Data_Analysis,Data_visualization_and_basics_of_Machine_Learning.ipynb

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        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.linear_model import LinearRegression"
      ]
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        "df=pd.read_csv(\"/content/engine size and its price2.csv\")\n",
        "df"
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              "   engine size   price vehicle  horsepower  mpg  top\n",
              "0          100  200000      4w         110   22    1\n",
              "1          150  234000      2w         120   70    4\n",
              "2          200  345000      4w         175   34    2\n",
              "3          300  500000      4w         210   34    3\n",
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              "(5, 6)"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5 entries, 0 to 4\n",
            "Data columns (total 6 columns):\n",
            " #   Column       Non-Null Count  Dtype \n",
            "---  ------       --------------  ----- \n",
            " 0   engine size  5 non-null      int64 \n",
            " 1   price        5 non-null      int64 \n",
            " 2   vehicle      5 non-null      object\n",
            " 3   horsepower   5 non-null      int64 \n",
            " 4   mpg          5 non-null      int64 \n",
            " 5   top          5 non-null      int64 \n",
            "dtypes: int64(5), object(1)\n",
            "memory usage: 368.0+ bytes\n"
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              "engine size     int64\n",
              "price           int64\n",
              "vehicle        object\n",
              "horsepower      int64\n",
              "mpg             int64\n",
              "top             int64\n",
              "dtype: object"
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              "   engine size   price vehicle  horsepower  mpg  top\n",
              "1          150  234000      2w         120   70    4\n",
              "2          200  345000      4w         175   34    2\n",
              "3          300  500000      4w         210   34    3\n",
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              "    }\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-7c562026-e1d6-40aa-919f-9747503b4a63 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-7c562026-e1d6-40aa-919f-9747503b4a63');\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-0f68043e-722a-4802-91de-53e5419bac98\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0f68043e-722a-4802-91de-53e5419bac98')\"\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",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
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              "\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-0f68043e-722a-4802-91de-53e5419bac98 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "eydGPaB8Y7kG",
        "outputId": "29a744d0-c415-4f18-fb7b-18d3fc658a25"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   engine size   price vehicle  horsepower  mpg  top\n",
              "0          100  200000      4w         110   22    1\n",
              "1          150  234000      2w         120   70    4\n",
              "2          200  345000      4w         175   34    2"
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              "\n",
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              "      <td>110</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>150</td>\n",
              "      <td>234000</td>\n",
              "      <td>2w</td>\n",
              "      <td>120</td>\n",
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              "      <td>4</td>\n",
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              "      <th>2</th>\n",
              "      <td>200</td>\n",
              "      <td>345000</td>\n",
              "      <td>4w</td>\n",
              "      <td>175</td>\n",
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              "      <td>2</td>\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6749da09-e7e7-4c68-9bfc-1c73f858541c 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-6749da09-e7e7-4c68-9bfc-1c73f858541c');\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",
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              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
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              "  </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",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\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-afcb07c0-b044-47fc-a978-87f085f94ae4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "JE_OMBZ8ZD-g",
        "outputId": "245195df-0dd5-492a-b70e-ebd1874700a5"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       engine size          price  horsepower       mpg       top\n",
              "count     5.000000       5.000000    5.000000   5.00000  5.000000\n",
              "mean    240.000000  395800.000000  183.000000  45.00000  3.600000\n",
              "std     138.744369  206453.868939   77.103826  21.18962  2.701851\n",
              "min     100.000000  200000.000000  110.000000  22.00000  1.000000\n",
              "25%     150.000000  234000.000000  120.000000  34.00000  2.000000\n",
              "50%     200.000000  345000.000000  175.000000  34.00000  3.000000\n",
              "75%     300.000000  500000.000000  210.000000  65.00000  4.000000\n",
              "max     450.000000  700000.000000  300.000000  70.00000  8.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-785038ea-9361-43f8-a1e3-fd91112e8fd4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>engine size</th>\n",
              "      <th>price</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>mpg</th>\n",
              "      <th>top</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.00000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>240.000000</td>\n",
              "      <td>395800.000000</td>\n",
              "      <td>183.000000</td>\n",
              "      <td>45.00000</td>\n",
              "      <td>3.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>138.744369</td>\n",
              "      <td>206453.868939</td>\n",
              "      <td>77.103826</td>\n",
              "      <td>21.18962</td>\n",
              "      <td>2.701851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>100.000000</td>\n",
              "      <td>200000.000000</td>\n",
              "      <td>110.000000</td>\n",
              "      <td>22.00000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>234000.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>34.00000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>345000.000000</td>\n",
              "      <td>175.000000</td>\n",
              "      <td>34.00000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>300.000000</td>\n",
              "      <td>500000.000000</td>\n",
              "      <td>210.000000</td>\n",
              "      <td>65.00000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>450.000000</td>\n",
              "      <td>700000.000000</td>\n",
              "      <td>300.000000</td>\n",
              "      <td>70.00000</td>\n",
              "      <td>8.000000</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-785038ea-9361-43f8-a1e3-fd91112e8fd4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-785038ea-9361-43f8-a1e3-fd91112e8fd4 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-785038ea-9361-43f8-a1e3-fd91112e8fd4');\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-d313ffbc-22c4-4538-8c44-0b4169fe0585\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d313ffbc-22c4-4538-8c44-0b4169fe0585')\"\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",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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",
              "      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-d313ffbc-22c4-4538-8c44-0b4169fe0585 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.get_dummies(df[\"engine size\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "dpgtQMxLZIcq",
        "outputId": "16ad5b93-4c3b-4daa-ad31-a7de5e484619"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   100  150  200  300  450\n",
              "0    1    0    0    0    0\n",
              "1    0    1    0    0    0\n",
              "2    0    0    1    0    0\n",
              "3    0    0    0    1    0\n",
              "4    0    0    0    0    1"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>100</th>\n",
              "      <th>150</th>\n",
              "      <th>200</th>\n",
              "      <th>300</th>\n",
              "      <th>450</th>\n",
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              "      <th>0</th>\n",
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              "      <th>2</th>\n",
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              "      <td>0</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "      padding: 0 0 0 0;\n",
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              "\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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-88e6779a-ff8d-49ef-8ced-4ac749763490 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-88e6779a-ff8d-49ef-8ced-4ac749763490');\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",
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              "\n",
              "\n",
              "<div id=\"df-738c5c09-05d8-4de6-b2ff-34a562f93449\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-738c5c09-05d8-4de6-b2ff-34a562f93449')\"\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",
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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",
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              "      --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",
              "      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-738c5c09-05d8-4de6-b2ff-34a562f93449 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"top\"].drop"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v01r1LDEZT3U",
        "outputId": "0fff9c3a-4ec2-4f57-f521-627d98a58044"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method Series.drop of 0    1\n",
              "1    4\n",
              "2    2\n",
              "3    3\n",
              "4    8\n",
              "Name: top, dtype: int64>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k=df.dropna(subset=[\"price\"],axis=0,inplace=True)\n",
        "k#remove nan values"
      ],
      "metadata": {
        "id": "7cTBhs5RZT67"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=df[\"engine size\"]\n",
        "y=df[\"price\"]\n",
        "plt.plot(x,y,color=\"deeppink\",alpha=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "0XtKP8ehaYQN",
        "outputId": "8b6ed614-8970-4e02-877c-611e2fb9c126"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ff2107dd030>]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "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": [
        "x=df[\"price\"]\n",
        "y=df[\"engine size\"]\n",
        "x1=df[\"price\"]\n",
        "y1=df[\"horsepower\"]\n",
        "plt.scatter(x,y,color=\"deeppink\",alpha=1)\n",
        "plt.scatter(x1,y1,color=\"yellowgreen\",alpha=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "kBfAPpIda45n",
        "outputId": "076745ae-5e2b-4a0b-9d80-2569d284d25c"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7ff20fb6a350>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "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": [
        "x=df[\"price\"]\n",
        "y=df[\"engine size\"]\n",
        "x1=df[\"price\"]\n",
        "y1=df[\"horsepower\"]\n",
        "plt.plot(x,y,color=\"deeppink\",alpha=1)\n",
        "plt.plot(x1,y1,color=\"yellowgreen\",alpha=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "ZDGLssRXbywR",
        "outputId": "b1aba5e9-af83-4ce6-cadf-29dddb4c8ef6"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ff20faf0100>]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "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": [
        "x=df[\"engine size\"]\n",
        "y=df[\"price\"]\n",
        "plt.bar(x,y,color=\"green\",alpha=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "gc4zM1gZb-Mx",
        "outputId": "243e6c25-d892-45f0-d37d-7b4417635ae8"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 5 artists>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "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": [
        "x=np.array([1,2,3])\n",
        "y=np.array([4,5,6])\n",
        "z=x+y\n",
        "z"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c5urM3PbcLJV",
        "outputId": "3af7d572-2508-4350-a0f5-124c10cb8363"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 7, 9])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x.ndim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HHjw6H8ZcXog",
        "outputId": "1a3d6091-6531-4ebb-d636-4232199a3b08"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_B3NqBJncpC6",
        "outputId": "882ecdbc-4463-4ca1-ec73-9a55dc14fc01"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3,)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x.size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WZOAeb5wcsPw",
        "outputId": "c93e70c4-8fd6-4a6f-be42-c1a5e0856285"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "z[0:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yK2aHosbcuJN",
        "outputId": "2b31cf4f-6c25-480d-ee64-f939a6fc3ae6"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 7])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "z*y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GXf4cgy6cuMw",
        "outputId": "545c406b-596f-487b-e371-c488a3576de7"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([20, 35, 54])"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.dot(x,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7-vw_2Ehc-1i",
        "outputId": "85ea8c4c-903e-4729-dbde-b4b97ceef682"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=df[\"price\"]\n",
        "y=df['engine size']\n",
        "sns.barplot(x=x,y=y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "Rcw6-uHudMEo",
        "outputId": "b79a4204-62c4-4568-d992-c8d86933fab6"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='price', ylabel='engine size'>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "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": [
        "lm=LinearRegression()\n",
        "x=df[[\"mpg\"]]\n",
        "y=df[\"price\"]"
      ],
      "metadata": {
        "id": "NyAmhdH1dtTQ"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lm.fit(x,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "TyQf6p9heMIX",
        "outputId": "a12d6963-dcf5-46bb-e788-9337d1748559"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "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>LinearRegression()</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\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k=lm.predict(x)\n",
        "print(k)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I54QWeLkeGmR",
        "outputId": "400a04a8-08d0-487f-9c66-9d79a563a74f"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[319538.86414254 478692.5389755  359327.28285078 359327.28285078\n",
            " 462114.0311804 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kk=lm.intercept_\n",
        "print(kk)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0rTscRXleCQ3",
        "outputId": "d4d9b469-5b37-42e4-b15c-58ac4cf14c20"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "246593.429844098\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kkk=lm.coef_\n",
        "print(kkk)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DYdNsmtLeb6x",
        "outputId": "9b2157d0-c34c-4623-b2d2-ee5e5199de92"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3315.70155902]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "price=kk+kkk*x\n",
        "print(price)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dmETtQvBepQI",
        "outputId": "c4db603d-7811-4012-e9ca-a948defb5949"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             mpg\n",
            "0  319538.864143\n",
            "1  478692.538976\n",
            "2  359327.282851\n",
            "3  359327.282851\n",
            "4  462114.031180\n"
          ]
        }
      ]
    }
  ]
}