
              "                 yes       1.673077  0.692308  \n",
              "unfurnished      no        1.641379  0.413793  \n",
              "                 yes       1.969697  0.818182  "
            ],
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              "    <div>\n",
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              "      <th>price</th>\n",
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              "      <th rowspan=\"2\" valign=\"top\">furnished</th>\n",
              "      <th>no</th>\n",
              "      <td>5.121072e+06</td>\n",
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              "      <td>3.731000e+06</td>\n",
              "      <td>4512.717241</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.165517</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7c393310-bf7c-4e5f-8881-d76479283ca6 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-7c393310-bf7c-4e5f-8881-d76479283ca6');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e74179a8-195c-4a80-8052-860b30665301')\"\n",
              "            title=\"Suggest charts\"\n",
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              "\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e74179a8-195c-4a80-8052-860b30665301 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# Creating train/test split\n",
        "X = housing[['area', 'bedrooms', 'bathrooms', 'stories', 'parking']]\n",
        "y = housing['price']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Creating model\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Making predictions\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Evaluating model performance\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "rmse = np.sqrt(mse)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "print(f\"Mean Squared Error: {mse}\")\n",
        "print(f\"Root Mean Squared Error: {rmse}\")\n",
        "print(f\"R-squared (R2) Score: {r2}\")\n",
        "\n",
        "# Create a DataFrame for visualization\n",
        "df= pd.DataFrame({'True Values': y_test, 'Predictions': y_pred})\n",
        "\n",
        "# Create a scatter plot using Plotly Express\n",
        "fig = px.scatter(df, x='True Values', y='Predictions', title='True vs. Predicted Values')\n",
        "fig.update_traces(marker=dict(size=10, opacity=0.5, line=dict(width=2, color='black')))\n",
        "\n",
        "# Add hover data (x and y coordinates)\n",
        "fig.update_traces(customdata=df.index)\n",
        "\n",
        "# Customize the hover tooltip\n",
        "fig.update_traces(hovertemplate='True Value: %{x}<br>Predicted Value: %{y}<br>Sample Index: %{customdata}')\n",
        "\n",
        "# Add a trendline (optional)\n",
        "fig.add_trace(go.Scatter(x=df['True Values'], y=df['True Values'], mode='lines', name='Trendline'))\n",
        "\n",
        "# Customize the layout\n",
        "fig.update_layout(\n",
        "    xaxis_title=\"True Values\",\n",
        "    yaxis_title=\"Predictions\"\n",
        ")\n",
        "\n",
        "# Show the interactive plot in the Jupyter Notebook\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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            "Mean Squared Error: 2292721545725.3623\n",
            "Root Mean Squared Error: 1514173.5520492233\n",
            "R-squared (R2) Score: 0.5464062355495871\n"
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