House_Price_Prediction.ipynb

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      "cell_type": "markdown",
      "source": [
        "##Importing the dataset"
      ],
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      }
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      "cell_type": "code",
      "source": [
        "#imported the dataset\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import sklearn.datasets\n",
        "from sklearn.model_selection import train_test_split\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn import metrics\n"
      ],
      "metadata": {
        "id": "rd0kwxrc9g3u"
      },
      "execution_count": 426,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "house_price=sklearn.datasets.fetch_california_housing()"
      ],
      "metadata": {
        "id": "-Ja1T5OVZMTl"
      },
      "execution_count": 427,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#name_of_the_dataset.function_name(imput_arguments)."
      ],
      "metadata": {
        "id": "MEValEkH9RHW"
      },
      "execution_count": 428,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(house_price)"
      ],
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      },
      "execution_count": 429,
      "outputs": [
        {
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          "text": [
            "{'data': array([[   8.3252    ,   41.        ,    6.98412698, ...,    2.55555556,\n",
            "          37.88      , -122.23      ],\n",
            "       [   8.3014    ,   21.        ,    6.23813708, ...,    2.10984183,\n",
            "          37.86      , -122.22      ],\n",
            "       [   7.2574    ,   52.        ,    8.28813559, ...,    2.80225989,\n",
            "          37.85      , -122.24      ],\n",
            "       ...,\n",
            "       [   1.7       ,   17.        ,    5.20554273, ...,    2.3256351 ,\n",
            "          39.43      , -121.22      ],\n",
            "       [   1.8672    ,   18.        ,    5.32951289, ...,    2.12320917,\n",
            "          39.43      , -121.32      ],\n",
            "       [   2.3886    ,   16.        ,    5.25471698, ...,    2.61698113,\n",
            "          39.37      , -121.24      ]]), 'target': array([4.526, 3.585, 3.521, ..., 0.923, 0.847, 0.894]), 'frame': None, 'target_names': ['MedHouseVal'], 'feature_names': ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude'], 'DESCR': '.. _california_housing_dataset:\\n\\nCalifornia Housing dataset\\n--------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 20640\\n\\n    :Number of Attributes: 8 numeric, predictive attributes and the target\\n\\n    :Attribute Information:\\n        - MedInc        median income in block group\\n        - HouseAge      median house age in block group\\n        - AveRooms      average number of rooms per household\\n        - AveBedrms     average number of bedrooms per household\\n        - Population    block group population\\n        - AveOccup      average number of household members\\n        - Latitude      block group latitude\\n        - Longitude     block group longitude\\n\\n    :Missing Attribute Values: None\\n\\nThis dataset was obtained from the StatLib repository.\\nhttps://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html\\n\\nThe target variable is the median house value for California districts,\\nexpressed in hundreds of thousands of dollars ($100,000).\\n\\nThis dataset was derived from the 1990 U.S. census, using one row per census\\nblock group. A block group is the smallest geographical unit for which the U.S.\\nCensus Bureau publishes sample data (a block group typically has a population\\nof 600 to 3,000 people).\\n\\nA household is a group of people residing within a home. Since the average\\nnumber of rooms and bedrooms in this dataset are provided per household, these\\ncolumns may take surprisingly large values for block groups with few households\\nand many empty houses, such as vacation resorts.\\n\\nIt can be downloaded/loaded using the\\n:func:`sklearn.datasets.fetch_california_housing` function.\\n\\n.. topic:: References\\n\\n    - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\\n      Statistics and Probability Letters, 33 (1997) 291-297\\n'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "house_price_df=pd.DataFrame(house_price.data, columns=house_price.feature_names) #created a datframe by the help of pandas"
      ],
      "metadata": {
        "id": "-Myu18Dbu8c2"
      },
      "execution_count": 430,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "house_price_df.head() # top values of the dataset"
      ],
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              "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
              "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
              "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
              "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
              "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
              "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
              "\n",
              "   Longitude  \n",
              "0    -122.23  \n",
              "1    -122.22  \n",
              "2    -122.24  \n",
              "3    -122.25  \n",
              "4    -122.25  "
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          },
          "metadata": {},
          "execution_count": 431
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#how to include string/category variable in the code"
      ],
      "metadata": {
        "id": "A8FslBBa9vDh"
      },
      "execution_count": 432,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "house_price_df.describe(include='all')  #to see the all features of the dataset"
      ],
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          "height": 300
        },
        "id": "FTWP3kkQ9yjJ",
        "outputId": "5ff59fd8-88b9-4b69-f197-fdbf096f42d9"
      },
      "execution_count": 433,
      "outputs": [
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          "data": {
            "text/plain": [
              "             MedInc      HouseAge      AveRooms     AveBedrms    Population  \\\n",
              "count  20640.000000  20640.000000  20640.000000  20640.000000  20640.000000   \n",
              "mean       3.870671     28.639486      5.429000      1.096675   1425.476744   \n",
              "std        1.899822     12.585558      2.474173      0.473911   1132.462122   \n",
              "min        0.499900      1.000000      0.846154      0.333333      3.000000   \n",
              "25%        2.563400     18.000000      4.440716      1.006079    787.000000   \n",
              "50%        3.534800     29.000000      5.229129      1.048780   1166.000000   \n",
              "75%        4.743250     37.000000      6.052381      1.099526   1725.000000   \n",
              "max       15.000100     52.000000    141.909091     34.066667  35682.000000   \n",
              "\n",
              "           AveOccup      Latitude     Longitude  \n",
              "count  20640.000000  20640.000000  20640.000000  \n",
              "mean       3.070655     35.631861   -119.569704  \n",
              "std       10.386050      2.135952      2.003532  \n",
              "min        0.692308     32.540000   -124.350000  \n",
              "25%        2.429741     33.930000   -121.800000  \n",
              "50%        2.818116     34.260000   -118.490000  \n",
              "75%        3.282261     37.710000   -118.010000  \n",
              "max     1243.333333     41.950000   -114.310000  "
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              "      <th></th>\n",
              "      <th>MedInc</th>\n",
              "      <th>HouseAge</th>\n",
              "      <th>AveRooms</th>\n",
              "      <th>AveBedrms</th>\n",
              "      <th>Population</th>\n",
              "      <th>AveOccup</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.870671</td>\n",
              "      <td>28.639486</td>\n",
              "      <td>5.429000</td>\n",
              "      <td>1.096675</td>\n",
              "      <td>1425.476744</td>\n",
              "      <td>3.070655</td>\n",
              "      <td>35.631861</td>\n",
              "      <td>-119.569704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.899822</td>\n",
              "      <td>12.585558</td>\n",
              "      <td>2.474173</td>\n",
              "      <td>0.473911</td>\n",
              "      <td>1132.462122</td>\n",
              "      <td>10.386050</td>\n",
              "      <td>2.135952</td>\n",
              "      <td>2.003532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.499900</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.846154</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.692308</td>\n",
              "      <td>32.540000</td>\n",
              "      <td>-124.350000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.563400</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>4.440716</td>\n",
              "      <td>1.006079</td>\n",
              "      <td>787.000000</td>\n",
              "      <td>2.429741</td>\n",
              "      <td>33.930000</td>\n",
              "      <td>-121.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.534800</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>5.229129</td>\n",
              "      <td>1.048780</td>\n",
              "      <td>1166.000000</td>\n",
              "      <td>2.818116</td>\n",
              "      <td>34.260000</td>\n",
              "      <td>-118.490000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.743250</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>6.052381</td>\n",
              "      <td>1.099526</td>\n",
              "      <td>1725.000000</td>\n",
              "      <td>3.282261</td>\n",
              "      <td>37.710000</td>\n",
              "      <td>-118.010000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>15.000100</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>141.909091</td>\n",
              "      <td>34.066667</td>\n",
              "      <td>35682.000000</td>\n",
              "      <td>1243.333333</td>\n",
              "      <td>41.950000</td>\n",
              "      <td>-114.310000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
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              "    .colab-df-convert:hover {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    fill: var(--fill-color);\n",
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              "    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",
              "      border-right-color: var(--fill-color);\n",
              "    }\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-d1fecb20-6452-48bb-86fd-a8a865f22efc 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": 433
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "house_price_df.shape #shape row * column of the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4aZ2XvdlwfD0",
        "outputId": "024fc85a-6a60-4ed1-dba7-a6b38a904d90"
      },
      "execution_count": 434,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20640, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 434
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "house_price_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KuIE0MsY9yfP",
        "outputId": "f37dbe1a-e3ec-45d2-a566-2ee456120762"
      },
      "execution_count": 435,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 20640 entries, 0 to 20639\n",
            "Data columns (total 8 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   MedInc      20640 non-null  float64\n",
            " 1   HouseAge    20640 non-null  float64\n",
            " 2   AveRooms    20640 non-null  float64\n",
            " 3   AveBedrms   20640 non-null  float64\n",
            " 4   Population  20640 non-null  float64\n",
            " 5   AveOccup    20640 non-null  float64\n",
            " 6   Latitude    20640 non-null  float64\n",
            " 7   Longitude   20640 non-null  float64\n",
            "dtypes: float64(8)\n",
            "memory usage: 1.3 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Float are continous variable.\n",
        "##This means For example: 2   Sale Price- 21609 non-null  float64 21613-21609=4 values are not availabe in ROWS\n",
        "#9 ROWS values are not available\n",
        "#How to find descriptive statistics for the single variable"
      ],
      "metadata": {
        "id": "t9tTgBBg9ycp"
      },
      "execution_count": 436,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "house_price_df['MedInc'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5PdMQoyh9yaL",
        "outputId": "cd4bb0f4-a669-44ba-e24d-3d5ae24de7bb"
      },
      "execution_count": 437,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.8706710029069766"
            ]
          },
          "metadata": {},
          "execution_count": 437
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "house_price_df['MedInc'].std() #standard deviation of the MedInc column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gll1jkqu9yXX",
        "outputId": "068eee23-180b-4392-c0e7-42b06a85bac7"
      },
      "execution_count": 438,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.8998217179452688"
            ]
          },
          "metadata": {},
          "execution_count": 438
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "house_price_df['MedInc'].min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O0jv2q2A9yUx",
        "outputId": "27dcbf9a-1bbb-4ef1-bdfb-c4433cf027e3"
      },
      "execution_count": 439,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4999"
            ]
          },
          "metadata": {},
          "execution_count": 439
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "house_price_df['MedInc'].quantile(.25)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZHnF7V2z9ySV",
        "outputId": "118ace4f-4ebd-4a1c-98f2-f9ce93854853"
      },
      "execution_count": 440,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.5633999999999997"
            ]
          },
          "metadata": {},
          "execution_count": 440
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#unique function for the string variable"
      ],
      "metadata": {
        "id": "JCLOYsWy9yPy"
      },
      "execution_count": 441,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "house_price_df[\"MedInc\"].unique()"
      ],
      "metadata": {
        "id": "uvJtXmYD9yNU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3bb1d8d-06e5-4678-d31b-848b8fd70d4e"
      },
      "execution_count": 442,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([8.3252, 8.3014, 7.2574, ..., 2.3598, 2.3661, 2.0943])"
            ]
          },
          "metadata": {},
          "execution_count": 442
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Using the Numpy Library for standard deviation"
      ],
      "metadata": {
        "id": "Z0jRHz7H9yKs"
      },
      "execution_count": 443,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Library_name.inpuut_name(function_parameteres)\n",
        "np.std(house_price_df['MedInc'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CZQxhcCZ9yIB",
        "outputId": "dbc9e5e6-5823-4707-d4cd-62a40b60cf59"
      },
      "execution_count": 444,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.8997756945748738"
            ]
          },
          "metadata": {},
          "execution_count": 444
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Plotting and Graphs(Line, Pie and bar Graph)"
      ],
      "metadata": {
        "id": "vu6z1YPvAdJm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Help to discover certain patern and information"
      ],
      "metadata": {
        "id": "QGG7986__-Nf"
      },
      "execution_count": 445,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "AA95dqRUAhnU"
      },
      "execution_count": 446,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####1.) Line graph"
      ],
      "metadata": {
        "id": "qgdpuG-jAnKU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#x-axis-plt.plot(data_set_name(variable_name))"
      ],
      "metadata": {
        "id": "KJ1F9odOAl1R"
      },
      "execution_count": 447,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#y-axis-plt.plot(data_set_name(variable_name))"
      ],
      "metadata": {
        "id": "1pbUkKmnAktR"
      },
      "execution_count": 448,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(house_price_df['MedInc'], color='red')   #in red color\n",
        "plt.xlabel(\"Population\")\n",
        "plt.ylabel(\"Median Income\")\n",
        "plt.title(\"Graphical Representaion\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "4JmP1glwA3vu",
        "outputId": "912db6db-bb37-4a9a-c479-01aaed23ede7"
      },
      "execution_count": 452,
      "outputs": [
        {
          "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": [
        "# Group the data based on the condition of the house\n",
        "#groupby function for string and categorical value\n",
        "plt.plot(house_price_df['MedInc'],marker='o', markerfacecolor='blue', markersize=5,color='red',linewidth=5,linestyle='dashed')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "CUi5WE7iA7ET",
        "outputId": "e33168c2-2e94-4de0-c695-5e4e14db0721"
      },
      "execution_count": 455,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7b31d2943df0>]"
            ]
          },
          "metadata": {},
          "execution_count": 455
        },
        {
          "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": [
        "plt.scatter(x=house_price_df['AveRooms'], y=house_price_df['MedInc'], color='green')\n",
        "plt.xlabel('Average Rooms')\n",
        "plt.ylabel(\"Median Income\")\n",
        "plt.title(\"Median Income Vs Average Rooms\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "qVzEvHHjB0ny",
        "outputId": "eb9858dc-1b45-4c01-fbca-30531976eaab"
      },
      "execution_count": 463,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Median Income Vs Average Rooms')"
            ]
          },
          "metadata": {},
          "execution_count": 463
        },
        {
          "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": [
        "#histogram is use to plot frequency count(or simple count of records over range of the values a variable can have)"
      ],
      "metadata": {
        "id": "G62cjb7cB0h5"
      },
      "execution_count": 348,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#divide the data into bins"
      ],
      "metadata": {
        "id": "2szSc2-MB0ei"
      },
      "execution_count": 349,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#It will create 100 equal variable"
      ],
      "metadata": {
        "id": "Fz9Af81iB0cC"
      },
      "execution_count": 350,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.boxplot(house_price_df['HouseAge'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "id": "W_LPFa2dCDX2",
        "outputId": "618a05d4-803e-4059-9cb6-043d7295bb97"
      },
      "execution_count": 467,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'whiskers': [<matplotlib.lines.Line2D at 0x7b31d24a98d0>,\n",
              "  <matplotlib.lines.Line2D at 0x7b31d24aae60>],\n",
              " 'caps': [<matplotlib.lines.Line2D at 0x7b31d24aa980>,\n",
              "  <matplotlib.lines.Line2D at 0x7b31d24aa3b0>],\n",
              " 'boxes': [<matplotlib.lines.Line2D at 0x7b31d24a9fc0>],\n",
              " 'medians': [<matplotlib.lines.Line2D at 0x7b31d24a9420>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7b31d24aa530>],\n",
              " 'means': []}"
            ]
          },
          "metadata": {},
          "execution_count": 467
        },
        {
          "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": [
        "#data is more skewd or center around high values"
      ],
      "metadata": {
        "id": "LJ2T1jgACDVU"
      },
      "execution_count": 353,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Positive and Negative Correlation"
      ],
      "metadata": {
        "id": "gEzh5Oy-rLJL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "correlation=house_price_df.corr()"
      ],
      "metadata": {
        "id": "dnQqFDLUCDNI"
      },
      "execution_count": 469,
      "outputs": []
    },
    {
      "source": [
        "house_price_df.fillna(house_price_df.mean(), inplace=True)"
      ],
      "cell_type": "code",
      "metadata": {
        "id": "zYHdx9R_sTOp"
      },
      "execution_count": 473,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 15))\n",
        "\n",
        "sns.heatmap(correlation, cbar=True, square=True, fmt='1f', annot=True, annot_kws={'size':8},cmap='Reds')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 917
        },
        "id": "1WrwHhcssjhR",
        "outputId": "34538dd4-73a3-4f8b-9722-9229a2fdf2eb"
      },
      "execution_count": 492,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 492
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=house_price_df.drop(['MedInc'], axis=1)\n",
        "Y=house_price_df['MedInc']\n",
        "\n",
        "print(X)\n",
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z-_M4TjftL__",
        "outputId": "9489cf02-a29d-4056-dbc1-f62f0ac6c88e"
      },
      "execution_count": 493,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
            "0          41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
            "1          21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
            "2          52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
            "3          52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
            "4          52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
            "...         ...       ...        ...         ...       ...       ...   \n",
            "20635      25.0  5.045455   1.133333       845.0  2.560606     39.48   \n",
            "20636      18.0  6.114035   1.315789       356.0  3.122807     39.49   \n",
            "20637      17.0  5.205543   1.120092      1007.0  2.325635     39.43   \n",
            "20638      18.0  5.329513   1.171920       741.0  2.123209     39.43   \n",
            "20639      16.0  5.254717   1.162264      1387.0  2.616981     39.37   \n",
            "\n",
            "       Longitude  \n",
            "0        -122.23  \n",
            "1        -122.22  \n",
            "2        -122.24  \n",
            "3        -122.25  \n",
            "4        -122.25  \n",
            "...          ...  \n",
            "20635    -121.09  \n",
            "20636    -121.21  \n",
            "20637    -121.22  \n",
            "20638    -121.32  \n",
            "20639    -121.24  \n",
            "\n",
            "[20640 rows x 7 columns]\n",
            "0        8.3252\n",
            "1        8.3014\n",
            "2        7.2574\n",
            "3        5.6431\n",
            "4        3.8462\n",
            "          ...  \n",
            "20635    1.5603\n",
            "20636    2.5568\n",
            "20637    1.7000\n",
            "20638    1.8672\n",
            "20639    2.3886\n",
            "Name: MedInc, Length: 20640, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Spliting the data into training and test data"
      ],
      "metadata": {
        "id": "YemyYFtnwGrd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test= train_test_split(X, Y, test_size=0.2, random_state=2)\n",
        "#80% and 20% ratio of the split dataset"
      ],
      "metadata": {
        "id": "hye5onY-wGKo"
      },
      "execution_count": 494,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NDiHZigRwrC8",
        "outputId": "90913964-bccb-4eec-bb76-07d505631774"
      },
      "execution_count": 495,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(20640, 7) (16512, 7) (4128, 7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Training using XGBoost- More than one model because of the decision Tree based Ensemble Machine Learning Algorithm"
      ],
      "metadata": {
        "id": "nbKvs5lgxMTB"
      },
      "execution_count": 496,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=XGBRegressor()"
      ],
      "metadata": {
        "id": "pdlkoRItxtQX"
      },
      "execution_count": 497,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "house_price_df.loc[:, 'MedInc'].tolist()  #to see the full column in the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k0vYLy-VmrXM",
        "outputId": "a0c088b6-d86b-46ee-dbec-5cb9fc49a574"
      },
      "execution_count": 500,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " 5.5,\n",
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              " 10.3203,\n",
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              " 3.62,\n",
              " 7.3588,\n",
              " 5.7239,\n",
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              " 4.8624,\n",
              " 9.1531,\n",
              " 4.7361,\n",
              " 5.4324,\n",
              " 4.9375,\n",
              " ...]"
            ]
          },
          "metadata": {},
          "execution_count": 500
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "BzlZem6L5I1B",
        "outputId": "86e3259b-e378-441f-bbfd-ca324d87f170"
      },
      "execution_count": 502,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 502
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_data_prediction=model.predict(X_train)"
      ],
      "metadata": {
        "id": "i1vZkwvl5Ixt"
      },
      "execution_count": 503,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(training_data_prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ErbKrwba5Iu9",
        "outputId": "e4819272-03e9-49fc-bac0-62e3c0557fcc"
      },
      "execution_count": 504,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1.7997783 6.951314  1.8129938 ... 3.7587268 2.240801  2.4026408]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Evaluation Phase"
      ],
      "metadata": {
        "id": "wm_ZcKjA5kkH"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kyq6y5OM5Is1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#R^2  (R- Square)\n",
        "score_1=metrics.r2_score(Y_train, training_data_prediction)\n",
        "#Mean Absolute Error\n",
        "score_2=metrics.mean_absolute_error(Y_train, training_data_prediction)"
      ],
      "metadata": {
        "id": "CuP3ble25Ipp"
      },
      "execution_count": 508,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"R^2 Error: \", score_1)\n",
        "print(\"Mean Absolute Error: \", score_2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wrFpAJR07Q6i",
        "outputId": "4982f9da-887a-4166-b7c5-f3ff21a862d0"
      },
      "execution_count": 509,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R^2 Error:  0.9336592798828024\n",
            "Mean Absolute Error:  0.36781328983357137\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "testing_data_prediction=model.predict(X_test)"
      ],
      "metadata": {
        "id": "pGHxJXeh5InA"
      },
      "execution_count": 513,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score1_test=metrics.r2_score(Y_test, testing_data_prediction)\n",
        "score2_test=metrics.mean_absolute_error(Y_test, testing_data_prediction)"
      ],
      "metadata": {
        "id": "HZYsXzsY5Ikm"
      },
      "execution_count": 514,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"R^2 Error: \", score1_test)\n",
        "print(\"Mean Absolute Error: \", score2_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C3DlL71F858c",
        "outputId": "63b9451a-cc57-444f-ad42-0c6b7783d775"
      },
      "execution_count": 515,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R^2 Error:  0.8044795741728923\n",
            "Mean Absolute Error:  0.588140578418716\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plotting the scatter plot of the Actual vs Predicted Outcome\n",
        "plt.scatter(Y_train, training_data_prediction)\n",
        "plt.xlabel('Actual Outcome')\n",
        "plt.ylabel('Predicted Outcome')\n",
        "plt.title('Actual Vs Predictied Outcome')\n",
        "plt.show()   #In the below graph we can see that both value are in the similar range"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "J5D2Amqk9A2g",
        "outputId": "d4b5aed9-b50b-40f2-d432-72958b3a311d"
      },
      "execution_count": 518,
      "outputs": [
        {
          "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": [],
      "metadata": {
        "id": "T2eybK_v-Cbo"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}